Most Senior Executives Barely Use AI. Answer Their AI Mandate With a Charter

About 69% of CEOs, CFOs and senior executives use AI less than one hour a week, and 28% never use it at all. That is how Kevin Indig and Amanda Johnson, writing in Growth Memo on September 21, 2026, summarise an NBER working paper (w34836) that surveyed about 6,000 senior executives across four countries. The figure reaches this article through their reading of the paper, so treat it as their summary of the survey rather than a number I pulled from the tables myself.

Hold that figure next to the instruction Growth Memo’s readers are answering: use more AI. The people writing the mandate are, on this reading, mostly light users. The people receiving it have to turn a sentence into work.

Indig and Johnson’s answer is a document the team hands back upward. I will call it a charter. It has three parts: a 30-day tally of where AI hours actually go, eight rules every experiment runs under, and a cap on how much team capacity experiments may consume. Their post is written for marketing teams and sponsored by Semrush. I see nothing in the charter that depends on marketing, and applying it to any function is my extension of their argument.

Why a Mandate Needs a Container

We have already argued that resistance to AI mandates is rational, and we have covered Meta turning a mandate into org structure and Ramp’s adoption through org design. This piece skips the debate about whether to comply and goes straight to the artifact.

Two more figures from Growth Memo explain why an artifact is needed. Citing the Validity State of CRM Data Report 2026 (N=500), the authors write that “nearly 60% of C-suite respondents and 52% of SVP/VPs said they feel pressure to implement AI tools now, while knowing the data inputs underneath those tools aren’t ready.” Citing EY’s survey of 15,000 employees and 1,500 employers in 29 countries, they report that “88% use AI every day, while only 5% use it to genuinely change how their work gets done.”

Both reach us second-hand through the same post. Read together, they describe pressure from above and shallow use below. A mandate with no scope, no end date and no spend limit sits between those two numbers and can absorb whatever time people give it. My position is simple: an unbounded mandate is ungoverned spend. The charter turns it into a budget line that someone can approve or stop.

Part One: The 30-Day Tally

Before proposing anything, each person logs AI hours for 30 days in four categories:

Checking and fixing outputs
Learning tools
Running AI workflows
Copyediting and fact-checking long-form outputs

The instruction attached to the tally matters as much as the categories: “you’re counting, not grading.” Nobody is scored on the result. The point is to see the shape of current use before arguing about future use.

Look at what the categories separate. Two of them are rework: fixing outputs and fact-checking long outputs. One is investment in skill. Only one, running AI workflows, is the thing the mandate probably imagined. A team that reports only “hours using AI” hides that split. A team that reports the four categories can show leadership how much of its AI time goes to correcting AI.

Part Two: Eight Rules for Every Experiment

Growth Memo lists eight rules. I quote them as written, group them myself, and add after each group what I think they do for governance.

Ownership and limits

“Name one owner.”
“Set an end or kill date before you start.”
“Budget for tokens and cap the spend.”
“Budget for time and record it.”

These four make an experiment accountable. One owner means one person answers for the outcome. A kill date set in advance removes the negotiation that happens when an experiment is half-working and nobody wants to be the one who ends it. Token and time budgets are the two currencies an AI experiment spends, and the rule names both.

Definition of success

“Write down what winning looks like in one sentence.”
“Turn use more AI into a defined outcome.”

The second rule translates the mandate directly. “Use more AI” has no finish line. A defined outcome does, and it gives the kill date something to be measured against.

How the team talks and deploys

“Present options rather than objections.”
“Run new workflows in shadow mode.”

The first of these is the upward-management rule. In my experience, a team that answers a mandate with objections gets overruled, and one that answers with a few scoped options gets a decision. Shadow mode, as I read it, means the new workflow runs alongside the old one while the output that ships still comes from the trusted process.

Part Three: The Capacity Cap

The cap is the part that turns the charter into a budget. Growth Memo’s schedule:

Start with 10% of your marketing team’s available capacity, measured over four weeks.
Move to 15% only after a completed pilot delivered measured gains.
Allow 20% as a temporary ceiling for a fixed rollout sprint.
Reduce by 5% if experiments delay planned work.

And the line that closes the loop: “Anything past the cap is a project, not an experiment, and projects get a budget line like everything else.”

Two cautions on these numbers. The percentages are the authors’ prescriptions, and the post reports no measurement behind them. They work as a starting position that a team can defend in a meeting because it is written down. Second, the post refers to “the other 80%” of capacity, which matches the 20% ceiling. The 10% default leaves more. At the default, 90% of capacity stays outside experiments.

The governance value sits in the transitions. Capacity moves past 10% only after a completed pilot delivered measured gains, and 20% is reserved for a fixed rollout sprint. It shrinks by 5% when experiments delay planned work. Neither move depends on how enthusiastic anyone feels about AI that quarter. And the ceiling has a clear exit: once work outgrows it, it stops being an experiment and competes for budget like any other project.

What the Charter Does Upward

Put the three parts on one page and the conversation with leadership changes shape. The tally shows where time goes today. The rules show that every experiment has an owner, an end date and a spend limit. The cap shows how much of the team’s week the mandate will consume and what has to happen for that share to grow.

Leadership can say yes to that page, or no, or ask for different numbers. Each of those answers is better than the likely outcome, as I see it, of an unscoped mandate, where AI work expands quietly into planned work and nobody can say what it cost.

This also answers the 69% figure without making it personal. A leader who uses AI lightly may have little first-hand basis for judging a workflow. The charter asks for a judgment on scope, cost and results, which is the kind of judgment leaders make about every other budget line.

Do This Now

Start the tally on Monday. Give every person on the team the four categories and the “counting, not grading” instruction, and collect the logs at day 30. While the tally runs, draft the charter: one page with the eight rules, the 10% starting cap measured over four weeks, and the conditions for moving to 15% or 20%. Pick one experiment, name its owner, write its kill date and its one-sentence definition of winning, and set it to run in shadow mode.

When the 30 days end, send the tally and the charter upward together. You will be presenting options, which is the seventh rule, and you will be asking for a budget line, which is what an AI mandate should have had from the start.

This analysis synthesizes Managing the AI mandate upward (Growth Memo, Kevin Indig and Amanda Johnson, September 2026), which is sponsored by Semrush and cites the NBER, Validity and EY figures used here.

Victorino Group helps leadership teams turn AI mandates into governed experiments with owners, kill dates and capacity caps. Let’s talk.