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Belgium 15-09-2026 Squad Only Virtual english

Turning AI Pilots into Daily Workflows: How to move from experimentation to adoption by redesigning work, ownership, controls, and habits.

AI pilots are easier to launch than to embed into daily operations. The challenge is changing how work happens once AI becomes part of decisions, tasks, handovers, and controls. Three pressure points tend to decide whether adoption holds. - Workflow fit matters: AI has to enter the flow of work as part of the process. - Ownership needs to be clear: someone must own the process, the controls, the exceptions, and the outcome. - User trust has to be built: people need to know when to rely on AI, when to challenge it, and when to escalate. The working question is simple: how do we move AI from promising pilots into daily work while keeping clarity, accountability, and risk control? If this is part of your current reality, let’s compare choices, constraints, and lessons learned.

Belgium 17-09-2026 Country Members Physical french

Les Rencontres: CTO: Le Saint Graal?

Pendant longtemps, le TCO donnait une impression de contrôle. On investissait, on amortissait, on optimisait. Les coûts étaient visibles, relativement prévisibles, et structurés autour d’une logique claire entre CAPEX et OPEX. Ce modèle ne suffit plus à expliquer où va réellement l’argent. Aujourd’hui, une part croissante des dépenses IT ne repose plus sur ce que l’on possède, mais sur ce que l’on consomme: des tokens générés par l’IA des agents qui exécutent des actions en continu des outils SaaS qui se multiplient dans les équipes du cloud qui s’adapte en permanence à l’usage Le coût ne disparaît pas, mais il devient plus diffus, plus dynamique, et souvent plus difficile à attribuer, à expliquer, et à maîtriser. C’est là que des approches comme le Technology Business Management, ou TBM, redeviennent particulièrement utiles: non pas comme un exercice de reporting supplémentaire, mais comme un moyen de relier concrètement les dépenses aux services, aux usages, et aux décisions. Et c’est là aussi que la question du “Saint Graal” revient, mais sous une autre forme. Vous vous demandez peut-être: Comment garder de la maîtrise quand la dépense dépend du comportement, des usages, et parfois même de systèmes autonomes ? Comment piloter l’équilibre CAPEX/OPEX quand le cloud, le SaaS et l’IA déplacent progressivement les coûts vers des modèles variables ? Quels coûts restent encore trop souvent hors radar: ressources métier, formation, support, intégration, sécurité, gouvernance ? Et surtout, qu’est-ce qu’un “bon TCO” veut encore dire quand il faut le mettre en regard d’objectifs parfois plus stratégiques, plus longs à mesurer, ou plus difficiles à quantifier: qualité de service, résilience, agilité, expérience utilisateur, capacité d’innovation ? Ce sont précisément ces questions qui seront au cœur de notre rencontre: une discussion ouverte entre pairs, ancrée dans la réalité du terrain, pas pour débattre de modèles idéaux, mais pour comprendre comment chacun tente, concrètement, de mesurer, d’attribuer et de reprendre le contrôle, sans réduire la valeur d’un choix technologique à son seul coût.

Belgium 25-09-2026 Squad Only Physical english

Managing Team Capacity Under Continuous Change; How to sequence initiatives, protect capacity, and maintain execution

Transformation has become constant. The challenge is managing the amount of change teams are expected to absorb while they continue to deliver existing work. Three pressure points show up quickly. - Capacity becomes stretched when teams face new tools, processes, AI practices, security requirements, and operating model changes at the same time. - Prioritisation becomes critical when many initiatives compete for attention. - Managerial discipline becomes essential because leaders need to decide what to stop, simplify, delay, or protect. The working question is simple: how do we keep transformation moving while giving teams enough focus and capacity to execute it properly? If your teams are absorbing constant change, let’s compare how others are managing capacity, sequencing, and focus.

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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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