You've bought AI tools, scaffolded the system, onboarded the teams, but adoption is happening at a seemingly glacial pace. Here are a few ideas you might try to grease the wheels:
Loop in Finance
Finance has seen one hundred AI vision decks and zero P&L transition models. Help them see the future.
Engineer salaries on R&D work frequently get capitalized—turned into an asset and amortized—under software capitalization rules (and, in the U.S., mandatory R&D amortization under Section 174). Inference spend is plain opex. It hits the P&L immediately.
So when you replace $2M of capitalizable engineering labor with $300K of inference, current-year reported earnings can look worse even though the real cost just collapsed. Your CFO looks at the plan and sees an earnings dip in year one, a crossover, and then a structurally lower cost base. They're not wrong.
Write out your strategic plan to paper over the dip with harness development—evals, context repositories, agent infrastructure. That work is itself capitalizable as internally developed software. The accounting asymmetry between "engineer building features" and "engineer building the system that builds features" is mostly a categorization fight, and Finance owns the categories.
Agree on one non-GAAP metric to report alongside the official numbers—output per fully-loaded dollar—so the board can see the real trajectory while GAAP works through the hangover. Helping your CFO co-author the transition model ensures he feels comfortable defending it. If finance is on your side, no one can stop you.
Loop in HR
Legacy company metrics still assume output scales with humans. Promotions are largely based on scope, span, and the complexity of herding other humans. Leading six people through a multi-quarter migration is still more likely to get you promoted than shipping the same migration alone in three weeks with an agent fleet.
To the existing HR system, agents register as smaller scope, fewer dependencies, and no one coordinated. Until "span of agency"—how much output you can actually be accountable for—replaces "span of control" in the leveling rubric, AI adoption will keep stalling while everyone performs the old playbook.
If you want to move the needle this quarter, ask your friendly HR leader to add a "leverage case" packet to promotion evidence: output shipped, harness built, verification responsibility carried. Then actually promote one visibly AI-native engineer on that basis. Once someone actually gets paid for working the new way, people will start taking your strategy decks seriously.
Loop in Management
Let's be honest about what most managers actually do: aggregate information upward and disaggregate instructions downward. Management is a routing layer for context. That is exactly the function a well-maintained context repository plus agents performs.
The new senior roles—harness owner, context steward, verification lead—are the highest-leverage jobs in an AI-native org. Your best managers are the natural candidates because they are already the org's context-routing layer. Show them how writing down what they route turns their tribal knowledge into compounding momentum for the organization and builds their next role in the process.
You might also try running one reference team openly—small, AI-native, real production surface—and publishing their numbers internally. Envy is a change-management program that runs itself.
Don’t Forget Your Quietest Engineers
If you are already running an AI-native team, you know the secret: your best engineers are heavily AI-leveraged and they are hiding it from you.
If a task scoped at two weeks with the sanctioned tools takes two days with covert AI, revealing that fact gets them a policy violation and more work. So they deliver in nine days as expected and bank the margin. Why wouldn't they?
Consequently every internal pilot is corrupted. That 15% lift you're celebrating is not the delta between "No AI" and "AI." It is the delta between covert adoption and open adoption.
Give them a Scoping Amnesty. Don't punish the saved time with more work. Suspend judgment on the methods. Let them use the reclaimed time for harness investment—better evals, context repo work, tooling. Now the engineer who reveals a 5x speedup gets something for it instead of losing something.
Good Luck and Godspeed
The entire AI transition is a repricing exercise. Your organization currently prices coordination. You need it to price leverage.
Prices only move when someone is visibly paid the new way. Everything else is commentary, slide decks, and another pilot that won't turn into a funded project.
Fix the incentives before your next strategy offsite.