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Belgium 10-09-2026 Squad Only Physical english

Building the Infrastructure AI Needs: From computer and data access to scalability, cost control, and operational readiness.

AI is moving from pilots into operational use, and the infrastructure question is becoming more concrete. The challenge is deciding what needs to be built, expanded, controlled, or delayed as usage grows. Three pressure points usually appear first. - Compute capacity requires careful planning: teams need enough GPU, cloud, or specialised processing power, while keeping utilisation and cost under control. - Data movement becomes more demanding: AI needs access to data across systems, with latency, security, integration, and quality managed from the start. - Operational control becomes essential: monitoring, access rights, resilience, cost visibility, and ownership need to scale with usage. The working question is simple: how do we support AI at scale while keeping infrastructure efficient, governed, and financially sustainable? If you are working through these choices, let’s compare approaches with others facing similar constraints.

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.

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Nordstrom Analytical Platform and Enhancing the Customer Experience

Nordstrom develops the Nordstrom Analytics Platform (NAP), which uses AI-powered analytics to personalize the shopping experience through deeper customer insights and predictions.

Chief Technology Officer, Edmond Mesrobian, recently participated in a conversation with VentureBeat's Hari Sivaraman, discussing the role of data infrastructure to enhance the customer experience. Focusing on AI-powered analytics, Edmond described the journey of building the Nordstrom Analytical Platform (NAP) to leverage deeper insights and predictions for a personalized experience. See highlights from their conversation below.

With our Nordstrom Analytical Platform (NAP), we are improving on our ability to provide personalized product discovery and service.

When we think about analytics as opposed to data reporting, timing is the most precious commodity, and making the timeliest decisions required us to build a platform. Whether it's stores, fulfillment centers or online, we built and translated those signals through models and actions. It's difficult to buy that type of solution off-the-shelf because you need to wire your enterprise. We asked, can we generate a prediction cost-effectively and translate that into a customer benefit and service? That's where we started, and we realized we needed to create it as opposed to buying it.

We embarked on this journey to get the business aligned so that we can capture events and stream them in real-time or near real-time into our analytical platform. We then layered analytical models of a variety of forms to translate into predictions—today's predictions are the new KPIs. Our goal was not to be technology providers for the components, but rather stitch it together to create the kind of analytical platform that we can then robustly drive machine learning. What we call our one-hop engine essentially translates our business events into something that could then be activated through a model. And so that's the special sauce—how we put the business events together, how we transformed them from one hop into our models and then generated predictions on the other end.

Our goal is to make the digital experience personal by offering our customers curated product choices on an individual level.

There are over a hundred AI models that we leverage daily at Nordstrom—a significant portion of them are backward-facing capabilities from inventory control to fulfillment and how to route orders to the nearest store and more. Customers will experience those capabilities indirectly in terms of getting orders shipped and delivered on time. Our mission and holy grail from a digital perspective is delivering the capability of discovery. Taking what we know about our customers we can deliver personalized products and better selection—through improved Looks, style boards and more. We are using AI in a more robust way to drive product discovery and personalization.

We are also focused on what we call our fashion map. The fashion map essentially takes a natural language-based approach with deep learning to interpret images and information from social networks—this allows us so to get to know customers through a natural language conversation, as opposed to keyword searches that are very taxonomy driven and hardcoded. We are continuing to work on achieving AI discovery to get closer to our customers in a personalized way.

See the full conversation below.

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