MedTech organisations are being encouraged to treat artificial intelligence as the final stage of digital transformation rather than its starting point, after discussions at this year's Veeva MedTech Summit in Amsterdam set out the groundwork that has to precede any deployment.
Across presentations, panel sessions and interviews, a consistent theme emerged: reliable data, consistent processes, appropriate governance and the people responsible for oversight have to be in place before AI can be used effectively.
Medtronic spent four years preparing
Becki Brasher of Medtronic described a four-year journey towards becoming ready to implement AI. The company began not with the technology but with the organisational fundamentals needed to support it, harmonising processes, improving data consistency, consolidating systems and establishing governance structures. Her summary of the programme was that Medtronic did not start with AI, but had to earn it.
That sequencing reflects a practical risk. Introducing AI into fragmented or inconsistent environments can create more problems than it solves, because without reliable information and standardised processes organisations may produce outputs that are difficult to trust, validate or scale.
Brasher also stressed preparing employees for change, noting that technology alone cannot deliver transformation and that staff need the knowledge, confidence and oversight arrangements to fold new tools into their everyday responsibilities.
Governance from the outset, in proportion to risk
The summit's closing keynote, given by Erik Orzechowski and Benjamin Rochette, focused on balancing innovation with regulatory compliance. As adoption accelerates, they argued, MedTech organisations have to find ways to move quickly without compromising the quality, safety and regulatory standards the industry is held to. Their central point was that governance should be embedded in technology development from the start rather than treated as a compliance exercise at the end.
That requires closer collaboration between IT, Quality and Regulatory teams: IT understands 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 relatively low-risk administrative activity does not necessarily need the same level of control as one influencing patient safety or regulatory decisions. Setting clear responsibilities, risk assessments and appropriate controls early allows a more flexible approach that supports innovation without overlooking obligations.
Human judgement retained
Brasher said AI should assist human reviewers rather than replace them, and that human judgement would stay in place as it is today until organisations build trust that what agents deliver is accurate. AI can analyse large volumes of information, generate documentation and offer recommendations, but its outputs still need scrutiny, since a convincing answer is not necessarily a correct one.
The closing keynote compared relying on AI without understanding its underlying subject matter to using a calculator without knowing the mathematics behind it. The analogy raises the question of how organisations can introduce AI into everyday workflows without eroding the expertise needed to challenge its outputs, particularly for the next generation of regulatory and quality professionals who might otherwise develop reliance before knowledge.
Testing value through controlled pilots
With a growing list of potential applications, organisations were urged to resist pursuing every opportunity at once and instead target areas where technology addresses 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, and recommended starting with controlled pilots that keep human oversight in place and build confidence before scaling.
Because the AI landscape will keep evolving, governance structures will need to adapt alongside new capabilities and changing regulatory expectations. Orzechowski's phrase for the approach was that organisations sometimes need to slow down in order to speed up.
The broader conclusion was that technology, regulatory compliance, data management and organisational culture cannot be tackled independently, and that progress will depend on collaboration inside companies as well as engagement between manufacturers, technology providers, regulators and other stakeholders.