2026 Predictions for Nuclear Energy: Innovation, Safety, and the Path to a Sustainable Future

The nuclear energy industry stands at a pivotal moment where innovation and tradition intersect to tackle the world’s most urgent challenges: decarbonization, energy security, and sustainability. From the emergence of small modular reactors (SMRs) and advanced reactor designs to the adoption of AI, automation, and digital engineering, the sector is embracing transformative technologies that are set to redefine how nuclear power is designed, operated, and perceived.

Key trends shaping the nuclear landscape include the transition from conceptual innovation to deployable solutions, the role of digitalization in enhancing safety and efficiency, and the evolution of regulatory frameworks to support next-generation technologies. Additionally, cybersecurity, workforce development, and global collaboration are becoming essential pillars of the industry’s future, ensuring that growth and innovation remain firmly grounded in the safety-first principles that define nuclear energy.

In this final blog of the 2026 prediction series, we bring these insights to life with perspectives from Jama Software’s industry expert, Patrick Garman, Solutions Manager for Energy, Industrial, and Consumer Electronics sectors. Patrick shares a forward-looking vision for 2026 and beyond, exploring the deployment of SMRs and advanced fuels, the integration of predictive analytics and real-time monitoring, and the innovations, strategies, and cultural shifts that will shape the nuclear industry’s role in a clean energy future.

Curious to read leading thought leaders’ predictions for their industries in 2026 and beyond? Dive into each blog below:

• • Part One: Consumer Electronics
  • Part Two: Medical Devices & Life Sciences
  • Part Three: Aerospace & Defense
  • Part Four: Automotive
  • Part Five: Semiconductors
  • Part Six: Architecture, Engineering, Construction, and Operations (AECO)

Emerging Technologies

Q: What next-generation technologies (e.g., small modular reactors, advanced reactor designs, digital control systems) will have the most significant impact on the nuclear industry in the next five years? How can organizations prepare to adopt and regulate these innovations safely?

Patrick Garman: Over the next five years, the nuclear industry is likely to be shaped by a practical shift from conceptual innovation to deployable technology. Small modular reactors (SMRs) and microreactors are expected to lead this transition, moving beyond pilot projects toward early commercial use thanks to their modular construction, smaller footprints, and ability to serve diverse applications, from grid support to industrial process heat and remote operations. In parallel, advanced non-light-water reactors, such as high-temperature gas, molten salt, and fast reactors, are gaining traction as long-term solutions for high-efficiency power generation and emerging use cases like hydrogen production and industrial decarbonization. These reactor designs are closely linked to advanced fuels, including HALEU and TRISO, making fuel availability, qualification, and supply chain readiness a central factor in how quickly projects can move forward. At the same time, the industry is embracing digital instrumentation and control, automation, and data-driven operations to improve performance, reliability, and safety while also introducing new considerations around software assurance and cybersecurity. Underpinning all of this is a growing reliance on factory-based manufacturing, modularization, and robotic inspection, which promise to reduce construction risk and improve quality, provided these methods can be consistently qualified and aligned with regulatory expectations.

Safety and Risk Management

Q: Safety has always been central to the nuclear industry. How can digitalization, real-time monitoring, and predictive analytics further strengthen plant safety and reliability? What cultural or procedural shifts are needed to sustain a modern safety-first approach?

Garman: Digitalization is giving the nuclear industry new ways to reinforce its longstanding safety-first foundation by improving visibility, consistency, and foresight across plant operations. Real-time monitoring and predictive analytics allow operators to detect early signs of equipment degradation, performance drift, or abnormal conditions well before they escalate into safety or reliability concerns, while modern digital control and decision-support systems help reduce human-factor risk by delivering clearer, more contextual information during both normal and off-normal operations. To fully realize these benefits, organizations must evolve their safety culture and procedures to treat software, data, and analytics as safety-relevant assets governed with the same rigor as physical systems, while strengthening the human-automation partnership through training, validation, and clear operational boundaries. A modern safety-first approach, therefore, extends beyond traditional engineering excellence to include disciplined digital governance, cybersecurity resilience, and continuous learning, ensuring that advanced technologies enhance the conservative decision-making that defines nuclear safety.

Digital Modernization

Q: How do you see digital engineering and integrated data environments improving plant lifecycle management, from design through decommissioning? What challenges exist in migrating from legacy systems to modern digital platforms?

Garman: Digital engineering and integrated data environments are changing how nuclear plants are managed across their entire lifecycle, helping teams maintain clarity and control from early design decisions all the way through operations and eventual decommissioning. By creating a connected digital thread that links requirements, design models, safety analyses, construction records, and operational data, organizations can avoid the information loss that often happens at handoffs between phases or teams. This continuity makes it easier to manage design changes, maintain configuration control, respond to regulatory questions with confidence, and use operational insight to plan maintenance, life extensions, or decommissioning activities more effectively.

The biggest challenge is not the technology itself, but the transition. Many nuclear organizations are working with decades of legacy systems, documents, and institutional knowledge that were never designed to work together. Migrating to modern digital platforms requires careful, phased approaches that preserve trust in the data, maintain regulatory confidence, and respect the realities of long-lived assets that cannot pause operations for wholesale transformation. Success depends on strong data governance, disciplined change management, and a clear understanding that digital modernization is a long-term capability investment.

Regulatory and Compliance Evolution

Q: As global interest in nuclear energy grows, particularly across the EU, how can the industry ensure regulatory frameworks keep pace with innovation? What best practices can help organizations streamline compliance without compromising safety?

Garman: As interest in nuclear energy accelerates, the challenge is ensuring regulatory frameworks evolve alongside innovation without undermining the industry’s uncompromising safety standards. New reactor designs, fuels, and digital technologies don’t fit neatly into licensing models that were built around large, conventional plants, which means regulators and industry alike must continue shifting toward risk-informed, technology-inclusive approaches. This evolution works best when developers engage regulators early and often, clearly articulate their safety case, and align on expectations for evidence, review milestones, and decision points before designs are finalized.

Best-in-class organizations are streamlining compliance by treating it as an integrated engineering discipline rather than a late-stage documentation exercise. That means embedding regulatory requirements directly into design and development workflows, maintaining clear traceability from safety objectives to implementation and verification, and reusing proven arguments, data, and analyses wherever possible. At the same time, harmonization efforts across jurisdictions, transparent regulatory collaboration, and disciplined change control help reduce duplication without sacrificing diligence. The result is a more predictable path to licensing that supports innovation while preserving the conservative, safety-first principles that underpin public trust in nuclear energy.

RELATED: Accelerate Nuclear Design Assessments and Reduce Certifications and Engineering Costs with Jama Connect^® for Nuclear Reactor Design & I&C Development

Cybersecurity in Nuclear Operations

Q: As nuclear facilities adopt more connected technologies, how can organizations guard against cyber threats while maintaining system integrity and safety? What proactive measures should become industry standard?

Garman: As nuclear facilities adopt more connected and digital technologies, cybersecurity is becoming inseparable from plant safety and reliability. Guarding against cyber threats starts with treating operational technology as safety-relevant infrastructure that is designed from the outset to limit the impact of any compromise through strong segmentation, controlled data flows, and isolation of critical functions. Leading organizations focus less on individual tools and more on disciplined system architecture, configuration control, and integrity protection, ensuring that digital systems behave predictably even under adverse conditions.

The industry is converging on practices such as secure-by-design engineering, rigorous access and change management, continuous monitoring tailored to OT environments, and well-rehearsed incident response that includes operations and engineering, not just IT. Ultimately, sustaining system integrity in a more connected nuclear plant depends on a cultural shift that recognizes cybersecurity as an extension of nuclear safety itself, governed with the same conservative mindset and operational rigor that public trust in the industry depends on.

AI and Automation

Q: What role will AI and automation play in improving design and manufacturing of nuclear reactors and efficiency, safety inspections, and predictive maintenance across nuclear facilities? What safeguards are needed to ensure responsible, transparent use?

Garman: AI and automation are set to play an increasingly practical role in the nuclear industry, particularly in areas where consistency, pattern recognition, and early detection matter most. In design and manufacturing, AI-assisted analysis can help engineers explore design alternatives, identify potential safety or manufacturability issues earlier, and improve quality through automated inspection, welding verification, and non-destructive evaluation. Across operating plants, automation and advanced analytics support more efficient inspections and predictive maintenance by detecting subtle equipment degradation, prioritizing risk-significant issues, and reducing unnecessary exposure of personnel to hazardous environments. Used appropriately, these technologies strengthen safety and reliability by helping teams act earlier and with better information.

Responsible AI deployment in the nuclear industry means applying the same conservative, evidence-based mindset that governs other safety-relevant systems. That means clearly defining where AI provides decision support versus where humans retain authority, validating models against real-world data, monitoring performance and drift over time, and maintaining full transparency into how recommendations are generated. Strong data governance, configuration control, and cybersecurity protections are essential, as is the ability to audit and explain outcomes to regulators and operators alike. When paired with clear safeguards and human oversight, AI and automation can become trusted tools that enhance the nuclear industry’s long-standing commitment to safety and public confidence.

Sustainability and Public Perception

Q: How can the nuclear industry strengthen public trust while positioning itself as a key player in the clean energy transition? What strategies are most effective for communicating safety, sustainability, and innovation to the public?

Garman: Strengthening public trust is ultimately about consistency between what the nuclear industry says, what it does, and what people experience over time. As nuclear positions itself as a critical enabler of a reliable, low-carbon energy system, the industry has an opportunity to connect its long-standing safety culture with today’s clean energy priorities, emphasizing not just carbon-free electricity, but resilience, energy security, and long-term environmental stewardship. Trust grows when organizations are transparent about both benefits and risks, communicate clearly how safety is engineered and governed, and demonstrate that lessons learned are actively shaping modern designs and operations.

The most effective communication strategies focus on clarity, credibility, and relevance to everyday concerns. That means moving beyond technical jargon to explain safety, waste management, and sustainability in plain language, using real data and independent validation rather than promises. Engaging early and continuously with communities, regulators, and policymakers helps demystify nuclear technology and humanize the people behind it. By pairing transparent communication with visible innovation, strong regulatory oversight, and measurable climate impact, the nuclear industry can reinforce public confidence while positioning itself as a trustworthy and essential contributor to the clean energy transition.

RELATED: Transmutex Wastes No Time Choosing Jama Connect for Developing Nuclear Waste Reprocessing Systems

Workforce and Knowledge Transfer

Q: With a generational shift in the workforce, how can the nuclear industry retain institutional knowledge while equipping new engineers with the digital and safety-focused skills needed for the next era of nuclear operations?

Garman: The challenge is to preserve institutional knowledge while preparing a new generation of engineers to operate in a far more digital, data-driven environment. Leading organizations are addressing this by deliberately capturing design intent, operating experience, and lessons learned in structured, accessible formats while pairing this with training that blends systems thinking, digital engineering tools, and a strong grounding in nuclear safety culture. Mentorship, cross-generational teams, and scenario-based training help bridge experience with innovation, reinforcing conservative decision-making even as new technologies are adopted. Ultimately, success depends on treating knowledge management and workforce development as long-term strategic investments, ensuring that the next generation has access not just to new tools, but have the mindset and discipline that have define safe nuclear operations.

Global Collaboration and Standardization

Q: How can international collaboration and harmonized safety standards support the safe expansion of nuclear energy, particularly as more nations revisit nuclear as part of their net-zero strategies?

Garman: Collaboration and harmonized safety standards are essential to the safe and timely expansion of nuclear energy as more countries turn to nuclear power to meet net-zero goals. Shared regulatory principles, common safety objectives, and mutual recognition of technical assessments help reduce duplication, improve consistency, and raise the global safety baseline – especially as new reactor technologies are deployed across multiple jurisdictions.

Collaboration among regulators, operators, and international bodies also accelerates the exchange of operating experience and lessons learned, allowing emerging nuclear programs to benefit from decades of global expertise. When paired with strong national oversight, this alignment supports innovation without compromising rigor, enabling countries to expand nuclear capacity with confidence while reinforcing public trust in nuclear safety worldwide.

Future Outlook

Q: What trends—technological, regulatory, or geopolitical—will most influence the global nuclear industry over the next decade? How can companies balance growth, innovation, and safety as nuclear energy plays a larger role in global sustainability goals?

Garman: Over the next decade, the global nuclear industry will be shaped by a convergence of technological innovation, evolving regulatory approaches, and shifting geopolitical priorities tied to energy security and decarbonization. Technologically, the progression of small modular and advanced reactors, digital engineering, and data-driven operations will expand where and how nuclear can be deployed, while fuel supply chains and cybersecurity will remain strategic constraints. Regulators are increasingly adapting frameworks to accommodate new technologies through risk-informed, technology-inclusive approaches, even as geopolitical dynamics, such as supply chain resilience, international collaboration, and regional energy independence, reshape investment and deployment decisions. To balance growth, innovation, and safety, companies will need to embed safety and compliance into their innovation processes from the outset, engage regulators and stakeholders early, and maintain disciplined governance over digital and organizational change. Those that succeed will be the ones that treat safety not as a brake on progress, but as the foundation that allows nuclear energy to scale credibly and sustainably in support of global climate and energy goals.

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2026 Predictions for AECO: AI, Digital Twins, and the Path to Sustainable Transformation

As we step into 2026, the Architecture, Engineering, Construction, and Operations (AECO) industry is poised for a transformative leap. From the integration of AI and digital twins to the adoption of robotics and advanced materials, the sector is embracing innovation to tackle its most pressing challenges: sustainability, efficiency, and collaboration in a hybrid world.

This year’s predictions explore how emerging technologies like generative design, predictive analytics, and automation are reshaping the project lifecycle. We’ll dive into the role of advanced digital tools in achieving net-zero goals, the growing importance of cybersecurity in a connected ecosystem, and the long-term trends that will define the industry for years to come.

In part six of this year’s predictions series, we bring these insights to life with perspectives from Jama Software’s own AECO experts: Joe Gould – Senior Account Executive, and Michelle Solis – Associate Solutions Architect, who share their vision for the future. From AI-driven decision-making to the rise of modular construction and lifecycle optimization, this piece highlights the innovations and strategies that will shape 2026 and beyond.

Curious to read leading thought leaders’ predictions for their industries in 2026 and beyond? Dive into each blog below and stay tuned for part 6, the finale of this year’s series:

• Part One: Consumer Electronics
• Part Two: Medical Devices & Life Sciences
• Part Three: Aerospace & Defense
• Part Four: Automotive
• Part Five: Semiconductors

Emerging Technologies

What specific emerging technologies (e.g., AI, digital twins, generative design, robotics) do you believe will have the most transformative impact on the AECO industry in the next five years? How can firms prepare to adopt and integrate these technologies effectively?

Joe Gould: AI and Machine Learning will become foundational across the entire project lifecycle.

• Design & Planning: AI accelerates generative design by evaluating thousands of options against constraints like cost, performance, and sustainability—helping teams reach optimized solutions faster.
• Predictive Insights: By analyzing large datasets, AI can forecast risks, schedule impacts, cost overruns, and potential failures, enabling earlier and more informed decisions.
• Workflow Automation: Routine tasks such as data entry, document review, and quantity takeoffs are increasingly automated, allowing teams to focus on higher-value, strategic work.

Digital Twins extend these capabilities into operations.

• Operational Optimization: Real-time digital replicas of assets enable continuous monitoring and simulation, improving energy performance, asset utilization, and long-term operating costs.
• Predictive Maintenance: Simulating asset behavior under different conditions helps identify issues before failure, reducing downtime and extending asset life.
• Collaboration: A shared, real-time data environment ensures all stakeholders are aligned on the most current information throughout the asset lifecycle.

