The McKinsey numbers do not need a second read to be sobering. The first read does the job.

The State of Organizations 2026 surveyed more than ten thousand senior leaders across fifteen countries and sixteen industries. Eighty-eight percent of organizations are deploying AI in at least parts of their business. Fewer than twenty percent see significant impact on the bottom line. Eighty-six percent of leaders say they are not ready to embed AI into day-to-day operations. One in six organizations has no clear C-level owner for AI adoption at all.

HBR’s “last mile” diagnosis from a closed-door Harvard summit fills in the texture McKinsey’s spreadsheet cannot. The summit included a global investment bank with more than two hundred and fifty LLM-connected applications in production, a payments network where copilot adoption sits above ninety-nine percent of employees, and an apparel group running eighteen thousand automated finance processes. In every one of those firms, finance teams hunt for measurable impact in headcount and cycle-time numbers and come up empty. The work is happening. The value is not landing.

Andrei Savine pulled both pieces together in March: enterprise AI dies in the last mile because organizations under-funded the operational layer that turns model output into business value. He calls it the Production Layer. He cites an executive in the McKinsey report who put the corrective ratio at five to one — for every dollar spent on technology, five should be spent on people, process, and verification.

That diagnosis is correct.

That diagnosis is also a tourist map.

Inside the firm, the disconnect is on the calendar. A partner explains why AI will not materially change the way they practice. The IT department, meanwhile, is months into a slow restructure built on the opposite assumption. Both positions are held with conviction. Neither side can yet describe the outcome on the other side of the change.

The general diagnosis works at the macro level because it abstracts away the structural features of the firms it describes. Software companies can absorb productivity gains by reducing headcount. They have CEOs who can decree adoption. Their work product is a piece of software, not a lawyer-hour. None of that is true in a law firm.

This piece is a map of what the last mile actually looks like inside one. The hazards in the terrain. The path that does not end in either a mass layoff or a restructuring announcement masquerading as strategy. The question is not whether five-to-one is the right ratio. It is whether law firms know what the five is supposed to build.

Why the General Diagnosis Underestimates Legal

The five features that make a law firm break the standard model are not exotic. They are the structure of the business. Anyone who has worked inside a firm knows them. Anyone trying to apply a generic enterprise-AI playbook to a firm runs into them within the first quarter.

Start with the billable hour. The standard productivity gain from AI is a productivity gain. In a firm billing by the hour, the same gain is a revenue cut unless something changes upstream. Two software companies have absorbed their productivity gains by reducing headcount in the last few months — roughly 1,600 people at Atlassian, and a 2,000-person restructure at WiseTech shortly before. Law firms cannot run that play. The work product is the lawyer-hour itself. Wolters Kluwer’s 2026 Future Ready Lawyer survey of 810 lawyers found 54% expect firms to use AI efficiency for more clients or competitive pricing; the 2024 edition projected that AI automation could reduce hourly billing per lawyer by roughly $27,000 a year. Those numbers cut against revenue, not toward it. The inversion is what makes generic AI advice land badly in partner meetings: efficiency arguments are heard as compensation arguments.

Beneath the billable hour sits the apprenticeship. Junior associates learn judgment by doing the work AI does best — first-pass document review, citation checks, due diligence summaries, contract redlining. The training pipeline runs through the same activities AI is now positioned to consume. Automate the work without redesigning the apprenticeship, and the firm produces senior associates whose calibration never developed. The Citi Hildebrandt 2026 Client Advisory reports revenue growth of 11.3% across surveyed firms, productivity per lawyer down 0.6%, and 88% of firms planning continued associate growth — built on the assumption that the apprenticeship pipeline still works. The assumption may not hold.

Governance compounds the problem. McKinsey’s number — one in six organizations without a clear C-level AI owner — understates the legal case. In an equity partnership, even a C-level owner cannot decree adoption. Decisions move through practice-group chairs, executive committees, and partner votes. The ILTA 2025 Technology Survey of 580 firms found that user resistance is the top barrier to AI adoption at 57%, up from 54% the year prior; half of those firms have no formal AI policy at all. The gap is not a failure of legal IT. It is a structural feature of how authority is held and exercised in a partnership.

Privilege and conflicts sit beneath the policy gap. Technical friction in legal includes constraints other industries do not face. Solicitor-client privilege. Conflict-of-interest screens. Data residency for regulated client matters. Prohibitions on third-party model training. Ethical-wall enforcement that must survive at the model and the prompt level. ABA Formal Opinion 512 and the Federation of Law Societies of Canada Model Code both treat AI use as a competence question, a confidentiality question, and a supervision question simultaneously. The provincial regulators — Ontario, British Columbia, Alberta, and the Barreau du Québec — have all issued guidance that constrains which tools are usable for which matters. None of this is a security checklist. It is a substantive constraint on adoption.

Last comes the asymmetry. The price of being wrong exceeds the benefit of being right. Mata v. Avianca made the failure mode public in 2023. Damien Charlotin’s AI Hallucination Cases Database has been logging the failure mode ever since, at the rate of dozens of new entries per month from courts in more than a dozen countries. The downside of an AI-fabricated citation is career-ending. The upside of an AI-generated brief is incremental. When the loss function is asymmetric, rational professionals default to avoidance — which produces exactly the shallow utilization HBR’s last-mile diagnosis describes.

Generic diagnoses do not survive these five features. A different map is needed.

The Map: Five Hazards in the Terrain

These hazards are not theoretical. They are already arriving.

Hazard 1 — The Apprenticeship Gap

The work AI handles best is the work juniors learn from. Document review. Due diligence summaries. Citation checks. Contract markup. Strip those activities out without replacement and the firm is running an apprenticeship system whose training data has been deleted.

The signal does not appear in the first year. It appears two and three years in, when an associate who learned to draft alongside an LLM is asked to negotiate a non-standard term in a deposition prep meeting and cannot triangulate why the standard term existed. The output looks fine. The pattern-matching against past work — the thing that turns a third-year into a fourth-year — has not happened.

The hazard sits at the intersection of two operating questions. The decomposition question asks what the junior was actually doing inside the document review. Some of it was the review itself. Some of it was learning what a representation looks like when it is doing real work in a credit agreement, learning which questions to ask before flagging an issue to a senior, learning the difference between a problem and an artifact of the drafting party’s preferences. The collaboration-quality question asks what the junior was learning by doing the work, not what they were producing. If only the production is automated, the learning evaporates with it.

The Citi Hildebrandt advisory caught the contradiction. Firms are still planning associate growth on the old assumption — that the same training pipeline still works — while productivity per lawyer ticked down. The senior-heavy staffing model some firms are leaning into assumes juniors continue to develop into seniors capable of senior work. The pipeline that produced that development is being quietly automated. The hazard arrives on the promotion clock. Juniors will keep being hired; the calibration that used to develop alongside them will not appear on schedule, and the firm will not notice until the class it tries to promote is already on the partnership runway.

Hazard 2 — The Pricing Collision

The hourly model inverts under AI. The same brief that took twenty hours last year takes seven this year. The client knows. The client has run the spreadsheet.

The signal arrives in the form of an RFP that asks specific questions. Which tools does the firm use? On which matter types? What productivity assumption is baked into the proposed rates? The 2025 ACC Chief Legal Officers Survey, summarized in subsequent analysis, found that 59% of general counsel see no clear cost savings from outside counsel using AI. A transparency gap, the report called it. That phrase will appear in a procurement deck within twelve months.

Pricing models that align with the new reality already exist. Fixed fees. Capped fees. Success fees. Capacity-based arrangements. AI-augmented hourly with a productivity discount. The menu is not new. What is new is the pressure to choose, and the pressure to choose visibly. Thomson Reuters’ 2025 Future of Professionals projects up to 240 hours per professional saved per year by AI-augmented workflows; the 2024 edition projected 12 hours per week saved by 2029 and roughly $100,000 in additional billable hours per U.S. lawyer. Those numbers are useful to a firm that has redesigned its pricing to capture them. They are punitive to a firm that has not.

This hazard is the one most likely to drive the same failure mode inside legal. A firm that does not redesign pricing and roles will eventually balance the books with headcount. The redesign is uncomfortable. The alternative is worse. The choice is whether to lead the pricing conversation or inherit one shaped by clients who started running their own AI math two budget cycles ago.

Hazard 3 — The Judgment Hollowing

Apprenticeship is about who comes next. Judgment hollowing is about who is already in the chair.

Senior judgment was built on years of doing junior work. Take that work away through AI mediation — not removal, mediation — and judgment does not develop the same way. The hazard appears in two opposing modes. One is overtrust: lawyers who accept AI output uncritically because the immersion that produced calibration has thinned out. The other is blanket rejection: lawyers who refuse to engage with AI assistance because the output feels uncalibrated even when it is sound. The first failure ships errors. The second ships sluggishness. Both come from the same root.

This is what skill-formation research in adjacent professions has been pointing at. When the practice that produced calibration is mediated, calibration drifts. Output calibration and signal discrimination — the two senior capacities that distinguish a partner from a senior associate — degrade in opposite directions when the underlying practice that built them disappears. The senior who used to read a memo and feel the wrong sentence is still reading the memo. The reading is faster. The feel is duller.

The signal is not visible on a dashboard. It is visible in review cycles. A senior who used to spot the problem in the first pass now spots it in the third. A junior whose draft used to be rebuilt is now approved with light edits — not because the draft is better but because the senior’s bar moved. The work product reads acceptable in both cases. The institutional capability does not develop. The hazard is the part of the iceberg the engagement letter cannot describe and the matter close-out cannot bill.

Hazard 4 — The Partnership Fracture

Adoption splits the equity table. Litigation moves on one tool while M&A holds out. IP runs a different stack than tax. Practice groups operate as small businesses inside the firm; their adoption velocities can diverge by a factor of five within the same year, and the divergence shows up in realization rates before it shows up in compensation discussions.

The ILTA 2025 survey captured the inflection point. Eighty percent of firms reported using or exploring generative AI. Half reported no formal AI policy. Those two numbers describe a partnership in which adoption is happening faster than governance, and where the governance vacuum is being filled at the group level by whoever decided to act first.

The signal is not the adoption rate. The signal is the compensation tension that follows. Two practice groups that bill different realization rates against similar matter types invite a comp-committee conversation no managing partner enjoys. Recruitment messaging that promises one thing across a firm where the lived experience varies by group invites a different conversation with laterals. The fracture is not technological. The fracture is political, and it arrives on the partnership’s calendar approximately one fiscal year after the first group commits to its first material AI deployment.

Inside the firm, the divergence is already concrete. One practice group, pressed by a client to deliver AI-driven efficiencies, has mandated tool use and is rebuilding workflows accordingly. Other groups have not opened the conversation. All of them sit inside the same partnership. The comp committee will eventually have to reconcile what the calendar already shows.

The leadership challenge in this hazard is velocity matching, not adoption. Getting compatible adoption velocities across groups is a different problem from getting any adoption at all, and it is not solved by the same instruments.

Hazard 5 — The Asymmetric Stakes

The price of being wrong exceeds the benefit of being right.

Mata v. Avianca was the warning shot — a Southern District judge, a $5,000 sanction, and the first widely circulated story of fabricated citations submitted to a federal court. Charlotin’s database has been the running count ever since: well over a thousand decisions tracked across more than a dozen countries, with new entries arriving at the pace of dozens per month. Johnson v. Dunn, decided in the Northern District of Alabama in July 2025, extended the line into BigLaw. A practice-group co-leader at a large, well-regarded firm signed a motion containing fabricated citations. The court signaled that monetary sanctions are no longer sufficient to deter AI-generated errors, and that future cases may see referrals to bar counsel and other escalations.

The signal is not the headline cases. The signal is the malpractice underwriting questionnaire. Carriers have started asking which tools a firm uses, for which matter types, with which verification process. Some are writing AI-specific exclusions. The Federal Court of Canada has issued a notice to the parties and the profession on AI in court proceedings; provincial superior courts have followed; U.S. district courts have done the same on a docket-by-docket basis. Disclosure of AI use is no longer optional in many jurisdictions, and in some it must include disclosure of how the AI was used.

