MedTech companies are being urged to treat artificial intelligence as an outcome of stronger foundations rather than a shortcut to digital transformation. At the Veeva MedTech Summit in Amsterdam, speakers and panel discussions repeatedly returned to the same theme: reliable data, consistent processes, appropriate governance and accountable people must be in place before AI can be trusted at scale.
Medtronic's four-year groundwork
Becki Brasher of Medtronic described a four-year effort to prepare the company for AI. The work began not with algorithms but with organisational basics: harmonising processes, improving data consistency, consolidating systems and establishing governance structures. Brasher explained that Medtronic did not start with AI; it had to earn the right to use it by building the conditions for reliable adoption.
That sequence matters because introducing AI into fragmented or inconsistent environments can create more problems than it solves. Without dependable information and standardised processes, organisations risk producing outputs that are difficult to trust, validate or scale. Medtronic's experience also underlined the workforce dimension. Technology alone cannot deliver transformation, according to the summit discussion; employees need the knowledge, confidence and oversight to use new tools in their daily responsibilities.
Governance embedded from the start
Balancing innovation with regulatory compliance was a central theme of the closing keynote by Erik Orzechowski and Benjamin Rochette. As AI adoption accelerates, MedTech organisations must find ways to move quickly without compromising the quality, safety and regulatory standards expected of the sector. The speakers argued that governance should be embedded in technology development from the outset rather than treated as a final compliance exercise.
That requires closer collaboration between IT, Quality and Regulatory teams. IT teams understand the technology and its implementation, while Quality and Regulatory professionals assess risks and define the evidence needed to demonstrate compliance. Orzechowski and Rochette also called for proportionate governance: an AI application supporting low-risk administrative work does not necessarily need the same controls as one influencing patient safety or regulatory decisions. Clear responsibilities, early risk assessments and appropriate controls can give organisations a more flexible approach that supports innovation without ignoring obligations.
Human judgement remains essential
Despite advances in AI capability, the summit stressed the continued need for human judgement and professional expertise. Brasher said AI should assist human reviewers rather than replace them. She expected human judgement to remain in place until organisations build trust that the outputs delivered by AI agents are accurate.
AI can analyse large volumes of information, generate documentation and provide recommendations, but its outputs still require scrutiny. A convincing answer is not necessarily a correct one, particularly in a highly regulated environment. The closing keynote compared relying on AI without understanding its underlying subject matter to using a calculator without knowing the mathematics behind it. The concern is particularly relevant for the next generation of regulatory and quality professionals: if employees rely on AI before developing foundational knowledge, organisations could end up with fewer people able to challenge inaccurate or inappropriate recommendations. The consensus was that AI should complement human expertise, not replace it, and that accountable individuals must retain responsibility for important decisions.
Pilots, value and adaptability
Another consideration is whether AI investments deliver genuine business value. With many potential applications available, organisations need to resist pursuing every opportunity at once. Instead, they should identify where technology can address existing operational challenges and establish clear measures of success. The closing keynote highlighted time savings, reduced process cycle times, less rework, improved quality and employee adoption as ways to assess value.
Controlled pilot projects can help organisations understand where AI is most effective, retain human oversight and build confidence before expanding successful applications. Because the AI landscape will continue to evolve, governance structures must adapt alongside new capabilities and changing regulatory expectations. Success will depend less on the number of AI initiatives launched and more on the ability to turn promising applications into reliable, scalable business capabilities.
A collective challenge
The summit presented digital transformation as a collective challenge. Technology, regulatory compliance, data management and organisational culture cannot be addressed independently. Progress will require closer collaboration within companies as well as continued engagement between manufacturers, technology providers, regulators and other industry stakeholders. The overall message was optimistic but pragmatic: AI offers significant opportunities for medical technology, yet realising them requires patience, preparation and clarity about where human expertise remains indispensable. Orzechowski framed the approach as slowing down in order to speed up later, with stronger foundations positioning the industry to take advantage of future technological opportunities.