Robotics and Automation have been moving from experimentation to real jobsite adoption.

• On-Site Execution: AI-enabled robotics handle repetitive and high-risk tasks with greater precision and safety.
• Autonomous Equipment: Drones and self-operating machinery are increasingly used for surveying, inspections, and material movement, improving efficiency while reducing labor constraints.

Sustainability and Net-Zero Goals

With the AECO industry under increasing pressure to meet sustainability and net-zero targets, what role do you see advanced software, materials innovation, and digital tools playing in achieving these goals? Are there specific technologies or strategies you think will lead the way?

Gould: Important question! Advanced digital tools allow teams to understand and manage environmental impact early in the process, long before construction begins.

At the core is Building Information Modeling (BIM), which provides a data-rich model that supports ongoing analysis of energy performance, material use, and constructability as designs evolve. Energy modeling and simulation extend this by forecasting real-world performance early, allowing teams to optimize efficiency and integrate renewables before decisions are locked in.

AI and machine learning add another layer by analyzing large datasets to improve decision-making, optimize resources, and surface risks earlier. Generative design helps teams evaluate thousands of design options that balance sustainability, cost, and performance. Digital twins, fed by real-time sensor data, carry this forward into operations—enabling predictive maintenance, smarter energy management, and continuous performance optimization over the life of the asset.

Life-cycle assessment tools tie it all together by informing material choices based on embodied carbon and long-term environmental impact, not just upfront cost.

Materials innovation focuses on reducing embodied carbon and supporting a more circular approach to construction.

This includes a shift toward low-carbon materials such as mass timber, green steel, and advanced concrete alternatives, along with greater use of recycled and reusable content. High-performance insulation and composites further improve operational efficiency by reducing long-term energy demand while maintaining durability and performance.

The real impact comes from integrating these tools into a single, data-driven approach—connecting design, construction, and operations.

Key strategies:

• Data-driven decarbonization, using reliable project data for transparent reporting and continuous optimization
• Prefabrication and modular construction, reducing waste, emissions, and schedule risk
• Circular design principles, enabling reuse and recovery at end of life
• Predictive maintenance, extending asset life and reducing long-term operational waste

By aligning digital tools, materials innovation, and lifecycle thinking, the industry can move beyond incremental gains and make measurable progress toward net-zero and long-term sustainability goals.

RELATED: Best Practices Guide to Requirements & Requirements Management

Collaboration in a Hybrid World

As hybrid and remote work models continue to evolve, how do you see these changes impacting collaboration, innovation, and project delivery in the AECO industry? What tools or processes will be critical for maintaining efficiency and creativity?

Gould: Hybrid and remote work are reshaping AECO, driving efficiency, expanding access to talent, and accelerating digital adoption—but they require more discipline around how teams collaborate and deliver work.

Collaboration has shifted from informal to intentional. Cloud-based platforms, shared models, and virtual design reviews are now standard, enabling distributed teams to stay aligned without being co-located. Innovation hasn’t slowed—it’s evolved. Access to broader talent pools and increased automation of routine tasks allow teams to spend more time on higher-value problem-solving.

From a delivery standpoint, hybrid models often reduce cycle times and costs. Work continues across time zones, travel is minimized, and documentation improves because communication has to be clearer by default.

Success in this environment depends less on tools alone and more on how they’re used. Cloud BIM, collaboration platforms, and project management systems form the backbone, but clear communication norms, standardized workflows, and outcome-based accountability are what keep teams productive.

To me, the shift isn’t about where people work—it’s about building repeatable, digital-first processes that support speed, clarity, and consistent project outcomes.

AI and Automation

How do you foresee AI and machine learning shaping decision-making, risk management, and project optimization in AECO? What are the biggest challenges or limitations the industry might face in scaling these technologies to automate processes?

Michelle Solis: While AI itself will make an impact on AECO companies, one additional area where we will see impact is in building the infostructure to handle the increase of AI usage across all industries. This will mean more jobs, job sites, data centers, and projects.

Gould: AI and machine learning are shifting AECO from reactive to proactive. When applied well, they improve decision-making, surface risk earlier, and optimize how projects are planned, built, and operated.

AI helps teams make better decisions by analyzing large volumes of historical and real-time data—highlighting patterns and risks humans typically miss. Generative design accelerates this by evaluating thousands of options against constraints like cost, performance, and sustainability. On the risk side, predictive analytics and real-time monitoring help identify schedule, cost, and safety issues before they escalate. AI also drives operational gains through task automation, smarter maintenance planning, and more resilient supply chains.

The challenge isn’t the technology—it’s scaling it. Most AECO firms struggle with fragmented data, limited system integration, and inconsistent standards. There are also a real skills gap and natural resistance to changing long-standing workflows. Add in high upfront costs, unclear use cases, unclear ROI, and legitimate concerns around data privacy and accountability, and adoption slows quickly.

The opportunity is real, but success depends on getting the fundamentals right: clean data, integrated systems, clear ownership, and practical use cases that tie directly to project and business outcome

Responsible AI Adoption

As AI and machine learning become more integrated into AECO workflows, what challenges or considerations should companies be mindful of to ensure successful implementation? How can firms address these challenges while maximizing the benefits of these technologies?

Gould: AI adoption in AECO isn’t a technology problem—it’s a fundamentals problem. Success depends on data, people, and how firms manage change.

Most organizations struggle with fragmented data, legacy systems, and limited AI-ready skills. Add natural resistance to new workflows, unclear ROI, and concerns around data security and accountability, and progress stalls quickly.

The path forward is straightforward:

• Get the data right: standardize, govern it, and make it accessible
• Upskill teams: treat AI as a productivity multiplier, not a replacement
• Start small: focus on high-impact pilots that prove value fast
• Modernize platforms: move toward cloud-based, integrated systems
• Keep humans in the loop: clear ownership, transparency, and oversight matter

Firms that focus on these basics will scale AI effectively—and turn experimentation into measurable business outcomes.

Data-Driven Project Management

With the growing emphasis on predictive analytics, real-time monitoring, and data-driven decision-making, what strategies would you recommend for AECO firms to better harness data for optimizing project outcomes and resource allocation?

Gould: To use data effectively, AECO firms need to focus less on dashboards and more on fundamentals: integrated systems, clean data, and teams that actually trust and use it.

That starts with moving off siloed tools and spreadsheets and into cloud-based, integrated platforms that create a single source of truth across design, delivery, and operations. Strong data governance—clear ownership, standards, and quality controls—is non-negotiable. Without clean, consistent data, analytics don’t matter.

From there, predictive analytics should be embedded directly into project workflows, not buried in reports. Tracking the right KPIs and using data to flag schedule, cost, safety, and resource risks early shifts teams from reactive to proactive.

Finally, this only works if people are brought along. Start small with high-impact use cases, involve field teams early, and invest in basic data literacy, so insights drive decisions—not just meetings.

RELATED: Five Key Challenges AEC Project Owners Face and How to Solve Them with Jama Connect^®

Regulatory Changes

What upcoming regulatory changes or compliance requirements do you anticipate having the biggest impact on the AECO industry in 2026? How can companies stay ahead of these changes?

Gould: The biggest regulatory shifts hitting AECO in 2026 will center on ESG (Environmental, Social, and Governance), energy performance, and digital risk. ESG reporting is moving from “nice to have” to mandatory, with climate disclosure requirements cascading through supply chains. Energy codes will continue tightening, pushing firms toward higher-performance, low-carbon, and “zero-ready” buildings. At the same time, increased use of AI and cloud platforms is driving new expectations around transparency, governance, and cybersecurity.

The firms that stay ahead won’t treat this as a compliance exercise. They’ll lean on digital platforms to track energy, carbon, and materials from design through operations, put clear AI and data governance in place, and strengthen cybersecurity practices as reporting requirements tighten. Just as important, they’ll build regulatory awareness into project planning early—before requirements show up as cost, schedule, or risk surprises.

Cybersecurity in AECO

As digital tools and connected systems become more prevalent in AECO, what role do you see cybersecurity playing in protecting sensitive project data and ensuring operational continuity? Are there specific threats or solutions companies should prioritize?

Solis: As digital tools, connected platforms, and AI become more embedded in AECO workflows, cybersecurity will play a critical role in protecting sensitive project data and maintaining operational continuity. With the growing use of AI, firms must clearly define what data can and cannot be shared with AI models, particularly when working with proprietary designs, client information, or critical infrastructure data.

Beyond data leakage, organizations also need to address risks such as AI hallucinations, bias, and model misuse, which can directly impact design decisions, safety, and compliance if left unchecked. To mitigate these risks, companies should prioritize strong access controls, data governance policies, employee training, and secure AI deployments. Establishing clear guidelines around AI use, along with continuous monitoring and validation of outputs, will be essential to ensuring both cybersecurity and trust in digital systems as adoption accelerates.

Future of Innovation

What is the most innovative trend, tool, or process you’ve seen in the AECO industry recently? How do you anticipate it influencing the industry in the coming years?

Solis: One of the most impactful trends I’ve seen recently is the increased focus on Requirements Management across rail and broader AECO organizations. While this shift is often driven by hard lessons such as losing a contract or discovering unmet requirements late in a project, it signals a growing recognition that informal or disconnected requirement processes are no longer sustainable for complex, regulated projects.

Gould: The most meaningful innovation in AECO is the convergence of AI, digital twins, and integrated platforms. Together, they’re turning projects into connected, data-driven systems that move teams from static modeling to prediction, automation, and lifecycle optimization.

At the center is the digital thread. Requirements are no longer buried in PDFs and spreadsheets—they’re connected directly to BIM, schedules, costs, and real-time performance data. AI continuously validates designs against requirements, flags deviations early, and maintains traceability from concept through operations. That shift alone reduces rework, misalignment, and late-stage surprises.

AI-powered digital twins then extend this into delivery and operations, keeping stakeholders aligned and enabling smarter, faster decisions. The result is leaner execution, better compliance, and assets that actually perform as intended—not just on day one, but over their full lifecycle.

Long-Term Trends

What trends or technologies do you think will still be shaping the AECO industry five years from now? Ten years? How can companies position themselves to remain competitive in the long term?

Solis: I don’t think there’s one technology specifically that will shape the AECO industry. Companies who make an effort to welcome new technologies and not go against them will see success. This industry doesn’t want to evolve, but it will.

Gould: Over the next 5–10 years, AECO will be defined by digital maturity and industrialization. AI, BIM, and digital twins will move from tools to core infrastructure, while sustainability and offsite construction become standard, not optional.

In the next five years, BIM becomes the project command center—fully cloud-based and connected to schedule, cost, and lifecycle data. AI is embedded in planning and design to surface risk early, optimize decisions, and improve predictability. Modular and offsite construction scale quickly as firms respond to labor constraints and schedule pressure. Sustainability shifts from “nice-to-have” to a requirement.

Hard to say but looking ten years out I would predict that digital twins manage assets end-to-end, robotics handle more field execution, and buildings operate as connected systems within smart cities. Design, construction, and operations blur into a continuous, data-driven lifecycle.

The firms that win will invest early in integrated platforms, clean data, and workforce upskilling. They’ll focus on collaboration, specialization, and strong technology partnerships—turning digital capability into real project outcomes, not just innovation theater.

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2026 Predictions for Semiconductors: AI, Chiplets, and the Path to Sustainable Innovation

As we step into 2026, the semiconductor industry stands at the crossroads of unprecedented technological advancements and complex global challenges. From the rise of AI-driven chip design and heterogeneous integration to the growing emphasis on sustainability and geopolitical shifts, the sector is navigating a transformative era.

The next wave of innovation will be defined by breakthroughs in advanced lithography, chiplet architectures, and quantum computing, while sustainability efforts will reshape manufacturing processes to address energy efficiency, water usage, and materials recycling. At the same time, the industry faces critical hurdles, including talent shortages, supply chain realignments, and the need for robust cybersecurity measures.

In this year’s predictions series, we’ve gathered insights from leading semiconductor experts:

• Simon Bennett – Principal Solutions Consultant, AI Tech Sales
• Sarah Crary Gregory – Principal Engineer, Systems and Solutions Methodologies, Crary Labs
• Neil Stroud – General Manager Automotive and Semiconductor, Jama Software
• Steve Rush – Principal Consultant, Jama Software

Together, they explore the trends and technologies shaping the future of semiconductors. From AI-driven automation and edge computing to the challenges of regulatory shifts and the promise of chiplet-based architectures, this piece highlights the innovations and strategies that will define 2026 and beyond.

Curious about what’s happening in other fields? Read part one on consumer electronics, part two on medical device & life sciences, part three on aerospace & defense, part four on automotive, and stay tuned for our upcoming predictions for AECO.

1: Emerging Technologies

Q: What emerging technologies (e.g., advanced lithography, AI-driven chip design, quantum computing, heterogeneous integration) will have the most transformative impact on the semiconductor industry in the next five years?

Simon Bennett: In the next five years, the semiconductor industry will continue to grow, almost doubling in size from today to $1Trillion by 2030. But to sustain that growth, the industry will go through some extreme changes and challenges. The first trend to note is actually due to a declining trend as Moore’s Law continues to slow. [Editor’s note: Moore’s law is the observation that the number of transistors in an integrated circuit (IC) doubles about every two years.]

Moore’s Law has driven the growth of the Semiconductor industry for many decades, but it is bumping up against the fundamental laws of physics. The economics of scaling to the next node are increasingly prohibitive and taking longer and longer to reach fruition.

Whilst keeping an eye on what is coming out of China, there will be some more mundane but equally challenging technology trends that are emerging and will become increasingly important in 2026 and beyond. These are AI driven design, and both chiplet and wafer scale designs (two opposite ends of the spectrum, but both an engineering reaction to the slowing of Moore’s Law).

Neil Stroud: Given the ever-increasing innovation around AI and its associated deployment, chip development is under continued pressure to keep up. This is applicable across all architectures, including Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Neural Processing Units (NPUs). Naturally, continued optimization will happen around acceleration and emerging technologies like process node shrinks (advanced lithography), AI-driven chip design, and the chiplet approach (heterogeneous integration). Process node shrinks will contribute. However, the chiplet approach will also drive heterogeneity across architectures and nodes. All these factors will intimately impact the next generation of chip families for AI in the datacenter and at the edge.

2: Sustainability and Manufacturing Efficiency

Q: How do you see sustainability influencing semiconductor manufacturing, particularly in areas like energy efficiency, water usage, and materials recycling? What strategies will help the industry achieve greener fabrication processes?

Bennett: This is a great question, and right now, the elephant in the room. From Fabs to datacenters, the environmental impact is huge. Water consumption alone is a huge factor. Twenty years ago, visionary realtors quietly purchased acres of land close to a bountiful supply of water and close to a large data pipe. Those realtors are now wealthy, and the secret is out. Now the price of that land is at a premium. So, the investors behind the fabs and the datacenters are using government subsidies and their own funds to find alternative sources of energy and resources. Nuclear is making a comeback, driven in part by the energy demands of the datacenters. Municipal areas like Phoenix are making guarantees of plentiful water to companies to attract them to their region; that will put them in direct conflict with farmers in California.

Most of this is happening off the radar of the mainstream media, and the political arena is presented as a battle for the best jobs. The concern over the environmental impact is not yet front and center. Two events will likely happen to change this:

The AI bubble will inevitably burst. Just like in the early days of the internet, there will be market correction as reality catches up to expectation. Just like the internet bubble, this doesn’t mean that AI is not going to be a societal change; it just means the market got too overheated.

