MedTech companies risk creating more problems than they solve if they layer artificial intelligence onto fragmented data and inconsistent processes, according to discussions at the Veeva MedTech Summit in Amsterdam. Across presentations, panel sessions and interviews, a consistent argument emerged: successful digital transformation depends less on how quickly organisations adopt AI than on whether reliable data, consistent processes, appropriate governance and accountable people are already in place.
The technology is already being used to streamline regulatory work, improve access to information and support more efficient operations, and its potential is hard to ignore. The summit's focus was on the groundwork that determines whether those applications can be trusted at scale.
Medtronic spent four years earning AI readiness
Becki Brasher of Medtronic described a four-year effort to prepare the company to implement AI. The programme did not begin with algorithms: the company harmonised processes, improved data consistency, consolidated systems and established governance structures, Brasher said during her session, adding that Medtronic "didn't start with AI" but "had to earn AI".
Her account illustrated why introducing AI into a fragmented environment can generate more problems than it resolves. Without reliable information and standardised processes, organisations risk producing outputs that are difficult to trust, validate or scale. Medtronic's experience also pointed to the workforce side of readiness: employees need the knowledge, confidence and oversight to fold new tools into daily responsibilities, since technology alone does not deliver transformation.
Governance embedded from the start
Balancing innovation with regulatory compliance was the theme of the closing keynote by Erik Orzechowski and Benjamin Rochette. They argued that governance should be built into technology development from the outset rather than treated as a final compliance exercise, and that IT, Quality and Regulatory teams must collaborate more closely: IT understands the technology and its implementation, while Quality and Regulatory specialists assess risk and define the evidence needed to demonstrate compliance.
The keynote also made the case for proportionate governance. An AI application supporting low-risk administrative work does not necessarily need the same controls as one influencing patient safety or regulatory decisions. Setting clear responsibilities, risk assessments and controls early, the speakers suggested, gives organisations a more flexible approach that supports innovation without losing sight of obligations. Compliance and innovation, in their framing, are not opposing priorities.
Human judgement and the calculator analogy
Retaining human judgement ran through the summit. Brasher said AI should assist human reviewers rather than replace them, and said in a subsequent interview that human judgement will stay in place "until we can build up some trust that what we're getting, what the agents are delivering is accurate". AI can analyse large volumes of information, draft documentation and offer recommendations, but a convincing answer is not automatically a correct one — a particular concern in a highly regulated sector.
The closing keynote compared relying on AI without understanding its subject matter to using a calculator without knowing the mathematics behind it. The analogy raised a question about the next generation of regulatory and quality professionals: if staff lean on AI before building underlying knowledge, organisations may end up with fewer people able to challenge inaccurate or inappropriate recommendations. The consensus was that AI should complement human expertise, with accountable individuals still responsible for important decisions.
Pilots and measurable value
With potential applications proliferating, speakers cautioned against pursuing every opportunity at once. Organisations should instead target areas where technology addresses existing operational problems and define success measures in advance, including:
- time savings
- shorter process cycle times
- less rework
- improved quality
- employee adoption
Controlled pilots were presented as a way to learn where AI is most effective, keep human oversight in place and build confidence before scaling the applications that work. Because both the technology and regulatory expectations will keep shifting, governance structures must adapt alongside new capabilities.
The wider conclusion was that transformation is a collective challenge: technology, compliance, data management and organisational culture cannot be tackled in isolation, and progress depends on collaboration inside companies and between manufacturers, technology providers, regulators and other stakeholders. Orzechowski's phrase for the approach — "slow down to speed up" — summed up a summit that was optimistic about AI but pragmatic about the preparation it demands.