Artificial intelligence is moving into medical technology workflows. At the Veeva MedTech Summit in Amsterdam, speakers and panelists argued that the main constraint is not how fast companies can adopt AI but whether they have the foundations to use it reliably.
Those foundations, as described across sessions, include dependable data, consistent processes, suitable governance and clearly assigned responsibility. Without them, introducing AI into fragmented operations can create more problems than it solves, because outputs become difficult to trust, validate or scale.
Medtronic’s Becki Brasher described a four-year effort to prepare for AI. The company concentrated first on harmonising processes, improving data consistency, consolidating systems and setting up governance structures. Brasher’s account made clear that Medtronic did not begin with the technology; it worked to earn the right to use AI by building the organisational base. She also stressed that employees need the knowledge, confidence and oversight to incorporate new tools into daily work, since technology alone does not deliver transformation.
Governance from the start
Balancing innovation with regulatory compliance was a central theme of the closing keynote by Erik Orzechowski and Benjamin Rochette. They argued that as adoption accelerates, MedTech companies must move quickly without weakening quality, safety or regulatory standards. Governance, in their view, should be built into technology development from the beginning rather than added as a final compliance step.
That requires closer work between 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 oversight as one that affects patient safety or regulatory decisions. Clear responsibilities, early risk assessments and appropriate controls can give organisations a more flexible path that supports innovation while preserving obligations.
Human judgement remains central
The summit also pushed back against the idea that AI can replace professional expertise. Brasher said AI should assist human reviewers rather than take their place. In her view, human judgement will remain essential until organisations build confidence that what AI agents deliver is accurate.
AI can analyse large volumes of information, generate documentation and make recommendations, but its output still needs scrutiny. A confident-sounding output may still be wrong, especially in a highly regulated field. Speakers likened reliance on AI without understanding the subject to trusting a calculator without understanding the maths. They warned that if regulatory and quality professionals depend on AI before developing underlying knowledge, companies could end up with fewer people able to challenge inaccurate recommendations. The conclusion was not to avoid AI but to use it alongside human expertise, with accountable individuals retaining responsibility for important decisions.
Pilots and measurable value
On business value, the summit advised companies to resist pursuing every possible application at once. Instead, they should target existing operational problems and define success measures. Suggested measures included time savings, shorter 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 wider deployment.
Because AI capabilities and regulatory expectations will keep changing, governance structures must adapt as well. Success will depend less on the number of initiatives launched than on the ability to turn promising applications into reliable, scalable capabilities. The summit framed digital transformation as a collective challenge: technology, compliance, data management and culture cannot be handled separately. Progress will require collaboration inside companies and continued engagement among manufacturers, technology providers, regulators and other stakeholders.
The overall message was optimistic but pragmatic. AI offers significant opportunities in medical technology, yet realising them requires patience, preparation and a clear view of where human expertise remains indispensable. Orzechowski’s phrase about slowing down in order to speed up captured the argument: getting the foundations right now should leave the industry better prepared for later technological opportunities.