The hazard lives in the loss function. As long as one fabricated citation costs more than ten useful drafts save, the rational play is to avoid the tool — and that avoidance is what produces the shallow utilization the survey data keeps measuring. The accountability avoidance pattern McKinsey identifies is rational behavior in an asymmetric environment. Resolution is not the elimination of risk. Resolution is clarity about who is responsible for which verification, at which step, with what record.

Five hazards. None of them appear on the McKinsey or HBR map. The path through them is what the next section describes.

The Path: What Implementation Actually Looks Like

Six moves. Each one resolves a hazard. None of them resolve all the hazards. The work has to be done in sequence and in combination.

Move 1 — Start with measurable operations before client-facing work

Knowledge management. Marketing. Finance. IT. Recruiting. These are the practice areas of the firm where measurement is possible, where the stakes are bounded, and where the team can build the muscle for measurement before pointing AI at client matters.

The reason this comes first is not theoretical. A firm that cannot measure value in its own back office has no business claiming to measure value in client work. Most firms skip the step anyway, because the political appeal of a client-facing pilot is too strong. There is a partner who wants it. There is a vendor who will demo it. There is a press release in the budget. The internal pilot is less photogenic. It is also where the failure modes show up cheaply.

What measurable internal deployments produce is more valuable than the deployments themselves. Real numbers. Real failure modes. Real governance precedents. The early adopters of internal AI become the people who teach the practice groups, because they have already had the embarrassing first conversation with privacy, the awkward second conversation with risk, and the corrective third conversation with finance.

A firm that has not staffed its internal AI team before standing up its client-facing AI team is not running an enterprise AI program. It is running a procurement exercise with extra steps. The move that says “we will start where it matters most” sounds bold and usually fails. The starting point that matters most is the one where mistakes cost the least.

Move 2 — Run the Task Audit at the practice-group level, not the firm level

A litigation matter is not an M&A transaction is not a regulatory filing is not an IP prosecution. The work is structurally different. The AI fit is structurally different. The realistic adoption pace is structurally different. Firm-wide rollouts produce shallow utilization because they ignore those differences and try to deploy one set of tools, with one set of metrics, across groups that need different things.

What the federated version looks like is unglamorous. Each practice group decomposes its own work into the activities that compose a matter — research, drafting, review, analysis, communication, project management. Each group runs its own audit of where AI fits, where it does not, and where the answer is uncertain. Litigation may find AI fits document review and timeline construction; M&A may find AI fits diligence summaries and disclosure schedule drafts; tax may find narrow but high-value uses around regulatory text comparison. Each group owns its own map.

This is what the HBR prescription — redesign roles, budgets, and processes — looks like when the redesign is translated into a firm. Not a firm-wide redesign. A federated one, in which the firm-level work is to set guardrails and standardize the verification, and the group-level work is to choose the use cases.

The Task Audit is the artifact. The honest version takes a quarter per group and produces something a partner can defend in a comp committee. The dishonest version takes a week, looks like a deck, and ages badly.

Move 3 — A single accountable owner per practice group, not a committee

Committees defer. Owners decide. McKinsey’s number — one in six organizations without a clear C-level AI owner — is worse in legal, because even where an owner exists at the firm level, the partnership structure dilutes accountability. The committee is the partnership’s default response to a contested decision, and AI adoption is a contested decision.

What works is a named partner in each practice group who owns AI decisions for that group. Not a committee chair. Not a project sponsor. Not a steering-group member. An owner — someone whose performance review includes a line item for the group’s AI capability development, and whose decisions do not require a partnership-wide vote to take effect inside the group. The owner reports up to a chief AI officer or equivalent at the firm level, but the accountability lives at the group level because that is where the work lives.

This configuration is unfashionable in firms that prefer consensus. It is also the only configuration that produces decisions on the timeline AI requires. The alternative is a steering committee that meets monthly, defers two of the three decisions on the agenda, and ratifies the decision a partner already made between meetings.

The named-owner model has a useful side effect. Partners who own decisions become accountable for outcomes. Partners on committees do not. Functional AI capability over the next three years will sit with firms that staffed for ownership early, not with firms that staffed for governance theater.

Move 4 — Redesign the apprenticeship deliberately

The apprenticeship gap does not close by accident. It closes by design — or it does not close.

The redesign question is not how the firm automates junior work. The redesign question is what juniors do instead, such that they emerge with the judgment senior practice requires. The automation question is the easy one. The redesign question is where the actual capacity sits.

What the redesign looks like in practice is structural. A second-year associate who used to spend a third of their hours on first-pass review now spends a third of their hours on something else. The “something else” has to do for the second-year’s development what the first-pass review used to do. Possibilities exist. Supervised secondary review, where the associate critiques the AI’s output and learns to spot what a senior would spot. Structured client-interaction time, where the associate develops the judgment that is hardest to automate. Deliberate cross-practice exposure, where the associate sees how a deal partner and a litigator weight the same fact pattern differently. None of these is automatic. All of them require partner time, which is the scarcest resource in a firm.

The cost of the redesign is real. The cost of not redesigning is realized in five years, when the firm tries to promote a class of senior associates and discovers their calibration is not where it needs to be. The redesign work flows into Section 4, where role redefinition becomes the unit of analysis.

Move 5 — Address pricing before clients force the conversation

The general counsel office is going to ask. Better to have an answer than to be asked. Better to have proposed the answer than to be in defensive negotiation when the question arrives, because the question will not arrive politely.

Fixed fees. Success fees. Capacity arrangements. AI-augmented hourly with a productivity discount. The menu exists. The choice is which structure fits which matter type and which client. The choice is also which structure preserves margin while signaling that the firm has thought seriously about the productivity assumption clients are making.

Pricing is not a finance department problem. Pricing is a partnership problem, because the realization-rate consequences of every pricing experiment land on individual partner P&Ls. The right pattern is to run experiments inside specific practice groups, with specific clients, on specific matter types, and harvest the data before the pricing committee tries to set a firm-wide policy. Firm-wide policy on pricing arrives last, after the experiments.

This is uncomfortable. It is also where the strategic self-direction question gets answered. Firms that lead the pricing conversation define the market. Firms that wait inherit a market other firms defined. The Wolters Kluwer survey found 54% of lawyers expect firms to use AI efficiency for more clients or competitive pricing — the client side has already done the math. The question for the firm is whether the math gets done in the partnership’s frame or in the client’s frame.

Move 6 — Measure capability development, not hours saved

Hours saved is the metric that produced those software-industry cuts. It is the wrong metric in legal, because the answer to “what did we do with the saved hours” cannot be “we cut the people.” The model does not work if the people are gone. The whole apprenticeship hazard, the whole judgment-hollowing hazard, every reason a firm has a future at all is grounded in the people being there to develop into the next generation of senior practitioners.

What to measure instead lives at the capability layer. Practice-group adoption depth — how many lawyers in the group are using AI for substantive work, not for one-off email drafts. Output calibration scores against blinded review. Role evolution — whether roles are changing in the direction the firm intends, or drifting. Time reinvested — when a task that took ten hours takes three, where do the seven hours go, and what shows up at the end of the quarter that did not exist at the beginning.

The five-to-one ratio from the McKinsey advisor surfaces at this move. Five-to-one is not a budget rule. It is a measurement principle. If the dollar count on the people side is small, the measurement on the people side will be small, and the capability the people side is supposed to build will not develop. A firm that spends one dollar on tools and twenty cents on capability development is running a one-to-five program with extra slides, not a five-to-one program.

Role Redefinition Through Live Operating Questions

The Production Layer cannot be installed. It has to be developed.

Savine’s prescription assumes installation — build the layer, fund it five-to-one, install the agentic control plane, run the pipeline. Inside a firm, the layer is not infrastructure. The layer is a set of capabilities held inside a set of roles that are changing shape faster than HR can document them. The Production Layer in a law firm looks less like a new platform team and more like existing roles being re-aimed at capability development, with new measurement underneath.

The work is still early. That matters. If the last mile were mainly a deployment problem, the evidence inside a firm would appear as tool rollouts, usage charts, and completed implementation plans. The evidence looks different inside the work. It appears as roles changing shape before the metrics are settled, and as operating questions the organization has to learn how to answer before outcomes are clean enough to report. Here’s what my team is doing to build the Production Layer in the firm I work in, and what the operating questions look like in each case:

Conversation sophistication scoring. We are building scoring metrics across LLM conversations in the firm’s AI products. The unresolved question is not whether conversations can be scored — they can. The hard question is which scores should change what happens next. A metric that does not connect to an intervention becomes dashboard ornamentation. A useful metric tells the organization where users need better scaffolding, where a workflow is too vague, where output calibration is weak, or where a product is encouraging shallow prompting. Someone has to decide what better conversations mean, what action follows the signal, and who owns the intervention. That deciding is role design, not analytics.

Technology Training becomes Learning Enablement. We’re moving from technology training to learning enablement. The old mandate was organized around software instruction. The new mandate is organized around business outcomes, particularly where AI software changes the work itself. The work moves from showing people how to use the tool to helping groups develop the capacity to get a better business result from the tool — which includes adoption, workflow fit, practice-group context, measurement, and reinforcement after the formal training ends. The Production Layer in this case looks less like new infrastructure and more like an existing team with a redefined mandate and a different measurement underneath.

Merging Business Intelligence, AI Enablement, and Application Development. We are reconfiguring a programming leadership role into an Engineering Manager with a portfolio across BI, AI Enablement, and Applications. The team is small. The mandate is to create an AI-native development process — which is not the same as a faster ticket pipeline. Artifacts are cheap. Judgment and systems are the hard part. The role is not only about producing more code or more applications. It is about building a system where AI changes the development process without hollowing out architecture, review, accountability, or product judgment. The hazard inside the role is the same hazard inside the firm: surface productivity gain at the cost of underlying capability.

These are signs of the terrain, not success stories yet. In each case, the software is the easy part to name. The hard part is deciding what new capacity the organization needs, which role owns it, and how the firm will know whether that capacity is improving. The Production Layer thesis points at this. It cannot specify it. The specification has to come from inside the practice, inside the roles, and inside the unresolved questions that appear before outcomes are clean enough to report.

Closing

The map drawn here is real. The hazards are observable inside the work. The path is operational. None of that does the work.

Generic diagnoses produce generic responses. The McKinsey numbers will be quoted at every legal industry conference for the next eighteen months. The HBR last-mile language will become a slide-deck staple, then a panel topic, then a vendor positioning line. None of that will move a single firm forward, because none of it engages with the structural features that determine whether the firm has a future on the other side of the curve.

What moves a firm forward is the development work that happens at the practice-group level, in the pricing conversation, in the apprenticeship redesign, in the named partner who decides to own a decision instead of distributing it across a committee. The Production Layer is not a build. It is a development arc. The arc takes years and runs through individual roles whose mandates are changing faster than the org chart can document them.

The five-to-one ratio at the top of this piece is the right principle. It is not the right plan. The plan has to be built inside a partnership, against billable-hour math the math does not want to give back, inside a regulatory landscape that does not allow shortcuts, and with case law and malpractice carriers ratcheting up the cost of being wrong while the survey data ratchets up the expectation of efficiency. The work is not optional. The pace of the work is not negotiable. The map is the first artifact. The path is the second. Neither is the work.

AI does not make a law firm better. It exposes whether the firm was already doing the work.

In the late Republic, Cicero set himself the project of translating Greek philosophy into Latin. He set out to render Plato and the Stoics in his own language, and he discovered fairly quickly that Latin could not hold the concepts he was trying to import. The vocabulary did not exist. In the Academica he wrote — half-anxiously, half-ambitiously — that he had been forced to manufacture the words. He coined qualitas to render Plato’s poiotes. He coined moralis from mos, the Latin for custom, to render the Greek ethikos. To these he added evidentia, humanitas, quantitas, essentia — by modern accounts, more than two hundred Latin equivalents of Greek philosophical and rhetorical terms, many of which survive in English as quality, moral, individual, vacuum, property, definition, infinity, science. The Romans got their philosophical vocabulary because one man manufactured it in Latin, sentence by sentence, while writing about it.

Cicero did not describe his work as translation. He described it as construction.

