The English language has a talent for always finding the right word to describe a situation
This time, it’s The GenAI Divide.
Behind the catchy phrase, the latest MIT report reveals a brutal truth:
95% of GenAI projects deliver zero measurable ROI.
Billions invested. Hundreds of pilots launched.
And yet, very few scaled.
And it’s not the technology’s fault.
It’s the gap between innovation and adoption.
A gap I know well.
I spent over 15 years in large organizations, leading some of the first digital projects, then innovation, new ventures etc
Different roles across strategy, business units and corporate venture capital, but all serving the same purpose: building the future while running the present.
It taught me that innovation must be visionary yet pragmatic — and above all, agile.
Because agility isn’t just a startup privilege. It’s a shared discipline.
Corporates must evolve faster.
Startups must adapt deeper.
And investors must help both align toward real, measurable impact.
The MIT findings confirm what many of us have lived through:
Big firms lead in pilots but lag in scale-ups : the “innovation paradox” of enterprises that experiment but rarely transform.
Only 2 out of 8 industries show real structural change.
Over 50% of AI budgets go to Sales & Marketing, because it’s easier to measure visibility than transformation. Meanwhile, back-office automation often yields better ROI but lacks board-level KPIs.
External partnerships succeed twice as often as internal builds ; yet many companies still try to “own everything” internally.
And perhaps most importantly: the gap isn’t about intelligence ; it’s about memory, adaptability, and learning.
From P&L to Use Case Bulk
Too often, innovation is still measured only by P&L output, when it should be measured by use-case impact. What we call use case bulk:
the ability of a technology to create repeatable, scalable and demonstrable value, even before it directly hits financial KPIs.
This shift in mindset , from quarterly ROI to long-term learning and replication, is what truly turns experimentation into transformation.
Lean & AI Adoption
In many ways, successful AI adoption follows the same logic as lean management; it’s not about doing more, but about doing better, continuously.
Like lean, it’s about framing, measuring, and valuing what truly creates value, while progressively eliminating waste and inefficiencies.
Some AI initiatives have a direct and visible impact on the P&L, driving immediate revenue or cost savings.
Others, often in support functions like HR, IT, procurement, or finance, generate indirect yet powerful returns through productivity, process optimization, and operational learning.
Both matter.
Both create ROI , one visible today, the other sustainable tomorrow.
That’s why the right approach to AI adoption must be structured, measurable, and iterative: framing each initiative around its contribution to long-term value creation, while continuously learning and improving over time.
What this means for innovation and for all of us
This divide isn’t just technological. It’s organizational and cultural.
Startups and corporates both play their part in bridging it:
Corporates must break silos, rethink procurement and change management, and measure ROI beyond immediate P&L.
Startups must adapt their technology to enterprise realities: data, workflows, governance, and change dynamics.
Innovation cannot stay trapped in POC theatre.
It needs structure, pedagogy, and a shared language between technology creators and adopters.
This is where true value creation happens when technology becomes understood, integrated, and evolving.
Technology moves fast but organizations learn slow.
If we don’t invest in learning and adoption, even the most advanced tech will plateau and fade.
At Yaday, we’re building that bridge.
Together with Romain Afflelou and the team, we created Yaday with this ambition: an ecosystem connecting startups, corporates, and investors to bridge the gap between technology and adoption.
Because the next generation of innovators won’t come from one side or the other.
They’ll come from those who think transversally,
act collectively,
and scale responsibly.
Applied AI: From Use Case to Use Chain
The real revolution won’t come from the labs, but from adoption at scale.
That’s where applied AI becomes transformative.
We look at AI agents and tools that reshape not only front-line functions but also support roles where productivity and process learning generate long-term competitive advantage.
And we focus on core technologies with transversal potential: proven in one industry, adaptable to others, as long as they remain agile and contextualized around real use cases.
Innovation starts with ideas.
Transformation starts with adoption.
https://www.artificialintelligence-news.com/wp-content/uploads/2025/08/ai_report_2025.pdf