I’m one of six voices in a new HBR report on agentic AI. Here’s the gap nobody wants to talk about.
Harvard Business Review Analytic Services published a new report, “Agentic AI: Expectations, Readiness, Results,” sponsored by AWS.
It’s built on a July 2025 survey of 623 decision-makers from the HBR audience. They featured six expert voices in it. I’m one of them, quoted as Jonathan Moss, EVP of Revenue Growth and Operations at Experity, alongside people from Syngenta, Vanguard, and McAfee.
I’m proud of it. Getting a full-page pull quote in an HBR report is a milestone, and I’m not going to pretend it isn’t.
But I want to use this post to talk about the thing in the report that actually keeps me up at night, because it’s the part most people will skim right past.
Here’s the headline everyone will quote: 84% of respondents agree agentic AI will transform their business. 90% expect most organizations in their industry to be using it. Big numbers. The kind of numbers that make a board meeting feel exciting.
Now here’s the number nobody’s putting on a slide. Only 5% say their organization has well-defined success metrics for agentic AI. Five.
Sit with that gap for a second. Eighty-four percent are certain it’ll change everything. Five percent can tell you whether it’s working.
That’s not an AI problem. That’s a system problem. And it’s the difference between a company that’s actually building something and a company that bought a collection of apps with a budget line and called it a strategy.
The messy-data excuse, and why it’s an excuse
The most common thing I hear from executives is some version of “we can’t really do agentic AI yet, our data is a mess.” And look, they’re right that the data’s a mess. Everyone’s data is a mess. If we’re all honest about it, there isn’t a company on earth sitting on pristine, perfectly governed data waiting for the robots to show up.
But here’s where the logic breaks. People treat “fix all the data” as a prerequisite, a multiyear cleanup project that has to finish before the real work can start. So the project gets scoped, gets funded, gets a steering committee, and three years later you’ve got a slightly cleaner data warehouse and zero agents in production.
Here’s what I told HBR, and it’s the thing I’d put on the wall: you don’t have to embark on a multiyear project to get data right before you adopt agentic AI. Align on which data you actually need for the specific workflow the agent is doing. Find where that data lives. Make sure it’s good and consistent. Just start there.
That reframe is the whole game. The workflow tells you which data has to be good. Not all of it. The slice the agent touches.
This is why I think of agentic AI as a forcing mechanism. It’s the thing that finally makes you get serious about data quality, because now there’s a job on the line that depends on it. For years “good data” was a virtue nobody could schedule. Agentic AI gives it a deadline and a reason. You don’t boil the ocean. You clean the one cup of water the agent is about to drink from, and you go.
Directed autonomy, because the stakes are real
The other thing the report surfaces is readiness, and it’s brutal. Only 5% say their workforce is very prepared. The top barriers are a lack of talent and skills (48%) and no clear roadmap or strategy (46%). Everyone wants the outcome. Almost nobody has built the system that produces it.
In healthcare, where I spend my days, you can’t hand-wave this. The cost of an agent getting it wrong isn’t a bad email. So the governance question isn’t optional, it’s the design.
The model I use is what I call directed autonomy. It’s three tiers, and you place every workflow into one of them.
For routine workflows, agents run fully autonomous. Let them go.
For context-dependent workflows, it’s shared control. The agent and the human work the problem together.
For high-impact workflows, there’s always a human in the loop, with explicit escalation pathways built in.
A concrete one: we would never let agentic AI produce a medical diagnosis on its own. That’s not the job. The job is to put every relevant piece of patient information in front of the clinician so they make the correct diagnosis faster. The agent does the gathering. The human does the deciding. That’s the line, and it doesn’t move.
There’s a quieter payoff to all of this that I love. One of the oldest complaints in medicine is that people who trained to practice medicine have become typists,burning their time and energy on notes and admin. Orchestrate the workflows between the provider, the front desk, the biller, and the patient inside an agentic ecosystem, and you give that time back. The point of removing the admin burden was never the admin. It’s letting the human be fully present for the work that needs ahuman. Make the delivery of care more human, not less.
What I’d actually do Monday morning
If you read the report and feel the 84%-versus-5% gap in your own org, don’t start with a tool. Start with one question: which single workflow, if an agent ran it, would create value you could measure this quarter?
Pick that one. Define what good looks like before you build anything, so you’re not the 95% who can’t tell if it’s working. Clean only the data that workflow needs. Slot it into the right autonomy tier. Ship it. Measure it.
That’s the whole thing. Not a transformation. A workflow with a number attached to it. Do that three times and you’ve got a system. Skip it and you’ve got a press release.
The report is worth your time. I’d read it for the gap, not the hype.
Read the full HBR Analytic Services report here.
If you want the longer version of how I’d build this out layer by layer, the Revenue Nervous System breakdown, that’s what Sunday’s Under the Hood is for. See you there.
— J