Artificial intelligence is opening new possibilities in MedTech, from streamlining regulatory work to improving access to information and making operations more efficient. But at this year’s Veeva MedTech Summit in Amsterdam, the central question was less about how quickly companies can adopt AI than whether they have the foundations to use it well.
Across presentations, panels and interviews, a consistent message emerged: digital transformation succeeds when organisations first put reliable data, consistent processes, appropriate governance and accountable people in place. Without those elements, AI can amplify existing weaknesses rather than resolve them.
Medtronic’s four-year foundation
Becki Brasher of Medtronic described a four-year effort to prepare the company for AI implementation. The work did not begin with algorithms or tools. Medtronic focused on harmonising processes, improving data consistency, consolidating systems and establishing governance structures.
Brasher summed up the approach in her conference session: “We didn’t start with AI. We had to earn AI.” The remark captured a broader warning from the summit. 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 hard to trust, validate or scale.
Medtronic’s experience also underlined the need to prepare employees. Technology alone cannot deliver transformation, according to the discussion. Staff need the knowledge, confidence and oversight to incorporate new tools into their day-to-day responsibilities.
Governance from the outset
Balancing innovation with regulatory compliance was another major theme, particularly in 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. Governance should be built into technology development from the start, not treated as a final compliance exercise.
That requires closer collaboration between IT, Quality and Regulatory teams. IT teams understand the technology and how it is implemented, while Quality and Regulatory professionals assess risks and determine the evidence needed to show compliance.
The speakers also called for proportionate governance. An AI application supporting relatively low-risk administrative work does not necessarily need the same level of control as one that influences patient safety or regulatory decisions. By setting clear responsibilities, risk assessments and appropriate controls early, organisations can create a more flexible approach that supports innovation without ignoring their obligations.
Human judgement remains essential
Despite AI’s growing capabilities, the summit stressed the continued importance of human judgement and professional expertise. Brasher said AI should assist human reviewers rather than replace them. In an interview, she said human judgement will remain in place until organisations build trust that what agents deliver is accurate.
AI can analyse large volumes of information, generate documentation and offer recommendations, but its outputs still require scrutiny. A convincing answer is not necessarily a correct one, particularly in a highly regulated environment. During the closing keynote, the speakers compared relying on AI without understanding its underlying subject matter to using a calculator without knowing the mathematics behind it.
The point has particular relevance for the next generation of regulatory and quality professionals. If employees rely on AI to complete tasks before developing the necessary knowledge, organisations could end up with fewer people able to challenge inaccurate or inappropriate recommendations. The consensus was not that AI should be avoided, but that it should complement human expertise. Accountable individuals must retain responsibility for important decisions.
Pilots and measurable value
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 areas 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 AI’s value. Controlled pilot projects can help organisations understand where AI is most effective, maintain human oversight and build confidence before expanding successful applications. That approach matters given the pace of technological change: governance structures need to adapt alongside new capabilities and evolving regulatory expectations.
Ultimately, success will depend less on the number of AI initiatives a company launches and more on its ability to turn promising applications into reliable, scalable business capabilities. The summit’s message was optimistic but pragmatic. AI offers significant opportunities for medical technology, but realising them requires patience, preparation and a clear understanding of where human expertise remains indispensable. As the closing keynote put it, organisations sometimes need to “slow down to speed up.”