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Belgium 10-3-26 All Members Physical english

The Composable Enterprise: Engineered with AI

From modular business design to AI-driven pipelines, architectures, and operationsA composable enterprise is built on modular processes, API-driven ecosystems, low-code platforms, and cloud-native services. It promises speed and adaptability by allowing organisations to reconfigure their capabilities as conditions change. However, modular design alone does not guarantee resilience; the way these systems are engineered and operated is just as important.This is where AI is beginning to make a difference. Beyond generating snippets of code, AI is already influencing how entire systems are developed and run: accelerating CI/CD pipelines, improving test coverage, optimising Infrastructure-as-Code, sharpening observability, and even shaping architectural decisions. These changes directly affect how quickly new business components can be deployed, connected, and retired.In this session, we will examine how CIOs can bring these two movements together:Composable design is the framework for flexibility and modularity.AI-augmented engineering is the force that delivers the speed, quality, and intelligence needed to sustain it.The pitfalls of treating them in isolation: composability that collapses under slow engineering cycles, or AI that only adds complexity without a modular structure.The discussion goes beyond concepts to practical implications: how to architect organisations that can be recomposed at speed, without losing control or reliability. The outcome is an enterprise that is not only modular in design but also engineered to adapt continuously under real-world conditions.

Belgium 12-3-26 Physical english

The Third-Party Risk Reckoning: How far can you trust your vendors?

Tomato! Tomato! Tomato! Get your tomato now! Every vendor sells security. And every company depends on vendors, partners, and suppliers. The more digital the business becomes, the longer that list grows, and so does the attack surface. One weak link, and there is always one, or one missed update, and trust collapses faster than any firewall can react. What used to be a procurement checklist has become a full-time discipline. Questionnaires, audits, and endless documentation prove that everyone’s “compliant,” yet incidents keep happening. So it’s clear: the issue isn’t lack of policy, or maybe a bit, but mostly lack of visibility. Beyond a certain point, even the most secure organisation is only as safe as its least prepared partner (or an employee who hadn’t had their morning coffee). So how far can you trust your vendors? How do you check what you can’t control? And when does assurance become theatre instead of protection? Does it come at a different cost? Let’s exchange what works and what fails in third-party risk management: live monitoring, shared responsibility models, contractual levers, and the reality of building trust in a chain you don’t own. A closed conversation for those redefining what partnership means when risk is shared but accountability isn’t.

Belgium 19-3-26 Country Members Physical french

Vendor Detox: La consolidation vaut-elle le risque ?

Moins de Partenaires : La consolidation vaut-elle le risque ? Le problème est la prolifération des fournisseurs : trop d'outils causant de la complexité, une taxe d'intégration paralysante et de la redondance. La Taxe d'Intégration est le coût caché (en temps, en échecs et en ressources) d'essayer de faire fonctionner ensemble des systèmes disparates. Cet échange se concentre sur des stratégies éprouvées pour simplifier de manière agressive le parc technologique, consolider les fournisseurs et élever certains fournisseurs clés au rang de partenaires stratégiques.

March 12, 2026 Squad Session Invitation Only Physical english

The Third-Party Risk Reckoning: How far can you trust your vendors?

March 24, 2026 Squad Session Invitation Only Physical english

Killing Zombie Projects: Knowing when to stop, restart, or quietly let go

Every organisation has them, projects that keep running long after their purpose has faded. No one remembers who asked for them, but shutting them down feels riskier than keeping them alive. And eventually, people stay assigned, budgets stay allocated, and energy drains into work that no longer matters. Inertia at its finest.

March 26, 2026 Squad Session Invitation Only Physical english

Measuring Value in AI Initiatives: Defining ROI, accountability, and measurable outcomes in complex environments

AI projects continue to multiply, but proving their value remains difficult. Most organisations can track activity, not impact. Dashboards count pilots and models, yet few translate to measurable business outcomes. The result is familiar: success stories without clarity on what they actually delivered.

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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.”

A man and a woman in green lanyards and green hats look at their smartphones.

Technologies

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

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