Most modern advice for people working at the seam between technical teams and business leadership does not seem to know that Cicero had this problem. The advice treats the cognitive load as a communication issue. Slow down. Find better metaphors. Bring people along. The advice is everywhere, and it isn’t wrong. It is also about two thousand years late, and it misreads the actual work.

The work of sitting between two worlds is not principally about explaining one to the other. It is about building the vocabulary that lets either side describe the territory in the first place. The cognitive load that comes with the work — the exhaustion, the reaching for words that aren’t quite right, the tedious circumlocution because the precise term hasn’t been settled yet — is not evidence of a failure to communicate. It is evidence of construction underway.

The Romans understood the position well enough to give it a god. Janus, two-faced, looked simultaneously at the past and the future, and in some traditions at the inside and the outside, the known and the unknown. He presided over thresholds and transitions. The month of January is his. Janus was not a metaphor for the experience of holding two views at once — he was an acknowledged cosmological role, important enough that the calendar opened with him. The Romans accepted that someone had to sit at the threshold between worlds, and they gave that someone a name.

They did the same for the institutional version of the role. The Latin word pontifex — the title borne by Roman priests, including the Pontifex Maximus — literally means bridge-builder. Pons (bridge) and facere (to make). The role had a name, an office, and a temple. The state recognized that the seam between worlds was load-bearing, and it appointed someone to carry it. The Romans were precise about this, and they were not romantic about it. They simply understood that a bridge does not build itself, and the person who builds it deserves an institutional title.

Move the picture forward two thousand years. AI governance, in any institution serious about AI, sits in the same position Cicero sat in. Two domains that do not natively share a vocabulary.

The technical world has its terms: model weights, retrieval-augmented generation, zero-data retention, prompt injection, agentic loops, fine-tuning, MCP servers, data residency. Each one carries a long technical tail and assumes a body of operational knowledge to be useful.

The legal and business world has its terms: privilege, fiduciary duty, materiality, retention obligations, regulatory exposure, duty of competence, professional secrecy. Each one carries its own long tail, and it assumes a different body of operational knowledge.

Neither vocabulary natively contains the other’s concepts. There is no Latin for poiotes and no business term for prompt injection. The person doing AI governance is, structurally, doing what Cicero did. They are manufacturing the language that one side can use to describe the other. The phrase AI governance itself is a coinage of the last few years, and it isn’t fully settled — different institutions mean different things by it. The vocabulary is being built, in real time, by the people occupying the position.

This is why communication advice misses the work. Communicate better assumes the words already exist and the speaker is choosing the wrong ones. The actual problem is more like Cicero’s. The words don’t exist yet. The practitioner is coining them. They are testing whether guardrail lands, whether AI risk lands, whether agentic system lands, whether the legal team will accept prompt-level controls as a meaningful category. They are doing what Cicero was doing in the Academica, and they are doing it under board-level scrutiny rather than over a quiet correspondence with Atticus.

The cost is structural and ancient

The cognitive load that comes with the position is not a personal weakness, and it is not unique to the modern moment. Cicero complained about it openly. In his letters to Atticus he expresses anxiety about whether Latin can carry the work, whether his coinages will hold, whether the Romans will accept words that did not exist yesterday. Cicero’s anxiety in those letters was diagnostic. He was naming the structural reality of vocabulary construction, and the cost it produced. The work was ambitious, and the ambition came with a cost the work itself created.

What changes when the position is understood this way is mostly the optimization function. The work is generative work. It produces vocabulary that did not exist before, and that vocabulary does work long after any given meeting ends. Cicero’s qualitas persisted for two thousand years. Boards in the present moment are not going to remember any individual conversation about AI governance, but the categories that get coined now — the names for risks, controls, accountability structures, evaluation methods — those will persist. The careful manufacture of the right word is the most durable form of leadership available in liminal positions.

That recognition reframes what the work actually is. The temptation in the position is to optimize for fluency — for speed, for clarity, for the smooth handoff between domains. Fluency is useful, but it is downstream of the actual work. Upstream is the vocabulary. The careful, sometimes tedious, often unfinished construction of words that one side can use to describe the other accurately. Pontifex labour, in the original sense.

What we have not given the role

The Romans did one thing better than the present moment is doing. They named the position and they put it inside an institutional structure. Pontifex was an office. Janus was on the calendar. The role of bridge-builder between worlds was understood to be public, recognized, and load-bearing.

The same recognition has not been given to AI governance. The people doing the work hold a hundred different titles, most of them generic — head of innovation, AI lead, director of emerging technology, occasionally something like responsible AI. None of these names the actual work, which is closer to vocabulary engineer or bridge-builder for a new domain. The cost of the absent name is the same cost Cicero observed in his letters. When the role is unnamed, the work is invisible. The load gets interpreted as personal weakness, and the standard advice — given to invisible work that wasn’t supposed to look like work in the first place — is fluency. Fluency does not build the bridge.

The systemic leadership question — how do you bring others with you — has an older answer than the modern leadership literature gives. You do not bring people with you by communicating better. You bring them with you by manufacturing the language that lets either side describe the territory you stand on. Cicero did it for philosophy. The Romans built the office for it. The work is not new. The institution has not yet caught up to the work.

That is the work behind the work in liminal positions. The slow construction of the words that don’t exist yet.

The Work Behind the Work is the umbrella under which all of this thinking lives — six interdependent capability questions that AI structurally cannot answer for you. Subscribe at andrewlewis.ca.

Eighty-eight percent of organizations now use AI in at least one business function. Six percent are getting measurable financial impact from it.

Both numbers come from the same source: McKinsey’s State of AI in 2025, published last November and based on responses from 1,993 organizations across 105 countries. The first number is in the headline. The second is in the methodology, where “high performers” — defined as the 6% reporting at least 5% EBIT impact from AI use — are described as a small but meaningful cohort.

The gap between those two numbers is the entire story.

What you do with that gap depends on whose framing you accept. McKinsey’s framing is that the 6% are pulling ahead because they are committing more aggressively to the playbook: pursuing what the report calls transformative innovation, redesigning workflows, scaling faster, investing more. The implication for the other 94% is straightforward: do more of the same thing, harder.

That is the firm selling transformation engagements explaining why most transformation engagements aren’t working.

This essay is about why that framing is wrong, why the data inside the same report points at a different answer, and what executives should actually do with the gap. It draws on Shakespeare, Sutton, three years of operating AI inside a regulated industry, and McKinsey’s own contradictory evidence — and ends with a diagnostic question every executive can answer for themselves.

The half of the metaphor most people have stopped reading

Most executives carry around a half-version of an old metaphor: a rising tide lifts all boats. The implication is that benefit is automatic. Buy the licenses. Enable the seats. The water does the work.

There is a more honest version of the same metaphor in Shakespeare. Brutus, in Julius Caesar, says “there is a tide in the affairs of men, which, taken at the flood, leads on to fortune.” Most quotations stop there. The next line is the part that should keep executives up at night: “Omitted, all the voyage of their life is bound in shallows and in miseries.”

The tide doesn’t lift everyone. It lifts the ones who are ready to take it. The rest get stranded.

This isn’t a stylistic distinction. It’s a structural one. A boat with holes in the hull doesn’t rise with the water; it fills up and goes down faster. As the tide of AI capability rises — and it is rising, on a curve that doesn’t depend on anyone’s organizational readiness — the gap between seaworthy organizations and unseaworthy ones widens, not narrows.

Standing still is not neutral either. Competitor vessels are getting ready. The water is going up regardless of what any single executive does.

That is what the 88-vs-6 gap actually describes. Not a gradient of effort or commitment. A gradient of seaworthiness.

The vantage point matters here. Working inside a regulated industry sharpens this. When you cannot just deploy and learn — when data residency, regulator scrutiny, and partner trust constrain every decision — the gap between buying capability and capturing value becomes uncomfortably visible. You watch teams roll out impressive-looking pilots that produce no measurable change in how cases get worked or matters get billed. You watch licensing spend that nobody can defend in front of a finance committee. You watch the same governance committee revisit the same policy questions for the third quarter in a row because nobody wants their signature on the approval. Regulated industries don’t get to skip the operational work. The unregulated ones don’t either; they just discover it later, after the licensing spend is already sunk.

Why this looks like Sutton’s Bitter Lesson, and isn’t

Anyone watching the AI capability curve from a distance can recognize the trajectory. It tracks a pattern Richard Sutton named in 2019 in an essay called The Bitter Lesson. His argument was that across seventy years of AI research, general methods that scale with computation have eventually beaten approaches built on human knowledge of the domain. Chess. Go. Speech recognition. Computer vision. The pattern repeats: human-knowledge-based methods feel principled and produce early wins, then get overtaken by general methods plus more compute. Sutton’s claim has aged exceptionally well. Large language models are the most emphatic confirmation of his thesis since he wrote it.

Read carelessly, the Bitter Lesson is the case for executive optimism. The tide is rising because compute keeps growing. General methods keep absorbing what humans used to scaffold. The eventual end state is models that handle the integration work themselves. Just wait. Just deploy. The capability will arrive; the value will follow.

That reading is wrong, in a specific way.

Sutton was making a claim about model capability and training paradigms — what wins benchmarks at the research frontier. He was not making a claim about what creates value in deployed enterprise systems. The frontier of value creation in 2026 is not the model layer. It is the layer around the model: governance, retrieval, evaluation, workflow integration, accountability, measurement. Almost all of that is what Sutton called “human knowledge” — exactly the kind of scaffolding the Bitter Lesson predicts will eventually be absorbed. But it has not been absorbed yet, and won’t be for a while, because it is not benchmark-shaped. It is context-shaped, organization-shaped, accountability-shaped. There is no test set for “did the matter close faster” or “would the partner sign off on this advice.”

The honest synthesis is that Sutton’s lesson and the operational discipline argument live at different layers. Sutton describes the trajectory of model capability, which is rising reliably. The operational discipline argument describes what happens between that rising capability and the outcomes a board recognizes. That is exactly where the 94% are stuck. Both observations are true; they are not in competition. The executive who reads the Bitter Lesson as license to wait is making a category error, confusing what wins research benchmarks with what compounds in P&L.

What McKinsey’s report actually shows, and what it conveniently omits

Read the report carefully and a different argument emerges than the one in the executive summary.

McKinsey’s own correlation analysis tested twenty-five organizational attributes against EBIT impact from generative AI. The single biggest predictor was workflow redesign. Not licensing volume. Not tooling sophistication. Not training programs. Operational rewiring.

That finding is in their data. It is not in their headline recommendation. The headline recommendation is to pursue transformative innovation — a phrase that maps conveniently to a multi-year consulting engagement and not to anything an internal team could execute on its own. Their data identifies operational rewiring as the binding constraint. Their recommendation identifies engagement scope as the answer. Those are different things.

There is another revealing detail. The report identifies twelve “best practices for gen AI adoption and scaling.” Read them in sequence and notice what they describe: establishing a dedicated transformation office, creating a comprehensive change story, developing role-based capability training programs, defining a phased rollout roadmap, establishing employee incentives, mechanisms to incorporate feedback, fostering trust through structured communications. Each of these is a defensible activity. Together, they describe the typical scope of a transformation consulting engagement almost exactly. The list is not wrong. It is just suspiciously shaped.

The high-performer framing has its own problem: it is partly tautological. McKinsey defines high performers as the 6% getting EBIT impact, then observes that they redesign workflows and pursue transformation. That doesn’t establish causation. It is equally consistent with a different reading: organizations capable of producing real EBIT impact are also the ones with the institutional discipline to redesign workflows. The methodology and the outcome correlate. The report assumes the first causes the second.

There is also a quiet methodological choice worth surfacing. The EBIT impact figures are self-reported, by respondents who are typically the same executives sponsoring the AI initiatives in their organizations. There is no independent verification. People who have sponsored a major program and put their name on it are not neutral evaluators of whether the program worked. The 6% number may be generous; the actual share of organizations capturing real, board-defensible value from AI is probably lower.

This matters because the prescription most executives are following is built on these unexamined assumptions.

