MedTech organisations are being urged to fix the fundamentals of data, process and governance before scaling artificial intelligence, according to discussions at this year's Veeva MedTech Summit in Amsterdam. In medical technology, AI is already being applied to streamline regulatory processes, improve access to information and support more efficient operations.
The consistent message across presentations, panels and interviews was that the challenge is not simply how quickly organisations can adopt AI, but whether the right foundations are in place: reliable data, consistent processes, appropriate governance and the people responsible for overseeing them.
Medtronic's four-year groundwork
Becki Brasher of Medtronic described a four-year effort to become ready to implement AI. Rather than beginning with the technology, the company 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 session.
That experience illustrates 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 stressed the need to prepare employees, who require the knowledge, confidence and oversight to incorporate new tools into everyday responsibilities.
Governance built in from the outset
Balancing technological innovation with regulatory compliance was the focus of the closing keynote from Erik Orzechowski and Benjamin Rochette. They argued that governance should be embedded in technology development from the start rather than treated as a final compliance exercise, and that closer collaboration between IT, Quality and Regulatory teams will be required. IT teams understand the technology and its implementation, while Quality and Regulatory professionals assess risks and define the evidence needed to demonstrate compliance.
The speakers also called for proportionate governance: an AI application supporting relatively low-risk administrative activities does not necessarily need the same level of control 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. Compliance and innovation, they suggested, should not be viewed as opposing priorities.
Human judgement retained
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 in place until organisations build trust that what agents deliver 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 closing keynote compared relying on AI without understanding its underlying subject matter to using a calculator without knowing the mathematics behind it. A related concern is the next generation of regulatory and quality professionals: if employees lean on AI before developing the necessary underlying knowledge, organisations could end up with fewer people able to challenge inaccurate or inappropriate recommendations.
Measuring value through controlled pilots
On business value, the summit advised organisations to resist pursuing every opportunity at once and instead identify areas where technology can address existing operational challenges, with clear measures of success. Suggested measures include time savings, reduced process cycle times, less rework, improved quality and employee adoption. Controlled pilots can show where AI is most effective and build confidence before successful applications are expanded, while governance structures must adapt as capabilities and regulatory expectations evolve.
Success, the consensus went, depends less on the number of AI initiatives an organisation launches than on its ability to turn promising applications into reliable, scalable business capabilities. Orzechowski's phrase for the approach was to "slow down to speed up."