The AI “hare-tortoise”: sprint to impress, walk to endure

This op-ed by Julien Floch, AI expert at Wavestone, was published in La Tribune  (open in a new tab) in June.

This article has been translated by artificial intelligence.

The rollout of artificial intelligence in business is starting to resemble a modern version of La Fontaine’s fable: today’s organizations must embody both the hare’s agility and the tortoise’s perseverance.

In a market where competition accelerates with every new model announcement, executives demand spectacular demos within weeks. Meanwhile, IT leaders and risk managers know that a shaky foundation—disorganized data, missing ethical guidelines, or absent MLOps processes—will inevitably stall or derail long-term ambitions.

Success lies in embracing this duality: deliver quick wins, but build with care and durability.

The hare’s pace: creating a “wow” effect, fast

In most boardrooms, patience is now measured in quarters—sometimes even months. Leaders who greenlight AI budgets expect tangible results before the next financial close: a Copilot assistant that summarizes meetings company-wide, an agent that speeds up marketing lead qualification, or a predictive model that cuts stockouts by 10% in its first pilot.

This “impact-first” approach serves two purposes: it delivers visible value and helps overcome lingering cultural skepticism around AI. Business users—whether legal advisors or quality engineers—see their daily frustrations resolved and quickly warm to the technology. This momentum strengthens executive sponsorship, increases budget appetite, and sets the stage for the next phase.

The tortoise’s stride: building a technical and ethical backbone

But behind every dazzling demo lurks the risk of accumulating “AI debt.” Hastily extracted data turns out to be incomplete, prompts crafted by power users become unmanageable, compute costs spiral out of control, and compliance alarms go off due to lack of explainability or bias management.
This is where the tortoise takes over.

Day by day, it standardizes data flows, builds a robust catalog, sets up a model registry with performance metrics, defines governance policies aligned with regulations (like the AI Act), and wraps it all in FinOps oversight—ensuring that a brilliant POC doesn’t blow the entire cloud budget.

Slowness isn’t a flaw—it’s the steady rhythm that lets you stack bricks without collapse.

Orchestrating both tempos: a discipline, not a compromise

It’s tempting to separate fast innovation from sustainable engineering. But experience shows that too much separation breeds two cultures that no longer understand each other. The key is to build explicit bridges: start in an agile sandbox, but define production-readiness criteria—data quality, robustness testing, monitoring plans—from day one.

Funding follows the same logic: a flexible discovery budget opens the door, but the larger industrial envelope is only unlocked after a validated business case and compliance audit.

This way, the hare’s enthusiasm stays in check, while the tortoise uses each quick win to reinforce its foundations.

The fable, reimagined

The lasting success of AI in business isn’t just about how fast you prototype or how solid your architecture is—it’s about synchronizing both dynamics. The organization that truly embodies the “hare-tortoise” runs just fast enough to outpace today’s competition, while moving patiently enough to remain a leader tomorrow.