Unfortunately, there will be some kind of accident related to the overbuild of the infrastructure around Datacenters and Fabs. A dam will burst (Phoenix – see Roosevelt Dam), or a multibillion fab will be damaged by a natural disaster (see fault lines in Taiwan). These two events will raise awareness of environmental costs relating to sustainability and manufacturing efficiency.

In other words, in the next five years, we will be forced to take a pause, a breath, and truly measure the value vs the cost. This isn’t a bad thing. Our human history of technology transformations is punctuated with these pauses and resets. Usually for the better.

Steve Rush: Sustainability is hugely influential and important. Energy demand is forecasted to accelerate with new data centers and the demand for AI. Semiconductor companies need a system to help manage their sustainability requirements and, very importantly, validate them. Implementation to hit targets and balance, power, efficiency, and sustainability will be a series of trade-offs – semiconductor organizations will need a tool to trace all of this information and prove that they meet sustainability targets and goals.

Sarah Crary Gregory: While the semiconductor industry is obviously fiercely competitive, it can match that intensity with fierce collaboration on critical issues. Sustainability is probably the most prominent area where industry consortia such as the Semiconductor Climate Consortium bring companies together to tackle common problems. Initiatives to enable water reclamation, reduce emissions, and produce data quantifying the return on investment of sustainability practices will be more critical with the burden placed on these resources from the exponential expansion of AI. The semiconductor industry is highly interdependent, and nobody believes that there’s a way to get a competitive advantage by monopolizing natural resources. The way forward is through innovations that decrease resource consumption and minimize waste, and initiatives for water reclamation/”net zero” resource use will continue to be essential investments.

Stroud: I think there are two parts to this. Firstly, the environmental impact of actually building the chips in foundries. A huge amount of effort and investment has gone into sustainability in semiconductor manufacturing, including energy efficiency, water usage, and materials recycling. semiconductor manufacturing and materials. A great example of this is massive recycling of water used in fab processes, as well as optimizing processes and the associated chemicals used, including minimizing atmospheric emissions.

Secondly, there is the environmental impact related to the deployment of the device itself, as it consumes power and emits heat. Of course, the extreme example of this is the data center where huge racks of GPUs or CPUs are deployed, collectively consuming Megawatts of power to both power them and cool them. Again, huge investment is going into driving data center efficiency. One way to contribute is through chip design optimization to improve ‘performance per Watt.’ That is simply a measure of how much computing can be done for a given Watt of power. This optimization can happen through design and architecture efficiencies as well as process node shrinks. Ensuring the software stack is also developed to drive efficient use of the underlying hardware platform also has a fundamental role to play. It’s easy to see that these steps can have a profound positive impact on the environment caused by the global electronics footprint.

RELATED: Buyer’s Guide: How to Select the Right Requirements Management and Traceability Solution

3: AI and Automation

Q: How is AI accelerating innovation in semiconductor design, verification, testing, and manufacturing? What challenges must companies overcome to fully leverage AI-driven automation?

Bennett: Natural language and agentic AI will continue to show up across the tool chain. But expect some resistance from SOC design engineers, who, ironically, since they are at the epicenter of the AI revolution, are traditionally conservative and slow to adopt new methods. Verification is the most in need of help with AI-driven automation, since there just aren’t enough engineers on the planet to drive the verification needs of an SOC. (see salaries on Glassdoor). It’s been estimated that with the use of AI, a team of 3 expert verification engineers can do the work of 5 traditional verification engineers with limited use of AI, in 3 to 5x less time. This is a compelling message to an (open-minded – see below for a caveat) engineering VP struggling to find the resources to deliver a fully validated product on time. These engineers and the tools they use will be in high demand in the next five years.

Beyond design, AI will show up in yield and manufacturing analytics. The challenge of inventory and yield management in the era of disaggregated chiplet-based designs is magnified. It’s essential that all the chiplets deliver the yield and volume needed at the exact same time. The overall package is only as good as the weakest tile. This is an underserved opportunity within the big three EDA companies, and the packaging OEMS tend to jealously protect their homegrown investments in solving these challenges. Expect emerging startups to come forward as disruptors in this particular segment in 2026 and beyond.

Rush: Every company is looking for ways to utilize AI in their organization. AI can play an important role in managing traceability, especially from siloed systems that are isolated from one another. Agentic experiences that improve engineer productivity really are key. The main challenge that AI has in the semiconductor space, in particular, is adoption with the engineering team. AI experiences must improve engineering productivity; they must be accurate, and they cannot be an impediment to use. If AI-generated content is of questionable quality or if the AI experiences become too burdensome to use, AI initiatives risk dying on the vine.

4: Supply Chain and Geopolitical Shifts

Q: How are global supply chain realignments and geopolitical factors shaping semiconductor strategy? What can companies do to mitigate risk and ensure resilience in developing complex products on their own or with co-development partners?

Bennett: A global supply chain developed over the past thirty years has delivered $1T in cost savings. This $1T is now under serious threat as the world is a very different place compared to when this globally interconnected environment was first conceived. In the next five years, expect China to become more self-sufficient as it replicates every aspect of what it previously relied on from overseas, from EDA to IP to fab equipment. Expect to see semiconductor-based products from coffee machines to phones to servers to (even) EVs sourced almost exclusively from China with little to no reliance on anything beyond the shores of China. This will trigger protectionist measures in the US and the EU as they work to protect homegrown industries from what will become increasingly consumer appealing products from the Chinese factories.

A more optimistic view may be that the tensions ease as the US / EU recognize the need for open trade with China, and continue to see its designs realized in Chinese factories (but I’m not holding my breath). In semiconductors, companies will be most susceptible to this shift in China as they move to homegrown alternatives. As the geopolitics ramp up, the focus on Provenance in the West will become a C-suite / US Senate / EU Parliament level of attention. Knowing where every component or piece of code originates, its genealogy will become paramount. A counterforce will emerge where the information is “buried” as the realization hits that we can’t possibly trace the root of every bit of code, every nanometer of design. Companies will emerge with one of two unique value propositions: 1) we can audit your product and provide the provenance, 2) everything you use is contaminated; we are a new company, built cleanly from the ground up. Somehow, all three will survive – the traditional companies, the auditors, and the new “clean” companies. But there will be some very interesting mergers and acquisitions, mostly off the radar as these three entities re-align and learn to co-exist.

Rush: These days, you can basically count on major geopolitical news covering the semiconductor industry week in, week out. At the end of the day, co-development and partnerships are key. The semiconductor supply chain is mind-bogglingly complex. Adopting modern, more collaborative tooling is on the rise. Historically, the semiconductor industry has even been hesitant to adopt cloud-based solutions, and I’ve definitely seen a change in the last few years around this.

Stroud: Like many other segments, the semiconductor market tends to be cyclic, which leads to times of undersupply and oversupply. This is a complex problem to manage with many factors, including global supply chain realignments and geopolitical factors. Naturally, foundry capacity has a big role to play, and we seem to be in an investment phase right now with a number of fabs being built. This is a massive investment with a modern fab costing tens of billions of Dollars and taking multiple years from construction start to mass production. Communication and collaboration across the ecosystem also has a role to play, especially now that we are accelerating into the chiplet era, which can help mitigate risk and ensure resilience in developing complex products.

5: Chiplet and Heterogeneous Integration

Q: What role will chiplet architectures and heterogeneous integration play in addressing performance and scalability challenges? What technical and ecosystem hurdles must be overcome?

Bennett: Chiplets are essential to the continued growth of Semiconductors. Without chiplets, the forecast CAGR ($1T by 2030) is unreachable (basic economics of Moore’s Law). The challenges are two-fold: 1) engineering challenges around interconnecting tiles from different suppliers running at high speed and with the thermal challenges of a modern chip; and 2) coherence – the coherence of the supply chain, compliance, and verification. More specifically, the standards emerging need to be better governed (e.g., Universal Chiplet Interconnect Express (UCIe) for interconnect and system architectures if they aren’t going to become bottlenecks stymying growth.

6: Talent and Workforce Development

Q: With growing global demand for skilled engineers and manufacturing specialists, how can companies address the talent shortage in the semiconductor industry?

Bennett: This is where AI needs to step in and become more readily accepted within Semiconductor Engineering orgs. As stated above, studies show that a small team of AI proficient verification engineers are 5x+ more productive than a traditional team. However, the resistance comes from within – engineers are conservative, and within a traditional engineering organization, the manager / Director / VP still measure their worth by the number of engineers the corporation is willing to fund. This leads to destructive behaviors, such as a VP of Verification Engineering employing 100 RTL validation engineers to do the job that 10 Functional Verification engineers could do because “it’s too expensive to hire the functional verification engineers” – the companies that will thrive and succeed in the next five years are the ones who break down this cultural impasse.

Rush: There are a lot of talented people in the job market right now who can help fill the gap. Hopefully, semiconductor companies will look to hire talent from across industries – automotive, medical, and aerospace. There are certain challenges in getting enough skilled foreign workers to fill certain roles – I’m more concerned that there are many highly skilled, talented people out there looking for jobs!

7: Regulatory and Export Controls

Q: How do evolving export controls, trade policies, and security regulations impact semiconductor innovation and competitiveness? How can companies adapt strategically?

Bennett: They don’t impact semiconductor engineering innovation or competitiveness – in fact, they improve it. Case in point is China – as access to advanced GPUS / EDA tools was limited, they innovated, and actually improved on the technologies they didn’t have access to. Another example is where the Russian engineers working for US companies prior to the war in Ukraine were let go and went to work for Russian companies, helping boost the AI business in Russia. But where the question applies is the innovation at the corporate level. Engineering innovation can be stymied by a C-suite overly concerned about trade or political issues. The paradox is that smaller companies with less of a global or political reach could feel less compelled to avoid the risk associated with innovation.

Gregory: “Evolving” is an understatement! The volatility around export controls and trade policy in the United States right now is simply unprecedented, and 2026 looks like more of the same. Companies can strategically navigate these unsettled times by implementing systems –people, processes, and tools – that enable maximum response flexibility. Modular architectures, whether they’re chiplet-based, specific configurations of IP cores, highly modular software, or other building blocks, will enable the development and delivery of products whose configurations can be changed and modified as circumstances warrant. Variant management is a critical capability to be able to swap features in and out based on policy changes. Solid, well-governed data foundations will be critical to stay on top of the wildly shifting policy landscape.

RELATED: Engineering Governance is a Critical Business Strategy for Product, Project, and System Development Excellence

8: AI and Edge Computing Demand

Q: As demand for edge AI and high-performance computing grows, what innovations are most critical to meet performance and power efficiency goals?

Bennett: There are many ways to answer this, but I’ll focus on the chip-level design aspect. First, the interconnect, as previously described – the clean adoption of UCIe and a strong governing body to oversee its evolution (think Universal Serial Bus, or USB.) 3D packaging needs to keep up with the thermal demands of a heterogenous package – this may lead back to the engineering talent pipeline previously discussed since the engineers who have the combination of skills to analyze and design (future-proof) these packages are unique (think warping of a substrate as it reacts to thermal pressures, leading to subtle issues with the interconnect manifesting as signal integrity.)

Rush: I’ll answer this more from a – data isolation – perspective. Design and testing are really important, but more important is tracing all the way to the highest level and validation. I think responsible AI will help with efficiency here, but companies need a way to trace from the top down. In all honesty, this is a challenge for the semiconductor industry – having one single source of truth that can prove you’re hitting sustainability goals.

9: Cybersecurity and IP Protection

Q: With increasingly complex global supply chains, how can semiconductor companies protect intellectual property and secure their design-to-production ecosystems?

Bennett: Expect a lot more reference to initiatives such as Software Bill of Materials (SBOM) and Engineering Bill of Materials (EBOM.) Expect the concept of a Bill of Materials (BOM) to evolve and take on more significance in the next few years. Expect the term Provenance to take on more importance. Traditional PLM companies will position themselves as the answer, but there will be significant pushback from the semiconductor industry, and rightly so – these PLM systems were never developed with semiconductors in mind. They are monolithic in nature, expecting the end user to move their data into their environments. The C-Suite will sign on, the engineers won’t. This will lead to QMS and IT organizations emerging to manually clone the data inside the PLM systems. For a while, this will seem just fine, until one or more issues come to public light, and the C-suite exec realizes they have spent a lot of money on tools and resources, and it didn’t solve the problem. Those companies that invested in a more lightweight engineer-friendly solution, providing traceability, compliance, and coherence insights without the costly overhead of monolithic tools and the resources that go along with them, will grab the attention of those who lost out. And yes, AI will play a part. A well-managed digital thread with the ability to expose itself in a controlled manner to intelligent insights will win out.

Rush: I mentioned earlier that semiconductor companies are adopting more cloud-based tooling. But they are not slacking in terms of security needs. By selecting best-in-class tools with exceptional infosec track records (like Jama Connect), they are effectively balancing speed and agility with security and not sacrificing either. They are pushing their vendors to expand their tool sets to deliver best-in-class experiences with rationale, scalable permission structures that are tightly governed. They’re looking for tools and vendors that are putting AI at the center of their vision – but need their vendors to offer closed, secure LLMs or integrations with in-hours AI systems.

Stroud: This is not a new issue! The semiconductor industry has been wrestling with intellectual property protection and securing the design-to-production ecosystem for years. The challenge is how to build enough flexibility in the ‘fixed’ silicon that, when combined with software (across all layers), is able to guard against future exploits and vulnerabilities. It’s almost impossible to build a modern chip without multiple integrated security capabilities. Also, it’s worth noting that security has to be a multidimensional approach in this age of hyperconnectivity, spanning seamlessly from cloud to edge. This is why we see an ever increasing number of emerging security standards that apply to both implementation and development processes, impacting hardware, software, and system design and deployment.

10: Future Outlook

Q: What do you see as the most important technological and market shifts that will define the semiconductor industry five to ten years from now? How can companies position for sustained leadership?

Bennett: 1) Semiconductor Technology: Chiplets, and the packages that are needed to realize their promise to alleviate the decline of Moore’s Law. 2) Companies: very different answer–the companies that will succeed in the future are those that completely obfuscate the hardware considerations from their customers—it’s all software, don’t worry about the hardware – we have taken care of that.

In summary, in some ways it’s the same old story – recognize and reward the unique engineering talent that helps differentiate your product, understand what the customer wants, and remove the barriers to growth. Sounds simple, right?

Rush: With AI, the amount of data that companies will manage is going to increase tremendously. Trying to manage that traceability is going to be extremely challenging. Jama Connect, with the new scaling improvements and AI vision, is at the forefront of the market and uniquely positioned to help semiconductor companies here.

Gregory: Agreed. AI is already reshaping the demand side of the market equation. The supply-side will evolve to support highly customized semiconductor design, even purpose-built and assembled solutions that are rapidly defined and fabricated. Edge AI and NPUs (neural processing units), along with open architectures such as RISC-V (and the RISC SW Ecosystem), will further broaden the horizons for semiconductor companies. How to be positioned for success? Again, it’s all about response flexibility. Sensing both strong and weak signals in the market and systematically building resilience into the company’s organizational practices will determine which companies emerge stronger from the challenges of the next five to ten years.

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2026 Predictions for Automotive: AI, Electrification, and the Road to a Connected Future

As 2026 approaches, the automotive industry is about to enter an exciting phase marked by cutting-edge technologies, sustainability requirements, and shifting consumer expectations. The industry is navigating a changing landscape of opportunities and challenges, from the emergence of autonomous driving systems and vehicle-to-everything (V2X) communication to developments in electrification and AI-driven innovation.

