Artificial intelligence is being applied across medical technology to streamline regulatory processes, improve access to information and support more efficient operations. But discussions at this year's Veeva MedTech Summit in Amsterdam focused less on how quickly companies can adopt the technology than on whether they have the foundations to use it effectively.
Across presentations, panel sessions and interviews, a consistent message emerged: reliable data, consistent processes, appropriate governance and the people who oversee them need to be in place first. Introducing AI into fragmented or inconsistent environments, the argument ran, can create more problems than it solves, producing outputs that are difficult to trust, validate or scale.
Medtronic's four-year groundwork
Becki Brasher of Medtronic described a four-year effort to become ready to implement AI. Rather than starting with the technology, the company harmonised processes, improved data consistency, consolidated systems and established governance structures. Brasher said the company did not begin with AI but had to earn it first. She also stressed preparing employees for change, noting that technology alone cannot deliver transformation and that staff need the knowledge, confidence and oversight to fold new tools into everyday work.
Governance embedded from the outset
The summit's closing keynote, delivered by Erik Orzechowski and Benjamin Rochette, examined how organisations can move quickly without compromising the quality, safety and regulatory standards the industry is held to. Their central argument was that governance should be built into technology development from the start rather than treated as a final compliance exercise.
That requires closer collaboration between IT, Quality and Regulatory teams, with IT understanding the technology and its implementation while quality and regulatory professionals assess risk and define the evidence needed to show compliance. The speakers called for proportionate governance, arguing that an AI application supporting low-risk administrative work does not need the same controls as one influencing patient safety or regulatory decisions. Clear responsibilities, early risk assessments and appropriate controls, they suggested, allow a more flexible approach that supports innovation.
Human judgement and demonstrable value
Brasher argued that AI should assist human reviewers rather than replace them, and said human judgement will remain essential until organisations can build trust that what agents deliver is accurate. The summit heard that a persuasive output is not automatically a correct one, particularly in a highly regulated sector. The closing keynote compared relying on AI without understanding its underlying subject matter to using a calculator without knowing the mathematics behind it, a concern that also bears on how the next generation of regulatory and quality professionals develops its expertise.
On value, the keynote advised organisations to resist pursuing every opportunity at once and instead target existing operational challenges with clear measures of success, including time savings, shorter process cycle times, less rework, improved quality and employee adoption. Controlled pilot projects were recommended to establish where AI is most effective, keep human oversight in place and build confidence before scaling.
The overall message was optimistic but pragmatic. Orzechowski's framing was that organisations sometimes need to slow down in order to speed up, and delegates heard that getting the foundations right now will leave the industry better placed to take up the opportunities that follow.