Agentic AI: How to Transform Your Operating Model to Scale

As a Partner and AI, Agentic & Data Strategy Specialist at Converteo, Guilhem Bodin advises Marketing and Digital executives on transforming their operations. He focuses on defining agentic strategies, optimizing customer journeys in the conversational era, and measuring the business impact of their AI investments.

Key Takeaways:

• Industrializing use cases: Agentic AI is blending into the standard tech stack and completely transforming the Operating Model, from the middle office to customer touchpoints.
• Adoption is the ultimate test: A solution’s value lies entirely in its actual usage. Without full buy-in from employees or customers, the investment is worthless.
• A mandatory Product approach: Every AI project must now be managed like a traditional digital product: broken down into iterative batches, short sprints, and data-driven steering.
• The probabilistic paradigm: Shifting to non-deterministic systems redefines technical risk. Human business judgment becomes the ultimate quality control mechanism.

Reducing AI to a mere peripheral productivity tool is tempting—but it is a massive strategic mistake. Market feedback proves it: integration is now systemic.

This new operating system profoundly restructures the organization. When a market player deploys up to 2,000 simultaneous use cases, every business unit—from logistics to HR—is overhauling its processes to embed an agentic layer. Initiatives like “Mira” at Fnac Darty, the “René” agent at Lacoste, or Club Med’s booking conversational agents are already running in production. The R&D experimentation phase is officially over.

Adoption: The Only True Success Metric for Your Agentic AI Project

Moving to production requires validation through actual use. The ROI of an agentic AI project is directly measured by its adoption rate, because the end user instantly penalizes the slightest friction. To guarantee the investment’s viability, flawless business integration is mandatory. In this context, change management sheds its support-function status to become the absolute central pillar of the deployment strategy.

This buy-in is mechanically built on trust. Baking in transparency by design is a non-negotiable prerequisite. Technically speaking, the relevance of autonomous agents relies entirely on robust data foundations: operational reliability is dictated by data governance excellence.

Steering Agentic AI Like a Product: Speed and Probabilities

Securing this adoption forces a fundamental shift in workflows. Deploying AI is now a Product Management discipline: you must define clear strategic goals, ship in iterative batches, and steer strictly by value.

Accelerated development cycles change the game. Where traditional software engineering took months, low-code platforms and LLMs now let teams build, test, and optimize specific agents in a matter of hours. This extreme velocity demands a massive update to the technical frameworks used by engineering and management teams.

Furthermore, shifting from deterministic logic (strict if/then rules) to a probabilistic model fundamentally transforms risk management. Since it is impossible to pre-define every edge case upfront, human judgment reclaims its central role in quality control.

Setting the Course and Clearing the Path to Deliver Your Agentic AI Project

Faced with biases, hallucinations, and the probabilistic nature of LLMs, humans remain the ultimate guarantors of the system. Their capacity for contextualization, empathy, and final decision-making are the mandatory guardrails for these deployments. The autonomous agent executes the tedious; the human resolves the complex.

As leaders and decision-makers, our job is no longer to micromanage technical processes. The core mission is to structure the data, enforce a governance framework compliant with regulations like the AI Act, and align the corporate culture. Mastering this transformation now dictates an organization’s competitiveness for the next economic cycle.

To dive deeper into these strategic challenges and explore our full analysis, watch the replay of our RUNTIME.AI session on YouTube.