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Belgium 19-3-26 Country Members Physical french

Vendor Detox: La consolidation vaut-elle le risque ?

Moins de Partenaires : La consolidation vaut-elle le risque ? Le problème est la prolifération des fournisseurs : trop d'outils causant de la complexité, une taxe d'intégration paralysante et de la redondance. La Taxe d'Intégration est le coût caché (en temps, en échecs et en ressources) d'essayer de faire fonctionner ensemble des systèmes disparates. Cet échange se concentre sur des stratégies éprouvées pour simplifier de manière agressive le parc technologique, consolider les fournisseurs et élever certains fournisseurs clés au rang de partenaires stratégiques.

Belgium 24-3-26 Invitation Only Physical english

Killing Zombie Projects: Knowing when to stop, restart, or quietly let go

Every organisation has them, projects that keep running long after their purpose has faded. No one remembers who asked for them, but shutting them down feels riskier than keeping them alive. And eventually, people stay assigned, budgets stay allocated, and energy drains into work that no longer matters. Inertia at its finest. Ending a project is rarely a technical decision. It’s emotional, political, and often tied to past promises or personal reputation. The longer something runs, the harder it becomes to admit it’s time to stop. Yet clearing that backlog of half-dead initiatives is often the only way to make room for new ones. So how do you decide when to pull the plug? What signals show that value is gone, and who gets to say so? How can governance encourage honest calls without punishing those who make them? Let’s discuss how to end gracefully, refocus teams, and turn closure into confidence rather than blame. Bullet in the head? Is that how you kill a zombie, or was it a silver bullet in the heart? A closed conversation on how to make progress by learning to stop.

Belgium 26-3-26 Invitation Only Virtual english

Measuring Value in AI Initiatives: Defining ROI, accountability, and measurable outcomes in complex environments

AI projects continue to multiply, but proving their value remains difficult. Most organisations can track activity, not impact. Dashboards count pilots and models, yet few translate to measurable business outcomes. The result is familiar: success stories without clarity on what they actually delivered. The real issue is measurement. Traditional ROI metrics fail when AI changes decisions more than results. Financial indicators miss the operational gains, while qualitative benefits often sound too vague to defend. Without clear evidence, budgets come under scrutiny and confidence erodes. This session focuses on how to connect AI work with business outcomes through structured metrics, governance, and accountability. We’ll explore how value tracking evolves from experimentation to scale, which indicators earn trust at board level, and where measurement stops being meaningful. A closed exchange for comparing methods, tools, and lessons learned in defining, proving, and sustaining AI impact.

March 24, 2026 Squad Session Invitation Only Physical english

Killing Zombie Projects: Knowing when to stop, restart, or quietly let go

March 26, 2026 Squad Session Invitation Only Physical english

Measuring Value in AI Initiatives: Defining ROI, accountability, and measurable outcomes in complex environments

March 31, 2026 Squad Session Invitation Only Physical english

Composability in Practice: Building modular systems that stay coherent as they grow

Composability sounds elegant in theory: small, independent parts that come together to form something greater, but in practice, it’s messy. What happens when modules overlap, APIs evolve differently across domains, and governance struggles to keep pace? What was meant to simplify architecture sometimes ends up multiplying it.

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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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