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Belgium 13-11-2026 Squad Only Physical English

Building the Business Case for New IT Capabilities; How to justify investment when value is strategic, shared, or only visible over time.

Many IT capabilities are essential and difficult to fund through a simple project ROI. The challenge is showing why architecture, governance, resilience, cybersecurity maturity, data foundations, and AI readiness deserve investment before the gap becomes expensive. Three pressure points make the case harder. - Shared value means the benefit often sits across several teams, while the budget sits in one place. - Timing creates friction because the cost is immediate while the value appears over time. - Risk translation is needed so business and finance leaders understand the consequence of delaying the investment. The working question is simple: how do we turn foundational IT capabilities into credible investment cases that link cost, risk, business value, and future flexibility? If you are building these cases internally, let’s compare how others are positioning value, risk, and timing.

Belgium 19-11-2026 All Members Physical English

Europe’s Digital Foundations

Public organisations are re-evaluating the digital foundations on which critical services depend. Cloud strategy is increasingly shaped by questions of operational control, legal exposure, supplier dependency, interoperability and long-term resilience. For public-sector leaders, the issue is not only where workloads are hosted, but how much strategic control remains over the technologies that support essential public services. This is where European cloud initiatives, sovereign infrastructure and open-source technologies become strategically important. Together, they can increase transparency, preserve portability and reduce dependency on individual providers. But they also raise practical questions around maturity, procurement, integration, support models and long-term sustainability. Open source brings a specific dimension to this discussion. It claims to better support transparency, interoperability and strategic autonomy, but it is also requiring clear governance, security controls and operational support. The opportunity is to understand where open technologies can strengthen the public-sector technology stack, where commercial platforms remain essential and how both can coexist within a coherent European digital strategy. Three questions shape the discussion: Which parts of the public-sector technology stack require stronger European control? What role should open-source technologies play in reducing dependency and strengthening transparency? Which governance and procurement decisions will determine whether a sovereign cloud strategy succeeds? Bringing these perspectives together can define where sovereign cloud and open technologies create strategic value, identify the decisions that deserve executive attention, and establish practical principles for strengthening Europe’s digital foundations while preserving flexibility and innovation. See you at the table?

Belgium 24-11-2026 Country Members Physical French

Les Rencontres: Innovation de l'Ombre

Créativité vs. Chaos : Quelle est la gouvernance minimale ? Le défi principal est d'équilibrer le risque et le rythme du changement. Les équipes métier adoptent de nouveaux outils d'IA et low-code de leur propre chef, créant une véritable créativité mais aussi un risque massif et non géré. Cette discussion se concentre sur l'établissement de voies simples et sûres pour l'innovation, en débattant de la gouvernance minimale qui protège la réputation de l'entreprise tout en évitant l'erreur d'écraser les initiatives utiles dans l'ombre. Questions clés d'Échange entre pairs : Où l'innovation de l'ombre aide-t-elle plutôt que de nuire, et comment éviter de l'écraser ? Quelle est la gouvernance minimale qui protège la réputation tout en maintenant la vitesse, et à qui appartient-elle réellement ? Comment les pairs ont-ils réussi à transformer les efforts initiaux de l'ombre en services officiels soutenus sans perdre la confiance de l'entreprise ? Comment pouvons-nous rendre plus facile d'innover de manière responsable que de simplement agir en franc-tireur ?

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Exscientia Uses Generative AI to Reimagine Drug Discovery

Exscientia uses generative artificial intelligence (AI) throughout the design-make-test-learn (DMTL) cycle to discover new therapies for patients quickly and relatively inexpensively.

“Using AWS, we reduce bottlenecks and accelerate the pipeline.”

David Hallett
Interim CEO and Chief Scientific Officer, Exscientia

Exscientia uses generative artificial intelligence (AI) throughout the design-make-test-learn (DMTL) cycle to discover new therapies for patients quickly and relatively inexpensively. Conventional drug discovery methods can take up to 15 years and cost over 2 billion dollars, with an average failure rate of 90–96 percent because scientists hunt for specific drug candidates among 10⁶⁰ bioavailable small molecules.

Built on Amazon Web Services (AWS), Exscientia’s innovative DMTL solution incorporates in silico design—using generative AI algorithms to design compounds in the cloud—and automated robots that make drug candidates in a lab. “We use generative AI to solve for efficiency and effectiveness,” says David Hallett, Exscientia’s interim CEO and chief scientific officer. “By predicting the molecular features of a safe and effective drug in silico, we minimize the number of costly experiments. Our platform, built in collaboration with the AWS team, is optimized for speed. We can repeat many DMTL learning loops, improving our drug candidates with every iteration.”

Using Generative AI for More Efficient Drug Discovery at Reduced Cost

Working backward from patient needs, Exscientia defines precise target product profiles (TPPs) that specify the complex combination of properties required by a well-tolerated and effective medicine. AI engineers design algorithms that generate panels of potential drug candidates to meet the TPPs. Active learning algorithms help expert designers to select a short list of drug candidates to synthesize in the lab, because they either move the TPPs forward or refine the models for future DMTL cycles.

Exscientia’s algorithms are trained on publicly available pharmacology data and proprietary in-house data generated from patient tissue samples, genomics, single-cell transcriptomics, and medical literature. By encoding data throughout the process and analyzing experimental results and previous design cycles, Exscientia can optimize upcoming design cycles and promote compound designs that are physically synthesizable. Using this synthesis-aware, iterative approach built on AWS, Exscientia makes 10 times fewer compounds than the industry average. “The idea is to iron out chemical liabilities to make safer and more effective drug candidates before we ever test them in patients,” says Hallett.

Exscientia has accelerated drug design by up to 70 percent while decreasing capital cost by 80 percent, compared with industry benchmarks. Using generative AI with other tools, Exscientia not only developed better drug candidates faster but also identified the right drug combinations to trial with patients in the clinic.

Cutting Time and Costs Using Automated Robotics to Make Better Medicines

Exscientia has incorporated cutting-edge chemistry synthesis and biology assay lab equipment with automation robots to avoid manual handling of lab equipment. Thus, its lab—orchestrated by AWS microservices—can operate 24/7 with minimal human supervision, “When our designs are ready, we can push a button, and within a few days, the robots are making the drug,” says Hallett. Maintaining extremely high levels of security and comprehensive disaster recovery, Exscientia will use this automated robotic capability to reduce the make and test timelines resulting from traditional offshore research contracts used across the industry.

As the company closes the loop with its robotic automation lab, it expects further productivity improvements. Data generated in the lab improves algorithmic predictions and speeds up DMTL cycles.

Introducing AI-Designed Molecules into Clinical Trials

Six molecules that Exscientia designed using AI have entered clinical trials. “Using AWS, we reduce bottlenecks and accelerate the pipeline,” says Hallett. “By switching on this highly integrated and automated DMTL loop, we can make drug candidates faster and more cost-effective.”

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