At the Veeva MedTech Summit in Amsterdam, speakers and presenters focused on a practical question for the medical technology industry: not how quickly companies can adopt artificial intelligence, but whether they have the foundations needed to use it well. The event highlighted uses ranging from streamlining regulatory processes and improving access to information to supporting more efficient operations, while repeatedly returning to the organizational work that must come first.
The common message was that reliable data, consistent processes, appropriate governance and clearly assigned responsibilities are prerequisites for successful digital transformation. Introducing AI into fragmented systems or inconsistent workflows, participants warned, can create more problems than it solves, because outputs become difficult to trust, validate or scale.
Medtronic’s four-year groundwork
Becki Brasher of Medtronic described a four-year effort to prepare the company for AI. The work did not begin with the technology itself. Instead, Medtronic concentrated on harmonizing processes, improving data consistency, consolidating systems and establishing governance structures. Brasher’s account made clear that the company sought to earn the right to use AI by building the organizational conditions for it first.
That preparation also extends to employees. Technology alone cannot deliver transformation, according to the summit discussion; staff need the knowledge, confidence and oversight arrangements to incorporate new tools into their responsibilities. Without that human readiness, even well-designed systems may struggle to produce dependable results.
Governance embedded from the start
In the closing keynote, Erik Orzechowski and Benjamin Rochette addressed the tension between faster AI adoption and the quality, safety and regulatory standards expected in MedTech. They argued that governance should be built into technology development from the outset rather than treated as a final compliance step. That requires closer collaboration among IT, quality and regulatory teams: IT understands 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 that influences patient safety or regulatory decisions. Clear responsibilities, early risk assessments and appropriate controls can give organizations a more flexible route to innovation without ignoring their obligations. Compliance and innovation, the discussion suggested, need not be opposing priorities.
Human judgment remains essential
Despite advances in AI capability, the summit stressed that human expertise must remain in the loop. Brasher said AI should assist human reviewers rather than replace them, and that human judgment will remain essential until organizations build trust that agents are delivering accurate results. AI can analyze large volumes of information, generate documentation and make recommendations, but its output still requires scrutiny. A convincing answer is not necessarily a correct one, especially in a highly regulated environment.
Closing keynote speakers compared relying on AI without understanding its subject matter to using a calculator without knowing the mathematics behind it. The analogy raised a concern for the next generation of regulatory and quality professionals: if employees rely on AI before developing underlying knowledge, companies may have fewer people able to challenge inaccurate or inappropriate recommendations. The consensus was that AI should complement human expertise, with accountable individuals retaining responsibility for important decisions.
Pilots and measurable value
Organizations also need to resist pursuing every potential AI application at once, the summit heard. Instead, they should target existing operational challenges and define clear measures of success. Suggested measures include time savings, reduced process cycle times, less rework, improved quality and employee adoption. Controlled pilot projects can show where AI is most effective, preserve human oversight and build confidence before successful applications are expanded.
The broader conclusion was that digital transformation is a collective challenge. Technology, regulatory compliance, data management and organizational culture cannot be addressed in isolation. Progress will depend on closer collaboration within companies and continued engagement among manufacturers, technology providers, regulators and other stakeholders. The mood was optimistic but pragmatic: AI offers significant opportunities, yet realizing them requires patience, preparation and a clear view of where human expertise remains indispensable. As one keynote phrase put it, organizations sometimes need to slow down to speed up.