In 2026, robotics and AI will continue to receive substantial attention across all industries, including medical devices. The expanding adoption of robotics and AI will coincide with other significant pressures that will impact medical devices. These include the recent 10993-1 update, and how the FDA views this update, as well as ongoing tariff and onshoring pressures. Collectively these forces will reshape how products are designed, validated, and manufactured in the coming year. As a result, in medical devices in particular, a deep understanding of the underlying material science, coupled with robust testing, will become more critical to reduce risk, increase performance, and control time-to-market.
At Cambridge Polymer Group, we have spent decades helping companies navigate the intersection of material science and product innovation in medical devices and beyond. As we look toward 2026, regulatory evolution, robotics, AI, and supply chain dynamics are converging in ways that place materials at the center of product reliability, regulatory success, and commercial viability.
Regulatory Pressures: Complexity in Biocompatibility
The recently revised ISO 10993-1 standard has introduced new complexity and uncertainty into biocompatibility evaluations, while uncertainty surrounding the FDA’s pending recognition leaves many teams in limbo. From our vantage point, this environment emphasizes the need for thorough material characterization and a fundamental understanding of the materials and their properties. Sterilization compatibility, resistance to cleaning agents and disinfectants, additive packages, extractables/leachables, and long-term aging behavior can no longer be addressed as an afterthought. They are central to development and approval timelines, costs, and patient safety.
Robotics and Reliability: Materials at the Core
In robotics, particularly in healthcare, we are seeing a growing awareness that reliability is not just about software or control systems — it is about the materials that bear load, resist fatigue, and survive sterilization and repeated cleaning. Failures tied to creep, environmental stress cracking, and chemical degradation are increasingly traceable to early-stage material mis-selection. Not giving these choices sufficient thought early on can lead to substantial loss of time. More generally, outside of healthcare, we have seen concerns around unexpected wear on wheels, tracks and guides, as well as gear material selection issues. Our work in polymer testing and failure analysis continues to show that the right material, selected with the full performance requirements in mind and validated under realistic conditions, is often the difference between field success and costly redesign.
AI and Materials: Promise and Caution
Recent press on the potential of AI suggests that it may have application throughout the material selection and characterization space. AI has the potential to transform how materials are modeled, selected, and even predicted to fail. In fact, as an example, we have collaborated with clients who use machine learning to discern patterns in complex data sets, simulate polymer behavior, optimize formulations, and accelerate testing. However, the models are only as good as the data used to train them.
The potential constraint on adoption of AI in this space (or the hidden catch in using its output) is that it is rare for published values to include all relevant parameters for material selection. For example, few elastomer data sheets will include information on sterilization compatibility or cleaning resistance, and data on the impact of additives on specific resins is scarce. We therefore caution that AI generated results must be considered from the perspective of the final use-case and must be grounded in physical data. Without robust experimental validation, predictive models risk drifting from reality with heavy consequences — especially in regulated environments like medical devices. CPG is well placed to provide both the fundamental material science and the critical test-based validations of the proposed materials.
Supply Chain Resilience: A Material Challenge Intensified
Finally, trade dynamics, tariffs, and regionalization are adding another layer of complexity to sourcing specialty polymers, medical-grade resins, and advanced composites, which remain heavily globalized and prone to high costs and disruption. We are increasingly seeing suppliers seek second-source vendors or find on-shore (or near-shore) alternatives to support and de-risk existing supply chains. We have also seen firsthand how material substitution under pressure can introduce unexpected risks. That is why we advocate for proactive risk assessment, testing, supplier qualification, and geographic diversification — not just as a procurement strategy, but as a reliability imperative.
Our View
2026 looks to be a year impacted by the convergence of several forces, each of which would be significant on its own and could disrupt companies’ operations if not addressed upfront. For this reason, we feel that 2026 will reward organizations that treat material science as a strategic discipline. Whether it is navigating regulatory ambiguity, designing reliable robotic systems, validating AI-driven insights, or building resilient supply chains, materials are at the center.
How Material Science Connects 2026’s Industry Challenges
| Domain | 2026 Challenge | Material Science Role |
|---|---|---|
| Medical Devices | Regulatory ambiguity around ISO 10993 and sterilization | Validating biocompatibility, sterilization resilience, and long-term aging of polymers |
| Robotics | Reliability and field failure risks | Selecting materials that resist creep, fatigue, and chemical degradation |
| AI | Trust, validation, and model drift | Providing physical data to train and validate AI simulations of material performance |
| Trade & Tariffs | Supply chain fragility and cost volatility | Diversifying sources of specialty polymers and qualifying substitutes under real conditions |
At Cambridge Polymer Group, we believe that thoughtful material selection, rigorous testing, and transparent cross-functional communication between engineering, quality, and regulatory teams derisks the development and production pipeline and provides the foundation for innovation that lasts.