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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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Validating Employment Candidate Expertise and Reputation at Inserm

Inserm relies on external experts to assess job candidates' expertise. This manual process is time-consuming and lacks transparency. To streamline recruitment and enhance efficiency, Inserm seeks an automated solution to verify candidate expertise.

CUSTOMER

Inserm (French National Institute of Health and Medical Research) has historically been the only public research institute in France dedicated to biological and medical research and human health. It employs nearly 13,000 researchers, engineers and technicians across more than 300 research laboratories.

CHALLENGE

To evaluate the quality of job candidates, Inserm coordinates with up to five external experts to analyze candidates based on their achievements and descriptions of their field of expertise. To shorten the recruitment process and increase transparency, Inserm sought an automated solution to verify the
expertise of their candidates.

SOLUTION

Expert.ai technology enables Inserm to rapidly identify the top experts in their candidate pool by aggregating and analyzing published scientific articles and reports from hundreds of globally respected journals. The talent team simply enters a candidate or project name in the search field and activates the ‘Expert lens’ to view a full list of domain experts.
Behind the scenes, expert.ai evaluates a candidate’s level of expertise based on the reputation rating of the journals in which they have been published. Keywords associated with the candidate are also extracted which can help quickly verify areas of expertise. The system also presents publication metrics and collaboration networks so Inserm can identify both established opinion leaders as well as rising stars.

BENEFITS

- Reduced risk of cronyism in candidate selection via automated expert identification
- Increased search productivity with a user-friendly interface
- Highly reliable, quantifiable and complete results enable informed decision-making

"Expert.ai technology generates highly relevant expert profiles through a global overview including keywords related to experts, the list of contributed journals, production trends, close collaborators as well as contact information."
Nicole Haeffner-Cavaillon, Unit Manager - Measurements, Indicators, Bibliometrics,
Department of Programs Evaluation and Monitoring, Inserm

Technologies

expert.ai Insight Engine expert.ai Platform

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