AWS for Industries
Executive Insights from the Inaugural AWS Life Sciences Symposium EMEA
Watch the full keynote recording: View on YouTube
Introduction
The life sciences industry stands at an inflection point. The technology is ready, the use cases are proven, yet many organisations remain stuck between promising pilot and production-grade impact. At the inaugural Amazon Web Services (AWS) Life Sciences Symposium EMEA, hundreds of pharma, biotech, and medtech leaders converged to address this paradox, sharing a unified urge to translate AI investments into outputs that can ultimately improve patient health.
In his opening keynote, Dr. Boris Bogdan, Director of Life Sciences at AWS, addressed the pilot-to-production challenge head on, sharing four strategic differentiators that set successful organisations apart from those stuck in pilot mode.
Strategic Differentiator 1: Developing a connected strategy
As with most new initiatives and emerging technologies, many life sciences companies approach AI as a siloed project, running pilots division by division. R&D runs its own pilots. Clinical may have a separate roadmap. And Commercial experiments independently.
While this methodology can deliver valuable proof points, it does not work at scale.
The pattern is consistent across the industry, from start-ups to top global biopharma companies. The organizations that are achieving step-function returns on their AI investments are establishing connected foundations that enable innovation-at-sale.
This includes establishing shared enterprise ontologies, common data models, metadata and context, agent infrastructure, and AI governance. Having these components in place establishes a common foundation that enables your divisions to run their own innovations while operating on a unified platform.
Strategic Differentiator 2: The Power of a Dual-Track Mindset
Leadership teams face compounding pressure to deliver near-term wins while needing to define and invest in their long-term AI strategy. This requires adopting a dual-track mindset that balances quick wins with deliberate investment in a scalable AI foundation
For this purpose, we distinguish use cases into two tracks. “Track 1” use cases deliver incremental value in specific priority areas, often layering AI on top of existing processes. There is no doubt that substantial value is being recognized through these efforts – they keep momentum and demonstrate quick wins such as reducing project timelines from months to days, cutting spend by double-digit percentage points, and so on.
But as addressed above, siloed projects are not what moves the needle long term.
That is where “Track 2” use cases come in. These use cases are platform-forcing, deliberately chosen to inform and enforce the build-out of your foundational layer. They are selected because of both their direct value and the enterprise capabilities they establish.
A great example is the development of a commercial agent. This is a high-value use case that requires establishing a semantic hub to answer physician questions. It solves a real problem and forces the build-out of the semantic layer. Another example is prioritizing clinical data transformation, requiring an AI-ready data platform. You recognize the value of accelerating the transformation process while creating the foundational infrastructure needed for future trials.
Organisations that solely focus on Track 1 use cases are often solving the same problem multiple times in multiple divisions rather than investing in a foundation that solves once and scales across divisions.
A sophisticated use case portfolio balances both tracks. Track 1 keeps the momentum. Track 2 ensures that the foundation grows quarter over quarter, so the next quarter’s use cases become faster, cheaper, and more powerful than those before.
Strategic Differentiator 3: Structural independence
Most organisations approach innovation within the confines of their core organisation, within the boundaries of how incentives are set, standard operating procedures, governance, and existing team hierarchies. This strategy works well for incremental improvements.
The real value of AI, however, comes from re-examining and re-engineering legacy processes and procedures. Reframing the question from “How can we reduce this timeline by 50%?” to “What is now possible with AI?”
This is not a new model, it is how Apple built the iPhone and Toyota built Lexus. Establishing satellites in both an organisational and geographical structure can help free your teams to focus on reimagining rather than evolving. For example, you may establish a team outside of your headquarters in Basel to sit alongside your AI/ML teams in London. It is about creating structurally independent teams that have the freedom to innovate unconstrained.
Strategic Differentiator 4: Owning Your AI Architecture
The imperative of owning your own data is well established. The same concept applies to your AI. Organisations establishing differentiated AI foundations view their architecture as proprietary investments, unique to their requirements and goals.
While there are many ways to segment AI architectures, the most successful approach is a three-layered architecture:
Layer 1 — Your Moat: Data & Semantics. This layer is designed to make your organisation AI-ready. It includes your data, taxonomies, ontologies, semantics, SOPs, and business relationships. It is the foundation of what makes your AI work for your organisation.
Layer 2 — Your Edge: Agents & Orchestration. This is where you establish your AI edge. Architecture choices matter here; build on open standards and avoid lock-in to maintain optionality and adaptability.
Layer 3 — Your Choice: Models & Intelligence. Keep maximum optionality. Maintain a multi-model architecture by design and ensure version control where models enter regulated workflows.
Establishing AI as a business differentiator does not come from one model. It comes from your data, your context, your workflows, and the learning loops underneath.
The architecture principle is simple: keep your data and semantic layer tight, be selective with the agents and orchestration layer, and ensure flexibility with your models.
The Value of Your Operating Principles
It is not just architecture, organisational structure, and use-case prioritisation that separates those succeeding with enterprise AI, operating principles play a vital role. As Dan Sheeran shared at the AWS Life Sciences Symposium in New York: you need to build for production from day one and create a builder culture.
Build for production from day one. Many Track 1 use cases remain stuck in prototype for similar reasons. When scaling to production, they face challenges in performance, scalability, security, and governance. It is not because the prototype was unsuccessful; it is because the surrounding infrastructure does not support it. Eli Lilly shared an insightful example of how they built Cortex, their unified data platform, for production from day one.
Create a builder culture. Some of the most innovative applications of AI come not from developers but from end users who deeply understand the challenge. Establishing a culture and infrastructure that empowers everyone to build, within the right guardrails is critical. This is where Amazon Quick is helping life sciences organizations across the globe.
Amazon Quick turns everyone within your organisation into a builder without writing a single line of code. Your marketing teams can generate sales decks leveraging insights from recent successes to inform positioning. Your regulatory affairs teams can draft submission documents by pulling from established SOPs and prior approvals, dramatically reducing cycle times.
Conclusion & Recommended Actions
The message from our inaugural European symposium is clear: organisations that move from isolated AI experiments to integrated, production-grade foundations are the ones achieving measurable, lasting impact. The technology is ready. The use cases are proven. The differentiator is execution, an integrated strategy, a dual-track portfolio, the organisational courage to establish satellites, architecture you own, and operating principles that prioritise production-readiness and a builder culture.
We would like to thank all our customers and partners who joined us for this landmark event. The conversations reinforced our conviction: when life sciences leaders invest in the right foundations, the impact on patients is not incremental, it is transformational.
We look forward to continuing these conversations and to welcoming you at future AWS events.