Artificial intelligence is being applied across MedTech to streamline regulatory processes, widen access to information and support more efficient operations. But discussions at this year's Veeva MedTech Summit in Amsterdam focused less on how quickly companies can adopt the technology than on whether the groundwork exists to use it effectively.
A consistent message ran through presentations, panel discussions and interviews: successful digital transformation requires organisations to address the fundamentals first, bringing together reliable data, consistent processes, appropriate governance and the people responsible for overseeing them.
Medtronic spent four years preparing
Becki Brasher of Medtronic described a four-year journey towards readiness to implement AI. Rather than starting with the technology, the company concentrated on the organisational foundations needed to support it: harmonising processes, improving data consistency, consolidating systems and establishing governance structures.
"We didn't start with AI. We had to earn AI," Brasher said during her conference session, according to the account of the event.
Her example 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 appropriate oversight to fold new tools into their everyday responsibilities.
Governance embedded from the outset
In the summit's closing keynote, Erik Orzechowski and Benjamin Rochette addressed how organisations can move quickly as AI adoption accelerates without compromising the quality, safety and regulatory standards expected of the industry. They argued that governance should be embedded into technology development from the start rather than treated as a final compliance exercise.
That, they said, will require closer collaboration between IT, Quality and Regulatory teams. IT teams understand the technology and its implementation, while quality and regulatory professionals help assess risks and determine the evidence needed to demonstrate compliance.
The speakers also emphasised 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. By setting clear responsibilities, risk assessments and appropriate controls early, organisations can build a more flexible approach that supports innovation without overlooking their obligations. Compliance and innovation, the message ran, should not be viewed as opposing priorities.
Human judgement and trust
Despite the growing capabilities of AI, the summit highlighted the importance of retaining human judgement and professional expertise. Brasher made clear her view that AI should assist human reviewers rather than replace them, and said human judgement will remain in place as it is today until trust is built 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. Speakers in the closing keynote compared relying on AI without understanding its underlying subject matter to using a calculator without knowing the mathematics behind it.
The point has particular relevance for the next generation of regulatory and quality professionals. If employees grow accustomed to relying on AI before developing the necessary underlying knowledge, organisations could end up 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, with accountable individuals retaining responsibility for important decisions.
Measuring value and scaling carefully
With an increasing number of potential applications available, organisations need to resist pursuing every opportunity at once and instead identify where technology can address existing operational challenges, with clear measures of success. The closing keynote highlighted time savings, reduced process cycle times, less rework, improved quality and employee adoption as ways to assess the value of AI.
Starting with controlled pilot projects can help organisations understand where AI is most effective, retain appropriate human oversight and build confidence before expanding successful applications. Given the pace of technological development, governance structures will also need to adapt alongside new capabilities and changing regulatory expectations.
Success, the summit's message suggested, will depend 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 "slow down to speed up" captured the argument: getting the foundations right today, speakers said, leaves the industry better positioned to take up the technological opportunities of tomorrow.