None of this is dishonest. Consulting firms make money on transformation programs. Their reports are not neutral observations of the AI landscape. They are demand generation for the engagements they sell. That is not a conspiracy theory. It is how the business model works. Executives know this about their own software vendors and somehow forget it about their consulting reports. The reports use the language of independent research and arrive accompanied by the prestige of the firm; both produce a halo that suspends ordinary critical reading.

The cleanest read of McKinsey’s own 2025 report is this: their data agrees that operational rewiring is the binding constraint on AI value capture. Their recommendation does not.

What seaworthy actually requires

A vessel that can take the tide needs two things, and you need both.

The first is the hull — operational readiness. Governance that can move at the speed of capability change. Procurement processes that start with a defined business problem rather than competitive anxiety. Data infrastructure that doesn’t break when the workload shifts. Measurement frameworks that distinguish adoption from value. These are the structural conditions that determine whether AI capability, once deployed, produces outcomes or evaporates into pilot purgatory.

The second is the crew — capable people. Practitioners who can see their own work structurally and identify where AI fits. People who can evaluate AI output with calibrated judgment rather than blind acceptance or reflexive rejection. Leaders who direct capability toward operational outcomes rather than activities.

Hull without crew is infrastructure waiting for capability nobody can direct. Crew without hull is capable individuals trapped in an unready organization. Most programs invest heavily in one and call it complete.

The version of this most executives miss is that both are operational disciplines. Hull readiness is obviously operational: governance, processes, infrastructure. But crew readiness is also operational. It is not produced by lunch-and-learns or tip sheets. It is produced by changing how work is decomposed, evaluated, and assigned. The training-program version of crew development is theatre. The work-redesign version is the actual thing.

What this looks like in practice is unglamorous. It looks like sitting with a team for an afternoon and watching them do their actual work, then identifying the four steps in their process that AI could change and the two steps it absolutely shouldn’t touch. It looks like building a measurement framework that distinguishes “we used the tool” from “the matter closed faster” or “the output was higher quality.” It looks like an executive willing to put their name on a governance position that will need to be defended six months from now when someone challenges it. None of these activities photograph well in a board deck. All of them produce more EBIT impact than another round of training rollouts.

The diagnostic question is which one is your binding constraint right now. A firm with strong governance and weak practitioner capability has a different next move than one with sophisticated practitioners trapped in a governance vacuum.

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The cascade between friction points

The friction points reinforce each other in a cascade that is worth naming, because most executive interventions miss the dynamic and treat each as independent.

Governance friction comes first because it sets the conditions for everything that follows. When nobody will sign the manifest — when policy decisions get distributed until no individual is accountable — the organization cannot articulate what it is willing to do with AI. That ambiguity perpetuates cultural friction. In the absence of clear positions, the rational behavior is to scrutinize every proposed initiative for flaws rather than commit to one. Why would a manager stake their reputation on a course that has not been institutionally sanctioned?

Cultural friction in turn shapes technical friction. When the organization rewards skepticism and penalizes commitment, procurement defaults to broad, defensive purchases. Tools get bought because peers bought them. Licenses get distributed widely so no single deployment can fail visibly. The technical environment that emerges is wide and shallow rather than narrow and deep, optimized for political safety rather than operational impact.

Wide-and-shallow technical environments produce workflow friction inevitably. People have AI tools available but no specific operational problem the tools were procured to solve, no clear quality framework for output, and no model for what good integration looks like. They use the tools at the margins of their work — rewriting an email, summarizing a document — and never integrate them into the core processes that would actually produce measurable outcomes. The capable AI tools that do exist get used at a fraction of their potential because the users have not done the work of understanding their own workflow well enough to know where AI belongs.

The cascade is reinforcing, but it is not strictly linear. You can have workflow friction without governance friction. You can resolve technical friction while cultural friction persists. The point is that addressing one friction point in isolation often reveals how much the others have been compensating for it. Organizations that address only the technical layer — buying better tools — discover that the governance and cultural blockages were the real constraints. Organizations that try to address culture without resolving governance discover they are asking people to commit in an environment where commitment carries no institutional support.

Identifying which friction point is dominant is the executive’s actual job. Not all four. The one that is the binding constraint right now.

The asymmetry has inverted

For most of the last three years, executive avoidance was the rational position. Committing to a major AI program carried real personal risk: an underperforming initiative would carry the executive’s name. Avoiding carried no symmetric cost. Boards were not asking pointed questions. There was no peer benchmark to be measured against. The water was rising slowly enough that being late looked safe.

That has changed. Boards now ask comparative questions. Industry surveys publish utilization data. McKinsey’s own report turns the visibility up further — every executive whose firm sits in the 94% now has a number describing their position that their board has access to. The cost of being late has become measurable, and it is compounding quarter by quarter.

There is a second-order effect worth naming. The compounding gap between high performers and the rest is not just about EBIT impact. It is about the institutional learning that comes with operating AI seriously over time. Firms that have spent two years building governance muscle, measurement frameworks, and integrated workflows are not just ahead in adoption. They have built organizational capability that the late-arriving firm cannot replicate by deploying the same tools faster. The advantage is structural, not procurement-driven, and structural advantages compound on a different curve than catch-up effort can match.

The executive who commits to operational readiness now is making the lower-risk bet. The one waiting for clarity is making the bet that used to be safe and no longer is. Most have not repriced.

Where to start

The diagnostic question is straightforward: which friction point is your binding constraint right now?

If governance is the binding constraint, no investment in capability development will produce outcome. The starting move is identifying who is willing to own a clear position on data handling, acceptable use, and risk tolerance — and accepting that the position may need to be defended later. The friction does not dissolve when risk is eliminated. It dissolves when accountability is claimed.

If culture is the binding constraint, the work is changing the incentive structure so that commitment is rewarded and avoidance carries cost. This is the hardest of the four because it requires changing what the organization values. Most cultural interventions fail because they try to change rhetoric without changing reward; people read the actual signals.

If your binding constraint is technical — tools acquired because competitors acquired them, with no specific business problem they were procured to solve — the move is to start over with success criteria first. Identify the operational problem, define measurable success, then select technology against those criteria. The tools you have may or may not survive the analysis. Sunk cost is not a strategy.

If workflow is the binding constraint — your governance is reasonable, your culture is permissive, your tools are deployed, but nothing has changed in how work actually gets done — the gap is structural understanding of work itself. Practitioners need to see their own workflows as systems of operations rather than streams of tasks. That capability is not built by tip sheets. It is built by sitting with the work.

Most executives can answer the diagnostic question honestly if asked plainly. The hard part is not the diagnosis. It is committing to the answer.

The line worth remembering

Read McKinsey’s data. Don’t read McKinsey’s recommendation. The tide is rising, the playbook you are being sold will leave you stranded, and the boats that take the tide will be the ones whose hulls were built before the water came up.

There is still time to build. Not much.

The work I publish at AndrewLewisWasHere goes deeper on what AI operational readiness actually looks like inside a regulated industry, where you can’t just deploy and learn. Subscribe and the next piece will land in your inbox.

Anthropic just published a study that quietly inverts how we talk about AI productivity. They paid 52 professional developers to learn a new Python library in 35 minutes. Half got an AI assistant. Half didn’t. Then everyone took the same comprehension quiz, with no AI.

The AI group scored 17% lower. About two grade points. A real effect, not noise — Cohen’s d of 0.738 if you care about the statistics.

That part has been making the rounds. The part that hasn’t is the productivity finding sitting right next to it.

On average, the AI group wasn’t faster.

People stayed about the same total time on the task. They just shifted from coding to interacting with the assistant. Some of them spent six minutes composing a single query in a 35-minute task. Which means the trade most people assume they’re making — accept some skill loss in exchange for speed — often isn’t even on offer. They lost the skill. They didn’t get the speed.

The variance is the story

What makes this study different from the usual productivity-of-AI-tools paper is that the researchers didn’t just measure averages. They watched every screen recording. And they found that the average is hiding something more useful than it’s showing.

There were six distinct ways people used the AI assistant. The researchers split them by quiz outcome. Three patterns scored between 24 and 39 percent on the comprehension test. Three scored between 65 and 86.

The low-scoring patterns share a structure. AI Delegation — handing the whole task over and pasting the result. Progressive Reliance — starting engaged, drifting into delegation as the clock ticks. Iterative Debugging — asking the AI to fix things you don’t understand, over and over.

The high-scoring patterns share a different structure. Conceptual Inquiry — asking the AI questions, but writing the code yourself. Hybrid Code-Explanation — asking for code with the explanation embedded. Generation-Then-Comprehension — getting the code, then asking the AI to teach you why it worked.

You can guess what divides the two halves. Cognitive engagement. Not how much AI was used. Not which tool. Not how skilled the developer was going in. Whether they stayed in the work or stepped out of it.

The participant feedback in the qualitative section is the part that stuck with me. Several developers in the AI group volunteered, unprompted, that they had “felt lazy,” or wished they had paid more attention to the explanations the AI gave them, or noticed afterward that there were “still a lot of gaps in their understanding.” Cognitive offloading from the inside. They could feel it happening in the moment and didn’t stop it, because the task pressure was telling them to keep moving.

Why debugging is the canary

The biggest gap between the groups wasn’t on the conceptual questions. It was on debugging.

The no-AI group hit errors. Trio errors specifically — runtime warnings about coroutines that were never awaited, type errors from passing the wrong kind of object. These errors force you to learn how the library actually works, because you can’t skip past them without forming a mental model. The AI group skipped past most of them. Their code worked the first time, more often than not. They never built the debugging muscle, because they never had to.

This is the part that should make organizations nervous.

The dominant workflow proposal for AI-assisted software development is “AI writes the code, humans review it.” Sometimes packaged as “human in the loop.” It depends on a workforce that can read code well enough to catch what’s wrong with it. But if AI assistance during the formation period systematically removes the error-encounter loop that builds review skill, you’re producing a workforce structurally unable to do the review you’ve designed your safety story around.

That isn’t a future risk. It’s a workflow currently being deployed.

The hidden second clause

The dominant claim about AI productivity has been: AI makes you faster. The fine print, based on this evidence, is — only if you stay cognitively engaged. And the empirical pattern says most people don’t.

There’s a clean way to think about this. AI doesn’t make you better. It amplifies whether you were doing the thinking in the first place.

For people who already engage with their work — who ask why something is structured the way it is, who validate against a model of what good looks like, who treat tools as collaborators rather than dispensers — AI extends what they can do. For people who don’t, AI gives them faster ways to look productive while the underlying skill quietly erodes.

This isn’t a moral observation. It’s a structural one. The patterns that preserved learning in the study weren’t the patterns of people working harder. They were the patterns of people staying in a particular mode of attention.

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What this changes

The implications break in three directions.

For individuals: how you use the tool matters more than which tool you use. Asking for code without asking for explanation is a learning shortcut that costs you compounding interest later. The patterns that work are slower in the moment and pay off in retention.

For teams: if you’re scaling junior contributions with AI, you need to deliberately build the engagement loops back in. Errors are not friction to eliminate. They’re where skill formation actually happens. Removing them feels like productivity. It produces fragility.

For organizations: the AI workflow you’re designing assumes a level of human capability you may be actively eroding through the same workflow. That’s not a contradiction you can prompt your way out of. It’s an architectural one.

The original framing — that AI is a productivity tool — was always slightly off. AI extends the cognitive work you’re already doing. Without that work underneath, the extension produces motion without progress.

AI doesn’t make you better. It exposes whether you were already doing the work.

Read the full Anthropic study: How AI Impacts Skill Formation by Judy Hanwen Shen and Alex Tamkin.

If this resonated, subscribe for more notes from inside a regulated industry trying to operationalize AI without breaking what makes it work.

The dominant narrative about AI and work goes like this: AI handles the production, you handle the thinking, everyone goes home early. It is a clean story. It is also wrong in a way that matters.

A principal engineer at a large telecom posted on Reddit this week about being all-in on agentic coding for two years and thinking about quitting software engineering entirely. Not because the tools don’t work. Because they work too well. The line that stopped me: “The cost of writing code in effort / time was a throttling middleware.”

