At the Veeva MedTech Summit in Amsterdam, speakers and presenters focused on a practical question for medical technology companies: not how quickly they can adopt artificial intelligence, but whether their data, processes, governance and people are ready to support it. The consistent message was that successful digital transformation depends on getting these fundamentals right before scaling AI.
Medtronic: 'We had to earn AI'
Becki Brasher of Medtronic described a four-year effort to prepare the company for AI. Rather than starting with the technology, Medtronic harmonised processes, improved data consistency, consolidated systems and established governance structures. Brasher summed up the approach during her session: “We didn’t start with AI. We had to earn AI.”
The rationale is that introducing AI into fragmented or inconsistent environments can create more problems than it solves. Without reliable information and standardised processes, organisations risk producing outputs that are difficult to trust, validate or scale. Medtronic’s experience also underlined the need to prepare employees: technology alone cannot deliver transformation, and staff need the knowledge, confidence and oversight to use new tools responsibly.
Governance from the outset
Balancing innovation with regulatory compliance was a major theme of the closing keynote by Erik Orzechowski and Benjamin Rochette. As AI adoption accelerates, they said MedTech organisations must move quickly without compromising the quality, safety and regulatory standards expected of the industry. A central argument was that governance should be embedded in technology development from the start, rather than treated as a final compliance exercise.
That requires closer collaboration between IT, Quality and Regulatory teams. IT understands the technology and its implementation, while Quality and Regulatory professionals assess risks and define the evidence needed to demonstrate compliance. The speakers also called for proportionate governance: an AI application supporting low-risk administrative work may not need the same controls as one influencing patient safety or regulatory decisions. Clear responsibilities, early risk assessments and appropriate controls can support a more flexible approach without ignoring obligations.
Human judgement and trust
Brasher argued that AI should assist human reviewers rather than replace them. She said human judgement will remain essential until organisations build trust that what AI agents deliver is accurate. AI can analyse large volumes of information, generate documentation and make 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 point has implications for the next generation of regulatory and quality professionals: if employees rely on AI before developing the necessary knowledge, companies 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, with accountable individuals retaining responsibility for important decisions.
Pilots and measurable value
Organisations also need to ensure AI investments deliver genuine business value. With many potential applications available, they should resist pursuing every opportunity at once and instead target existing operational challenges with clear success measures. The closing keynote identified time savings, reduced process cycle times, less rework, improved quality and employee adoption as ways to assess value. Controlled pilots can show where AI is most effective, preserve human oversight and build confidence before wider deployment.
Because the technology and regulatory expectations will keep evolving, governance structures must be able to adapt. 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 painted 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 and continued engagement among manufacturers, technology providers, regulators and other stakeholders. The tone was optimistic but pragmatic: realising AI’s opportunities requires patience, preparation and clarity about where human expertise remains indispensable. As Orzechowski put it, organisations sometimes need to “slow down to speed up.”