MedTech organisations are being urged to put data, process and governance foundations in place before scaling artificial intelligence, following discussions at the Veeva MedTech Summit in Amsterdam.
Across presentations, panel sessions and interviews, a consistent message emerged: the challenge is not simply how quickly organisations can adopt AI, but whether they have the right foundations to use it effectively. Speakers argued that successful digital transformation depends on reliable data, consistent processes, appropriate governance and the people responsible for overseeing them.
Medtronic's four-year groundwork
Becki Brasher of Medtronic described the company's four-year journey towards being ready to implement AI. Rather than starting with the technology, Medtronic concentrated on the organisational foundations needed to support it, harmonising processes, improving data consistency, consolidating systems and establishing governance structures. Brasher summed up the approach: "We didn't start with AI. We had to earn AI."
That groundwork matters because 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 stressed preparing employees for change, arguing that technology alone cannot deliver transformation and that staff need the knowledge, confidence and oversight to incorporate new tools into daily work.
Governance from the outset
Balancing innovation with regulatory compliance was another major theme, particularly in the closing keynote from Erik Orzechowski and Benjamin Rochette. As adoption accelerates, they said, MedTech organisations must move quickly without compromising the quality, safety and regulatory standards expected of the industry. Governance, they argued, should be embedded into technology development from the start rather than treated as a final compliance exercise.
That requires closer collaboration between IT, Quality and Regulatory teams. IT teams understand the technology and its implementation, while Quality and Regulatory professionals assess risks and determine the evidence needed to demonstrate compliance. The speakers also called for proportionate governance: an AI application supporting low-risk administrative activities does not necessarily need the same controls as one influencing patient safety or regulatory decisions. Clear responsibilities, early risk assessments and appropriate controls can create a more flexible approach that supports innovation without overlooking obligations.
Human judgement and trust
Despite AI's growing capabilities, the summit emphasised retaining human judgement and professional expertise. Brasher said AI should assist human reviewers rather than replace them, and that human judgement will remain essential until organisations build trust that what agents deliver is accurate. AI can analyse large volumes of information, generate documentation and make recommendations, but its outputs still require scrutiny; a convincing answer is not necessarily a correct one, particularly in a highly regulated environment.
The closing keynote compared relying on AI without understanding its underlying subject matter to using a calculator without knowing the mathematics behind it. That raises a question for organisations about how to introduce AI into everyday workflows without undermining the expertise needed to assess its outputs, especially for the next generation of regulatory and quality professionals. The consensus was not that AI should be avoided, but that it should complement human expertise, with accountable individuals retaining responsibility for important decisions.
Measuring value and scaling carefully
With more potential applications available, organisations need to resist pursuing every opportunity at once and instead target areas where technology addresses existing operational challenges. The keynote highlighted measures of value including time savings, reduced process cycle times, less rework, improved quality and employee adoption. Controlled pilot projects can show where AI is most effective, retain human oversight and build confidence before successful applications are expanded.
Because the technology landscape will keep evolving, organisations need governance structures that can adapt alongside new capabilities and changing regulatory expectations. Success, the summit suggested, will depend less on the number of AI initiatives launched and more on the ability to turn promising applications into reliable, scalable business capabilities. Orzechowski coined the phrase "slow down to speed up" to describe the approach.