Aug 3, 2026 · 24 min · 12 segments
Most AI programs cannot prove they paid off. That is the trap this episode is built to help you avoid — a portfolio of pilots that all demo well, and nothing you can defend when the budget review…
I'm Chris Bradley, Chief Marketing Officer at Veritiv and head of our AI Transformation Lab.
Today, we're gonna talk about the hardest question in this whole space right now.
Not which model is best, not which tool, not which demo looked the most impressive last week.
The question is simpler than any of that, and it's the one your CFO is already asking: Did it pay off? This year, one number really cut through the AI noise for me, and it has stuck with me since.
A widely cited MIT study looked across enterprise AI pilots, and it found that ninety-five percent of them delivered no measurable financial return.
Now, sit with that for a second because it is easy to hear that and assume the technology failed.
These are the best models we have ever had, more capable this quarter than last.
The problem is that the work never got finished and the value never got counted.
So the real question isn't whether the technology works, it's why the return isn't showing up on the P&L.
In the last episode, we talked about right-sizing the model, where the number that mattered most was cost per success, the cost to complete a single job.
Today, we scale that up from one job to a whole program, from what a demo promised to what actually landed in the business, because that is what enterprise AI transformation actually means.
And it is the work we do every day in the lab, not adopting a tool, not running experiments on the side, rebuilding how the operational core of the business runs, improving the return as you go.
There are two kinds: efficiency AI, doing things we already did, but better and faster, and opportunity AI, doing things that weren't possible before and now are.
Most of this episode lives in efficiency because that is where the return is provable.
But keep opportunity in view the whole time because the point of getting efficient was never a lighter week.
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I'm Chris Bradley, Chief Marketing Officer at Veritiv and head of our AI Transformation Lab.
Today, we're gonna talk about the hardest question in this whole space right now.
Not which model is best, not which tool, not which demo looked the most impressive last week.
The question is simpler than any of that, and it's the one your CFO is already asking: Did it pay off? This year, one number really cut through the AI noise for me, and it has stuck with me since.
A widely cited MIT study looked across enterprise AI pilots, and it found that ninety-five percent of them delivered no measurable financial return.
Now, sit with that for a second because it is easy to hear that and assume the technology failed.
These are the best models we have ever had, more capable this quarter than last.
The problem is that the work never got finished and the value never got counted.
So the real question isn't whether the technology works, it's why the return isn't showing up on the P&L.
In the last episode, we talked about right-sizing the model, where the number that mattered most was cost per success, the cost to complete a single job.
Today, we scale that up from one job to a whole program, from what a demo promised to what actually landed in the business, because that is what enterprise AI transformation actually means.
And it is the work we do every day in the lab, not adopting a tool, not running experiments on the side, rebuilding how the operational core of the business runs, improving the return as you go.
There are two kinds: efficiency AI, doing things we already did, but better and faster, and opportunity AI, doing things that weren't possible before and now are.
Most of this episode lives in efficiency because that is where the return is provable.
But keep opportunity in view the whole time because the point of getting efficient was never a lighter week.
