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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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Applying AI to help students find a career path they love

Working Eye is a startup headquartered in the UK that has turned traditional careers advice into a personalized AI-driven discovery experience using interactive cognitive technology and multimedia to help every pupil, student and working adult make better career decisions in the ever-evolving world of work.

When and how did you choose your career? Did you ever doubt whether you’d selected the right path?

“I’ve raised four children and guided each of them through subject selection at school, as well as further education, and job selection. Each time I observed the same challenges: they received minimal career guidance in school and there was no single source of knowledge to help them explore career paths that were unfamiliar to me personally,” explains Peter Cayless, Founder and CEO of Working Eye.

Cayless found that the existing guidance available to students was too broad-brushed and it came too late in the subject selection process. Students did not have a way to explore their options independently and thoroughly early on in their journey. In fact, studies have shown that 96% of graduates switch careers by the age of 24 and half of all graduates follow a career path that does not relate to their degree (source: New College of the Humanities [link resides outside of ibm.com]). In both cases, the main reason given was that they had little idea of what was really involved in the job before they took it. This led Cayless to launch Working Eye (link resides outside of ibm.com), an AI-driven career discovery platform that aims to inspire people by helping them discover themselves and all the possibilities for a career they will love.

Working Eye’s career discovery platform (link resides outside of ibm.com) is designed to be a digital coach that facilitates a student’s career exploration process. The assistant poses discovery questions to the student in natural language and then guides them on a highly unique exploration process that helps them identify a potential career path that is tailored to their specific needs, strengths and priorities. As the assistant guides the student through this dialogue process, it presents various pieces of written and video content which help the student better understand the career that is best suited to them and shows them how to get started on that path. All of this is powered by IBM technology. “IBM watsonx Assistant is the backbone of the chat between the user and the platform, while IBM Watson Discovery is the backbone of finding the right material, collating it for the student,” says Alan Joenn, Chief Operating Officer of Working Eye. The platform’s primary source of data is a government website called the National Career Service Database, as well as the video content created by the Working Eye team.

During its initial pilot of the platform, the Working Eye team tested the digital coach with approximately 1,500 students, parents and teachers. The platform received a 100% approval rating in school panels and 48% parental uptake. “We like to refer to Working Eye as a hand to hold throughout your working life. It is a beautiful example of how AI can work for good. AI isn’t necessarily going to take your job. It has the potential to help you get the job you want,” adds Cayless.

100%

approval rating in school panels during initial pilot

48%

parental uptake during initial pilot

“

We like to refer to Working Eye as a hand to hold throughout your working life. It is a beautiful example of how AI can work for good. AI isn’t necessarily going to take your job. It has the potential to help you get the job you want. ”

Peter CaylessFounder and CEOWorking Eye

About Working Eye Ltd.

Technologies

IBM Watson Discovery IBM Watsonx Assistant

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