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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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AI21 Labs Collaborates with Google Cloud to Integrate Generative AI Capabilities with BigQuery

AI21 Labs is using Google Cloud infrastructure to speed its model training and inferencing.

Leading AI research startup, running on Google Cloud's AI/ML infrastructure, to bring industry-specific generative AI capabilities to BigQuery

SUNNYVALE, Calif., Aug. 29, 2023 /PRNewswire/ -- Google Cloud today announced a new collaboration with AI21 Labs, an Israeli startup that fundamentally changes the way people read and write using generative AI and large language models (LLMs). AI21 Labs is leveraging Google Cloud's purpose-built AI/ML infrastructure to speed its model training and inferencing, and is also one of the first partners to offer generative AI features on top of BigQuery.

As a result of AI21 Labs' and Google Cloud's collaboration, customers can now use BigQuery connectors and functions to natively integrate several industry-specific generative AI capabilities. For example, retail customers can now use generative task-specific APIs for creating product descriptions or extracting insights from customer reviews, while financial services companies could use them to quickly analyze reports.

AI21 Labs has deployed Google Cloud's home-grown AI supercomputers, Tensor Processor Units (TPUs), for training and serving its advanced LLMs. By leveraging Google Cloud TPUs, AI21 Labs can take advantage of the same custom-designed, machine-learning infrastructure that powers many Google products. In combination with Cloud TPUs, AI21 Labs also benefits from Google Cloud's extensive portfolio of GPU VMs, including the recently announced A3 VMs powered by NVIDIA H100 GPUs. Google Cloud's open approach to AI development and leadership in custom silicon equips AI21 Labs with a more seamless experience rooted in its scalable, secure, and stable infrastructure.

"Google Cloud's vast portfolio of impressive AI hardware is why we chose to work with the company, and we're proud to now double down on expanding our generative AI solutions alongside them," said Ori Goshen, Co-CEO and co-founder, AI21 Labs. "Our partnership with Google Cloud has had a profound effect on our work to advance the possibilities of artificial intelligence and natural language processing, and today's news of our deepened collaboration marks an important step forward in that journey."

"AI21 Labs is a leading generative AI startup that is taking advantage of the incredible performance that Google Cloud's leading infrastructure offers," said Thomas Kurian, CEO, Google Cloud. "Our new BigQuery integrations are a great example of how we are working together to bring the value of generative AI to businesses in every industry."

To explore LLM-based capabilities from AI21 Labs, visit its website. To learn more about the AI/ML infrastructure powering AI21 Labs, visit cloud.google.com/tpu.

Additional Resources

Keep up with the latest Google Cloud news on our newsroom and blog.
Learn more about Google Cloud's BigQuery here.
Learn more about Google Cloud TPUs here and Cloud GPUs here.
Learn more about AI21 Labs here.

About Google Cloud
Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

SOURCE Google Cloud

Technologies

Google Cloud A3 instances Google Cloud TPUs

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