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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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Quality Digital transforma experiência das áreas administrativas do Grupo Servopa com a automatização de processos

O Problema A Quality Digital enfrentou o desafio de viabilizar o download de mais de 16 mil arquivos XML dentro de uma demanda extremamente urgente e pontual. O processo, que levaria mais de um mês para ser executado manualmente por um colaborador, precisava ser acelerado para garantir agilidade e eficiência. Além disso, era essencial que a solução desenvolvida permitisse a reutilização do procedimento no futuro de forma simples e prática. A Solução A solução adotada foi a automação por meio do RPA da AutomationEdge. Desde sua implantação em 2019, a ferramenta foi estrategicamente utilizada para otimizar processos, automatizando operações de backoffice, processamento de faturas, geração de relatórios e integração de sistemas de TI. Atualmente, quatro anos depois, a Servopa, que conta com aproximadamente 2 mil colaboradores, já opera 123 processos automatizados com dois robôs, e está avaliando a implementação de novas rotinas para aumentar ainda mais sua produtividade. O segundo robô, implantado no final de 2020, foi dedicado à área de faturamento, automatizando a geração de notas fiscais de veículos novos, trazendo maior eficiência e agilidade para o setor. Principais Desafios do Projeto O maior desafio foi garantir a confiabilidade do robô, pois qualquer falha no processo poderia comprometer a operação. No início, houve uma falta de confiança na automação, o que gerou cautela por parte da equipe. No entanto, à medida que o time adquiriu experiência no desenvolvimento e domínio da ferramenta, a segurança no processo evoluiu significativamente, atingindo um nível de confiança total na solução. Por que a Quality Digital foi escolhida? Inicialmente, a empresa buscou alternativas internas para solucionar o problema, tentando implementar soluções em Python e banco de dados por meio do time de TI da Servopa. No entanto, essas abordagens apresentaram desafios significativos, como alta propensão a erros, complexidade na manutenção, falta de assertividade e dependência de múltiplas configurações. A Quality Digital se tornou a escolha definitiva ao apresentar uma solução eficiente e confiável com a AutomationEdge, eliminando os obstáculos enfrentados anteriormente e garantindo um processo automatizado, preciso e de fácil manutenção.

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