Artificial intelligence is drawing attention across medical technology for its potential to streamline regulatory work, improve access to information and make operations more efficient. But discussions at the Veeva MedTech Summit in Amsterdam focused on a less visible prerequisite: whether companies have the data, processes, governance and people needed to use the technology reliably.
A recurring theme was that adoption speed should not be the first measure of progress. Speakers and panel participants argued that digital transformation works better when organisations first harmonise how they work and make information dependable. Without that base, AI can amplify inconsistency rather than resolve it, producing outputs that are hard to trust, validate or scale.
Medtronic’s groundwork
Becki Brasher of Medtronic described a four-year effort to prepare the company for AI implementation. The work began not with AI tools but with organisational foundations: aligning processes, improving data consistency, consolidating systems and putting governance structures in place. Her message was that the company had to earn the right to use AI by making its environment ready for it.
That preparation also extends to employees. The summit heard that technology alone cannot deliver transformation; staff need knowledge, confidence and clear oversight to incorporate new tools into daily responsibilities. Introducing AI into fragmented workflows risks creating more problems than it solves, particularly when the outputs must be checked and repeated at scale.
Governance from the start
In the closing keynote, Erik Orzechowski and Benjamin Rochette addressed the tension between faster adoption and regulatory compliance. They argued that governance should be built into technology development from the beginning, rather than treated as a final compliance step. That requires closer cooperation among IT, quality and regulatory teams: IT understands implementation, while quality and regulatory specialists assess risks and define the evidence needed to show compliance.
The speakers also called for proportionate controls. An AI application supporting low-risk administrative work does not need the same level of oversight as one that could affect patient safety or regulatory decisions. Clear responsibilities, early risk assessments and appropriate controls can give companies a more flexible route to innovation without ignoring their obligations. In this view, compliance and innovation are not opposing priorities; well-designed governance can support faster progress with greater confidence.
Human judgement and measurable value
Participants repeatedly returned to the role of human expertise. Brasher said AI should assist human reviewers rather than replace them, and that judgement will remain essential until organisations build trust that AI agents deliver accurate results. AI can analyse large volumes of information, draft documentation and make recommendations, but those outputs still need scrutiny. A convincing answer is not necessarily a correct one, especially in a regulated industry.
The summit also heard a comparison between relying on AI without understanding its subject matter and using a calculator without knowing the mathematics behind it. For the next generation of regulatory and quality professionals, that raises a concern: if staff depend on AI before developing underlying knowledge, companies may have fewer people able to challenge inaccurate recommendations. The consensus was that AI should complement expertise, while accountable individuals retain responsibility for important decisions.
On business value, speakers advised companies not to pursue every possible application at once. Instead, they should target existing operational problems and define success measures such as time savings, shorter process cycles, less rework, improved quality and employee adoption. Controlled pilots can show where AI is most effective, preserve human oversight and build confidence before wider deployment. Because AI capabilities and regulatory expectations will keep changing, governance structures must adapt as well.
The broader conclusion was that digital transformation is a collective challenge. Technology, compliance, data management and organisational culture cannot be addressed in isolation. Progress will depend on collaboration inside companies and continued engagement among manufacturers, technology providers, regulators and other stakeholders. The mood was optimistic but pragmatic: AI offers significant opportunities, yet realising them requires patience, preparation and a clear view of where human expertise remains indispensable.