That phrase deserves to sit for a second. Writing code used to be slow enough that your brain could keep up. The effort of typing, compiling, debugging — all of that created natural pace. You had time to sense when a pattern was wrong. Time to think through the shape of a class or the implications of an architectural choice. The slowness wasn’t a bug. It was cognitive infrastructure.

Now that infrastructure is gone. And this engineer — 13 years of experience, principal level — reports making ten whiteboard-level architectural decisions before his second cup of coffee. Decisions that used to happen once a sprint, maybe twice, gated by the slow, expensive process of actually building things. The dam broke. The decisions didn’t disappear. They accelerated.

The production layer was never the bottleneck you thought it was

When people talk about AI making work faster, they’re usually describing the production layer — the part where raw effort turns into output. Drafting, coding, formatting, researching. And yes, AI compresses that layer dramatically. What nobody accounted for is what happens to the other layer — the judgment layer — when production speeds up by an order of magnitude.

Every piece of AI-generated output requires evaluation. Is this right? Is this good enough? Does this fit the architecture? Does this solve the actual problem or just the surface symptom? Those questions existed before AI, but they arrived at a pace your brain could absorb. Production time was thinking time. The gap between “I need this” and “here it is” gave your mind room to prepare for the decision.

That gap is gone. And the result is not faster work. It’s faster judgment demands on a brain that hasn’t changed speed.

A Boston Consulting Group study published in Harvard Business Review in March 2026 put a name to this: “AI brain fry.” They surveyed 1,488 full-time U.S. workers and found that high AI oversight — the kind that requires reading, interpreting, and evaluating AI output — was associated with 14% more mental effort, 12% greater mental fatigue, and 19% greater information overload. Workers described a fog or buzzing that forced them to physically step away from their screens. One of the study’s authors told Fortune the pattern was consistent: people were getting more done but hitting the limits of their cognitive capacity because there were simply too many decisions to make.

An eight-month study of a 200-person tech firm, led by researchers at UC Berkeley, found the same dynamic from a different angle. AI wasn’t reducing work. It was intensifying it. Employees processed more information, made more decisions, and experienced more burnout — not less — as AI adoption increased.

Decision fatigue is not a new concept. The delivery mechanism is.

Psychologists have studied decision fatigue for decades. The core finding is straightforward: the quality of your decisions degrades as you make more of them. Roy Baumeister’s ego depletion research established that decision-making draws from a finite cognitive resource. Make enough decisions and you start defaulting to heuristics, avoiding trade-offs, or simply deferring. The average American adult reportedly makes around 35,000 decisions a day. Most of those are trivial. The ones that matter are the ones that require actual evaluation.

What AI does is change the ratio. It doesn’t increase the total number of decisions. It increases the density of consequential ones. When production was slow, your day was a mix of low-stakes mechanical work and occasional high-stakes judgment calls. The mechanical work gave your brain recovery time between the hard decisions. It was boring, but it was load-bearing.

Remove the mechanical work and what’s left is a continuous stream of judgment. Architecture choices. Quality assessments. Risk evaluations. Strategic trade-offs. All day. No recovery intervals. The Reddit poster described it precisely: running ten whiteboard-level decisions before morning coffee, decisions that used to be spaced across a sprint. His brain isn’t slower than it was. The demand on it is faster.

We don’t understand the cost of running a judgment machine all day

This is the part almost nobody is talking about. We’ve spent two years celebrating AI’s ability to remove the production burden. We have not spent two minutes thinking about what happens to the human on the other side when the production burden was also a cognitive pacing mechanism.

The Reddit engineer said something else that stuck: “I feel like for the devs that have survived layoff rounds, AI has raised the bar of required skills, not lowered it.” That maps directly to the Jevons Paradox applied to AI — as AI efficiency increases, the demand for human capability doesn’t decrease. It increases. The skills that matter shift upward. The judgment, the architectural thinking, the ability to evaluate quality at speed — those become the job. And the job becomes relentlessly, uninterruptedly hard.

This isn’t a coding problem. It’s a knowledge work problem. Every profession that adopts AI tools effectively will hit this same wall. Lawyers reviewing AI-drafted contracts. Financial analysts evaluating AI-generated models. Marketers assessing AI-produced campaigns. The production layer compresses. The judgment layer concentrates. And the person in the middle has to run their brain at a sustained intensity that the old workflow never required.

We don’t have infrastructure for this yet. We don’t have pacing strategies. We don’t have cognitive load frameworks adapted for AI-augmented work. We don’t even have language for the problem — which is why “brain fry” and “throttling middleware” resonate so immediately. People recognize the feeling before anyone names it.

The work behind the work just got more urgent

The conventional response to this problem will be training. Run a workshop on managing AI output. Distribute a tip sheet on decision prioritization. That approach will fail for the same reason it always fails — it treats the symptom without touching the structure.

The actual work is harder than a workshop. It’s developing the ability to see your own workflow clearly enough to know which judgments matter and which don’t. To calibrate your trust in AI output so you’re not re-evaluating everything at full intensity. To build the signal discrimination that lets you spot the 5% of output that needs real attention and let the rest move.

That is not a training problem. That is a capability development problem. And it’s one that gets more urgent, not less, as the tools get faster.

Your brain was always a judgment machine. AI just made it the only machine that matters.

I'm writing from inside a regulated firm doing AI adoption in real time. Every week I publish what I'm seeing — the frameworks, the friction, the decisions that actually move work. If that's useful to you, subscribe. It's free, and I'll send you the next one.

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Every enterprise AI dashboard in the world has an adoption number on it. Seats activated, prompts per user, tools deployed, percentage of workforce with access. The number goes up every quarter. The board is pleased. The CIO presents it with confidence.

And almost none of it tells you whether AI is actually working.

The metric everyone loves is the metric that matters least

Adoption measures activity. Someone logged in. Someone typed a prompt. Someone opened Copilot and asked it to summarize an email they could have read in thirty seconds. All of that registers as adoption.

What it doesn’t measure is whether anyone’s work changed. Whether a contract review that used to take four hours now takes ninety minutes because the lawyer iterated through three rounds of AI-assisted markup. Whether the finance team built an automated reconciliation workflow or just asked ChatGPT to explain what a pivot table does. Whether the senior associate used AI to surface a pattern across two hundred documents that no one would have caught manually — or whether they used it to rewrite a slightly awkward email.

Those are fundamentally different behaviors. One is sophisticated use. The other is expensive autocomplete. And the adoption dashboard doesn’t distinguish between them.

The evidence arrived this week, from three directions at once

A Wharton School study tracking enterprise AI adoption over multiple years found a widening disconnect between executive enthusiasm and managerial reality. Nearly two-thirds of executives report becoming significantly more optimistic about AI over the past year. The managers implementing those same tools inside actual workflows report something closer to frustration. They see the constraints. They carry the operational burden. They don’t feel supported.

A separate survey of 2,400 knowledge workers found that 29% admit to actively undermining their company’s AI rollout. Among Gen Z workers, that figure reaches 44%. The tactics include feeding proprietary data into unapproved tools, refusing to complete AI training, and in some cases deliberately producing poor-quality output to make the tools look bad.

And a third study, from HFS Research, found that only 14% of enterprises have a clear AI strategy at all. The other 86% are deploying tools into a vacuum — no framework for what good use looks like, no feedback loop for what’s working, no definition of success beyond the adoption dashboard.

These aren’t three separate problems. They’re three symptoms of the same one: organizations measuring the wrong thing, at the wrong level, and mistaking activity for progress.

What sophistication actually measures

The Conversation Sophistication Score is a framework built on research from KPMG and the University of Texas at Austin. The study analyzed 1.4 million AI interactions across 2,500 employees and identified thirty behavioral characteristics that separate the highest-performing AI users from everyone else.

The finding that matters: sophistication isn’t correlated with frequency. The people using AI most often aren’t the ones using it best. The distinguishing behaviors are things like interaction depth (multi-turn conversations that build on previous outputs), task complexity (applying AI to genuinely difficult problems rather than simple lookups), iterative reasoning (treating AI output as a draft to be refined, not an answer to be accepted), breadth of application (using AI across multiple domains rather than one narrow use case), and fluency signals (adapting communication style and prompt structure to the specific task).

None of those show up on an adoption dashboard. You can have 95% adoption and 10% sophistication, and your metrics will tell you everything is going great until it very clearly isn’t.

The sequence that actually works

The enterprises getting AI right aren’t doing anything exotic. They’re solving the structural problems before chasing the visible ones. They define what good AI use looks like before measuring whether it exists. They build governance — not as a compliance exercise but as a shared understanding of what’s allowed, what’s encouraged, and what’s off-limits. They create conditions where experimentation is safe and failure is data, not career risk.

And then — after the structure exists — they start measuring sophistication alongside adoption. Not instead of it. Alongside it. Because adoption without sophistication is just expensive access. And sophistication without adoption means you have a few brilliant users surrounded by a workforce that’s opted out.

The 14% of organizations that have a clear strategy? They’re the ones building this foundation. The other 86% are wondering why their adoption numbers keep climbing and their results don’t.

The dashboard isn’t broken. The measurement is.

If you found this useful, consider sharing it with someone leading an AI rollout right now. They probably have an adoption number. They probably don’t have a sophistication score. That gap is the article.

Something odd is happening inside organizations right now, and it showed up clearly in a study the Wharton School published this week. The executive suite and the management layer are both talking about AI. They’re using similar language. They’re attending the same town halls and reading the same strategy decks. But they are not having the same conversation.

Nearly two-thirds of executives say they’ve become significantly more positive about AI over the past year. They see it as a strategic priority. They’re investing heavily. Some are restructuring their organizations around it. For senior leadership, AI has moved from an interesting capability to an existential commitment — the question isn’t whether to go all in, it’s how fast.

The managers one or two levels below them? They’re drowning.

The gap isn’t disagreement — it’s altitude

The Wharton/GBK Collective study has been tracking this dynamic for multiple years, and the pattern is consistent: executives experience AI as an opportunity. Managers experience AI as a workload.

That’s not because managers are resistant or uninformed. It’s because they sit at the exact altitude where strategy becomes operations. They’re the ones who have to reconcile the CEO’s enthusiasm with the fact that the approved tool doesn’t integrate with the case management system. They’re fielding questions from their teams about what’s allowed and what isn’t — often without clear answers, because the governance policy is still being drafted. They’re absorbing the productivity overhead of learning new tools while still delivering on every existing deadline.

Executives don’t see this overhead because it doesn’t appear in the metrics they review. Adoption dashboards show seats activated and usage trends. They don’t show the manager who spent three hours this week answering the same AI governance question from four different direct reports because the policy FAQ doesn’t exist yet.

The executive sees a line going up. The manager feels the weight behind that line.

Where the pressure compounds

This altitude gap produces a specific set of downstream problems that I see repeatedly from inside an organization going through this.

The first is unfunded mandates. Leadership communicates that AI adoption is a priority. But the time, training, and governance infrastructure required to adopt responsibly aren’t budgeted separately. They’re absorbed by the existing management layer on top of everything else. The implicit expectation is that managers will figure it out — will become AI champions in addition to their actual roles, without reduced workloads or additional resources.

The second is phantom consensus. Strategy decks present AI adoption as an aligned organizational priority. Everyone nods in the planning meeting. But alignment at the strategy level doesn’t mean alignment at the implementation level. The manager who nodded in the meeting goes back to a team that has no idea what’s expected, using tools that half-work, under policies that are still in draft. The strategy deck says “aligned.” The floor says “confused.”

The third is consequence asymmetry. The Fortune/Workplace Intelligence survey found that 60% of executives would consider cutting employees who refuse to adopt AI. But only 14% of enterprises have a clear AI strategy. Executives are prepared to enforce adoption of something they haven’t fully defined. The consequence falls on the people closest to the work, while the strategic ambiguity originates at the top.

What the sabotage data is actually telling us

This is where the 44% Gen Z sabotage number from this week’s headlines becomes less scandalous and more predictable.

When managers are unsupported, their teams feel it. The confusion rolls downhill. If a manager doesn’t have clear governance guidance, their team gets inconsistent answers about what’s allowed. If a manager hasn’t been given time to understand the tools, they can’t coach their team on effective use. If a manager is overwhelmed by the implementation burden, their team reads that stress and interprets it — correctly — as a signal that AI adoption is creating problems, not solving them.

