Closing the AI Value Gap : A CEO’s Playbook
6 minutes read
By Laurent “LT” Lathieyre, Senior Partner at Dataveon, an ENEON Group company
It all started, as many corporate fads do, with fear of missing out. Confronted with rivals proclaiming machine‑learning breakthroughs and investors urging digital narratives, executives rushed toward AI with the grace of a three‑legged race. Artificial intelligence shifted from option to status symbol a badge of sophistication, a talking point for earnings calls, and an easy way to add zeros to a valuation, at least in a press release.
With the hype receding and budgets tightening, a sober question is echoing in boardrooms: where’s the return on investment? Many companies discover that their early AI bets are not minting money. The reasons vary, but a pattern is emerging: well‑meaning ambitions colliding with shallow strategy, mismatched infrastructure, and all‑too‑human behaviours. Let’s take a tour.
The FOMO‑Fueled Launchpad
The initial sprint into AI was often motivated less by clear problems than by anxiety about being left behind. Strategy sessions became innovation pageants, with leaders demanding: “Where’s our chatbot?” or “Why don’t we have predictive analytics like that start‑up we saw at the conference?” In this context AI became more sizzle than substance. A quarter of projects may meet expectations, which implies that three out of four remain PowerPoint deep. Initiatives that begin without a defined business need end up as expensive theatre: demo reels that impress only their own creators.
Defining ROI: Not Exactly Last Century
One simple reason many AI projects fail is that no one agrees on what success looks like. Initiatives launch with grand ambitions to “revolutionize customer service,” yet there are no measurable outcomes. In the absence of clear goals, teams default to vanity metrics: dashboard views, usage counts, or uptime. Meanwhile the real impact on the business cost reduction, revenue lift, customer retention remains ambiguous or absent.
Even when metrics exist, they can mislead. Teams often fixate on binary targets (“reduce call‑centre volume by 20%”) that invite gaming. Success becomes a checkbox exercise rather than a transformation. The result is AI that looks good in quarterly updates but doesn’t move the needle.
Data: The Rotten Core
Even the most sophisticated AI can’t compensate for poor data. Yet, in the dash to go live, organizations often gloss over this inconvenient truth. Many discover too late that their information resembles a digital landfill: fragmented across systems, riddled with inconsistencies, and full of historical bias. Feeding this into a model is like expecting haute cuisine from a microwave burrito.
Integration then becomes painful. Stitching together systems that were never meant to talk to each other produces a tangle of spaghetti architecture that causes even hardened IT leaders to blanch.
Tech‑Stack Tension and Infrastructure Failings
Once you fix your data assuming you do you still confront the architectural elephant in the room. Many enterprises attempt to graft AI onto legacy systems like a jet engine strapped to a lawnmower. The results aren’t pretty. Outdated CRM platforms, brittle ERPs, and siloed cloud tools make AI initiatives fragile at best and unscalable at worst.
Equally damaging is a lack of operational readiness. Models require governance, monitoring, retraining, and infrastructure. Without robust MLOps and governance, teams build one‑offs that can’t be trusted, reused, or scaled. The wheel gets reinvented, badly, every time.
Humans: The Uncanny Valley of Adoption
Even when the tech works, people still need to use it. That’s where a subtler dysfunction emerges. Employees are understandably skeptical of AI. Some fear being replaced; others simply don’t trust outputs they can’t explain. Explainability might be a buzzword in academic circles, but to a frontline worker it is the difference between using a tool and ignoring it.
Transformation fatigue is real, too. When every six months brings a “strategic initiative,” employees disengage. Top‑down mandates fall flat without training, communication, and incentives. A chatbot might answer customer queries faster, but if no one on the team uses it or worse, sabotages it the net benefit rounds down to zero.
The Leadership Black Hole
Above all these challenges reigns one issue: the absence of real leadership. Too many AI programmes live in organizational limbo launched with enthusiasm but without an executive sponsor to fight for adoption, integration, or funding. Projects vanish into the ether of “that thing we tried last year.”
This is not just a failure of will; it is a failure of systems thinking. Many leaders treat AI as a discrete tool, not recognizing that it touches everything: processes, customer experiences, org charts, and risk. Treating AI as another IT initiative ensures its impact will be just as small.
Then there are the frozen frameworks leaders clinging to old playbooks in a time of unprecedented change. Organizations that once thrived through rigid processes and operational efficiency now find themselves out‑maneuvered by companies willing to experiment, iterate, and rethink everything.
More Pilots Than an Airline
A final, often fatal, trap is the obsession with pilots and prototypes. Pilots are valuable but easy. The real challenge is moving from prototype to production. Yet many companies get stuck in an endless loop of demonstrations that never leave the innovation lab. Without clear transition plans, change management, and production infrastructure, promising ideas die quietly celebrated in internal demos, forgotten by customers and frontline employees alike.
Turning the ROI Frown Upside‑Down
This may sound bleak, but there is hope. Organizations can realise value from AI; it requires a shift in mindset. Start with the problem, not the model. AI should serve real business needs, not exist for its own sake. Take data seriously: clean, connected, context‑rich data is a prerequisite, not an afterthought.
Build governance into your process from the start. Track outcome metrics costs, revenue, satisfaction rather than usage metrics, and include both quantitative and qualitative feedback. Listen to users, learn from them, iterate.
Support your people. Communicate clearly, provide training, and create incentives. Make trust and transparency foundational principles, not buzzwords.
And above all, think in systems. AI isn’t a silver bullet; it’s a capability that must be embedded into the fabric of your business. That requires changes to processes, technology, and how teams work together.
Finally, resist the urge to boil the ocean. Focus resources on a few high‑impact use cases. Scale what works. Shut down what doesn’t. AI is not magic it’s math, data, and people. When those three work together, the returns will follow. Really.
At ENEON and Dataveon, we help leaders define and deploy AI strategies that deliver measurable impact. From maturity assessments to production deployment, our experts guide you past common pitfalls and scale what works.