MedTech organisations are being urged to treat artificial intelligence as the result of organisational readiness rather than a starting point, following discussions at this year's Veeva MedTech Summit in Amsterdam.
Across presentations, panel discussions and interviews, a consistent message emerged: successful digital transformation depends on addressing the fundamentals first, bringing together reliable data, consistent processes, appropriate governance and the people responsible for overseeing them.
Medtronic spent four years laying the groundwork
Becki Brasher of Medtronic described the company's four-year journey towards becoming ready to implement AI. Rather than beginning with the technology itself, Medtronic harmonised processes, improved data consistency, consolidated systems and established governance structures. "We didn't start with AI. We had to earn AI," Brasher said during her conference session.
That groundwork explains why introducing AI into fragmented or inconsistent environments can create more problems than it solves: without reliable information and standardised processes, organisations risk producing outputs that are difficult to trust, validate or scale.
Brasher also emphasised preparing employees for change, noting that technology alone cannot deliver transformation and that staff need the knowledge, confidence and appropriate oversight to incorporate new tools into everyday responsibilities.
Governance built in, not bolted on
Balancing technological innovation with regulatory compliance was a central theme of the closing keynote from Erik Orzechowski and Benjamin Rochette. As AI adoption accelerates, they argued, organisations must move quickly without compromising the quality, safety and regulatory standards expected of the industry — and governance should be embedded in technology development from the outset rather than treated as a final compliance exercise.
That requires closer collaboration between IT, Quality and Regulatory teams, they said: IT teams understand the technology and its implementation, while Quality and Regulatory professionals help assess risks and determine the evidence required to demonstrate compliance.
The speakers also called for proportionate governance. An AI application supporting relatively low-risk administrative activities does not necessarily require the same level of control as one influencing patient safety or regulatory decisions. Clear responsibilities, early risk assessments and appropriate controls can support a more flexible approach that allows innovation without overlooking obligations.
Human judgement remains in the loop
Despite improving AI capabilities, the summit stressed retaining human judgement and professional expertise. Brasher said she expects human judgement to remain in place as it is today "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, generate documentation and provide recommendations, but its outputs still require scrutiny — a convincing answer is not necessarily a correct one, particularly in a highly regulated environment.
The keynote compared relying on AI without understanding its 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: if employees lean on AI before developing the necessary knowledge, organisations could end up with fewer people able to challenge inaccurate or inappropriate recommendations.
Measuring value and starting small
With more potential applications available, organisations should resist pursuing every opportunity at once and instead identify where technology can address existing operational challenges, with clear measures of success. The keynote cited time savings, reduced process cycle times, less rework, improved quality and employee adoption as ways to assess value. Controlled pilot projects can show where AI is most effective, retain human oversight and build confidence before successful applications are expanded.
Because the AI landscape will keep evolving, governance structures need to adapt alongside new capabilities and changing regulatory expectations. Success will depend less on the number of initiatives launched than on the ability to turn promising applications into reliable, scalable business capabilities.
The summit's overall message was optimistic but pragmatic. Technology, regulatory compliance, data management and organisational culture cannot be addressed independently; progress will require closer collaboration within companies as well as continued engagement between manufacturers, technology providers and regulators. As Orzechowski put it, organisations sometimes need to "slow down to speed up."