The 26% of sabotaging employees who say the strategy is poorly executed aren’t making an abstract complaint. They’re reporting what they observe at the manager level every day: pressure without support, mandates without clarity, consequences without strategy.

The sabotage isn’t coming from below. It’s flowing downhill from above.

The intervention that most organizations skip

The conventional response is more training, better tools, clearer communication from leadership. Those help. But they miss the structural problem.

The structural intervention is resourcing the middle. Giving managers dedicated time for AI governance and enablement work. Reducing their operational load during the adoption period rather than adding to it. Creating feedback channels that surface implementation friction to leadership before it calcifies into resistance.

The organizations I’ve seen move fastest on AI adoption aren’t the ones with the biggest budgets or the most ambitious CEOs. They’re the ones where middle management has actual capacity to do the work that adoption requires — the unglamorous, invisible, operational work of translating executive vision into something a team of eight people can actually execute on a Tuesday afternoon.

That work doesn’t appear on an adoption dashboard. It doesn’t generate a conference keynote. But it’s the difference between a strategy that lands and one that produces a 44% sabotage rate.

The executives and the managers aren’t enemies. They’re not even disagreeing. They’re just standing at different altitudes, describing different views of the same mountain — and nobody’s built the trail between them.

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I made a video this week breaking down the full research — the sabotage data, the Wharton findings, and a framework for measuring sophistication instead of activity.

Most organizations measure AI adoption the way they measure gym memberships. How many people signed up. How often they swipe in. Maybe how long they stay. None of which tells you whether anyone is actually getting stronger.

The AI equivalent: prompt counts, hours logged, tokens consumed, self-assessed skill levels. These numbers are easy to collect, and at most companies they look encouraging. Adoption is up. Usage is growing. The dashboards are green.

But adoption is not sophistication. And the gap between the two is enormous.

What the Research Actually Shows

KPMG and researchers at the University of Texas at Austin spent eight months studying this gap. They analyzed 1.4 million AI prompts from roughly 2,500 professionals — not surveys, not self-reports, but actual conversation logs at scale. The question was simple: what does sophisticated AI use look like, and how do you tell it apart from routine use?

The headline finding: about 90% of employees used AI regularly. Only approximately 5% used it in ways that generated differentiated value. That’s a 17:1 ratio between adoption and sophistication, and most organizations can’t see it because they’re measuring the wrong dimension entirely.

The researchers identified four behavioral patterns that consistently predicted sophisticated use. Not prompt length. Not frequency. Behaviors: treating AI as a reasoning partner rather than accepting first outputs, delegating complex multi-step tasks with clear constraints, applying AI across diverse task types instead of just writing assistance, and sustaining longer working-session-style interactions.

Here’s the part that caught me off guard. The most sophisticated users weren’t the youngest employees — they were above manager level. The conventional wisdom says junior employees are more natural with these tools. The data says otherwise. There’s a real difference between being comfortable with AI and being good at getting results from it. Comfort is about familiarity. Sophistication is about judgment.

The Problem with Averages

When I started building a scoring framework from this research, I ran into an interesting design problem. A weighted average of behavioral dimensions sounds clean, but it lies in predictable ways.

Consider someone who writes long, detailed initial prompts and sustains multi-turn conversations. Their Interaction Depth score is high — maybe an 8 or 9. But they never refine outputs. Never push back. Never ask the model to check its reasoning or explore alternatives. Their Iterative Reasoning score is a 2.

A weighted average might land them at “Proficient.” But they’re not proficient. They’re just verbose. The length of the prompt isn’t the signal. What the user does with the output is.

This is why the framework I built includes gating criteria — floor rules that prevent misclassification. You can’t reach the Advanced tier unless both Task Complexity and Iterative Reasoning hit at least 7 out of 10, regardless of what your weighted average says. Those two dimensions are the strongest differentiators in the research, and they carry 55% of the total score.

The gating mechanism is the single most useful idea in the framework. It forces honest measurement.

What This Means for How You Train

The dimension-level data tells you something activity metrics never can: where to invest in training.

If Iterative Reasoning is consistently low across your organization, another “intro to prompting” workshop won’t help. The gap isn’t in how people write prompts — it’s in how they think about the interaction. They need to learn to treat AI as a reasoning partner: assign roles, provide examples, test assumptions, ask the model to verify its own logic.

If Task Complexity is low, the problem is different. People aren’t delegating hard enough. They’re using AI for tasks they could do themselves in roughly the same time, instead of delegating the genuinely complex, multi-step work where AI creates real operating margin.

The dimension scores give you a specific diagnosis. The diagnosis gives you a specific intervention. That’s the difference between “use AI more” and “here’s what to change about how you use it.”

The Uncomfortable Implication

If only 5% of users are sophisticated at a firm where 90% are active — a firm that had invested heavily in AI tools and training — then sophisticated use doesn’t happen organically. Making tools available and running training sessions gets you to 90% adoption. It does not get you to sophistication.

Getting there requires measuring the right things, making specific behaviors visible and expected, and building the feedback loops that help people see the gap between where they are and where they could be. Activity metrics can’t do that. Behavioral metrics can.

You can’t operate what you can’t measure. And right now, most organizations are measuring the equivalent of gym swipes.

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Every few weeks, a new report lands cataloguing the barriers to enterprise AI adoption. Poor data management. Incomplete governance. Lack of training. Unclear ROI. Resistance to change.

No Jitter published one this week. BCG has one. McKinsey’s latest State of AI survey says 88% of organizations report using AI in at least one business function — but nearly two-thirds haven’t begun scaling it across the enterprise.

An HBR piece from earlier this year put a finer point on it: AI initiatives stall not because the technology fails, but because employees’ anxiety about relevance, identity, and job security drives surface-level adoption without real commitment.

None of this is new. And that’s exactly the problem.

We’ve been listing the same barriers for two years. The list hasn’t changed. Which raises an uncomfortable question: if we know what the barriers are, why haven’t we removed them?

The Barrier List Is Actually a Job Description

I lead AI enablement and delivery at a large Canadian law firm. My charter covers generative AI governance, production infrastructure decisions, cross-functional technology guidance, and organizational design for autonomous delivery teams.

When I read reports listing the top barriers to AI adoption, I don’t see obstacles. I see my to-do list.

Data readiness? That’s the conversation with InfoSec about what categories of data we can and can’t share with AI tools — and under what conditions. It’s the ongoing discussion about zero data retention and data residency requirements that shapes every infrastructure decision we make. And these aren’t theoretical concerns. The U.S. CLOUD Act allows American law enforcement to demand data held by U.S.-based providers regardless of where that data is physically stored. A Canadian firm using a U.S.-hosted AI tool with client data on a Montreal server is still exposed. When a Microsoft executive told the French Senate under oath in June 2025 that he couldn’t guarantee European data would be safe from U.S. authorities, the same logic applies to every law firm using a U.S.-owned cloud AI service.

Governance gaps? That’s the policy work — figuring out what guardrails are necessary when you’re building internal infrastructure like MCP servers that connect AI tools to live systems. The answer changes as the tools evolve, which means the governance has to evolve with it.

The other barriers on the list — training, middle management alignment, cultural resistance — those show up too. Every organization doing this work recognizes them. The question isn’t whether they’re real. It’s whether anyone is resourced to actually address them.

The barrier list isn’t a research finding. It’s a scope of work.

The Real Reason the List Doesn’t Change

From the inside, the pattern is pretty clear: the barriers persist because removing them requires a kind of work that most organizations don’t want to fund, staff, or prioritize.

Governance work is slow, political, and unglamorous. Nobody gets promoted for writing an AI acceptable use policy. Nobody gets a conference keynote for building a data classification framework. These are infrastructure projects — essential, invisible, and easy to defer.

Training is continuous, not one-shot. You can’t run a lunch-and-learn in Q1 and call it done. AI tools change. Use cases evolve. New risks emerge. Training has to be ongoing, role-specific, and practical. That takes dedicated time from people who are already stretched.

Middle management is where adoption lives or dies. A Workera analysis from earlier this year noted that most adoption bottlenecks sit in the middle of the organizational chart — managers who struggle to set expectations for AI-assisted work, don’t know what “good” looks like, and avoid the topic because it raises uncomfortable questions about headcount and value.

And cultural resistance isn’t about employees being Luddites. The HBR research showed it’s really about identity — people worry that using AI makes them look replaceable, or that their expertise is being devalued. You can’t train your way out of that. It requires sustained, honest messaging about what AI actually changes and what it doesn’t.

Every one of these barriers is addressable. None of them is easy. And most of them require organizational commitment that goes well beyond the technology team.

Where the Work Actually Lives

When I look at the barrier list through the lens of what has to happen, the work sorts itself pretty naturally.

Some of it you can systematize. Governance templates, usage policies, risk assessment frameworks, data classification standards — these are repeatable artifacts. Build them once, adapt across the organization. AI can even help with its own adoption here. Use it to draft the policies, generate training materials, structure rollout plans.

Some of it requires people working alongside people. Helping managers evaluate AI-assisted work. Coaching teams on where AI fits their specific workflows. Having the uncomfortable conversations about what changes when a task that used to take four hours now takes forty minutes. You can support this with tools and structured conversations, but you can’t skip the human part.

And some of it you just have to do the slow way. Building trust. Shifting culture. Showing through consistent action that efficiency gains won’t quietly become headcount cuts. That the goal is better work, not cheaper workers. No technology accelerates this.

Most organizations want to spend their AI budget on the first kind. The actual work is mostly the second and third.

The Patience Problem

There’s an additional dynamic that the barrier reports don’t capture: the gap between executive expectations and organizational readiness.

Leadership sees the reports about AI productivity gains. They hear the vendor pitches. They attend the conferences. They come back wanting to know why the organization isn’t moving faster.

The honest answer is: because we’re doing the barrier removal work. And that work doesn’t produce demo-ready results on a quarterly cadence.

Governance frameworks aren’t impressive in a board presentation. Training programs don’t generate viral LinkedIn posts. The slow, steady work of building organizational readiness for AI is the least visible and most important investment a company can make right now.

The organizations that are furthest along in AI adoption aren’t the ones that skipped the barrier work. They’re the ones that started it earlier and funded it properly. They invested in the boring infrastructure while everyone else was running pilot programs.

What Would Actually Change Things

The next time one of these reports drops, resist the urge to nod along and move on.

Instead, ask: which of these barriers are we actively working to remove? Who owns each one? What resources are behind it? What does progress look like on a 90-day horizon?

If you can’t answer those questions, the barrier list isn’t research. It’s a mirror.

These barriers have been known for two years. They’ll still be known next year if the only response is acknowledging them in another planning deck.

Somebody has to do the work. In most organizations, that role either doesn’t exist yet or doesn’t have the authority to act.

That’s the barrier nobody puts on the list.

I’d be curious to hear which barrier your organization spends the most time acknowledging and the least time actually working on.

There’s a term making the rounds in enterprise circles right now: shadow AI.

If you work in security or IT governance, you probably hear it as a threat. Employees using ChatGPT on personal accounts. Pasting client data into tools nobody approved. Building little automations that nobody knows about until something breaks.

And yes — that’s real. The data on it is pretty stark. Microsoft’s 2024 Work Trend Index found that 75% of knowledge workers now use AI at work, and 78% of them are bringing their own tools — what Microsoft calls “BYOAI.” A BlackFog survey found roughly half of workers admit to using AI tools without employer approval. A KPMG study puts it even higher — 57% say they’ve submitted AI-generated work as their own without telling their manager.

But the part most of the coverage misses is that people don’t hide their AI use out of malice. They hide it because nobody told them it was okay. No guidance, no approved tools, no one saying “yes, use this — here’s how to do it without creating a mess.”

The View From Inside

I run AI enablement and delivery at a large Canadian law firm. When I say “run,” I mean I’m the person in the room when we decide what tools to approve, how to govern their use, and what happens when someone finds a shortcut we didn’t anticipate.

And I can tell you: shadow AI doesn’t start with rebellion. It starts with silence.

