TL;DR Key Insights from the Emerging Technologies in Pharma & AI Summit

At the Emerging Technologies in Pharma & AI Summit in Vienna, one message stood out: the pharmaceutical industry does not lack AI innovation—it struggles to deploy it at scale. Key themes included the need to move beyond pilots, treat governance as core infrastructure, improve data quality, design AI tools people actually use, and clarify responsibility when systems fail. Companies making the most progress focus less on experimentation and more on integration, adoption, and collaboration between pharma and technology partners.

Intelligencia AI’s Marianna Esposito and Dorita Metallinou recently attended the Emerging Technologies in Pharma & AI Summit in Vienna, where researchers, technology providers, and pharmaceutical leaders gathered to discuss how artificial intelligence is reshaping drug development.

Marianna participated in a panel discussion titled “Tech + Pharma – Partnerships That Work.” The conversation focused on how collaborations between pharmaceutical companies and technology partners are evolving—and what separates promising experiments from real operational impact.

Across presentations and discussions, one theme surfaced repeatedly: the industry does not lack AI innovation. The real challenge lies in deploying AI in ways that deliver measurable value on a scale.

Why Is Moving Beyond AI Pilots So Difficult?

One of the most striking observations from the summit was a growing honesty about where the industry currently stands.

Many organizations have launched promising AI initiatives. Yet relatively few of these projects have progressed beyond the pilot stage. Too many pilots are creating not enough impact.

“We don’t have an AI problem—we have a deployment problem. A proof of concept that never scales is just an expensive experiment.”

Marianna Esposito

Senior Business Development Director, Strategic Accounts, Intelligencia AI

The takeaway was clear: success in AI is no longer defined by building models or running isolated proofs of concept. Instead, the real test is whether solutions can be integrated into everyday workflows and successfully adopted across organizations.

Why Does Governance Need to Be Built in from the Start?

Another recurring theme was the role of governance.

In many organizations, governance frameworks are introduced late in the process—after models have already been developed. The discussions at the conference made clear that this approach rarely works. Instead, companies making real progress tend to treat governance as part of the underlying architecture, not an afterthought.

The critical insight is that governance is not a final step; it’s the architecture. Without it, AI doesn’t fail gracefully—it fails expensively.

In practice, this perspective shifts governance from a compliance requirement to a foundational design principle. When responsibilities, oversight, and decision processes are defined early, organizations are better positioned to scale AI systems responsibly.

Why Is Data Still the Biggest Bottleneck for AI in Pharma?

Despite rapid advances in AI models and tools, the summit reinforced a familiar reality: data quality and accessibility remain the primary constraints.

Data, therefore, remains the biggest bottleneck—not models, not tools. If the data isn’t clean, curated, structured, and usable, nothing else works.

This observation resonated across multiple sessions. Pharmaceutical organizations manage vast and complex datasets spanning research, clinical trials, and regulatory documentation. Without strong data foundations, standardized formats, and better integration across research and clinical systems, even the most advanced AI models struggle to deliver reliable results.

Why Does Adoption Matter More Than Technology?

Another theme that surfaced repeatedly was the importance of human adoption.

AI tools succeed because people choose to use them. The solutions that gain traction are typically those that reduce friction in daily work rather than adding complexity.

In practical terms, this means AI should help teams:

• automate repetitive tasks
• reduce administrative burden
• accelerate decision-making

Tools that require extensive additional input, steep learning curves, or complicated workflows often fail to gain traction, regardless of their technical sophistication.

In other words, the most successful AI solutions are those that make work easier and solve real problems—not those that add complexity and require a long time to show their value.

Who Is Responsible When AI Systems Make Mistakes?

Panel discussions also explored a critical governance question: who is responsible when AI systems make mistakes?

Organizations that scale AI successfully tend to address this issue early.

Rather than creating complex approval structures, several companies have made progress by clarifying responsibilities within existing processes. When teams understand who owns decisions and outcomes, AI systems can be integrated more confidently into operational workflows.

A related insight emerged from the panel:

• Clear responsibility structures matter more than additional rules.
• Simplicity often enables faster adoption.

Companies such as Sanofi and Roche are examples of organizations that have begun embedding these principles into their operating models.

Is the Pharma–Tech Relationship Changing?

Beyond the technical discussions, one broader shift stood out.

Historically, collaboration between pharmaceutical companies and technology partners has sometimes been framed as a purely transactional relationship: one side provides domain expertise, the other delivers technical solutions.

However, the discussions showed that a different perspective is beginning to take hold: the pharma–tech relationship in the era of AI needs to be more collaborative and cooperative than before.

“Less ‘us versus them’ between pharma and tech, and there is more recognition that we’re all in this journey together.”

Dorita Metallinou

Senior Business Development Associate, Intelligencia AI

This mindset reflects a growing understanding that meaningful progress requires close collaboration across disciplines. Scientists, engineers, data experts, and technology providers all play a role in turning AI’s potential into practical outcomes.

What Ultimately Matters Most?

Ultimately, the discussions returned to a central point: AI initiatives in pharmaceutical research and development are more than just technology projects. Their purpose is to help organizations make better decisions, accelerate innovation, and ultimately improve patient outcomes.

As the industry continues to explore what works—and what does not—the conversations at the Emerging Technologies in Pharma & AI Summit highlighted a key lesson:

The future of AI in pharma will depend less on new algorithms and more on how effectively organizations deploy, govern, and adopt the tools they already have.

FAQ

Why do many AI projects in pharma remain stuck in the pilot phase?

Many organizations successfully build AI models but struggle to integrate them into everyday workflows. Without clear governance, strong data infrastructure, and user adoption, pilot projects often fail to scale.

What role does governance play in AI deployment?

Governance helps define responsibilities, oversight, and decision processes. When it is built into the architecture from the start, organizations can scale AI systems more safely and effectively.

Why is data still a major challenge for AI in pharma?

Pharmaceutical data is often complex, fragmented, and stored across multiple systems. Without clean, structured, and well-integrated data, AI models cannot produce reliable results.

What makes AI tools successful in real-world use?

The most successful tools reduce friction in daily work. They automate repetitive tasks, reduce administrative burden, and help teams move faster rather than adding complexity.

Why are pharma–tech partnerships becoming more important?

Drug development increasingly relies on advanced analytics and AI capabilities. Close collaboration between pharmaceutical experts and technology partners helps combine domain knowledge with technical expertise.

For more tailored, data-rich insights, let’s talk.