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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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ABN AMRO modernizes its infrastructure in 9 months with Datometry and Microsoft Azure Synapse Analytics

Dutch bank ABN AMRO was looking to move business-critical workloads from Teradata to Microsoft Azure Synapse Analytics. As a result of leaving its legacy vendor, ABN AMRO realized significant scaling advantages, gained flexibility, and laid the foundation for a comprehensive cloud strategy.

ABN AMRO is one of the largest retail banks in the Netherlands, with more than 5.2 million retail clients and 20,000 employees worldwide, operating in 12 different countries. Its wealth management exceeds €200 billion in assets under management, serving more than 100,000 clients.

ABN AMRO wanted to move its workloads from its legacy system, Teradata, to Azure Synapse Analytics but did not find the conventional approach of code converters appealing. ABN AMRO found that code converters have limitations in converting legacy code with poorly documented business logic. Often, manual intervention to extract, modify, and reinsert the code is required.

A further complication was the nature of the business process served by the application. Several applications served sophisticated risk models that are subject to stringent regulation. These models are certified by a regulatory body, and changes would require a time-intensive certification process before putting them into production.

Finally, the deadlines were tight. The switchover from the legacy system to Azure Synapse Analytics needed to be performed with as little downtime as possible for business analysts and other internal customers. As much as the migration was a technical problem, it was also a formidable challenge in risk management and execution.

Database virtualization leads the way

ABN AMRO turned to Microsoft partner Datometry. Based in San Francisco, California, Datometry specializes in bringing virtualization to the database stack. Its Hyper-Q platform, available in the Azure Marketplace, lets enterprises move from Teradata or Oracle Exadata to Azure Synapse Analytics without headaches.

Before the formal project kickoff, ABN AMRO had a complete blueprint of its workloads through Datometry qInsight. The qInsight report provided the team with a detailed analysis, identifying any potential challenges up-front. Equipped with this knowledge, the bank embarked on the implementation knowing there would be no major surprises.

Instead of modifying applications, ABN AMRO leveraged Datometry Hyper-Q. The Hyper-Q platform translates SQL and API calls in real time. Through Hyper-Q, existing applications work directly with Azure Synapse Analytics. The operation is fully transparent, making changes to application code unnecessary.

Hyper-Q enabled ABN AMRO to operate with a small tactical team. By drawing on strong in-house talent, the bank did not need to augment its staff significantly. Being able to keep the size of the team small was critical to maintain agility, and it also contributed to the overall cost savings.

Liberated from having to rewrite a large body of applications, the ABN AMRO team took the opportunity to selectively adjust a few individual applications. These modifications would play specifically to the strengths of Azure Synapse Analytics.

Meeting deadlines while avoiding downtime

ABN AMRO completed the project on an aggressive timeline. In nine months, the entire workload was migrated off the Teradata system and ABN AMRO was able to decommission its Teradata appliance.

Mike Waas, CEO of Datometry, said, “ABN AMRO just set a record for moving from Teradata to Azure Synapse: In only nine months, they pulled off a full migration that seemed completely impossible. Accomplishing all that, while maintaining full compliance with strict regulations that prevented any changes to the SQL text, is a phenomenal feat.”

After the migration, both the legacy system and the future stack were operated for several months side by side. This enabled business users to validate data and functionality using their original applications.

Finally, when it came time to switch over, ABN AMRO did not schedule explicit downtime. Instead, the company moved its business users to Azure Synapse Analytics, the new core data platform, completely seamlessly.

“With Datometry we have delivered a successful transformation,” said Marcel Kramer, Head of Data Engineering at ABN AMRO. “We moved our mission-critical workloads from Teradata to Azure Synapse seamlessly. With a legacy of more than 15 years of business logic, this is a remarkable result.”

The foundation for a comprehensive cloud strategy

By migrating on a short timeline, ABN AMRO realized considerable savings. The bank was able to decommission its legacy system, avoiding a costly renewal and potentially a refresh of the hardware that would have come due. Leaving the applications intact saved time and effort for rewrites and upheld regulatory requirements. Additionally, it eliminated the undue risk and room for error any modifications would have caused, ensuring the ABN AMRO team was in full control of any risk during the project.

Through Datometry's virtualization, ABN AMRO elegantly solved its migration challenge, and at a fraction of the cost of a conventional migration. Now on Azure Synapse Analytics, ABN AMRO has a strong foundation for the future.

ABN AMRO just set a record for moving from Teradata to Azure Synapse: In only nine months, they pulled off a full migration that seemed completely impossible. Accomplishing all that, while maintaining full compliance with strict regulations that prevented any changes to the SQL text, is a phenomenal feat.

Mike Waas: CEO

Datometry

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