MedTech companies need to put reliable data, consistent processes, appropriate governance and accountable people in place before they can scale artificial intelligence, according to discussions at the Veeva MedTech Summit in Amsterdam.
The event focused less on the speed of AI adoption than on the organisational groundwork required to use the technology safely and effectively. A consistent message across presentations, panels and interviews was that introducing AI into fragmented or inconsistent environments can create more problems than it solves.
Medtronic’s four-year groundwork
Becki Brasher of Medtronic described a four-year journey to prepare the company for AI implementation. Rather than starting with the technology, Medtronic concentrated on harmonising processes, improving data consistency, consolidating systems and establishing governance structures.
Brasher explained that Medtronic did not begin with AI; it first had to earn the conditions for using it. The implication was that without reliable information and standardised processes, AI outputs are difficult to trust, validate or scale.
She also stressed that technology alone cannot deliver transformation. Employees need the knowledge, confidence and oversight to incorporate new tools into their responsibilities.
Governance from the outset
Balancing innovation with regulatory compliance was another central theme, particularly in the closing keynote by Erik Orzechowski and Benjamin Rochette. They argued that governance 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 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 low-risk administrative work may not need the same controls as one influencing patient safety or regulatory decisions. Clear responsibilities, risk assessments and early controls can give organisations a more flexible approach that supports innovation without ignoring obligations.
Human judgement and measurable value
The summit also emphasised retaining human judgement. Brasher said AI should assist human reviewers rather than replace them, and that human judgement will remain essential until there is confidence that agents deliver accurate results. A persuasive output may still be incorrect, especially in a highly regulated sector.
Closing keynote speakers compared relying on AI without understanding its subject matter to using a calculator without knowing the mathematics behind it. They warned that if next-generation regulatory and quality professionals depend on AI before developing underlying knowledge, organisations could have fewer people able to challenge inaccurate recommendations.
On business value, the speakers advised organisations not to pursue every potential application at once. Instead, they should target existing operational challenges and set clear measures of success, including 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 wider deployment.
The broader conclusion was that success will depend less on the number of AI initiatives launched than on the ability to turn promising applications into reliable, scalable capabilities. As Orzechowski put it, organisations sometimes need to slow down in order to speed up.
The summit framed digital transformation as a collective challenge. Technology, regulatory compliance, data management and organisational culture cannot be addressed independently, and progress will require collaboration within companies as well as engagement between manufacturers, technology providers and regulators.