An associate finds that Claude drafts better first-pass research memos than starting from a blank screen. A paralegal discovers that summarizing long contracts is faster with a chat interface than with manual notes. A business services team member figures out that AI can write a decent first draft of an internal communication in a quarter of the time.

None of these people are trying to break rules. They’re trying to do their jobs. The organization just hasn’t caught up to the speed at which these tools became useful.

Most of the articles about shadow AI treat it like a leak to plug. Install monitoring software. Ban unsanctioned tools. Train people on the risks.

That framing has it backwards.

Shadow AI is what happens when your governance moves at committee speed and your employees move at tool speed. The gap between those two speeds is where unofficial use grows — not because people are reckless, but because the formal path either doesn’t exist or takes six months.

The Demand Signal

After watching this unfold inside a conservative institution, one thing keeps standing out:

The people using AI in secret are your early adopters. They already see the value. They’ve figured out where it fits. They’re exactly who you want on your side when it’s time to roll something out for real.

When you discover shadow AI, you have two options.

The first option is containment. Lock down access, monitor usage, send a compliance memo. This is the default in regulated industries, and it’s understandable. The risks are real — confidentiality, privilege, data residency. In legal, putting client information into an unapproved tool isn’t just a governance violation. It’s potentially a professional conduct issue. In February 2026, Judge Rakoff in United States v. Heppner ruled that documents a defendant created using a consumer version of Claude were neither privileged nor work product — privilege was gone the moment he hit enter on a public AI tool. That’s the kind of consequence that makes CISOs lose sleep.

And this isn’t hypothetical exposure. Clio’s 2025 Legal Trends Report found that 79% of legal professionals now use AI, but more than half say their firm has no AI policy or they’re unaware of one. That gap between usage and governance is where the risk concentrates.

The second option is acceleration. Take the signal seriously. Ask: what are people actually using AI for? Where is the demand highest? What would it take to offer an approved path that’s fast enough that people actually use it instead of going around it?

In my experience, the second option works better. Not because the risks don’t matter — they absolutely do — but because containment without an alternative just pushes usage further underground. People don’t stop using the tool. They get better at hiding it.

What the Enablement Response Actually Looks Like

When we discover unofficial AI use, the first conversation isn’t “you shouldn’t have done that.” It’s “tell me about your workflow.”

That conversation usually tells you more than any audit would.

The use case is almost always reasonable. People aren’t using AI to cut corners on judgment calls. They’re using it to speed up the mechanical parts — drafting, summarizing, organizing, reformatting. Work that takes time but doesn’t require expertise.

The risk is usually more contained than you’d expect. Someone using AI to draft an internal email isn’t creating the same exposure as someone pasting client financials into a public model. Understanding the actual risk surface matters more than treating all AI use as equally dangerous.

And the gap between what people want and what the organization provides is almost always about governance, not technology. We have access to enterprise AI tools. The problem is that the approval process, the usage policies, and the training haven’t kept pace with how fast people figured out the tools are useful.

Once you understand those three things, the path forward is pretty clear: fast-track the governance for the use cases people are already doing. Don’t build the perfect policy. Build the minimum viable policy that lets people work safely, and iterate from there.

The Uncomfortable Part

There’s something else going on that the shadow AI conversation mostly avoids: the reason employees hide their AI use isn’t always about missing policies.

Sometimes it’s about culture.

Some people hide AI use because they think their manager will see it as cheating. Some worry they’ll look less skilled — that if the work is partially AI-generated, it somehow doesn’t count. Others are afraid that admitting they use AI to work faster just means they’ll be assigned more work at the same pay.

That’s a leadership problem, not a technology one.

If your culture treats AI use as a confession rather than a competency, you’ll get secrecy. If your managers don’t know how to evaluate AI-assisted work, they’ll default to suspicion. And if your organization’s implicit message is “we’ll adopt AI eventually, but not yet,” your employees will hear “figure it out yourself and don’t tell anyone.”

The organizations that handle this well share a common trait: they treat AI use as a skill to develop, not a shortcut to police.

So What Do You Do With This?

Shadow AI isn’t a crisis to manage. It’s evidence that the transformation you’ve been planning is already happening — just without your involvement.

The question isn’t how to stop people from using AI. That ship sailed. The question is whether you can make the official path better than the workaround.

Because right now, in most organizations, the workaround is faster, easier, and more accessible than whatever the IT governance committee approved nine months ago.

If you’re responsible for AI adoption in any capacity, the existence of shadow AI should feel encouraging. The demand is real. The use cases are already proven. You don’t need to manufacture an adoption curve.

You just need to catch up to your own people.

What does shadow AI look like in your organization — and is the response containment, acceleration, or something in between?

I’ve spent the previous five articles in this series describing what I saw and heard at LegalWeek — the themes, the operating model shifts, the tactics that are working, and the hard conversations that aren’t happening yet.

This final piece is different. It’s the “so what” — the part where observation turns into action. I’ve organised it into three horizons based on urgency, complexity, and how much organisational consensus each move requires.

Do Immediately

These are moves that require minimal approval, no structural change, and can start generating signal within weeks.

Shift AI training to task-based, simulated matters. If your training programme still starts with “here are the features of this tool,” stop. Identify the ten to twenty most repeatable legal tasks in your practice groups and build training around those. Use realistic simulated matters with time pressure and incomplete facts. Measure both time saved and — critically — confidence gained. The confidence metric is what tells you whether adoption will stick.

Publish usage metrics by practice group. Take whatever AI tool usage data you have — Harvey, CoCounsel, whatever — and make it visible across the firm, broken down by practice group. Don’t attach mandates to it. Don’t set targets. Just publish the numbers and let the peer dynamics do the work. This is the lowest-cost, highest-signal adoption tactic I encountered at the entire conference.

Stand up a practice group AI roundtable. One representative from each practice group. Each brings one real use case. No slides, no vendor demos, no keynotes. The constraint is what makes it work: “one real use case” forces specificity, keeps sessions short, and creates the social proof that training programmes can’t replicate.Do Next

These require more coordination and some cross-functional buy-in, but they’re achievable within a quarter.

Formalise AI risk tiers and embed them in system prompts. If your AI policy is a standalone document that lives on an intranet page, it’s time to encode it. Define risk tiers for different use cases. Map approved tools to those tiers. Then embed the guardrails directly into the system-level prompts that shape how AI tools behave for your lawyers. Governance that’s invisible when done right is governance that actually works.

Map shadow AI usage to unmet needs. Talk to your IT or security team about which AI sites lawyers are visiting outside the firm’s approved tools. Don’t approach this as a compliance exercise. Approach it as market research. The patterns will tell you exactly where the gap is between what you provide and what practitioners actually need. That’s your next pilot programme.

Standardise vendor intake to reduce cycle time. Audit how long it takes a new AI tool to move from initial interest to production deployment. If the answer is measured in months, figure out where the bottleneck is — legal review, InfoSec, procurement — and build a repeatable, transparent process with target timelines for each stage. Speed of experimentation is a compounding advantage.Decide This Year

These are strategic decisions that require partnership-level conversation and real organisational commitment. They can’t be delegated to a working group or deferred to next year’s strategy cycle.

Where specialist AI talent fits. If your AI adoption effort is staffed entirely by repurposed knowledge management professionals and a single innovation lead, you need to have an honest conversation about what’s missing. Builders — people with technical depth who can also navigate firm politics — are a different kind of hire. Decide where they sit, who they report to, and what success looks like for them. Enablement without builders stalls.

How pricing reflects AI leverage. If AI is cutting the time on certain tasks by 60% or more, your pricing model is drifting out of alignment with the value you deliver and the efficiency you’ve gained. You may not need to overhaul your pricing framework this year, but you need to start the conversation — because clients are going to start it for you if you don’t.

Whether compensation rewards AI leadership — or punishes it. This is the decision that reveals whether everything else is real. If your compensation framework doesn’t recognise AI adoption, innovation leadership, or efficiency gains, then those things aren’t strategic priorities. They’re aspirations. Your compensation plan is your strategic plan. If AI doesn’t show up there, it isn’t real.

The Series in Review

Over these six articles, I’ve tried to describe what LegalWeek 2026 revealed about where law firm AI adoption actually is — not where the marketing says it is, but where the honest conversations are happening.

The tools are everywhere. Differentiation is gone. The constraint is now organisational: habits, trust, incentives, process, talent, and pricing. The firms that treat AI as a technology problem will keep stalling. The ones that treat it as an operating model problem will keep moving.

None of this is easy. But the path is clearer than it was a year ago, and the firms that take it seriously have a window to build a real advantage before the rest of the market catches up.

Andrew is a Director of AI and Innovation at a large Canadian law firm. He writes about what AI adoption actually looks like from inside the institution.

LegalWeek had plenty of optimism. Plenty of impressive demos. Plenty of panels about what’s working.

But the most useful conversations were the ones about what’s not working — and more specifically, about the structural problems that firms acknowledge privately and avoid publicly. Three stood out.

The Talent Gap Nobody Wants to Name

Most firms are now training lawyers on AI. That’s genuine progress. Two years ago, most weren’t.

But training lawyers to use AI tools is not the same thing as building the capability to push AI forward. Firms are investing in enablement — training, change management, communications — while underinvesting in the specialist talent needed to design, build, and evolve AI-powered workflows.

The pattern looks like this: you hire a Director of Innovation, or you expand the knowledge team, and you expect that small group to handle everything from tool evaluation to prompt engineering to strategic planning. It works for a while. Then it doesn’t, because enablement without builders stalls. You can train a thousand lawyers to use a tool, but somebody has to decide which tool to deploy, how to configure it, how to integrate it into existing systems, and how to iterate when the first version doesn’t work.

That “somebody” is a different kind of hire than most firms are comfortable making — someone with technical depth who can also navigate the political and cultural realities of a partnership. The talent market for those people is thin, and firms that wait too long to hire them will find that the gap between their ambitions and their capabilities keeps widening.

I feel this tension in my own work. The scope of what needs doing grows faster than the team. And the solution isn’t to work harder — it’s to build a team with the right mix of skills, which requires a hiring conversation that most firms haven’t had yet.Pricing Is Misaligned and Clients Will Notice First

Here’s the second hard conversation: AI is improving speed and leverage. In many cases, work that used to take forty hours now takes ten. That’s great for efficiency. It’s terrible for a business model built on billing hours.

The pricing models at most firms haven’t caught up. And here’s the uncomfortable part — clients are going to notice before firms do. A client who knows that AI-assisted contract review takes a fraction of the time it used to is going to ask why the invoice looks the same.

This isn’t a future problem. It’s a present one. The firms that address it proactively — by experimenting with fixed-fee arrangements, value-based pricing, or at minimum transparent disclosure of AI-assisted work — will build client trust. The ones that don’t will face the conversation on the client’s terms, which is always worse.

I don’t have a clean answer here. Nobody at LegalWeek did either. But the acknowledgment that this conversation is overdue was widespread, and the firms thinking about it seriously tend to be the same ones that are ahead on adoption more broadly.Compensation Is the Reveal

The third conversation is the one that separates firms that are serious about AI from firms that are performing seriousness.

Almost no firms — very few, maybe a handful — are aligning compensation with AI adoption, innovation leadership, or efficiency gains. Everyone agrees they should. Almost nobody has done it.

This matters more than it might seem, because compensation is the only signal that the partnership structure actually respects. You can issue all the strategy memos you want. You can create innovation committees and AI task forces and digital transformation programmes. But if the compensation framework doesn’t reward the behaviour you say you value, then the behaviour won’t change.

Your compensation plan is your strategic plan. That’s not a slogan. It’s a diagnostic. If AI adoption, innovation leadership, and efficiency gains don’t show up in how people are paid, then those things aren’t strategic priorities — they’re talking points.

The firm that figures this out first — that creates a credible, measurable link between AI-driven performance and partner compensation — will have an enormous talent and adoption advantage. Everyone else will keep having the same conversation at next year’s conference.

The Common Thread

All three of these problems — the talent gap, the pricing misalignment, and the compensation question — share a root cause. They require firms to change something structural about how they operate, not just add something new on top.

