MedTech companies need to build reliable data, consistent processes and fit-for-purpose governance before they can scale artificial intelligence, according to discussions at the Veeva MedTech Summit in Amsterdam.
The event brought together industry presentations, panels and interviews, with a common theme emerging: the challenge is not simply how quickly organisations can adopt AI, but whether their foundations can support effective use. Speakers pointed to fragmented systems and inconsistent information as barriers that can make AI outputs difficult to trust, validate or scale.
Medtronic’s foundation-first approach
Becki Brasher of Medtronic described a four-year effort to prepare the company for AI. Rather than starting with the technology, Medtronic focused on harmonising processes, improving data consistency, consolidating systems and establishing governance structures. Brasher said the company did not begin with AI; it had to earn the right to use it.
That groundwork also has implications for staff. Medtronic’s experience highlighted the need to give employees the knowledge, confidence and oversight required to incorporate new tools into daily work. Technology alone, the discussion suggested, cannot deliver transformation.
Governance embedded from the start
The summit’s closing keynote, featuring Erik Orzechowski and Benjamin Rochette, focused on balancing faster AI adoption with the quality, safety and regulatory standards expected in medical technology. They argued that governance should be designed into technology development from the outset rather than treated as a final compliance step.
Closer collaboration between IT, quality and regulatory teams is central to that effort. IT teams understand how technology is built and implemented, 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.
By setting clear responsibilities, risk assessments and controls early, organisations can create a more flexible approach that supports innovation while keeping obligations visible. Compliance and innovation, the keynote suggested, should not be treated as opposing priorities.
Human judgement remains essential
Despite advances in AI capability, speakers stressed that human judgement and professional expertise remain necessary. Brasher said AI should assist human reviewers rather than replace them, and that 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. In a highly regulated environment, a convincing answer is not necessarily a correct one. The closing keynote compared relying on AI without understanding the underlying subject matter to using a calculator without knowing the mathematics behind it.
That raises a question for the next generation of regulatory and quality professionals. If employees rely on AI before developing the necessary knowledge, organisations 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
Organisations also need to resist pursuing every possible AI application at once. Instead, speakers advised identifying areas where technology can address existing operational challenges and setting clear measures of success. Suggested measures included time savings, reduced process cycle times, less rework, improved quality and employee adoption.
Controlled pilot projects can show where AI is most effective, preserve appropriate human oversight and build confidence before successful applications are expanded. Because the AI landscape will continue to evolve, governance structures must be able to 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. The summit’s broader message was that digital transformation is a collective challenge: technology, regulatory compliance, data management and organisational culture cannot be addressed independently. Progress will require collaboration within companies and continued engagement among manufacturers, technology providers, regulators and other stakeholders.
The mood was described as optimistic but pragmatic. AI offers significant opportunities for medical technology, yet realising them requires patience, preparation and a clear understanding of where human expertise remains indispensable. Orzechowski characterised the approach as slowing down in order to speed up: by getting the foundations right today, the industry can be better positioned to embrace future technological opportunities.