The integration of emerging technologies is reshaping vehicles into interconnected, software-defined systems, while sustainability goals are driving rapid advancements in battery technology, charging infrastructure, and renewable energy integration. At the same time, the industry faces critical hurdles, including cybersecurity threats, regulatory complexities, and the need for seamless collaboration across OEMs, suppliers, and technology partners.

In this year’s predictions series, we’ve gathered insights from leading automotive experts:

• Florian Rohde – Managing Partner, iProcess LLC
• Ronald Melster – Managing Director, Melster Consulting GmbH
• Kevin Dibble – President, Reinnovate Consulting
• Matt Mickle – Director, Solutions & Consulting, Jama Software
• Sathiya Ramamoorthy – Senior Solutions Consultant, Jama Software

Together, they explore the trends and technologies shaping the future of the automotive industry. From AI-driven predictive maintenance and edge computing to the challenges of electrification and the rise of subscription-based ownership models, this piece highlights the innovations and strategies that will define 2026 and beyond.

Curious about what’s happening in other fields? Read part one on consumer electronics, part two on medical device & life sciences, part three on aerospace & defense, and stay tuned for our upcoming predictions on Semiconductors, AECO, and more.

Emerging Technologies

Q: What emerging technologies (e.g., autonomous driving systems, vehicle-to-everything (V2X) communication, advanced driver-assistance systems (ADAS)) do you believe will have the most transformative impact on the automotive industry in the next five years? How can companies prepare to adopt and integrate these advancements effectively?

Florian Rohde: There is no ONE next big thing. The most transformative impact will be created by the integration of many emerging technologies. We see fast-paced innovation in a lot of sectors, and the most successful product will be the one with the best overall user experience. Whether driving manually or autonomously, mobility will encompass much more, with integration into the environment and a fully customized experience emerging as the winning combination. The emergence of AI will definitively be the biggest enabler for the next generation of mobility, for several areas, first for the user interface, which will see orders of magnitude in improvement, and next then also for driving and integration functions, as well as shared mobility, or public transportation.

Ronald Melster: While ADAS and autonomous driving progress as expected, V2X (Vehicle-to-Everything) communication is the underestimated game-changer for the next five years. V2X addresses fundamental sensor limitations. Instead of struggling to recognize speed limit signs in poor weather, vehicles receive information directly from infrastructure. Studies suggest V2X-enhanced ADAS could address eighty-eight percent of vehicle collisions. Over ten million V2X-capable vehicles are expected by 2025, with regulatory mandates in Europe, the US, China, and Japan driving adoption. For companies integrating V2X, three areas are critical. First, functional safety, where ASIL-grade components are required to ensure reliable communication in safety-critical scenarios. Second, security architecture where authentication and privacy protection must be built in from day one to prevent spoofing and data breaches. Third, a clear technology strategy, as the landscape is rapidly consolidating around dominant standards. The challenge is infrastructure dependency. Systems must operate in mixed environments where V2X complements traditional sensors. This complexity demands structured development processes to maintain safety throughout the vehicle lifecycle.

Matt Mickle: All of these technologies will be impactful as they shift vehicles from isolated products to interconnected, software-defined systems, but only if they’re integrated safely and at scale, using AI to support a backbone of well-established processes and strong cross-industry partnerships.

Sathiya Ramamoorthy: 5G-V2X, satellite-enhanced V2X, high-precision GNSS, and the steady progress of L4 autonomous driving will strongly shape the industry over the next five years. Recent 5GAA demonstrations showed how reliable hazard warnings, emergency messages, and seamless satellite–terrestrial switching can support safer automated functions, while precise GNSS improves lane-level positioning. L4 autonomy is already moving from pilot projects to real robotaxi services in several cities, with more deployments expected from 2026 onwards, while L5 will remain long-term and limited to special scenarios. To prepare, companies need software-defined architectures, strong cybersecurity, and integrated testing that connects road, cloud, GNSS, satellite, and automated-driving systems.

Sustainability and Electrification

Q: As the automotive industry continues its journey toward electrification and sustainability, how do you see advancements in battery technology, charging infrastructure, and renewable energy integration shaping the future? What strategies will be critical for achieving these goals at scale, and how can companies navigate the challenges of changing regulatory landscapes?

Rohde: We are seeing extremely quick improvements in all areas related to EVs. A lot of engineering resources and investments are going into advancing cell technology, infrastructure, and electronics. Additionally, cars are transitioning into SDV architectures, which makes the ongoing integration of new technology faster and easier. The regulatory landscape needs to adapt to this new pace of the industry in order not to be the braking block of innovation. I observe openness on the lawmaker’s side; collaboration is key.

Melster: The technical challenges of electrification are well documented, but the software complexity is often underestimated. Charging systems require communication with external infrastructure. Unlike traditional vehicle functions in a closed embedded environment, two development worlds collide: embedded software with real-time and safety requirements meet cloud software with external interfaces and different security models. Every charging station becomes a potential attack vector. The solution lies in a unified development process across both domains. ASPICE-compliant processes must extend to backend development, and the new ASQMS standard explicitly requires this scope of expansion. Success requires structured processes that bridge these domains and integrated security practices throughout the development lifecycle.

Kevin Dibble: In many countries, the grid simply can’t support the charging infrastructure required to support a highly electrified mobile society. Cars, buses, and heavy trucks demand more power than grids can supply. New technologies for large energy stores will be critical for establishing charging infrastructure that is powered by green energy.

Mickle: Electrification and renewable integration are inevitable, and the technology is moving in the right direction; however, there will be challenges such as keeping up with the demand for batteries, expanding the grid capacity for widespread vehicle charging, and maintaining products that meet the needs of regulations that lack harmonization. All of this will require tight alignment between OEMs, suppliers, and regulators.

RELATED: Empowering Complex Development with Responsible AI

Connected Vehicles, Data, and Safety

Q: With connected vehicles becoming the norm, how do you see data collection and utilization evolving to improve safety, reliability, and customer experience? What opportunities and challenges do you anticipate in leveraging real-time data to enhance both innovation and road safety?

Ramamoorthy: Connected vehicles will use real-time data from onboard sensors, other vehicles, and smart infrastructure to improve safety, reliability, and overall driving experience. Recent 5GAA road tests showed how sharing hazard and sensor information can prevent accidents and support safer maneuvers. The main challenges will be protecting personal data, securing networks, and ensuring this information is used in a safe and trustworthy way.

Rohde: Until now, car makers had to over-engineer their products due to lack of knowledge of how they are actually used. The only form of feedback for the engineers came through return parts, an indicator that the product has not performed well. But there was no clear understanding to what extent the parts were over-engineered if they did not break. They might be using 99% of their capability and useful life, or maybe just 5%, engineers never knew at scale. Now, with data, either evaluated inside the vehicles or in an anonymized data lake, we can see the real use. How often are doors opened? How many turns does a steering gear do over the years? What capacity of batteries is necessary for 90% of the users? These and more questions can now be answered and add valuable insights for R&D engineers to make the product better, without making it exorbitantly more expensive.

Melster: Connected vehicles fundamentally change verification. Millions of vehicles capture edge cases no test team could ever cover. When all vehicles collect data from day one, you get comprehensive real-world coverage and real-time mapping of road conditions and system behavior. The challenge: companies drown in terabytes without clear processes for filtering and feeding insights back into development. Most collect everything and learn nothing. Success requires closing the loop from field data to requirements to implementation.

Mickle: There is a ton of opportunity in collecting vehicle data for things like predictive maintenance and improving ADAS functions with real-time road and traffic conditions, but data privacy and security still remain major concerns. Success will require strong data-governance processes and clear traceability from collected data to the actions that are taken in order to ensure that trust and security are maintained.

AI and Automation

Q: How do you foresee AI and machine learning influencing areas like autonomous driving, predictive maintenance, and design and manufacturing efficiency in the automotive industry? What are the biggest challenges companies might face in scaling these technologies, and how can they overcome them?

Rohde: The first big step to the success of AI is to understand it. There’s no “THE AI”; there are a lot of different components to AI, and the industry has to put in the effort to understand what all of these are and how they can come together and help us. Overall, it is without a doubt that artificial intelligence will change the way we are engineering our products and the way our products will behave. Already today, AI is greatly used in the areas of documentation, specification, and test engineering. But this is completely different AI than what will drive our autonomy or predictive maintenance. Right now, we’re talking server AI, machine learning producing algorithms it’s getting sent to the vehicle. The concept of edge AI, where we have real decision-making in the car based on ongoing learning, will be powerful, yet it’s still a while out (see last question).

Melster: AI will have a massive impact: in-vehicle systems, development processes, and predictive maintenance. The real challenge is the conflict between non-deterministic AI behavior and regulatory requirements for deterministic safety proofs. Non-determinism makes AI powerful, but regulations demand verifiable requirements and predictable behavior. How do you prove compliance when behavior emerges from training data rather than code? ISO 26262 and ASPICE weren’t designed for this. Companies need new verification approaches that demonstrate safety boundaries without requiring deterministic behavior. The scaling challenge isn’t computational – it’s process maturity.

Dibble: AI will continue to be the centerpiece of self-driving car technology. However, large gains are coming through the automation of the development workflow for many aspects of automotive engineering. The exponential growth of software in the car needs Agentic AI workers to improve quality and speed up delivery. Requirements management and test management are 2 areas that should light up in 2026.

Mickle: The biggest concerns here are model transparency and quality training data in order to maintain safety and regulatory expectations. AI-driven decisions need to be explainable and validated using solid governance practices. More standards, such as ISO PAS 8800, are still being developed to help with this and will need to be put into practice.

Responsible and Safe AI Adoption

Q: As AI and machine learning become more integrated into automotive workflows, what key considerations should companies focus on to ensure safe, ethical, and transparent implementation—especially in safety-critical systems? How can organizations address these challenges while maximizing the benefits of AI-driven automation?

Dibble: AI agents should be considered teammates or collaborators alongside systems and software engineers. Human-in-the-loop staffing practices will be critical for error reduction and to certify systems for safety, cyber, and quality. Planning for AI workflows must include consideration of ethical issues like bias.

Mickle: Organizations should treat AI as part of their safety and quality management system, rather than as a bolt-on technology. This means validating it against well-structured requirements and keeping humans in the loop for high-risk decisions.

Evolving Consumer Expectations

Q: With consumers increasingly prioritizing sustainability, connectivity, and personalized experiences, how do you see these expectations shaping vehicle design, features, and services in the coming years? What innovations will be critical to meeting these demands, and how can companies stay ahead of shifting preferences?

Rohde: Cars in the future will not have a selectable number of customizations for features. Instead, features will be truly customizable with the help of AI interfaces so that drivers or users can make them actually one of their own. While this is creating challenges on the development side for implementation of those AI-driven features, it creates even higher, bigger challenges on the side of validation. From that point on, the features will not be defined only by the requirements, but the user will have significant input in their design and use.

Mickle: Innovation will need to focus on energy efficiency, connectivity, and flexibility to adapt to each individual’s needs. Software-defined features delivered through over-the-air updates, along with the use of sustainable materials, will be critical to achieving this.

For example, Rivian’s “Smart Charging Schedule Recommendation” can automatically shift charging to off-peak hours. A software update which can help with environmental goals without a needed hardware change.

Ramamoorthy: Consumers will expect cars to feel like personalized digital devices, not just machines. We already see this with BMW adopting Android Automotive OS and offering paid digital features through its ConnectedDrive store and charging services. In the future, OEMs will rely more on software, subscriptions, sustainable materials, and regular OTA updates to keep vehicles fresh and aligned with fast-changing customer expectations.

Regulatory Landscape

Q: What upcoming regulatory changes or safety standards do you anticipate having the biggest impact on the automotive industry in 2026? How can companies stay ahead of these evolving requirements while maintaining innovation and competitiveness?

Melster: The biggest impact in 2026 won’t be any single new regulation—it’s the sheer volume of standards and norms hitting developers of a single product. ISO 26262, ISO 21434, ASPICE, ASQMS, UN R155/R156, EU Cyber Resilience Act—each brings its own audits and assessments. Developers spend more time in audits than actually developing. Every project gets audited separately, creating redundancy, inefficiency, and audit fatigue. The only viable solution is shifting from project-based to organization-based assessments. Certify the organization and its processes once, not every project individually. Build trust through organizational-level certificates. This allows developers to focus on development, makes audits efficient, and keeps innovation possible despite increasing regulatory complexity.

Mickle: Standards such as ISO 21434 and ISO 26262 will become even more tightly integrated into development processes, while SOTIF and ISO/PAS 8800 will take a growing foothold as AI-based systems expand. In addition, major updates to the Euro NCAP protocols planned for 2026 will have a significant impact on how vehicles are designed and validated.

Cybersecurity and Vehicle Safety

Q: As vehicles become more connected and autonomous, what role do you see cybersecurity playing in ensuring system integrity, passenger safety, and data protection? What strategies should companies prioritize to mitigate cyber risks and strengthen trust in connected vehicle ecosystems?

Rohde: Cybersecurity in automotive is still in its infancy. Both OEMs and Suppliers have yet to build up strong cybersecurity defense teams and strategies. Many systems in a car today are not designed to be resistant against cyber-attacks. The future will bring quantum computing, and with that, even bigger cybersecurity threats. The car industry has to react now in order to prepare for that scenario.

Melster: Cybersecurity is not a compliance checkbox – it is an operational discipline. Most OEMs treat ISO 21434 and UN R155 as audit exercises: pass the assessment, move on. Real security requires security by design: threat modeling in architecture, security champions in teams, continuous penetration testing—not just before audits. The bigger challenge is post-production. Threats will evolve after type approval. Companies need Security Operations Centers (SOCs) for vehicle fleets: continuous monitoring, incident detection, and coordinated OTA updates when vulnerabilities emerge. Security is not a milestone—it is ongoing operations.

Dibble: Developing secure vehicle architectures should be the focus. These architectures must be resilient to new and increasing threats from AI-based cyber-attacks.

Mickle: Cybersecurity will need to be treated as a continuous lifecycle activity, fully integrated with functional safety and requirements management processes rather than handled as an independent effort.

Ramamoorthy: Cybersecurity will be central to protecting system integrity, passenger safety, and vehicle data as connectivity increases. The 2025 JLR cyber-attack showed how a single breach can disrupt operations and expose supply-chain weaknesses. With new rules like the EU Cyber Resilience Act, the EU AI Act, and China’s GB 44495-2024, companies must focus on secure architectures, strong OTA processes, and continuous fleet monitoring. To build trust, OEMs should enforce strict supplier security audits, run regular penetration tests, secure OTA updates, and maintain fast, well-practiced incident-response actions.

RELATED: Buyer’s Guide: Selecting a Requirements Management and Traceability Solution for Automotive

Co-Development and Supplier Collaboration

Q: With automotive systems growing more complex and software-driven, co-development and shared requirements between OEMs, suppliers, and technology partners are becoming essential. How do you see collaboration models evolving to support faster innovation, stronger traceability, and consistent safety standards across the supply chain?

Rohde: The collaboration between the different players in the industry has to be redesigned. Those long-existing barriers between OEMs, Tier 1s, and Tier2s are hindering the progress. To achieve proper continuous integration and validation results, a much closer collaboration is necessary. On top of that, we’re seeing the emergence of open-source software in the automotive industry, which has its definite Pros like avoidance of double work and extra efforts. But it also comes with new challenges like certifications and responsibility questions.

Melster: Modern vehicle development is one integrated project spanning OEMs and multiple supplier tiers. However, assessments treat each company separately. The same processes are audited repeatedly at each supplier, creating massive redundancy. The solution requires two elements. First, supply chain certificates. If a supplier holds a valid certificate, the OEM accepts it without re-auditing. Second, agreed toolchains. Requirements management, change management, and configuration management must use compatible tools across company boundaries. Without tool alignment, traceability breaks down. Certificates reduce redundancy; shared tools enable traceability.

