MedTech companies risk creating more problems than they solve if they layer artificial intelligence onto fragmented data and inconsistent processes, according to discussions at this year's Veeva MedTech Summit in Amsterdam.
Across presentations, panel sessions and interviews, a consistent message emerged: successful digital transformation depends on getting the fundamentals right first, namely reliable data, consistent processes, appropriate governance and clearly assigned responsibility for overseeing them.
Medtronic spent four years getting ready
Becki Brasher of Medtronic described the company's four-year effort to prepare for AI implementation. Rather than beginning with the technology, Medtronic worked on organisational foundations: harmonising processes, improving data consistency, consolidating systems and establishing governance structures. "We didn't start with AI. We had to earn AI," Brasher said during her conference session.
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 their daily work.
Governance built in from the start
In the closing keynote, Erik Orzechowski and Benjamin Rochette argued that governance should be embedded in technology development from the outset rather than treated as a final compliance exercise. That, they said, requires closer collaboration between IT, quality and regulatory teams: IT understands the technology and its implementation, while quality and regulatory specialists assess risk and define the evidence needed to demonstrate compliance.
They also called for proportionate governance. An AI application supporting relatively low-risk administrative work does not need the same level of control as one influencing patient safety or regulatory decisions. Setting clear responsibilities, risk assessments and controls early allows a more flexible approach that supports innovation without neglecting obligations.
Human judgement stays in the loop
Brasher said AI should assist human reviewers rather than replace them, and that she expects human judgement to remain essential until organisations build trust that what agents deliver is accurate. AI can analyse large volumes of information, draft documentation and make recommendations, but its output still requires scrutiny; a convincing answer is not necessarily a correct one, particularly in a highly regulated sector.
Keynote speakers compared relying on AI without understanding the underlying subject matter to using a calculator without knowing the mathematics behind it. The analogy raises a question for the next generation of regulatory and quality professionals, who may lean on AI before developing the expertise needed to challenge inaccurate recommendations.
Measuring value and starting small
With a growing list of potential applications, organisations were urged to resist pursuing every opportunity at once and instead target existing operational problems with clear measures of success. The keynote identified time savings, shorter process cycle times, less rework, improved quality and employee adoption as ways to judge value, and recommended controlled pilots to establish where AI works best, keep human oversight and build confidence before scaling.
Because the technology and regulatory expectations will keep shifting, governance structures need to adapt alongside new capabilities. Orzechowski coined the phrase "slow down to speed up" to describe the approach.
The summit's overall message was optimistic but pragmatic: progress depends on collaboration inside companies and between manufacturers, technology providers and regulators, and on recognising where human expertise remains indispensable.