AI ROI Depends on Allocation Discipline

We've all worked hard at something we weren't sure would work.

And still been asked to work harder.

→ A year ago, only 6% of companies were AI high performers. (McKinsey, 2025)

→ Now 20% of companies capture 74% of AI's economic value.(PwC, April 2026)

That's progress.

It's also concentration.

We're not looking at a market where AI suddenly creates value everywhere.

A relatively small group is getting much better at turning it into economic results.

That raises a harder question:

What are they allocating differently?

Which use cases get funded matters.

So does where the best people go, how much compute the work gets,

and what leadership expects back.

I spent seven years leading global GTM digital transformation work

at top management consultancies.

We saw this then too.

The technology mattered.

But allocation determined whether the transformation created value.

Part of the AI ROI problem is an allocation problem.

Governance tells us what we can do.

Allocation decides where we place the bet.

If leadership gets that wrong, more AI won't fix it.

It'll just fund the wrong work faster.

Execution Beats Theory. Every Time.

👋 Hi, I'm Tim. I help engineering-led startups and mid-market scaleups build adaptive GTM systems, unify revenue, and scale with Agentic AI.

At Entry Point 1, we partner with engineering-led startups and mid-market scaleups that operate intentionally and play to win. We've helped generate $1B+ in revenue across our work.

If you're ready to win, we're ready to help you get there: https://lnkd.in/gB64Mt2D

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