MD&M West 2026 Booth Briefing—How AI and MES Are Transforming MedTech Production

A: AI without data is useless. AI paired with only a data platform misses the context necessary to be truly intelligent. MES provides the foundational context that enables AI’s understanding. We have found that the most effective architecture features an anchoring MES for shop-floor execution and transactional data, UNS as a real-time data hub, CDM for standardization and AI for intelligent insights. This setup creates a “digital thread” that enables seamless data flow from sensors and equipment to enterprise-level decision-making, minimizing silos and supporting agile responses to quality issues, supply chain disruptions and process optimizations.

Overall, this integrated approach shifts from reactive to proactive operations, with ROI seen in reduced recalls, faster time-to-market and compliance efficiencies. As of 2026, adoption is accelerating, with an increasing number of Life Sciences firms reporting digital transformation initiatives incorporating these elements.

Q: Engineers are under pressure to justify digital investments. What measurable operational gains (for instance, scrap reduction, faster deviations or improved OEE) are you seeing when data platforms and analytics are implemented effectively?

A: In medical device manufacturing, implementing data platforms and analytics on a solid MES foundation—such as Critical Manufacturing’s modular system—delivers tangible ROI by leveraging real-time data for predictive insights, process optimization and compliance. This integration turns MES-captured shop-floor data into actionable intelligence, addressing operational pressures through metrics like reduced scrap (via root-cause analysis), faster deviation resolution (through alerts and CAPAs) and improved OEE (by minimizing downtime and variability).

Based on recent case studies and analyses, gains vary by implementation maturity but consistently show 10-40% improvements in key areas, with ROI often realized within compelling timeframes. For instance, MedTech firms report average OEE uplifts of up to 20%, enabling up to 30% throughput gains without added capital.

These outcomes are amplified in MES-centric setups (e.g., with UNS/CDM for data unification), where analytics enable continuous improvement without disrupting validated processes. For Critical Manufacturing deployments in MedTech (e.g., Elekta, Integer), gains include reduced setup times, standardized operations and predictive quality, though specific percentages vary by site.

Q: Looking ahead, what should manufacturing and design engineers prepare for next? How will AI-enabled analytics change day-to-day decision-making on the shop floor over the next 2-3 years?

A: Looking ahead, manufacturers should prepare for a landscape increasingly defined by AI integration, digital transformation, sustainability mandates and workforce evolution. This preparation involves strategic mindset shifts to leverage emerging technologies like agentic AI, digital twins and advanced analytics. Based on recent industry outlooks, the focus will be on building resilience amid economic uncertainties (e.g., tariffs, supply chain volatility) while driving efficiency and innovation.

In the near term, AI-enabled analytics will transform shop floor decisions from reactive, data-siloed processes to proactive, real-time optimizations, boosting throughput and reducing downtime. This evolution will foster the move from manual monitoring to Predictive Insights. Operators will receive prescriptive alerts (e.g., “Adjust spindle speed to avoid defect”), shifting from post-event fixes to prevention.

Building on AI analytics, real-time optimization and autonomy will then be leveraged. Most scheduling systems will be infused with AI in the next several years, enabling dynamic adjustments to production flows. Decisions on resource allocation or sequencing will be AI-assisted, with agents autonomously rerouting workflows during disruptions, improving flow stability and recovery speed.

MES systems are modernizing to incorporate AI not just for analytics but for interface generation, too. By 2029, expect AI to automate the creation of dashboards—drawing them in real-time based on user roles, intent, context and data patterns.

For instance, an engineer might query the system conversationally (“Show me predictive downtime risks for Line 3”), and AI would generate a tailored visualization, complete with insights and recommendations, rather than relying on pre-built templates. This “Generative UI” (GenUI) approach reduces design time from weeks to minutes, speeding the timeline for MES implementations while making interfaces more intuitive and effective for shop-floor users.

AI agents will soon also be managing tasks around QC (Quality Control), QA (Quality Assurance), maintenance and similar functions. This aligns with the rapid evolution of Industry 4.0, where MES is shifting from passive data recorders to active decision engines powered by agentic AI. These agents, which can autonomously perceive data, reason and act (e.g., triggering alerts, adjustments or workflows), are already emerging in pilots and will become standard offerings.

Paul Straeten, VP Life Sciences — Industry Trends, AI Adoption & Regulation, Critical Manufacturing

Q: AI adoption in MedTech is accelerating, but unevenly. Where are you seeing manufacturers move beyond pilot projects into validated production use, and what technical or organizational barriers are still slowing broader deployment?

A: Manufacturers are moving AI into validated production where it augments controlled processes—such as vision inspection, anomaly detection and data-heavy documentation workflows—rather than replacing human decision-making. The main barriers are not the models themselves, but validation strategy, as an example. How do we update a model without re-validating everything? A second barrier is data quality and lineage, integration with MES/QMS and unclear ownership between IT, OT and Quality.