Mickle: It will be essential to maintain full traceability across integrated systems, with shared visibility across interfaces between organizations to ensure alignment on safety and security goals. This means a shared ecosystem of compatible tooling that allows for close communication feedback loops.

Long-Term Trends

Q: What trends or technologies do you think will still be shaping the automotive industry five years from now? Ten years? How can companies position themselves to remain competitive, safe, and innovative in the long term?

Rohde: Edge AI will be the biggest thing. AI that continues to get smarter and better while learning from the environment, eventually. I believe we will see a paradigm shift as soon as edge AI hardware makes a big impact, and from there on, mobility will be nothing like it is today.

Melster: Mastering AI will be the key. Not just deploying AI features in vehicles, but mastering AI-driven development, validation, and operations. AI for automated testing. AI for anomaly detection in fleets. AI for predictive maintenance. Companies that integrate AI across the entire development lifecycle will dominate.

Dibble: The megatrend that will change the industry permanently is the pay-as-you-go subscription type ownership models, and away from traditional ownership models. This will focus OEMs on developing more fleet cars, wipe out dealerships, give the OEM direct control over the customer experience, and allow for a new wave of middle-tier companies to potentially manage the service.

Mickle: Technology advancements such as central compute with zonal architecture will have a major impact, reducing complexity and improving overall reliability while enabling lower costs and much faster innovation. Otherwise, of course, AI is going to have a major impact with all avenues of advancement.

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2026 Predictions for Aerospace & Defense: AI, Sustainability, and the Digital Transformation Frontier

As we approach 2026, the aerospace and defense (A&D) industry stands at the crossroads of innovation and transformation. With rising geopolitical tensions, increased defense spending, and technological advancements, the sector is navigating a complex landscape of opportunities and challenges.

From the integration of AI and digital twins to the push for sustainable aviation and the modernization of legacy systems, A&D organizations are embracing cutting-edge technologies to enhance efficiency, safety, and mission readiness. At the same time, they face critical hurdles, including supply chain disruptions, evolving regulatory frameworks, and the need to attract a future-ready workforce.

In this year’s predictions series, we’ve gathered insights from leading industry expert professionals from Jama Software:

• Cary Bryczek – Director, Solutions & Consulting
• Matt Macias – General Manager, Aerospace & Defense
• Karl Mulcahy – EMEA Sales Manager, A&D

Together, they explore the trends and technologies shaping the future of aerospace and defense. From AI-driven design optimization and autonomous systems to the rise of sustainable aviation fuels and the challenges of digital engineering, this piece highlights the innovations and strategies that will define 2026 and beyond.

Curious about what’s happening in other fields? Read part one on consumer electronics, part two on medical device & life sciences, part three on automotive, and stay tuned for our upcoming predictions on semiconductors and AECO.

Please note: This blog features content from writers in the UK and the US. Spelling variations (e.g., ‘defense’ vs. ‘defence’) may appear due to regional differences.

Emerging Technologies

Q: What emerging technologies (e.g., digital twins, advanced materials, AI-driven design optimization, autonomous systems) do you believe will have the most transformative impact on the aerospace and defense industry in the next five years? How can organizations prepare to integrate these technologies effectively into existing programs?

Matt Macias: Dramatic product transformations are already underway, and we will see increasing fielding of cyber-physical systems that take advantage of software-based intelligence and features combined from the beginning to fully capitalize on extensive use of sensors and electronic systems, as well as the physical aspects of the system. I am very excited to see this next round of intelligent/cyber-physical systems in operation. Should processing capability and AI enable further breakthroughs in model performance, the opportunity to see live or near-live digital twins of craft used to monitor health or guide optimized operations/missions is a tantalizing possibility with enormous potential to decrease costs, increase availability, and mission success.

Karl Mulcahy: With increases in Defence spending occurring worldwide, I’m seeing a move towards Digital Transformation to help in all manners of A&D business. Whether this is for a larger Defence contractor or a new Space Innovator ‘Start Up,’ there’s much more of a focus on moving away from legacy methods and more towards adopting modern technology such as AI to help automate more in operations.

With larger organisations wanting to pivot to being more agile, competitive, and delivering innovation quicker, there’s more of a challenge to modernize legacy systems and to connect data sources, whereas I’m hearing that startups want to learn from time in industry to help define good processes now to aid scalability and drive efficiency.

The need to create digital twins to reduce risks, undertake cheaper / continuous improvement, and helping to innovate faster is a big driver for the customers I’m working with. Also, the need to strategically reuse items from previous projects for modernization programs, or even new variants/products, is a focus to help get to market faster and meet ever-changing market demands.

Cary Bryczek: One tangible example that nearly anyone who travels will benefit from is the modernization of the air traffic controller (ATC) to pilot communications system. Today, controllers unbelievably still use Very High Frequency (VHF) and Ultra High Frequency (UHF) radio signals technology developed in the 40s to communicate with pilots. While new technology aids decision-making, human error remains a significant factor in ATC operations. Voice commands spoken at a rapid pace due to air traffic congestion, received by pilots who may not have English as their native language, over VHF/UHF where signals can be interfered with or stepped on, increases the number of mishaps in aircraft flight takeoffs and landings. Mishaps are on the rise. As of December 2025, there have been 1,097 aviation accidents or incidents in the United States in 2025, according to the National Transportation Safety Board—not including the most recent crash by the UPS cargo jet in Kentucky. Many point the finger at poor ATC technology, policies, and failure to act on the numerous alerts at this location over the past decade as significant contributing factors to the deadly collision of the Army Blackhawk helicopter with the Bombardier CRJ7000 passenger airliner in Washington DC.

My prediction is that AI-assisted technology will dramatically improve the safety in our airspace. Navigation signals will be intelligently generated by the AI based on data and presented to air traffic control operators to be sent as a text message directly to the pilot. Pilots receive it and can even have the navigation message tell the aircraft to change course.

Sustainability and Green Aviation

Q: As the industry pushes toward decarbonization, how do you see advancements in sustainable aviation fuels (SAF), electrified propulsion, and hydrogen-powered systems shaping the future of aerospace? What strategies will be key for scaling these solutions globally?

Macias: While we have not seen the focus on these technologies recently due to a series of financial headwinds, we are just waiting for the next breakthrough in affordable power density solutions in batteries and alternative fuels. These alternatives could also become more viable as new craft become viable with more limited/focused missions that could benefit. In short, while this area may not be making the progress desired as of late, I am optimistic of surprises around the corner that might bring this back to the forefront.

Mulcahy: Despite challenges in this part of the industry, we’re starting to work more with companies retrofitting older aircraft with modern technology i.e. SAF (Sustainable Aviation Fuels), and whilst sustainable to reuse existing products out there and help to make them greener, this is arguably the fastest, lowest risk route to immediate CO2 reductions due to compliance with regulations and existing infrastructure around it.

Whilst we can all see innovation occurring within the eVTOL, UAV, AAM markets due to market needs and also to develop new compelling product lines, I’m curious to see how regulations will continue to emerge in these fields in line with new infrastructure being molded too, i.e., VertiPorts, charging bays.

But with more companies choosing not to develop everything in-house, there are emerging challenges of systems integration and ensuring that all parties are aligned to be fit for purpose and align with higher-level requirements to ensure risks are mitigated, and for example, range/weight calculations are verified correctly.

Bryczek: As much as I personally wish for technologies like hydrogen propulsion and battery propulsion to make our airspace cleaner, this is getting pushed farther out. The technology for batteries is not expanding rapidly enough to make this approach viable at a large scale. Many of the eVTOL startups have already changed their designs from pure electric to now hybrid-electric aircraft. For major manufacturers Airbus and Boeing, finance challenges are plaguing them in different ways. Boeing is still recovering from loss in sales and design/manufacturing problems with its jets and has less ability to focus on the necessary R&D for hydrogen propulsion. Airbus too has slowed its development in hydrogen, citing both infrastructure technology and regulatory difficulties. Interestingly, there have been press releases indicating Airbus shareholders are reaping sizable dividends, yet R&D budgets remain flat. Many in Europe argue that tax exemptions for delivery of aircraft using fossil fuels be eliminated, which does sound like a healthy step in the right direction. So, my answer to this question is that the industry is going the route of evolution rather than innovation.

Digital Transformation

Q: How is digital engineering transforming design, verification, and lifecycle management in aerospace and defense? What are the biggest opportunities and challenges in achieving a fully integrated digital thread?

Macias: In product development transformation, we are now seeing the true impact of model-based product development fully realized, where all disciplines across the enterprise can now both benefit from their own dedicated models, and perhaps even more importantly, the synergistic collaboration around holistic models that bring together all aspects of product, production, operation, and mission. This emerging success will be dramatically accelerated in the near future as Model-Based Systems Engineering (MBSE) and AI/ML concepts get more fully deploye,d with special benefit coming from the democratization of these iterative and collaborative data/model constructs, helping all understand how their work fits into the whole and how they can optimize all aspects of the product.

Mulcahy: The need for a digital thread is emerging more than ever to ensure interconnectivity between systems, reduce siloed working, and ensure the overall single source of truth. Whether companies are looking to deliver projects on time or reduce costs, there is a clear business case to establishing digital engineering practices. However, to get there a large challenge companies are facing is to embrace open technologies that can communicate to each other and allow data exchange. Furthermore, there’s a need to shift from document driven approach to model-based, data-centric workflows to connect teams and empower them with data to make better decisions.

Bryczek: The Department of War certainly is trying as hard as it can to get its workforce to change in step with newer digital engineering methods. It issued its new Digital Acquisition Strategy in November, which directly calls for leveraging digital engineering approaches and data over documents vs. traditional approaches. Requirements will be defined and validated in the context of a model and integrated with software and mechanical models. This vision is sound, but it is not happening across the board overnight. There are opportunities, but the biggest barrier remains the government personnel and their will to change the status quo and invest in the available technologies to make it happen.

We will continue to see increasing development converging around product families and feature-based development. Those who are smartly designing their products to follow Modular Open Systems Architecture (MOSA), which provides a higher degree of interoperability and vendor choice by the customer, will continue to have more success in the government market.

RELATED: CIMdata: Digital Thread in Aerospace and Defense

AI and Automation

Q: What role will AI and machine learning play in enabling autonomous flight, predictive maintenance, and mission readiness? What impact will AI have on design and manufacturing processes? What challenges might arise in ensuring safety, reliability, and certification?

Macias: I would like to see AI applied in three areas: 1) easing, broadening and acceleration of multi-disciplinary optimization of the product development process; 2) assistance and assurance of quality, comprehensively and consistency of development team work, preventing surprises and moving engineering further and further up-front opening up an order of magnitude of more possibilities; 3) combined with digital twins, AI could assist greatly in ensuring that all operational products are safe, healthy and operating effectively. All 3 of these effects would have a dramatic impact on safety, effectiveness, and cost/sustainability (not to be overlooked as a major driver of ecological concerns itself).

Bryczek: This question is endlessly broad, so I’d like to focus on the less glamorous segment of aircraft maintenance. I described already how there is a rise in air traffic control mishaps, some even leading to deaths. 2025 has been the most vivid year for aircraft accidents in my own personal memory. As more aircraft remain in service such as the aging MD11 that crashed in Kentucky killing all aboard and many on the ground due to a maintenance problem, and aging fleets being sold from one airline to the next often to younger international companies lacking the decades of the culture of safety that enable the processes and procedures for strict maintenance, we see evidence of aircraft slow to catch up to service bulletins and in some cases ignoring warning alerts leading to crashes and mishaps. Machine Learning will be able to use data to predict maintenance needs. It will analyze sensor data, as well as part requirements and testing data tracked even after part delivery, to predict part failures, preventing costly downtime and improving safety by alerting aircraft operators

Responsible AI Adoption

Q: As defense organizations expand their use of AI, how can they balance innovation with ethical and regulatory considerations? What frameworks should guide responsible AI adoption in mission-critical systems?

Mulcahy: There has to be a combination of human education/accountability, transparent governance, with security being a large part of this. With challenges like export control/data restrictions being a large consideration in defence projects, it’s important to test AI’s output and work before rolling out on a wider scale.

It will be interesting to see if organizations like the DOD and NATO release any guidance and/or frameworks for responsible & secure AI use in projects and/or missions.

Bryczek: In my observation, the US Government has taken a more responsible posture to AI than the commercial world. The Department of Defense has already published its Responsible AI (RAI) Toolkit, which is both a practical and public resource providing guidance to align AI projects with best practices and ethical principles as well as concrete activities that need to be taken when implementing AI. One of the five principles that jumps out to me is the “Traceable Principle: AI capabilities should be developed with transparent, auditable methodologies and data sources so personnel understand the technology and its operational methods.”

Traceability is Jama Connect’s core competency spanning engineering disciplines, bringing together the collaboration of both traceable decision-making and data. I predict we will see more use of Jama Connect in AI projects.

Macias: Karl and Cary’s answers are excellent and capture this topic well.

Supply Chain Resilience

Q: How do you see aerospace and defense companies adapting to ongoing supply chain disruptions? What technologies or practices will strengthen resilience and reduce risk in global production networks?

Mulcahy: Having worked with both sides of the supply chain here, with larger System Integrators / Consortium managing lots of parts/players, or with lower-tier suppliers who are changing their business model to become more diverse or enter into new markets, it’s clear how they want to adapt and streamline – by becoming digital.

By embracing technology to become more efficient, more collaborative, and robust, companies are able to differentiate by identifying gaps earlier with connected datasets and make decisions to take action quicker. With remote/international working still forming a large part of the Aerospace & Defence supply chain, it’s important to utilize secure communication to ensure continuous alignment. Furthermore, we’ve seen supply chains being strengthened due to mutual transparency and predictability, leading to more longer-term agreements and better future forecasting for future projects.

Macias: We believe strongly that the Aerospace and Defense supply chain can greatly benefit from increased model and digital data-based collaboration and traceability. As this becomes more adopted, we should see opportunities arise for more resilience and also avoidance of surprises and other quality impacts. At Jama Software, we are working hard to enable this.

Cybersecurity and Data Protection

Q: As aircraft and defense systems become increasingly digital and connected, what are the top cybersecurity challenges facing the industry? How can organizations safeguard sensitive data and critical assets?

Bryczek: We will see continued security mandates for Defense agencies as well as all contractors developing systems under contract, to be scrutinized heavily. Cybersecurity is no longer just an IT issue; it is a core element of national security. Threats have grown far beyond the days of old, with just malware and social engineering. Organizations will be putting more focus on Software bill of materials (SBOM) programs, which are driven by: Executive Order 14028. SBOMs provide full transparency into software components used in defense systems, helping mitigate supply chain compromise, hidden dependencies, and embedded malware and backdoors. This is especially important for weapons systems, avionics, and mission-critical software.

For example, U.S. departments of Defense, Homeland Security and Transportation all have launched cybersecurity initiatives affecting aviation. The Federal Aviation Administration mandated that airlines establish and maintain cybersecurity programs. The European Union Aviation Safety Agency developed a cybersecurity roadmap to address threats to the air traffic management system and operators. In addition, industry groups like the Aerospace Industries Association and National Business Aviation Association rank cybersecurity among key issues facing the aerospace industry.

Workforce and Skills Transformation

Q: With new technologies reshaping engineering and manufacturing, what skills will be most in demand in the aerospace and defense workforce of the future? How can organizations attract and retain this talent?

