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The $5 Million AI Project That Solved Nothing

A mid-market enterprise approves a $5M AI budget.

Leadership says: “We need AI.”

A task force is formed. Workshops are held. Consultants are hired. A list of 40 potential AI use cases is created.

They pick three:

• Chatbot for internal HR queries

• AI-powered invoice classification

• Automated meeting summaries

Twelve months later:

The models technically work.

The demos look impressive.

The dashboards are live.

But business impact? Minimal.

HR costs did not drop meaningfully.

Invoice processing time improved slightly — but not enough to move margins.

Meeting summaries are used by a handful of people, then forgotten.

$5 million spent. Near-zero measurable business value.

This is not a technology failure.

This is a use case failure.

And it is one of the most common reasons AI investments disappoint.

The Real Problem: Companies Choose Easy Use Cases, Not Valuable Ones

Most organizations start their AI journey by asking:

“Where can we apply AI?”

This is the wrong question.

It leads teams to pick use cases that are:

• Easy to implement

• Popular in vendor demos

• Technically interesting

• Low-risk politically

Instead of use cases that are:

• Tied to revenue

• Tied to cost structure

• Tied to strategic advantage

• Tied to executive priorities

The result is a portfolio of AI projects that look modern — but do not materially change business performance.

Why This Happens

Problem 1: Demo-Driven Decision Making

Most AI use cases are chosen based on what vendors showcase:

Chatbots.

Document processing.

Copilots.

Generic forecasting models.

These are easy to sell. Easy to demo. Easy to approve.

But they are rarely the highest-leverage opportunities inside a business.

Vendors optimize for repeatability.

Businesses need differentiation.

Problem 2: Technical Teams Pick What They Can Build — Not What Matters

Data science teams are often asked to propose use cases.

They naturally gravitate toward problems that are:

• Cleanly defined

• Have available data

• Fit standard ML patterns

This creates a bias toward technically convenient projects — not economically meaningful ones.

Revenue leakage.

Pricing inefficiencies.

Operational bottlenecks.

Customer churn drivers.

Working capital inefficiencies.

These are harder. Messier. More cross-functional.

So they get deprioritized — even though they matter more.

Problem 3: No Clear Owner for Business Impact

Many AI projects live in IT, innovation labs, or analytics teams.

Who owns the P&L outcome?

Often: no one.

So success is measured by:

• Model accuracy

• Deployment completed

• Dashboard usage

• Number of users onboarded

Instead of:

• Revenue increased

• Costs reduced

• Cycle time improved

• Risk exposure reduced

When no executive owns the business metric, AI becomes a science project.

The Hidden Cost of Low-Impact AI

Low-impact use cases create a dangerous illusion of progress.

Leadership believes:

“We are doing AI.”

In reality:

They are burning budget on marginal improvements.

This creates:

• AI fatigue across the organization

• Skepticism from business leaders

• Budget pressure on future AI initiatives

• A false narrative that “AI doesn’t deliver ROI”

AI is not failing.

Use case selection is failing.