Q: Regulatory pressure is reshaping design and manufacturing decisions. From your vantage point, how are evolving regulations influencing choices around automation, data traceability and system architecture on the factory floor?

A: Regulators aren’t mandating automation in MedTech manufacturing—but their expectations around traceability, data integrity and speed of evidence are making manual processes increasingly untenable. To meet rising regulatory expectations, MedTech manufacturers are digitizing execution, automating data capture at the source and standardizing traceability across materials, equipment and processes.

By replacing paper records with electronic, system-enforced workflows and structured exception handling, they can produce complete, audit-ready evidence faster—without sacrificing control or compliance. In practice, architecture choices are increasingly quality decisions—weak data lineage directly translates into compliance risk.

Q: Design engineers are being asked to think earlier about manufacturability and compliance. What upstream design or process decisions have the biggest downstream impact on audit readiness and long-term scalability?

A. The biggest impact comes from defining CTQs (Critical to Quality metrics) and control strategies early, designing for full traceability and configuration management, and standardizing execution patterns without locking in rigidity. When risks, controls and execution evidence are digitally connected from day one, audits become faster and scaling across products or sites becomes far more predictable.

To sum up, in Medtech, AI, automation and compliance are no longer separate conversations. The manufacturers that succeed are the ones designing their processes—and their digital foundations concurrently (no afterthought)—so quality, traceability and scalability come built in. That’s exactly the discussion we’re looking forward to having with engineers and manufacturers at MD&M West.

Mohamed Benkirane, VP Medical Practice — Customer Priorities & Live Deployments, Critical Manufacturing

Q: You work directly with device manufacturers in active deployments. What capabilities are customers prioritizing right now (throughput, traceability, quality analytics or flexibility), and how do those priorities differ between greenfield and brownfield facilities?

A: In active deployments, customers are prioritizing a combination of throughput, traceability and flexibility, with the balance shifting, depending on whether the facility is greenfield or brownfield. Across the board, end-to-end traceability has become a baseline requirement rather than a differentiator, driven by regulatory pressure and the need for faster root-cause analysis.

In greenfield facilities, the focus tends to be on flexibility and scalability. Manufacturers want modular automation, standardized data models and systems that can easily adapt to new products or volume changes. Because these plants are designed digitally from the outset, customers also place a strong emphasis on real-time quality analytics and closed-loop control to optimize performance from day one.

In brownfield environments, priorities are more pragmatic. Throughput and uptime often come first, as plants need to extract more value from existing assets without disrupting production. Traceability and quality analytics are still critical, but they are typically implemented incrementally, working around legacy equipment and heterogeneous control systems. Here, the emphasis is on interoperability and rapid time-to-value rather than full architectural purity.

Ultimately, while greenfield sites optimize for futureproofing, brownfield sites optimize for continuity, each shaping how capabilities are prioritized and deployed.

Q: Many plants are modernizing without disrupting validated processes. What practical strategies are manufacturers using to integrate new automation or digital tools while maintaining compliance and uptime?

A: Many manufacturers are modernizing by taking an incremental, risk-based approach rather than large-scale system replacements. A common strategy is to layer new automation and digital tools on top of existing, validated processes, allowing plants to enhance visibility and control without altering the core execution logic that has already been approved.

Parallel systems and phased rollouts are also widely used, enabling teams to validate new capabilities in controlled environments before full deployment. This minimizes disruption to uptime and reduces compliance risk. Strong change management practices, such as clear validation protocols, documented impact assessments, and early involvement of quality and IT teams, are critical to ensuring regulatory requirements are maintained.

Ultimately, manufacturers that succeed focus on interoperability, standardization and reuse, modernizing in steps while preserving process integrity and operational continuity.

Q: From a systems perspective, where do projects most often stumble? Are the biggest challenges mechanical integration, controls and data connectivity, or organizational alignment between engineering, quality and IT teams? 

A: From a systems perspective, projects most often stumble not because of technology itself but because of organizational misalignment. While mechanical integration, controls, and data connectivity can introduce complexity, these challenges are generally solvable with the right tools and expertise. The bigger issue across many manufacturers is the absence of a centralized Center of Excellence and the lack of effective communication between engineering, quality and IT teams.

When teams operate in silos, they struggle to understand each other’s requirements, priorities and constraints. This leads to fragmented system designs, duplicated efforts and late-stage rework. Without a Center of Excellence to define standards, governance and shared best practices, projects lose coherence and scalability. Ultimately, successful system integration depends less on individual technologies and more on cross-functional alignment, shared ownership and a common vision for digitalization.