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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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Paf’s engineering team creates 85 custom GPTs to surge developer productivity

Paf adopted ChatGPT Enterprise across its entire company, with engineers using custom GPTs on a daily basis to speed up routine development tasks. Paf also integrated ChatGPT Enterprise into the grit:lab coding academy. 70% of Paf employees actively use ChatGPT Enterprise, spanning business teams like finance, HR, marketing, and customer support.

Evaluating various AI models

Paf is an international gaming company founded in 1966 in the Åland Islands by the Red Cross, Save the Children, and Folkhälsan, and is an industry leader in responsible gaming. With around 315 employees from 29 countries, Paf has contributed over 447.5 million euros to benefit society since it was founded.

As an organization committed to staying ahead of the technology curve, Paf recognized the transformative potential of AI early on. When generative AI began gaining traction, the company swiftly adopted and evaluated various AI systems to understand how this technology could propel its employees and business operations forward.

In their search for the ideal generative AI solution, Paf experimented with LLAMA, Claude and GPT-4. When the team conducted head-to-head accuracy and cost comparisons, GPT-4 was 25% more accurate than competitors, without being more expensive. Paf decided to use GPT-4 as its AI solution.

"A man in a beige sweater and glasses speaks into a microphone at an event. People in green hats and lanyards are in the background.

Creating custom GPTs to streamline the development process

Paf rolled out ChatGPT Enterprise to the entire team of 100 developers, and now finds ChatGPT Enterprise indispensable for their daily tasks. “I use ChatGPT 20 times a day for tasks like boilerplate code creation or learning a new programming language,” says frontend developer Krista Koivisto. In addition to using ChatGPT Enterprise for general coding assistance, the engineering team has created over 85 custom GPTs to support specific use cases.

One of the engineering team’s favorite applications of custom GPTs is a suite of specialized coding GPTs that help streamline the development process, from creating backend infrastructure to generating frontend components:

Swagger GPT converts Swagger JSON API definitions into TypeScript service endpoint definitions per Paf’s coding standards.
TypeScript GPT writes the backend service code using the endpoint definitions, reusing existing session validation functions.
GraphQL Nexus GPT generates GraphQL Nexus schemas, integrating existing helper functions to interact with the frontend.
Relay GPT creates the React Relay hooks using GraphQL Nexus schemas for communicating with our backends.
React GPT writes React components using Paf’s React and TypeScript style guidelines and core component library.

Swagger GPT converts Swagger JSON API definitions into TypeScript service endpoint definitions per Paf’s coding standards.

TypeScript GPT writes the backend service code using the endpoint definitions, reusing existing session validation functions.

GraphQL Nexus GPT generates GraphQL Nexus schemas, integrating existing helper functions to interact with the frontend.

Relay GPT creates the React Relay hooks using GraphQL Nexus schemas for communicating with our backends.

React GPT writes React components using Paf’s React and TypeScript style guidelines and core component library.

“Focused GPTs avoid overloading models and curb hallucinations,” says Koivisto. “We automatically generate functioning boilerplate implementations with far less effort.” By chaining tailored GPTs together instead of relying on the general model, Paf's developers can quickly generate accurate, standardized application flows and APIs nearly automatically.

Empowering every engineer to be a systems architect

Building on its success with the development team’s custom GPTs, Paf has integrated ChatGPT Enterprise into the grit:lab coding academy to accelerate training for 65 aspiring developers. Grit:lab students use ChatGPT for a variety of coding-related tasks, including:

Understanding new programming concepts
Debugging code errors efficiently
Learning syntax and structure across different languages
Generating test data quickly

This AI-augmented software development approach is creating a new breed of software developer, one who has more systems architect knowledge from the start. “Using ChatGPT, the junior developers think at a higher, systematic level,” says Kim Gripenberg, a DevOps engineer, noting that both grit:lab students and junior developers at Paf progress years faster with AI assistance. Instead of getting bogged down in syntax errors and coding basics, developers can focus on the overall application and system design.

People working at desks with multiple computer monitors in an office. One person wears headphones, and a green coffee cup is on a desk.

ChatGPT delivers the equivalent output of 12 employees

In the next year, Paf plans to fully integrate ChatGPT Enterprise and the OpenAI API into all of its processes. “AI is here to stay. Either you are on the train,” says Fredrik Wiklund, Chief Technology Officer, “or you are back at the station, watching it leave.” The company envisions GPTs eventually handling more coding tasks like writing, testing, and deploying software, freeing up developers to focus on higher-level, systems-level work.

This AI-augmented approach will allow Paf to innovate with a velocity similar to that of a much larger company. By integrating generative AI into every part of its business, Paf is set to maximize its positive impact for employees, customers, and communities it serves.

“We estimate ChatGPT is doing the equivalent work of 12 full time employees,” commented Wiklund. “The impact to our business has exceeded our expectations, and this is only the start.”

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Technologies

OpenAI API OpenAI DALL-E OpenAI's GPT-4

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