Artificial intelligence is already being applied in MedTech to streamline regulatory work, improve access to information and support more efficient operations. But discussions at the Veeva MedTech Summit in Amsterdam focused on a less visible precondition: whether companies have the data, processes, governance and people needed to use the technology reliably. The message was that adoption speed matters less than readiness.
Foundations before algorithms
Medtronic’s experience offered one of the clearest examples. Becki Brasher described a four-year journey towards becoming 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.
The sequencing matters. 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. An AI tool layered onto inconsistent records may make errors faster or harder to trace, even if the underlying model is capable.
Preparation also extends to employees. Medtronic’s experience highlighted the need for people to have the knowledge, confidence and oversight required to incorporate new tools into everyday responsibilities. Technology alone cannot deliver transformation if the workforce is not equipped to use it and challenge its outputs.
Governance as a design input
Balancing innovation with regulatory compliance was a major theme of the summit’s closing keynote by Erik Orzechowski and Benjamin Rochette. As AI adoption accelerates, MedTech organisations must find ways to move quickly without compromising quality, safety and regulatory standards. A 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 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 emphasised proportionate governance: an AI application supporting low-risk administrative activities does not necessarily require the same level of control as one influencing patient safety or regulatory decisions.
By establishing clear responsibilities, risk assessments and appropriate controls early, organisations can create a more flexible approach. Compliance and innovation should not be viewed as opposing priorities; with the right structures, governance can help companies move forward with greater confidence.
Human judgment and trust
The summit also stressed that human judgment and professional expertise remain essential. Brasher made clear her view that AI should assist human reviewers rather than replace them. Asked where human judgment will remain critical as AI becomes more sophisticated, she said judgment will stay 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.
That analogy raises a practical question for employers and for the next generation of regulatory and quality professionals. If employees become accustomed to relying on AI before developing the necessary knowledge, organisations could find themselves with fewer people capable of challenging inaccurate or inappropriate recommendations. The consensus was not that AI should be avoided, but that it should complement human expertise. Accountable individuals must retain responsibility for important decisions.
Pilots, value and scalable capability
Another consideration is whether AI investments deliver genuine business value. With many potential applications available, organisations need to resist pursuing every opportunity at once. Instead, they should identify where technology can address existing operational challenges and define clear measures of success.
The closing keynote highlighted several ways to assess value: time savings, reduced process cycle times, less rework, improved quality and employee adoption. Controlled pilot projects can help organisations understand where AI is most effective, maintain appropriate human oversight and build confidence before expanding successful applications.
This approach matters because the AI landscape will continue to evolve. Governance structures need to adapt alongside new capabilities and changing regulatory expectations. Ultimately, success will depend less on the number of AI initiatives launched and more on the ability to turn promising applications into reliable, scalable business capabilities.
A collective readiness challenge
The summit reflected an industry increasingly aware that digital transformation is a collective challenge. Technology, regulatory compliance, data management and organisational culture cannot be addressed independently. Progress will require closer collaboration within companies, as well as continued engagement between manufacturers, technology providers, regulators and other stakeholders.
The overall message was optimistic but pragmatic. AI offers significant opportunities for medical technology, yet realising them requires patience, preparation and a clear understanding of where human expertise remains indispensable. As Orzechowski put it, organisations sometimes need to “slow down to speed up.” Getting the foundations right today is intended to leave the industry better positioned to embrace tomorrow’s technological opportunities.