Ninety-three cents of every dollar that enterprises spend on AI goes to technology and tooling. Seven cents goes to everything else: culture, change management, learning, and the redesign of how work actually gets done. That number comes from Deloitte’s Tech Trends research, now in its seventeenth year.

Bill Briggs is Deloitte’s Global Chief Technology Officer. He came on episode 912 of CXOTalk to make the argument a technologist is not supposed to make: companies should spend proportionally less on the technology. Here is how I opened the show:

Companies are caught in an AI investment trap, spending too much in the wrong places with too little benefit. It’s a mess.

Michael Krigsman, CXOTalk episode 912

Briggs did not argue with the framing.

Ninety-three cents of every AI dollar buys technology

I asked him for the headline finding from this year’s report.

The best stat coming out of it is that 93% of all AI spend is going toward the tech and the tooling, and only 7% is on everything else, which is the culture, the change, the learning, the how do we communicate the vision, how do we be thoughtful about trying to redesign or reimagine how we work and how we serve our markets and our customers.

Bill Briggs, Global Chief Technology Officer, Deloitte

Now put that next to the other number he gave me.

we’re at the place where 30% or less have agentic pilots that have reached production at scale.

Bill Briggs

Those are one finding seen from opposite ends. The seven percent is the work that carries a pilot into production, and nobody funds it. So I put the obvious objection to him.

You’re CTO, you’re a technologist, and so why do you have such a strong focus on this 7% that’s outside the core 93% of the technology?

Michael Krigsman

I’m a technologist who loves technology because of the potential it represents, but tech for its own sake has never been a winning hand.

Bill Briggs

AI on a bad process weaponizes inefficiency

And we’re in a moment in time where if you apply AI into an inefficient process, you apply AI on an overly complex process, business process, you’re gonna weaponize inefficiency and actually probably pay a lot.

Bill Briggs

Weaponize inefficiency. Automation laid over a broken process does not repair it. It runs the broken process faster, at scale, with an inference bill attached. Sangeet Paul Choudary made a version of this argument on episode 900. Briggs makes it from inside a Big Four firm.

AI is an incredibly powerful technology, and it’s a dangling modifier.

Bill Briggs

A dangling modifier attaches to nothing. He made the point with an image from a trade show floor.

As I walked around the 300,000 exhibitors, I didn’t see a single one with a sign that said, “Now with electricity.”

Bill Briggs

Nobody sells electricity as a feature. They sell what the electricity lets you do.

Nobody owns the inference bill

David Batz asked on LinkedIn how you predict AI financial risk quantitatively, with specific KPIs. Unit costs keep collapsing while the total enterprise bill climbs, so I asked whether organizations track inference as its own budget line.

And so the idea that you could have $100 a month bill one month and then a 500K dollar bill the next month won’t happen. But that requires some maturity and rigor that not every organization’s doing.

Bill Briggs

Maturity and rigor are the seven percent. No vendor sells them to you.

Trust collapses on the way down the org chart

The finding that stopped me had nothing to do with money.

So you can picture an org chart where the C-suite is the top of the pyramid, and the C-suite over all industries around the globe, the trust in AI from the C-suite was 70%, 7-0.

Bill Briggs

As you went down every layer of the org chart, it was like a logarithmic scale halving the trust every hop away until you got to the front line entry-level worker, and the trust was 6.7%. So from 70 to 6.7.

Bill Briggs

Seventy percent at the top. Six point seven percent at the bottom. The executives who approved the spending believe in the technology, and the people who have to use it do not. A tool the front line does not trust is a tool the front line routes around. Underneath the trust gap sits a governance gap.

You’ve raised the specter now of jobs. AI agents are multiplying inside enterprises faster than almost anybody can track, and many organizations do not have sufficient governance frameworks for a workforce that isn’t human.

Michael Krigsman

What’s a disciplinary action for an AI agent?

Bill Briggs

There is no settled answer, and every organization running agents at scale needs one.

Success theater has a tell

The sharpest question of the hour came from the audience. Chris Petersen wrote in on Twitter.

Are the organizations putting out the “we have tens of thousands of AI agents” headlines getting real value from these agents or just trying to game a system of bad metrics?

Chris Petersen, audience question, CXOTalk 912

But the prevalence of success theater is still real. And typically when we’re measuring volumes of use case or volumes of agents as the thing that’s the bar, it’s a tell.

Bill Briggs

A tell, not a metric. Agent count measures what you bought, not what changed. I said as much back to him.

I love that, because you’re really now talking about shifting the focus from the process, the process being the hand-waving, “We have thousands of agents”- shifting from that into, “Here’s what they’re doing. Here are the benefits. Here, they’re reducing inventory, they’re saving time, saving money,” what have you.

Michael Krigsman

They’re ingredients, they’re not recipes, and we need more chefs able to give us 3-star Michelin meal equivalents that we can then not talk about headlines of agents. We can talk about litany of real metrics that matter.

Bill Briggs

This is the old failure in new clothes

I spent two decades writing about IT project failure for ZDNet and founded Asuret to diagnose why enterprise projects go wrong. In close to 900 conversations, the shape has not moved. The technology usually works. The organization around it never gets built, because the budget line for building it does not exist.

The 93/7 split is that story with a new acronym. Money goes to the platform, because the platform has a vendor, a contract, and a demo. The work of changing how people operate has none of those, so it goes unfunded, and the pilot does not scale.

What to do with this on Monday

Work out your own ratio first. Split the AI money you committed this year into what buys technology and what buys change: training, work redesign, communication, and the people whose job is to make the new process real. If your number looks like 93 to 7, you know why your pilots stall, and you have Deloitte’s own research to show anyone who disagrees.

Then stop counting agents. Replace agent counts in your reporting with the outcomes Briggs named: inventory reduced, time saved, money saved. A program that cannot name its outcome is theater, and the volume number is the tell.

Take seriously the one skill Deloitte found matters most in its CIO program.

The single most effective, impactful session we do is not on multi-agent systems or ops or infrastructure. It’s on storytelling, of just how to structure and shape a vision and be able to help share that to influence and inspire.

Bill Briggs

That is a strange thing for a technology firm to publish, and it is consistent with the rest of the research. The seven percent is where the value lives. It has no vendor, and it is the difference between a demo and a business.