MedTech companies are being urged to fix their data, process and governance foundations before scaling artificial intelligence, following discussions at the Veeva MedTech Summit in Amsterdam.
AI is already being applied across the sector to streamline regulatory processes, improve access to information and support more efficient operations. But speakers and panellists at the event argued that the decisive question is not how quickly organisations can adopt the technology, but whether they have the right groundwork in place to use it effectively. A consistent message across presentations, panels and interviews was that successful digital transformation depends on addressing the fundamentals first: reliable data, consistent processes, appropriate governance and the people responsible for overseeing them.
Medtronic's four-year preparation
Becki Brasher of Medtronic described a four-year programme aimed at making the company ready to implement AI. Rather than starting with the technology, Medtronic concentrated on organisational foundations, harmonising processes, improving data consistency, consolidating systems and establishing governance structures.
Brasher told attendees that the company did not begin with AI, but instead had to earn the conditions in which it could be used.
That sequence reflects a wider warning from the summit. Introducing AI into fragmented or inconsistent environments can create more problems than it solves; without reliable information and standardised processes, organisations risk generating outputs that are difficult to trust, validate or scale. Brasher also stressed that technology alone cannot deliver transformation, and that employees need the knowledge, confidence and appropriate oversight to fold new tools into their everyday responsibilities.
Governance from the outset
Balancing innovation with regulatory compliance dominated the closing keynote, given by Erik Orzechowski and Benjamin Rochette. As adoption accelerates, they said, MedTech organisations must find ways to move quickly without compromising the quality, safety and regulatory standards expected of the industry. Their central argument was that governance should be embedded into technology development from the outset rather than treated as a final compliance exercise.
That requires closer collaboration between IT, Quality and Regulatory teams. IT functions understand the technology and how it is implemented, while Quality and Regulatory professionals assess risk and determine the evidence needed to demonstrate compliance. The speakers also called for proportionate governance: an AI application supporting relatively low-risk administrative activities does not require the same level of control as one influencing patient safety or regulatory decisions. Clear responsibilities, early risk assessments and appropriate controls, they argued, allow a more flexible approach that supports innovation without overlooking obligations, and compliance and innovation should not be treated as opposing priorities.
Human judgement retained
The summit also emphasised the value of human expertise. Brasher said AI should assist human reviewers rather than replace them, and argued that human judgement will remain in place until organisations build enough trust that what agents deliver is accurate. AI can analyse large volumes of information, generate documentation and provide recommendations, but its output still requires scrutiny: a convincing answer is not necessarily a correct one, particularly in a highly regulated environment.
Closing keynote speakers compared relying on AI without understanding its underlying subject matter to using a calculator without knowing the mathematics behind it. The point has particular weight for the next generation of regulatory and quality professionals. If staff become accustomed to relying on AI before developing the necessary underlying knowledge, organisations could find themselves with fewer people able to challenge inaccurate or inappropriate recommendations. The consensus was that AI should complement rather than replace expertise, with accountable individuals retaining responsibility for important decisions.
Measuring value
On investment, delegates were urged to resist pursuing every available application at once and instead identify areas where technology addresses existing operational challenges, with clear measures of success. Suggested metrics included time savings, reduced process cycle times, less rework, improved quality and employee adoption. Controlled pilots were presented as a way to understand where AI is most effective, retain appropriate human oversight and build confidence before expanding successful applications.
Because both the technology and regulatory expectations will keep changing, organisations need governance structures that can adapt alongside new capabilities. Success, the summit heard, will depend less on the number of AI initiatives launched than on the ability to turn promising applications into reliable, scalable business capabilities, supported by closer collaboration inside companies and between manufacturers, technology providers, regulators and other industry stakeholders.