Mulcahy: There’s a growing need for skills around MBSE / Digital Engineering methods, of course, knowledge about AI / M,L with more technology being developed and introduced into manufacturing today and, no doubt, in the near future. Further skills around cybersecurity and overall secure systems engineering are proving to be in demand. With more software now being embedded into products, both system safety and security are becoming more important to focus on, with companies looking to streamline more to various regulations such as DO-326.

Organisations can attract this talent by helping to innovate quickly by adopting modern tools/workflows, but also empowering employees to make decisions and be able to get on with the task at hand. There are cultural/financial aspects too, which I’m sure are important, but I feel a big thing is to provide opportunities for continuous learning. This will prove to be important to employees to understand new technologies, advance their skills, and also, in turn bring more benefits to their business by applying their learning to continuously enhance workflows and inspire future generations.

Macias: I couldn’t agree more with Karl! The workforce of the future will need the ability to work both in their area of specialization as well as appreciate the total system’s effects, hence the rise in importance of systems/requirements engineering and optimization competencies.

Bryczek: Modern aerospace projects are massive in scale and complexity, involving interdisciplinary teams and subsystems. Systems engineering is the glue that holds everything together, ensuring that avionics, propulsion, structural components, and software work seamlessly. Proficiency in systems thinking, risk management, and integration processes used to be vital but now the new systems engineer is an AI Engineer. AI engineers blend systems engineering, software development, computer science, and user-focused design. This mix helps them build smart systems that can tackle specific tasks or achieve set goals. The skills of an AI engineer are typically: building algorithms, model training, data preprocessing, and model deployment.

RELATED: Buyer’s Guide: Selecting a Requirements Management and Traceability Solution for Aerospace

Regulatory and Policy Evolution

Q: How do you see evolving regulations and policies, including new cybersecurity frameworks—impacting innovation and program timelines? How can organizations stay ahead?

Macias: The industry is demanding agility and rapid innovation to react to new technologies and new mission needs. We see this coming from government defense organizations across the globe, where acquisition reforms and digital engineering strategies are coming to the forefront to acknowledge the need to accelerate product to market/field at cost and on schedule. We can expect this to dominate focus going forward, with all product development organizations needing to leave behind legacy tools and processes and move to highly agile, innovative digital model-based approaches to keep up.

Bryczek: There are many moving pieces to the evolving regulatory and policy landscape, which include everything from revamping and rebranding AS9100 to the IA9100 series quality standards, acquisition reform acts such as SPEED and FoRGED that are supposed to stimulate faster technology adoption, and significant cybersecurity rules for AI and Zero Trust, all driven by the FY2026 National Defense Authorization Act. These policy and regulatory changes drive the key changes in what we will see is more open collaboration between government agencies to ensure systems being built do not overlap, and that systems are being developed using interoperable technology. The FACE and MOSA standards will become more important than ever. Commercial organizations need to prepare for the new international quality requirements, embrace digital transformation (AI, cyber), and adapt to faster, more agile defense acquisition processes to remain compliant and competitive.

Long-Term Trends

Q: What trends or technologies will continue to shape aerospace and defense over the next decade? How can organizations ensure sustained innovation while managing cost, risk, and compliance?

Mulcahy: We’ve seen a big theme of reuse and sustainability in industry recently. Reusable satellites, rockets, and even technologies in use such as autophage. No doubt innovation will continue to happen across the wider industry, to help solve global challenges, aid to defence efforts, and contribute to electronic warfare. I think AI will continue to be introduced to more areas of businesses and continue to aid moves towards Digital Engineering and overall efficiencies. I think as research continues and more innovation is created from academia for example, there may be closer links formed between Industries, academia, and potentially even governments to co-invest and accelerate technology development.

Organisations should continue to invest in education on these new technologies to protect themselves, but also to introduce better workflows, attract new talent, and help to deliver projects on time. But an important factor will be to use modern tools fit for today’s project needs that are open and facilitate a digital engineering way of working.

Macias: Sustained/accelerated innovation with improved efficiency, quality, and compliance will be the goal over the next decade, and those who capitalize on current digital engineering practices will be best positioned to both capitalize on emerging AI/ML technologies and improvements in modeling/processing capabilities. The key to this will be the establishment of traceable, agile, model-based environments that bring everyone together in a common view of the total system, giving all the ability to contribute to the total success of the product, production, and mission. This can only be accomplished if organizations focus on democratization of the digital thread and common (MBSE & RM) models by avoiding deepening or perpetuating silos.

Bryczek: Long-term trends in the defense industry are driven by rising geopolitical tensions, increased defense spending—particularly in Europe—and rapid advances in emerging technologies. Global military expenditure continues to grow as nations respond to a worsening security environment and pursue modernization, with NATO members increasingly meeting higher spending targets. The industry is shifting toward autonomous and unmanned systems, including UAVs, USVs, and ground platforms, to reduce human risk, with swarm technology becoming a major focus. Investment is also accelerating in hypersonic missiles and directed-energy weapons to counter evolving threats. Additionally, space is emerging as a critical military domain, with growing emphasis on autonomous spacecraft, satellite-based surveillance and communications, and managing the risks of space militarization and debris.

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2026 Predictions for Medical Device & Life Sciences: AI, Wearables, and Navigating Regulatory Change

With 2026 on the horizon, the medical device and life sciences industries are moving through a landscape defined by fast-paced innovation, changing regulations, and dynamic market shifts.

From the transformative potential of Artificial Intelligence (AI) in product development and diagnostics to the growing role of wearables and personalized medicine, the industry is embracing change while addressing critical challenges like cybersecurity, data privacy, and supply chain resilience.

In this year’s predictions series, we’ve gathered insights from leading experts across the field, including:

• Richard Matt, Principal Consultant at Aspen Medical Risk Consulting
• Adam Smith, Founder/CEO at AgentAstro.ai
• Mike Celentano, Owner & President at System Optimization Specialists
• Dan Purvis, Chairman and Co-Founder at Velentium Medical

Plus a team of seasoned professionals from Jama Software:

• Vincent Balgos, Senior Director, Solutions & Consulting
• Carleda Wade, Senior Solutions Consultant
• Jakob Khazanovich, Senior Solutions Consultant
• Romer De Los Santos, Principal Solutions Consultant
• Tom Rish, Senior Business Development Manager, Medical Device & Life Sciences

Together, they explore the opportunities and hurdles that lie ahead, offering a glimpse into the future of medical devices and life sciences.

Join us as these experts share their perspectives on the technologies, strategies, and innovations that will define the next chapter of the industry. From AI’s growing influence to the challenges of regulatory harmonization and the rise of wearables and personalized medicine, this piece highlights the trends shaping 2026 and beyond.

Curious about what’s happening in other fields? Read part one on consumer electronics, part two on medical device & life sciences, part three on aerospace & defense, part four on automotive, and stay tuned for our upcoming predictions on semiconductors and AECO.

AI and Automation

Q: How do you see AI shaping the future of medical device design and manufacturing, diagnostics, and patient engagement in 2026 and beyond?

Richard Matt: I see AI organizing and mining information that predicts more effective use of medical devices. AI will be used in product development to predict more effective product design and in post-market assessments to confirm or refute assumptions about the treatment’s effectiveness.

Adam Smith: AI has become the connective layer across the device lifecycle, replacing manual research with automated analysis of predicates, guidances, standards, and historical evidence. This reduces ambiguity, improves consistency, and supports more adaptive systems that learn from real-world performance. It also drives more personalized, device-integrated insights, bringing engineering, clinical, and regulatory teams into tighter alignment.

Mike Celentano: AI is already shaping the MedTech development space and will continue to increase its influence in 2026 and beyond. For example, systems engineers I work with already use AI to summarize and affinitize voice of customer interview verbatims into stakeholder needs. Some are also using AI to help organize their requirement statements. Others are using various AI personas as independent reviewers of their deliverables. In 2026, these uses will become more common. But other AI uses will emerge including AI-based trade analysis based on MBSE models since there is now a strong textual component to SysML. AI will also emerge more in risk analysis and root cause analysis. In short, wherever AI can make developers more efficient and/or increase quality, it will emerge as such.

Dan Purvis: AI has amazing abilities when harnessed well. There are many places where an algorithm can do a much better job than a person. I think that you are going to see more therapies with an AI component that makes a suggestion that is then reviewed by a person.

Vincent Balgos: What we’ve seen in industry so far is the continued strong interest in exploring how AI can contribute in developing safe and effective products, but with the limited ROI to date, industry seems to be taking a more methodical and deeper approach in discussing the more how and why of AI. Example, there is initiative to discuss data standardization of AI information following IEEE 2801 or other best practices gleaned from BigTech companies such as Microsoft, Amazon, and Google.

Carleda Wade: I’m seeing more customers looking to explore how they can incorporate AI into their development process. While many companies have yet to create full-blown policies on the use of AI at their organization, I can see this increasing in the coming years with the popularity of AI in everyday life. I think that people in our industry will be a bit conservative in their initial use of AI until FDA standards and guidelines are released. I could see it being very useful in processes like post-market surveillance.

Jakob Khazanovich: AI is becoming ubiquitous, but it will be a tool to work faster and smarter rather than a replacement for human engineers. In the future, initial draft requirements, test cases, or even entire trace matrices will be created by AI and then refined by engineers. Many companies will be slow to formally adopt the use of AI, but there is no question that engineers have a ChatGPT window open on the side to help them refine design artifacts quickly. In manufacturing generally, I could see AI being used to optimize part designs for strength, cost, and moldability.

Romer De Los Santos: AI has been growing fastest in imaging and genomic analysis for a while now. However, I’ve been seeing growing interest in using AI to accelerate their product development process by handling repetitive and tedious tasks. Jama Software is already moving towards automated test case generation, for example. I expect AI will help enable increased modularity of medical devices, manage complex product variants, and quickly identify and patch components with security issues.

Tom Rish: There is no escaping AI, and it is certainly poised to play a huge part in the evolution of the industry. I think most people thought it would revolutionize the products directly, and that will come with time. However, my takeaway from recent conferences is that companies are starting to take a more methodical approach to incorporating AI. After the initial surge in AI popularity, people are starting to realize how important it is to have a strong foundation of data. I believe companies will spend the immediate future organizing data and building good frameworks so that they can better incorporate AI into internal processes like product development and manufacturing.

Q: What ethical considerations should companies keep in mind as they integrate AI/ML into clinical decision-making and device functionality?

Matt: Companies need to rely on evidence of what AI can contribute and avoid rolling out product features based on speculation of what AI ‘should’ be able to accomplish.

Smith: I think companies need to be clear about how AI-driven decisions are made so clinicians can actually understand and trust what the system is doing. I also believe they need to watch for bias in the training data, because uneven performance across patient groups can create real clinical risk. And I think it’s important to stay accountable for how these models evolve over time, making sure updates are monitored so the systems remain safe and reliable in practice.

Celentano: ML in medical devices has been around for a couple of decades now. I worked on an ML fuzzy logic bG meter diagnostic algorithm in the early 2000’s. Then, and now, human verification and validation is essential. Just like when we use ChatGPT for something, we always double check the answer ourselves. Why? Because AI gets it’s knowledge from us, the internet, our databases, our programming, and all of that is not perfect. So the same applies for clinical decision making. Health Care providers must verify and validate the AI conclusions themself, and ultimately, humans must always take responsibility for the final answers.

Purvis: Keep a person “in the loop” as it allows for review, edit, and potential correction.

Balgos: Considering Med Industry’s ethos is to “do no harm,” I was happy to hear the talk about using standards such as ISO 42001 to ensure the responsible and ethical use of AI, including addressing the known bias in medical decision making in the clinical settings.

Wade: They should think about the inherited bias of the AI tool that they use, since it could unfairly classify data about certain demographics.

Khazanovich: Intellectual property concerns will need to be addressed to ensure AI-suggested content is not putting companies in any sticky situations.

De Los Santos: Companies need to have clear rules and controls around when and how to use AI when dealing with private health information.

Rish: It is hard to put anything other than data privacy at the top of this list. Whether it is patient data, information about clients, or proprietary product details, companies need to train their employees to use AI responsibly. It is so easy to copy/paste information into AI tools in the name of efficiency, but people need to think twice about what they are sharing.

Q: What emerging technologies do you believe will have the biggest impact on life sciences innovation in the next 12–18 months?

Matt: AI is the hands-down favorite.

Celentano: AI is one for sure. mRNA is also going to be huge in life sciences since it makes vaccines fast to develop, and any mRNA vaccine appears to have cancer-fighting benefits with immunotherapy that are next-level. One negative impact that will be felt for the next year to 10 years are the 2025 US budget cuts to NIH, CDC, and other long-term research activities.

Purvis: There are big things happening in wearables. The purchase of Nalu. The Medicare reimbursement for Cala. The market is beginning to realize that wearable neurotech has a lot of growth potential to benefit patients’ lives in a less invasive way.

Balgos: AI is the hottest tech right now to make the biggest impact, but the bigger impact is when these AI-enabled devices start talking to each other, with the common goal of supporting the patient and medical professionals. The Model Context Protocol (MCP) will be a key part of that impact.

De Los Santos: I expect that AI will be applied to product development processes to reduce bottlenecks.

Rish: Wearable devices have already had a profound impact on the industry, and I think their influence will only continue to grow. Companies are pushing the limits when it comes to providing excellent data, all from rather simple devices like rings, watches, etc. Details still need to be figured out on the regulatory side when it comes to indications, but patients want to know more about their health. My hope is that the trend of people taking a more proactive approach with their health continues with the continued rise of wearables.

RELATED: Integrate Cybersecurity and Safety Risk Management in Jama Connect® to Simplify and Accelerate Medical Device Development.

Regulatory & Compliance

Q: What regulatory shifts (e.g., EU MDR/IVDR enforcement, FDA changes, global harmonization) do you anticipate will most affect medical device and life sciences companies in 2026?

Matt: ISO 13485 brings with it a tremendous amount of explicit detail that was only present in regulations by ‘reading between the lines’. This increased detail about the behavior expected for compliance will affect medical device companies both broadly and deeply.

Smith: I think we’re about to see a wave of impact from AI systems that are purpose-built for regulated work, especially tools that can interpret standards, guidances, historical submissions, and clinical evidence in a structured way. I also believe digital twins and simulation platforms will start to play a bigger role in both device design and verification as companies look for faster ways to generate defensible evidence.

Celentano: There has been more regulatory focus on Interoperability and Cybersecurity lately. This will continue to intensify in terms of enforcement in 2026. More AI guidelines and perhaps regulations will also emerge.

Purvis: All agencies are continuing to focus on cybersecurity. Companies should make sure that they have a product cybersecurity (as opposed to general business/IT cyber) strategy right alongside development and manufacturing strategy.

Wade: The FDA’s harmonization of 21 CFR 820 with ISO 13485, which is slated to be effective in February 2026, will have a large impact on US-based companies. Many have known about this upcoming change for years, but will need to be fully compliant very soon.

De Los Santos: Of course, the FDA’s harmonization effort will have a large impact on the development of US medical devices. Meanwhile, in the EU, I expect that bottlenecks around full compliance with MDR for legacy medical devices will continue as manufacturers struggle, not only with making legacy development documentation compliant with the MDR, but getting it reviewed in a timely manner due to the limited capacity of notified bodies.

Rish: Without a doubt, QMSR is the thing I hear the most about. For those of us that have been in the industry for a while, we have seen a lot of changes (ISO13485 in 2016, ISO 14971 in 2019, EU MDR, and more). This is one change that feels like it is actually helping us out as the FDA is harmonizing with ISO 13485. It seems like this will help the industry become a little more streamlined, which hopefully leads to more and safer products being launched.

