How to Implement AI Projects in Enterprises: Aligning Leadership, Technology, and Business Efforts

Recently, many business leaders have been feeling frustrated—and it’s not an isolated phenomenon.
Why is that?
Companies launch AI initiatives with great fanfare.
For the first six months, everyone’s talking big:
chat assistants will be upgraded,
AI agents will be deployed across the board,
even the PPTs look like they’re about to disrupt the whole industry.
It feels like the business is on the verge of an AI-powered breakthrough.

But now?
If you really ask about project progress,
almost nothing meets expectations.
Outcomes fall far short of what was promised.
What’s more interesting is that
companies everywhere seem to be copying the same script.
Executives are pumped up—
constantly brainstorming AI strategy, excited beyond measure.
Meanwhile, business departments are pushing back,
unwilling to touch new systems.
The tech teams have it worst—
constantly being pulled in different directions,
and totally demoralized.

Behind this lies a fundamental organizational challenge within enterprises:
the “impossible triangle” of leadership, technology, and business.
Often, AI implementation stalls not because of the technology itself,
but misalignment among these three forces.
Most projects get stuck in this tug-of-war,
unable to move forward.

Let’s start with leadership.
Executives want clear and quick returns:
on one hand, they want a compelling story for capital markets—
something to attract investment, generate PR buzz,
and showcase an innovative AI strategy.
On the other hand, they demand fast ROI—
preferably within three months—
whether through cost savings or increased GMV.
So they often make impulsive decisions
without clarifying concrete implementation scenarios,
and lose patience if iterations take longer than a quarter.

Then there’s the tech team.
Technologists think from a pure engineering perspective.
They want to build effective multimodal solutions,
while controlling costs,
ensuring inference speed,
maintaining data compliance,
and creating versatile toolkits.
But often, they lose sight of what the business actually needs.
The result?
Flashy demos and beautiful slide decks—
that completely fall apart in real-world deployment.

The business side is even more pragmatic.
They care about user adoption,
conversion rates,
and hitting KPIs—
and they want immediate results.
If AI doesn’t deliver quickly,
it’s dismissed as a gimmick.
Already skeptical of new technology,
they won’t risk their performance metrics for experimentation.
If something doesn’t work right away,
they simply abandon it.

And so the classic tripartite deadlock takes shape:
Leadership pushes strategy →
Tech lacks clear direction →
Tech goes all-in on AI →
Business doesn’t engage →
Business wants quick wins →
Leadership wants long-term transformation.

Many say the solution lies in understanding technology or product.
But the hardest part is understanding the organization.
AI implementation is never just a tech project—
it’s a collaboration project.
It must be a top-level initiative,
with active involvement from leadership.
Just appointing a tech lead isn’t enough.
Executives must embrace MVP thinking—
stop aiming for perfection out of the gate.
Tech teams need to understand business funnels—
and recognize that technology must serve growth.
Business units should participate in prompt refinement—
and treat AI as their own tool.

So if you’re driving AI adoption within your company,
start by mapping your corporate power structure:
Who really has decision authority?
Who is creating friction?
Who is disengaged?
Often these three forces are so misaligned
that there isn’t even a true consensus—
everyone is just living under the illusion of agreement.

Remember:
In the age of AI,
the biggest risk isn’t falling behind in technology—
it’s organizational rigidity.
Successful AI adoption must start with fixing the organization.

One final question to ponder:
Is your company’s leadership-tech-business dynamic a productive iron triangle—
or a fatal triangle?