At a time when artificial intelligence is massively accelerating production, one question becomes unavoidable: how do we stay relevant and irreplaceable? At the La journée Agile event in Namur, François Francart and Adrien Clerbois shared a concrete field report on integrating AI into agile practices in a large enterprise.

AI in Service of Agility – Human Discernment and Augmented Production

At a time when artificial intelligence is massively accelerating production, one question becomes unavoidable: how do we stay relevant and irreplaceable? At the La journée Agile event, held on 22 May 2026 in Namur at the Ecolys site, François Francart (agile coach, leadership coach, mindfulness instructor) and Adrien Clerbois (Senior Technical Architect .NET/Cloud/AI, Microsoft MVP) shared a concrete field report on integrating AI into agile practices in a large enterprise.

Their message is clear: AI is transforming the way we produce. But above all, it is redefining where human value lies.

Abundant production: the new bottleneck

The central question the speakers raised is blunt: how do we stay relevant in a world where AI massively accelerates production?

Thanks to AI, generating content, breaking down tasks, producing code, or writing specifications becomes almost instantaneous. Production becomes abundant. The real bottleneck no longer lies in the capacity to do, but in the capacity to understand and decide.

“The issue is no longer producing quickly, but deciding with discernment.”

In other words, value shifts toward:

  • A fine-grained understanding of context
  • The quality of decision-making
  • Accountability taken on
  • Ethics and human relationships

AI increases production. Humans remain the guardians of meaning.

AI in agile ceremonies: concrete use cases

Rather than talking theory, François Francart and Adrien Clerbois shared concrete uses of AI across the different agile ceremonies.

Backlog Refinement: clarifying and challenging needs

During Backlog Refinement, AI can:

  • Reformulate user needs
  • Ask clarifying questions
  • Propose acceptance criteria (for example, in Gherkin)
  • Identify inconsistencies or blind spots

One interesting use is to leverage the persona pattern, by phrasing prompts such as: “You are a Scrum expert…”. This yields structured and contextualized suggestions.

However, AI does not replace team discussion. It enriches the thinking, but it holds neither the product vision nor the implicit knowledge of the field.

Sprint Planning: assistance with structuring

During Sprint Planning, AI can:

  • Break down user stories
  • Identify dependencies
  • Highlight risks
  • Suggest a coherent sprint goal

Its analytical capacity is impressive. But commitment remains human. It is the team that decides, that commits, and that takes ownership.

AI can propose. It cannot commit to delivering.

Daily Scrum: surfacing weak signals

For the Daily Scrum, AI can analyze the transcripts and:

  • Identify implicit blockers
  • Keep a log of impediments
  • Produce a concise summary

It reduces the cognitive load tied to routine and frees up time to focus on substance. By analyzing the exchanges, it can sometimes detect weak signals that the team does not clearly verbalize.

But here again, it does not experience the human dynamic. It observes words, not emotions.

Review & Retrospective: an augmented scribe role

During Reviews and Retrospectives, AI acts as a scribe:

  • Captures feedback
  • Synthesizes the decided actions
  • Proposes success indicators

It structures information and makes follow-up easier. Nevertheless, the speakers warn of two major risks:

  • Hallucinations
  • Confirmation bias

An AI can produce a convincing… yet inaccurate summary. Without vigilance, the team may accept an erroneous interpretation simply because it is well phrased.

The limits: accountability, context, and bias

AI does not have an organization’s full context. It knows neither the relational history, nor the political stakes, nor the individual sensitivities.

Above all, it takes on no accountability.

“The real danger is no longer not knowing, but believing you know.”

In an environment where answers are fast, fluent, and well argued, the risk is to confuse producing text with producing truth.

Poorly used, AI can amplify existing biases. It can reinforce a dominant view, render minority signals invisible, or oversimplify complex situations.

Human discernment then becomes the key skill.

Agility strengthened by AI

Contrary to some fears, agility does not disappear in a world augmented by AI. It becomes more relevant than ever.

Why?

Because agility rests on:

  • Continuous adaptation
  • Collaboration
  • Transparency
  • Collective accountability

The more AI accelerates execution, the more these principles are needed to stay on course.

AI boosts productivity on standardized tasks. It streamlines, structures, accelerates. But the more it progresses, the more human value concentrates on:

  • Discernment
  • Ethics
  • Strategic decision-making
  • The quality of human relationships

In this sense, AI is not the enemy of agility. It reveals its essence.

Conclusion: produce more… or decide better?

The experience shared by François Francart and Adrien Clerbois shows that integrating AI into agile practices is not only possible, but already operational.

Yet the real challenge is not technological. It is human.

In a world where production becomes abundant, scarcity shifts toward the capacity to understand, to question, to decide with accuracy. Agility, far from being obsolete, becomes an essential framework for navigating this acceleration.

The question is therefore no longer: how do we produce faster?

But rather: how do we decide better?

#Agility #ArtificialIntelligence #Leadership #Discernment #Innovation