Balgos: QMSR transition will cause some immediate local impact on medical companies, especially those that are non-compliant to ISO 13485. Even those that are compliant, a revisit oftheir Quality Procedures will be needed. On a broader, global level scale, the continual changes in general strategy and the reduction in force in the medical related US Federal Agencies (FDA, NIH, CDC, etc) experienced personnel, will have longer term impacts in the way industry and academia pursue new medical innovation, the path to bring products to market, and the overall medical welfare of the general population.

Q: How are companies adapting their software and systems to meet evolving cybersecurity and data privacy requirements across global markets?

Matt: Cybersecurity is greatly under-considered in medical device design, resulting in extensive and growing opportunities for medical cyberattacks.

Celentano: Well, most MedTech companies are finally getting serious about Cybersecurity and privacy as well as data integrity, now that regulators are enforcing the regulations and standards more. Years ago, MedTech companies used to hire one person to be responsible for Cybersecurity. Now most companies have cyber teams, privacy teams, and data integrity teams, all with standard operating procedures, which makes each employee responsible for compliance.

Purvis: The best way to answer this is “systemically.” Companies are setting a comprehensive product cybersecurity strategy that bakes cybersecurity into every aspect of the pre-market cycle. Also, companies are realizing that post-market cybersecurity (ongoing surveillance) must be budgeted and planned for.

De Los Santos: Companies are purchasing or repurposing tools to help them generate new cybersecurity deliverables and update their customer notification systems to be in compliance with the final guidance on Cybersecurity in Medical Devices released just this year.

Rish: I believe the best companies will take a step back and rethink their approach to risk management. A lot of organizations complete risk activities in separate buckets. Things like cybersecurity, human factors, process risk, and more are all done at separate times and then merged into a disjointed system. Since technology is rapidly evolving, I think people need to take a more holistic view of risk. Put the patient or end user first by thinking about everything that can go wrong and how you can mitigate those risks at a systemic level.

Balgos: Due to the FDA’s Final Guidance on Cybersecurity in mid 2025, organizations are taking a more proactive approach to cybersecurity since it is now a required deliverable for device submissions. In addition, Med companies are seeking an integrated approach to both security + safety risk management in the processes & tools since both can impact each other’s associated Risk level, especially in this early era of AI.

Market Forces & Strategy

Q: What macro trends (e.g., supply chain resilience, sustainability, workforce shifts) do you think will influence strategic decisions in the industry next year?

Matt: The macro trend to bring employees who worked remotely back to the office after the rapid and uncontrolled increase in remote workers during the COVID pandemic.

Celentano: 2025 tariff wars will still have residual supply chain impacts in 2026 for MedTech. Reduced funding for research and other economic factors will make MedTech jobs more precious and harder to get. Reduced emphasis on sustainability will continue to flood the employment market with those specialists who now need to become more multi-disciplined. Software-related MedTech jobs will likely grow in comparison to electrical and mechanical job opportunities. Systems Engineering and Program Management jobs will likely increase next year due to the need for more integration of existing technologies and less investment in new technologies.

Purvis: The industry is seeing some positive changes in reimbursement. Several firms are seeing their strategic plan around study data pay off with reimbursement.

Wade: A lot of companies are very conservative with their make or buy decisions due to current tariffs, which will impact how they design their products.

Rish: It seems like the economy has been the main question mark ever since 2020. There have been some major highs and major lows. While private investment seems to be down, there is no denying that large companies are making news lately with some big mergers and acquisitions. I believe the larger players will continue to identify promising technology and take steps to acquire or partner with the organizations developing that technology.

Balgos: With lessons learned from the Covid Era and the current potential dynamics with the US Federal government, companies are focused on strengthening their supply chain to prevent or lessen global market & trade changes. Whether sourcing more locally, identifying equivalent substitutes, or even manufacturing their own materials, flexibility will be key to mitigate any turbulence in the supply chain.

Q: What differentiates companies that are thriving in this rapidly evolving landscape from those that are struggling to keep up?

Matt: A laser focus on the patient. This drives everything in medical devices, but many companies get distracted by technology, profit margins, or timelines. A laser focus on the patient cures all of these ills, but many companies don’t see the connection.

Smith: I think the companies that are thriving are the ones treating regulatory and quality work as a strategic asset, not a bottleneck, and adopting tools that give them clearer evidence and faster decision cycles. I also believe they’re the ones breaking down silos between engineering, clinical, and regulatory teams, so requirements, risks, and documentation stay aligned from the start. And I think the organizations that struggle are usually the ones holding onto legacy systems and manual processes, which makes it much harder to keep pace with shifting standards, rising submission volume, and growing complexity.

Celentano: Adapting to the sometimes surprising demands of the public and the governments. Being nimble to move resources toward new cash cows. For example, marketing Trizepitide, GLP, and GIP more for weight loss rather than diabetes.

Purvis: There are four key stakeholders in every MedTech business: patients, caregivers, corporate (hospital, surgery center, payers), and investors (which includes employees, management, and financial backers). The thriving companies have found a way to satisfy all of them well.

De Los Santos: Companies that are slow to use AI/ML may start to feel like their competition is speeding ahead of them.

Rish: From my experience in the industry, the companies that thrive fully reject the idea that regulations slow you down. Instead, they use regulations to build business practices that create efficiency and excellence. Those that set up smart business processes as part of a QMS significantly increase their chance of hitting product deadlines. They get products to the market faster and are also typically producing much safer products. They increase their revenue and reduce their audit findings.

Balgos: With the constant dynamics in the regulatory landscape, having a solid regulatory strategy that includes sub-topics like cybersecurity, quality compliance, and an actual commercialization plan will help keep companies nimble in the face of change.

RELATED: Buyer’s Guide: Selecting a Requirements Management and Traceability Solution for Medical Device & Life Sciences

Looking Ahead

Q: What’s the most innovative thing you’ve seen in the industry this year that you believe others will adopt in 2026?

Matt: A novel method to assess whether the benefits of a treatment exceed its risks. This has the ability to both bring new products to market more quickly and relaunch existing products into new patient populations and indications for use.

Smith: I think the most innovative shift I have seen this year is the way AI is beginning to shape entire medical device roadmaps rather than just isolated tasks. The work we are doing with the University of California is a good example, where Agent Astro is being used from the earliest concept conversations all the way through regulatory planning, predicate selection, testing expectations, and submission strategy. I believe this end –to-end use of AI will accelerate a broader shift in the industry, where regulatory affairs is no longer treated as a process-driven function at the end of development, but as a strategic driver that informs design choices, materials decisions, and overall product direction. I think this approach will spread quickly in 2026 because it brings consistency, reduces rework, and gives teams a much clearer path from idea to approval.

Celentano: Weight loss drugs will continue to make record profits. mRNA treatments will emerge to fight cancers. The most innovative products next year will solve medical problems for all patients and doctors, perhaps related to common pain points like healthcare access, healthcare insurance, or prescription drug costs.

Purvis: Bioelectric therapies that directly target the patient’s condition. More firms are realizing that a device play is valuable (in addition to pharmaceutical-based solutions).

Rish: I probably can’t claim it is the most innovative thing I’ve seen, but one of the most surprising innovative ideas is the FDA committing to using AI in their review process. It is great to see that the FDA is willing to modernize a bit, and I hope that leads to more streamlined and effective reviews for all parties. The goal shouldn’t be to just catch random things, but to focus on important topics so that safer products will be launched. I know companies are starting to use AI to prep for things like submissions and audits, and I think that will ultimately help them launch better products and reduce audit findings.

Balgos: The extraordinary rise in continuous glucose monitoring (CGM) devices and at-home testing kits (ala Covid) in the market demonstrates that device manufacturers can effectively market directly to consumers. This may open a wider range of wearables, at-home kits, and DIY applications that may broaden the adoption of FDA’s initial “Healthcare at Home”

Q: What’s one mistake or blind spot you see companies making that could hinder their success in the coming years?

Matt: Focusing on compliance instead of the patient.

Celentano: Many MedTech companies do a terrible job of eliciting and analyzing their stakeholder needs. They often build what they think their stakeholders want instead of providing them solutions they actually need.

Purvis: For startups: stick with what you are uniquely gifted to do and outsource everything else to quality partners. Your IP, your clinical, and your science should stay with you – all other aspects can be handled more cheaply and effectively by others.

De Los Santos: One of the biggest mistakes I see is companies creating huge and complex product development and risk management processes in response to regulatory changes. Congress has directed the FDA to take the least burdensome approach to evaluation of premarket medical devices. The amount of documentation and evidence should be commensurate with the security and safety risk of the device.

Rish: As discussed previously, I think rushing the use of AI increases the risk of a company falling greatly behind the competition. I highly recommend focusing on organizing data, building processes around usage, and training employees on how to use it. The longer you wait to do that, the deeper the hole gets before you can use AI effectively.

Balgos: Believing that only technical prowess is needed for a successful device submission and market penetration. I like the colloquial phrase of “it takes a village to raise a child,” with adaption that it takes a “system of systems approach” to develop a safe, effective, and successful medical product.

Q: Are there any major disruptors on the horizon that you believe could reshape the industry in 2026?

Matt: I don’t believe any disruptors are on the horizon that are so powerful they could reshape the industry in just one year. AI will be the disruptor that will reshape the industry over the next decade.

Smith: I think one of the biggest disruptors will be the shift in how companies access regulatory expertise. For years, firms have charged tens or hundreds of thousands of dollars to help MedTech companies navigate predicates, draft documentation, and map out submission strategy, and there is still real value in working with consultants who bring human judgment and trusted relationships. But I believe the nature of that work is changing because AI is turning regulatory affairs into a strategic driver instead of a downstream, process-heavy function, and for only a few hundred dollars, any company can now access the equivalent of a team of regulatory veterans. I think this will make advanced regulatory support accessible to far more innovators than ever before and will reshape how new devices reach the market in 2026.

Celentano: The confluence of AI with other multipliers will be a dominating success factor in 2026. For instance, MBSE with AI will enable nearly automatize system architecture options based on requirements or vice versa, saving tons of manpower and reducing time to market.

Purvis: BCI is hot – and lots of investment has been thrown at it. I think that “data from the brain” is going to start opening more and more MedTech opportunity in the years ahead. Also, personalized medicine with tailored devices to individual anatomy will continue to grow (think Invisalign for many more conditions).

Wade: The recent government shutdown caused a huge backlog at the FDA for submissions, which will inevitably take a while to sort out.

De Los Santos: The possibility of more federal layoffs or cuts in funding to the sciences will cause uncertainty and may stall development. Innovation often requires significant public investment for technology to develop.

Rish: It is hard to think of anything that can match the potential AI holds when it comes to reshaping the industry. Those that use it wisely and effectively will equip their employees to do amazing things. I truly believe it will help the best minds in the industry spend more time on innovation, which will ultimately improve the quality of life of people all throughout the world!

Balgos: The continued dynamics of the US Federal Government and its impact on global businesses/trade, regulatory, international affairs, and the scientific and medical community.

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2026 Predictions for Consumer Electronics Product Development: AI, Sustainability, and the Rise of Connected Ecosystems

As we move closer to 2026, product development feels more like an evolving journey full of fresh ideas, new challenges, and real opportunities to create something better.

To kick off our annual predictions series, we turned to our own expert, Patrick Garman – Manager, Solutions & Consulting, Jama Software, for his take on what’s around the corner in the world of Consumer Electronics. If there’s one thing that stands out, it’s how fast everything is changing. New technologies are always pushing the boundaries of how products are dreamed up, built, and experienced.

In part one of this series, Patrick dives into how AI is shaking up the design process, why making products more sustainable and built to last matters more than ever, and how connected ecosystems are rewiring our expectations. He also tackles big-picture topics like data privacy and the need to build stronger, more adaptable supply chains.

Keep reading as Patrick takes a closer look at where consumer electronics might be headed, from the latest tech breakthroughs to the real-life hurdles and wins shaping the industry’s next chapter.

Curious about what’s happening in other fields? Read our predictions on Automotive, Aerospace & Defense, Semiconductors, Medical Devices & Life Sciences, and more.

Emerging Technologies

Q: What emerging technologies (e.g., edge computing, IoT, AI-driven automation, smart materials) will most transform the electronics industry in the next five years? How should companies prepare to adapt and innovate?

Patrick Garman: The next five years will be transformative for the electronics industry with innovations like modular chips, Edge AI, and AI driven engineering as the principal drivers.

Historically, chip performance has depended on how many transistors can fit onto a single die, and we are near a physical limit on this approach. Luckily, UCIe (Universal Chiplet Interconnect Express) open standard allows designers to mix and match process nodes, IP, and vendors to build tailor-made systems faster and cheaper.

Edge AI is moving intelligence and inference closer to the source of data – in the actual device. With neural processing units (NPUs) and advances in connectivity like WIFI 7 and 5G-Advanced, devices can perform sophisticated inference in real time. Consider Apple Intelligence, which runs most operations locally, only connecting to data centers or external services as needed. Edge AI means lower latency, better data privacy, and less dependence on cloud bandwidth – meaning smarter, more responsive products. For manufacturers, this also enables predictive maintenance, adaptive control, and more efficient energy use.

And finally, AI not just as a feature but as a collaborator in the design process. AI-assisted electronic design automation (EDA) is already accelerating design cycles, with early adopters reporting 2-3x productivity gains and faster time to market, often with improved design quality. These systems can learn from thousands of past layouts and simulations to guide engineers toward optimal designs faster than human intuition alone, and we are not far from reliable agentic design flows, where an AI model coordinates the entire toolchain, from schematics to verifications, autonomously.

Ultimately, competitive differentiation will no longer be based on performance and cost, but on how quickly and intelligently companies can adapt.

Sustainability and Circular Design

Q: How are sustainability initiatives—like reducing e-waste, improving recyclability, and minimizing carbon footprint—shaping product development and manufacturing strategies? What practices will define leaders in this space?

Garman: Sustainability is really starting to change how consumer electronics are designed and made. Companies are starting to think about how to make products that last longer and create less waste. That means designing things that are easier to repair or upgrade, using recycled materials, and finding ways to take apart and reuse components when a product reaches the end of its life. Some manufacturers are even rethinking how circuit boards are built so the parts can be separated more easily for recycling. On the production side, many are switching to cleaner energy sources and trying to reduce packaging and transportation emissions.

For a long time, sustainability has been more of a social cause, but now regulation is coming that will make sustainability its own requirement for products. The EU seems to be leading this charge with Sustainable Design Regulations and Digital Product Passports. I think savvy companies will be proactive in complying with the EU standards – taking the strictest state approach. In the long run, the brands that focus on making durable, repairable, and responsible products are the ones that will earn the most trust from customers.

RELATED: https://www.jamasoftware.com/webinar/agile-compliant-and-competitive-fast-trackingconsumer-electronics-innovation/

Smart and Connected Products

Q: How do you see connectivity and data analytics changing the way products are designed, used, and supported? What are the most promising opportunities for delivering value through connected ecosystems?

Garman: One of the biggest benefits is that designers no longer have to rely on assumptions about how products are used – embedded sensors and connected feedback loops provide real-world and real-time observations. This not only shortens design cycles; it reveals new use cases and patterns and supports predictive modeling so that companies can develop more reliable, efficient, and user-centered products.

This connectivity also provides benefits for consumers – over-the-air updates, edge AI, and cloud coordination allow products to adapt to users, optimize performance in context, and anticipate service needs before failures occur. HP’s ink subscription program is a good example – their connected printers track ink supply levels and proactively order replacement cartridges just in time to avoid outages.

The greatest opportunity, though, is to move from individually connected devices to connected ecosystems. When devices, analytics, and digital services share data securely, companies can deliver cross-domain experiences. Smart home hubs are just scratching the surface in terms of automation – they are still pre-programmed routines that are responsive to conditions rather than predictive or even contextual.

AI and Automation

Q: How is AI transforming design verification, testing, and quality assurance in electronics design and manufacturing? What challenges do companies face in scaling automation while maintaining flexibility?

Garman: Ultimately, AI will transform verification, testing, and quality assurance into intelligent, adaptive processes rather than static checklists. We are already seeing machine learning models that can predict where design flaws are most likely to occur, automatically generate test scenarios (a la Jama Connect AdvisorTM’s Test Case Generation feature currently in beta), and analyze simulation or production data to optimize coverage. This means faster V&V cycles without sacrificing quality – most likely increasing quality over time. Human judgement will not be replaced in our lifetime, but the efficiency gains mean engineers focused on engineering rather than administration and management.

Ethical and Responsible AI

Q: As electronics become more intelligent, how can companies ensure responsible use of AI and protect consumer privacy? What frameworks or standards are most critical for responsible implementation?

Garman: Data stewardship and privacy protection should be core design principles. Ensuring privacy and ethical use begins with transparency, consent, and control – consumers should know when AI is making decisions, what data is being collected, and how it will be used. It’s also incredibly important that AI systems are auditable – you can clearly trace outcomes and prove that they are justifiable, especially in safety-critical or consumer facing applications.

As for frameworks and standards, I recommend a strictest state approach – design for compliance with your strictest regulatory market, which today is probably the EU. The EU Artificial Intelligence Act, OECD AI Principles, and NIST AI Risk Management Framework all emphasize human oversight, transparency, and accountability, while GDPR, ISO/IEC 27701 and ISO/IEC 27001 provide a foundation for secure data governance.

Consumer Expectations

Q: With consumers expecting seamless connectivity, personalization, and sustainability, how do you see these preferences influencing the next generation of products? What innovations will drive brand loyalty?

Garman: Three pillars that influence consumer expectations and brand loyalty are seamless connectivity, meaningful personalization, and visible sustainability. The next generation of products will succeed not by adding more features, but by delivering frictionless, adaptive experiences that feel integrated across devices and ecosystems.

Products will increasingly communicate and learn from one another—phones coordinating with vehicles and wearables, appliances responding to home energy data—creating personalized environments that anticipate needs rather than react to commands. AI and edge computing will make this contextual intelligence local, fast, and privacy-preserving, while modular hardware and software platforms will allow updates and upgrades throughout the product’s life.

Sustainability will also become a defining factor in brand loyalty. Consumers want devices to be designed for longevity and repairability. Companies that combine intelligent design with ethical production—using recycled materials, energy-efficient architectures, and verifiable carbon reporting—will differentiate themselves as trusted, forward-looking brands. Ultimately, successful products will simplify ownership and offer more personal experiences.

RELATED: Buyer’s Guide: How to Select the Right Requirements Management and Traceability Solution

Supply Chain and Resilience

Q: What lessons from recent supply chain challenges can the electronics industry apply to improve resilience and reduce dependency on vulnerable regions or components?

Garman: The past few years have shown the electronics industry that running super-lean supply chains can backfire. When the pandemic and chip shortages hit, companies learned the hard way how risky it is to depend on just a few factories, regions, or single-source parts.

The big takeaway is that resilience matters as much as efficiency. Leading manufacturers are now spreading production across multiple regions, qualifying backup suppliers, and designing products that can use alternative components when needed. They’re also using data and digital twins to spot weak links early and plan around potential disruptions instead of reacting after the fact.

Modular products and standardized interfaces make it easier to swap parts or shift suppliers without starting from scratch. Teams are breaking down silos between engineering, procurement, and logistics so they can move faster when problems arise. In short, the focus is shifting from chasing the lowest cost to building smarter, more balanced supply chains—ones that can bend without breaking. Having live traceability from product requirements to parts is key to success.

Cybersecurity in Connected Devices

Q: As the number of connected devices grows, what cybersecurity threats are most pressing for manufacturers and users? How can companies build trust through secure-by-design principles?

Garman: Companies need to move from “add-on” security to secure-by-design thinking. There are probably more smart devices in market today than non-connected devices, making cyber security a top concern for consumers (and thus for companies designing products). The biggest risks come from things like hacked supply chains (where bad code slips in before a product ships), weak passwords or outdated firmware, and unprotected data in transmission.

Secure-by-design means building protection in from the start – using strong encryption, verified software updates, and secure hardware to keep data safe. It also means being clear and transparent with consumers about what data is collected and how it will be used. Conforming to standards like ISO 27001 and the NIST Cybersecurity Framework, and proactive compliance with the EU Cyber Resilience Act or US Cyber Trust Mark demonstrate a commitment to cybersecurity principles and build trust with consumers, but again, transparency is going to be key.

Regulatory and Compliance Challenges

Q: How are global regulations on safety, energy efficiency, and data protection affecting electronics innovation? How can companies balance compliance with speed to market?

Garman: Overall, governments have been slow to keep regulatory pace with technical innovations, but this is rapidly changing. We’re seeing new rules to help make products safer, more energy efficient, and to protect consumer data. Things like the EU’s Cyber Resilience Act or new energy labeling standards are pushing companies to design electronics that are not just clever, but also secure and sustainable. It does make development a bit more complicated, but it’s also forcing better design—like using parts that are easier to recycle, making software more secure, and being upfront about how data is handled.

It’s difficult to achieve compliance – especially when regulations are continually evolving – without sacrificing speed, but that does not mean it’s impossible! The key is to build compliance into your requirements management process so you have traceability from regulatory requirements to your product requirements, so you can show how you are complying, and V&V so that you can prove that you are compliant.

Future Trends

Q: What technological or market trends do you believe will still be shaping the electronics industry in five to ten years? How can companies remain agile and competitive in an era of rapid innovation?

Garman: For companies, staying competitive will mean staying flexible. That means designing products and organizations that can adapt quickly using modular architectures, software-driven features, and strong digital ecosystems that make updates easy. It also means keeping close ties between engineering, supply chain, and compliance teams so they can respond fast when technology or regulations shift. The winners will be the ones that move quickly and keep trust: innovating at speed, but with security, sustainability, and customer experience built in from the start.

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2026 Predictions Series: Insights from Leading Experts

As we move closer to 2026, product development feels more like an evolving journey than a fixed destination. It is a path full of fresh ideas, complex challenges, and real opportunities to create something better.

This multi-part series cuts through the noise to deliver actionable foresight. We have gathered leading experts to explore the critical shifts defining the next era of innovation. Whether you are looking to pivot your strategy or refine your roadmap, these insights will help you stay ahead of the curve.

What We Are Watching

Innovation doesn’t happen in a silo. The breakthroughs in one sector often spark revolutions in another. In this series, we dive deep into the specific dynamics of key global industries such as Consumer Electronics, Automotive, Aerospace & Defense, Semiconductors, Medical Devices & Life Sciences, AECO, and Nuclear.

Core Themes & Trends

Across every industry, we are tracking the threads that connect them all. This series provides a holistic view of the landscape, covering topics such as:

• Emerging Technologies
• AI and Automation
• Ethical and Responsible 
• Cybersecurity
• Regulatory & Compliance

Stay Ahead of the Curve

Predictions will appear below as they are published. Stay tuned to this space for ongoing updates and fresh expert insights as the series unfolds.

2026 Predictions for Consumer Electronics Product Development: AI, Sustainability, and the Rise of Connected Ecosystems

In part one of this series, Patrick dives into how AI is shaking up the design process, why making products more sustainable and built to last matters more than ever, and how connected ecosystems are rewiring our expectations. He also tackles big-picture topics like data privacy and the need to build stronger, more adaptable supply chains.

Keep reading as Patrick takes a closer look at where consumer electronics might be headed, from the latest tech breakthroughs to the real-life hurdles and wins shaping the industry’s next chapter.

2026 Predictions for Medical Device & Life Sciences: AI, Wearables, and Navigating Regulatory Change

With 2026 on the horizon, the medical device and life sciences industries are moving through a landscape defined by fast-paced innovation, changing regulations, and dynamic market shifts.

From the transformative potential of Artificial Intelligence (AI) in product development and diagnostics to the growing role of wearables and personalized medicine, the industry is embracing change while addressing critical challenges like cybersecurity, data privacy, and supply chain resilience.

In part two of this series, we’ve gathered insights from leading experts across the field, including:

• Richard Matt, Principal Consultant at Aspen Medical Risk Consulting
• Adam Smith, Founder/CEO at AgentAstro.ai
• Mike Celentano, Owner & President at System Optimization Specialists
• Dan Purvis, Chairman and Co-Founder at Velentium Medical

Plus a team of seasoned professionals from Jama Software:

• Vincent Balgos, Senior Director, Solutions & Consulting
• Carleda Wade, Senior Solutions Consultant
• Jakob Khazanovich, Senior Solutions Consultant
• Romer De Los Santos, Principal Solutions Consultant
• Tom Rish, Senior Business Development Manager, Medical Device & Life Sciences

Together, they explore the opportunities and hurdles that lie ahead, offering a glimpse into the future of medical devices and life sciences.

Join us as these experts share their perspectives on the technologies, strategies, and innovations that will define the next chapter of the industry. From AI’s growing influence to the challenges of regulatory harmonization and the rise of wearables and personalized medicine, this piece highlights the trends shaping 2026 and beyond.

2026 Predictions for Aerospace & Defense: AI, Sustainability, and the Digital Transformation Frontier

As we approach 2026, the aerospace and defense (A&D) industry stands at the crossroads of innovation and transformation. With rising geopolitical tensions, increased defense spending, and technological advancements, the sector is navigating a complex landscape of opportunities and challenges.

From the integration of AI and digital twins to the push for sustainable aviation and the modernization of legacy systems, A&D organizations are embracing cutting-edge technologies to enhance efficiency, safety, and mission readiness. At the same time, they face critical hurdles, including supply chain disruptions, evolving regulatory frameworks, and the need to attract a future-ready workforce.

In part three of this year’s predictions series, we’ve gathered insights from leading industry experts from Jama Software:

• Cary Bryczek – Director, Solutions & Consulting
• Matt Macias – General Manager, Aerospace & Defense
• Karl Mulcahy – EMEA Sales Manager, A&D

Together, they explore the trends and technologies shaping the future of aerospace and defense. From AI-driven design optimization and autonomous systems to the rise of sustainable aviation fuels and the challenges of digital engineering, this piece highlights the innovations and strategies that will define 2026 and beyond.

Please note: This blog features content from writers in the UK and the US. Spelling variations (e.g., ‘defense’ vs. ‘defence’) may appear due to regional differences.

2026 Predictions for Automotive: AI, Electrification, and the Road to a Connected Future

As 2026 approaches, the automotive industry is about to enter an exciting phase marked by cutting-edge technologies, sustainability requirements, and shifting consumer expectations. The industry is navigating a changing landscape of opportunities and challenges, from the emergence of autonomous driving systems and vehicle-to-everything (V2X) communication to developments in electrification and AI-driven innovation.

The integration of emerging technologies is reshaping vehicles into interconnected, software-defined systems, while sustainability goals are driving rapid advancements in battery technology, charging infrastructure, and renewable energy integration. At the same time, the industry faces critical hurdles, including cybersecurity threats, regulatory complexities, and the need for seamless collaboration across OEMs, suppliers, and technology partners.

In part four of this year’s predictions series, we’ve gathered insights from leading automotive experts:

• Florian Rohde – Managing Partner, iProcess LLC
• Ronald Melster – Managing Director, Melster Consulting GmbH
• Kevin Dibble – President, Reinnovate Consulting
• Matt Mickle – Director, Solutions & Consulting, Jama Software
• Sathiya Ramamoorthy – Senior Solutions Consultant, Jama Software

Together, they explore the trends and technologies shaping the future of the automotive industry. From AI-driven predictive maintenance and edge computing to the challenges of electrification and the rise of subscription-based ownership models, this piece highlights the innovations and strategies that will define 2026 and beyond.

2026 Predictions for Semiconductors: AI, Chiplets, and the Path to Sustainable Innovation

As we step into 2026, the semiconductor industry stands at the crossroads of unprecedented technological advancements and complex global challenges. From the rise of AI-driven chip design and heterogeneous integration to the growing emphasis on sustainability and geopolitical shifts, the sector is navigating a transformative era.

The next wave of innovation will be defined by breakthroughs in advanced lithography, chiplet architectures, and quantum computing, while sustainability efforts will reshape manufacturing processes to address energy efficiency, water usage, and materials recycling. At the same time, the industry faces critical hurdles, including talent shortages, supply chain realignments, and the need for robust cybersecurity measures.

In part five of this year’s predictions series, we’ve gathered insights from leading semiconductor experts:

• Simon Bennett – Principal Solutions Consultant, AI Tech Sales
• Sarah Crary Gregory – Principal Engineer, Systems and Solutions Methodologies, Crary Labs
• Neil Stroud – General Manager Automotive and Semiconductor, Jama Software
• Steve Rush – Principal Consultant, Jama Software

Together, they explore the trends and technologies shaping the future of semiconductors. From AI-driven automation and edge computing to the challenges of regulatory shifts and the promise of chiplet-based architectures, this piece highlights the innovations and strategies that will define 2026 and beyond.

2026 Predictions for AECO: AI, Digital Twins, and the Path to Sustainable Transformation

As we step into 2026, the Architecture, Engineering, Construction, and Operations (AECO) industry is poised for a transformative leap. From the integration of AI and digital twins to the adoption of robotics and advanced materials, the sector is embracing innovation to tackle its most pressing challenges: sustainability, efficiency, and collaboration in a hybrid world.

This year’s predictions explore how emerging technologies like generative design, predictive analytics, and automation are reshaping the project lifecycle. We’ll dive into the role of advanced digital tools in achieving net-zero goals, the growing importance of cybersecurity in a connected ecosystem, and the long-term trends that will define the industry for years to come.

In part six of this year’s predictions series, we bring these insights to life with perspectives from Jama Software’s own AECO experts: Joe Gould – Senior Account Executive, and Michelle Solis – Associate Solutions Architect, who share their vision for the future. From AI-driven decision-making to the rise of modular construction and lifecycle optimization, this piece highlights the innovations and strategies that will shape 2026 and beyond.

2026 Predictions for Nuclear Energy: Innovation, Safety, and the Path to a Sustainable Future

The nuclear energy industry stands at a pivotal moment where innovation and tradition intersect to tackle the world’s most urgent challenges: decarbonization, energy security, and sustainability. From the emergence of small modular reactors (SMRs) and advanced reactor designs to the adoption of AI, automation, and digital engineering, the sector is embracing transformative technologies that are set to redefine how nuclear power is designed, operated, and perceived.

Key trends shaping the nuclear landscape include the transition from conceptual innovation to deployable solutions, the role of digitalization in enhancing safety and efficiency, and the evolution of regulatory frameworks to support next-generation technologies. Additionally, cybersecurity, workforce development, and global collaboration are becoming essential pillars of the industry’s future, ensuring that growth and innovation remain firmly grounded in the safety-first principles that define nuclear energy.

In this final blog of the 2026 prediction series, we bring these insights to life with perspectives from Jama Software’s industry expert, Patrick Garman, Solutions Manager for Energy, Industrial, and Consumer Electronics sectors. Patrick shares a forward-looking vision for 2026 and beyond, exploring the deployment of SMRs and advanced fuels, the integration of predictive analytics and real-time monitoring, and the innovations, strategies, and cultural shifts that will shape the nuclear industry’s role in a clean energy future.

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