Adding a tool is easy. Rewriting a compensation framework is hard. Building a vendor intake pipeline is manageable. Rethinking pricing is existential. Hiring an enablement team is safe. Hiring builders is unfamiliar.

The firms that move on these hard conversations won’t do it because it’s comfortable. They’ll do it because the alternative — stalling out at the enablement stage while competitors build real capability — is worse.

Next in this series: A practical takeaway framework — what to do immediately, what to do next, and what to decide this year.

Andrew is a Director of AI and Innovation at a large Canadian law firm. He writes about what AI adoption actually looks like from inside the institution.

There’s a persistent assumption in law firm leadership that AI adoption needs to be mandated. Pushed from the top. Measured by compliance. Enforced through policy.

LegalWeek offered a different model — one that’s simpler, cheaper, and apparently more effective.

Make usage visible. Then get out of the way.

Transparency as Adoption Strategy

Several firms described a version of the same tactic: they publish AI usage data — often Harvey adoption metrics, but sometimes broader tool engagement numbers — broken down by practice group.

That’s it. No mandate. No minimum usage requirement. No punitive measures for groups that lag behind. Just the data, visible to everyone.

What happens next is predictable if you’ve ever worked in a partnership. Competitive dynamics kick in. The corporate group sees that litigation is running at 40% adoption. The IP group sees that employment is outpacing them. Partners start asking their associates why the numbers look the way they do.

This creates three effects simultaneously: friendly competition, peer pressure, and organic knowledge sharing. The groups that are ahead start fielding questions from the groups that aren’t. The knowledge doesn’t flow through a central training programme — it flows through the firm’s natural social architecture.

I find this approach compelling because it respects how law firms actually work. Partnerships are peer-driven organisations. Top-down mandates create resistance. Visible data creates conversation. And conversation, in a partnership, is how things change.Practice Group Roundtables

The second tactic that came up repeatedly is even lower-ceremony: practice group AI roundtables.

The structure is minimal. You convene representatives from each practice group. Each group shares one real use case — something they’ve actually done with AI, not something theoretical. The group discusses what worked, what didn’t, and whether the use case is transferable.

That’s the whole format. No keynotes. No vendor demos. No slides. Just practitioners telling other practitioners what they tried and what happened.

The value here isn’t in any single use case. It’s in the normalisation. When a senior partner in real estate describes using AI to draft lease abstracts, that gives the cautious partner in tax permission to try something similar. Use cases are the social proof that no amount of training material can replicate.

I’ve been experimenting with a version of this in my own context, and the constraint that makes it work is the specificity requirement. “One real use case” forces people past the theoretical and into the concrete. It also keeps the sessions short, which means people actually show up.

What’s Actually Happening Here

Both of these tactics — usage transparency and roundtables — are doing something that top-down adoption programmes struggle with. They’re creating social conditions where using AI becomes the norm rather than the exception.

That’s a fundamentally different approach from the standard playbook. The standard playbook says: train people, give them access, and measure adoption. These tactics say: make the behaviour visible, create peer contexts where it’s discussed, and let the social dynamics of the firm do the rest.

It won’t work everywhere. It won’t work for every practice group. And it won’t replace the need for solid training, good tooling, and clear governance. But as a complement to those things — as the social layer that makes everything else stick — I haven’t seen anything more effective.

No mandate required. Just sunlight.

Next in this series: The hard conversations firms are avoiding — on talent, pricing, and the compensation question nobody wants to answer.

Andrew is a Director of AI and Innovation at a large Canadian law firm. He writes about what AI adoption actually looks like from inside the institution.

There’s a particular flavour of adoption advice that shows up at every legal technology conference. It sounds like: “You need executive sponsorship, a clear communications plan, and training resources.” It’s not wrong. It’s just not useful. Everyone already knows that. The question is how — and LegalWeek this year actually had some answers worth paying attention to.

Start With the Task, Not the Tool

The most consistently cited training model across sessions was what I’d call task-first AI training. The structure is straightforward, even if the execution is hard:

Identify the ten to twenty most repeatable legal tasks in your practice groups. These aren’t the edge cases. They’re the drafting, review, summarisation, and research patterns that eat up the majority of associate and junior partner time. Then build your training around those tasks — not around the AI tool’s feature set.

The practical version looks like this: you drop a lawyer into a simulated matter. Time pressure, incomplete facts, realistic constraints. The AI tool is available, but so is everything else. The goal isn’t to teach the tool. The goal is to measure whether the lawyer can produce better work, faster, when AI is part of their workflow.

Firms doing this are tracking two things: time saved and confidence gained. The second one matters more than people think. A lawyer who finishes a task 30% faster but doesn’t trust the output will stop using the tool within a month. Confidence is the leading indicator that sticks.The System Prompt Is the New Policy Document

Here’s something that wouldn’t have made sense two years ago: firms are embedding their AI policies directly into system prompts.

They’re formalising AI policies, defining risk tiers, listing approved use cases — and then encoding those rules into the system-level instructions that shape how AI tools behave for their lawyers. The result is governance that feels invisible when done right. Instead of a PDF that nobody reads, you get guardrails that are baked into the tool itself.

This is a meaningful evolution. It’s the difference between telling people the rules and engineering an environment where the rules are hard to break. I find this approach far more promising than the compliance-training model most firms default to, because it shifts the burden from the individual lawyer to the system design.

Shadow IT Is Signal, Not Threat

This was my favourite insight of the conference, and the one I think has the most untapped potential.

Firms are starting to track which AI sites their lawyers are visiting. Not to police them — to understand what problems those lawyers are trying to solve that the firm hasn’t enabled yet.

Think about what that data tells you. When a corporate associate is spending time on ChatGPT drafting contract summaries, that’s not a compliance failure. That’s a product requirement. It’s telling you exactly where the gap is between what the firm provides and what practitioners actually need.

The question to ask isn’t “how do we stop this?” It’s “what are people trying to do that we haven’t enabled?”

I’ve started looking at shadow AI usage in my own context through this lens, and it’s clarifying. The patterns don’t lie. When you see clusters of unsanctioned tool use around a particular task or practice area, you’ve found your next pilot programme.AI Communications Are Changing Format

One more tactical note that’s worth flagging: the way firms communicate about AI internally is shifting away from long-form memos and town halls toward short, consumable formats.

The firms getting traction are producing three-minute, reel-style videos and plain-language explainers. That tracks with everything we know about how busy professionals actually absorb information. The forty-five-minute webinar has its place, but it’s not how you move a two-thousand-person organisation.

There’s also a shift in what the communications are about. The focus is moving from internal capability (“here’s what our AI tool can do”) toward client readiness (“how many AI questions are clients asking, and are our lawyers prepared to answer them?”). That reframing matters. It connects AI adoption to revenue and client relationships, which is the language that moves partners.

Next in this series: How usage transparency, peer pressure, and practice group roundtables are doing more for adoption than any top-down mandate.

Andrew is a Director of AI and Innovation at a large Canadian law firm. He writes about what AI adoption actually looks like from inside the institution.

If you only follow the headline-grabbing AI announcements — the new tools, the vendor partnerships, the “we’re using AI to do X” press releases — you’d miss the most significant operational shift happening inside law firms right now.

Knowledge teams are exploding.

Not in the dramatic, “we hired a Chief AI Officer” way that makes the legal press. In the structural, quiet, “we’ve doubled headcount in our knowledge and innovation function” way that only becomes visible when you compare org charts twelve months apart.

The Role Has Changed

This isn’t just growth for growth’s sake. The role of these teams is fundamentally different from what it was even two years ago.

The old knowledge management function was a library. Precedent banks, template repositories, know-how databases. Important work, but bounded. The new function is closer to an operating system for the firm’s relationship with AI. It spans tool evaluation, prompt design, lawyer enablement, and quality control — and increasingly, it’s the function that determines whether any given AI initiative actually lands with practitioners or dies on the vine.

I’ve seen this shift from the inside, and the scale of it catches people off guard. Every new AI tool that enters the firm needs someone to evaluate it, pilot it, build the guardrails around it, design the training, measure the adoption, and troubleshoot when things don’t work as expected. Multiply that across a dozen tools and a few thousand lawyers, and you start to understand why these teams are growing.

The insight that resonated most at LegalWeek: AI doesn’t eliminate knowledge work. It raises the bar on it. The firms that understand this are investing accordingly. The ones that don’t are burning out small teams and wondering why adoption numbers stay flat.Vendor Onboarding Is Now a Strategic Bottleneck

Here’s a related shift that doesn’t get enough attention: the path from “we’d like to try this tool” to “it’s live in production” has become a full pipeline — and at most firms, that pipeline is slow.

The common stages are familiar to anyone who’s lived through enterprise procurement: legal review, information security assessment, procurement negotiation. None of these steps are unreasonable. All of them take time. And the cumulative effect is that firms without a fast, standardised intake process are falling behind — not because they’re taking on more risk than their competitors, but because they’re taking longer to make decisions.

This is one of those operational details that doesn’t make it into conference keynotes, but it determines outcomes more than most strategic decisions do. A firm that can evaluate, approve, and deploy a new AI tool in six weeks has a compounding advantage over one that takes six months. Not because the tool itself is transformative, but because the speed of experimentation becomes a capability in its own right.

I’ve been pushing on this in my own context — trying to turn what was an ad hoc, relationship-driven intake process into something repeatable and predictable. It’s not glamorous work. But every week you shave off the onboarding cycle is a week your lawyers get to start learning what works and what doesn’t.What This Actually Means

The structural message from LegalWeek was clear, even if nobody put it on a slide: the firms that are serious about AI are building internal infrastructure that looks nothing like a traditional law firm support function. They’re building product teams, design capabilities, enablement programmes, and operational pipelines.

This costs real money. It requires real headcount. And it demands that firm leadership treat AI adoption as an ongoing operational commitment, not a one-time capital expenditure.

That’s the part most firms haven’t fully internalised yet.

Next in this series: The specific training, communications, and adoption tactics that are actually working — from task-first training models to the surprising value of tracking shadow IT.

Andrew is a Director of AI and Innovation at a large Canadian law firm. He writes about what AI adoption actually looks like from inside the institution.

If you walked the expo floor at LegalWeek this year, you’d be forgiven for thinking the AI problem in legal is solved. Every booth had a demo. Every demo was impressive. Every vendor had a pitch about how their particular flavour of generative AI was going to reshape legal work.

And yet the most honest conversations — the ones in the hallways, the ones over coffee, the ones where people dropped the marketing voice — kept circling back to the same uncomfortable admission: the tools aren’t the bottleneck anymore.

The bottleneck is us.

Everyone Has the Same Oven Now

Here’s the analogy that kept surfacing in different forms across panels and roundtables: buying a better oven doesn’t make you a better restaurant. Training the kitchen does.

That lands differently depending on where you sit. If you’re a vendor, it’s a threat — because differentiation is collapsing. If you’re a firm leader, it’s a mirror — because the constraint on AI adoption isn’t the technology. It’s habits, trust, incentives, and whether the tool actually fits into the way people already work.

Firms that treat AI as a software rollout are stalling. I’ve watched this pattern from the inside. You announce a tool, run an onboarding webinar, send a few follow-up emails, and then wait for adoption numbers that never come. It feels like a deployment. It is not a deployment. It’s an organisational change problem — the kind where you need to rebuild workflows, shift expectations, and get very honest about what’s actually blocking people.

The firms that are moving aren’t the ones with the best tools. They’re the ones that treat adoption as a change management initiative with executive air cover, practice-group-level ownership, and a willingness to redesign process rather than bolt AI onto existing ones.The Hiring Shift Nobody’s Talking About Enough

There was a quieter theme running underneath the big panels that I think matters more than most of what made the main stage. Firms are changing how they evaluate talent — not just AI specialist talent, but lawyers.

The shift is from answers to questions.

The best hires, as several panellists put it, don’t claim mastery. They ask sharp, practical questions. They frame problems well. They spot risks that others walk past. They stay curious when the ground is uncertain.

This applies equally to the lawyer you’re hiring for your M&A team and the AI strategist you’re hiring for your innovation group. The signal isn’t “I know how to use Harvey” — it’s “I understand what this tool can’t do, and I know when to stop trusting it.”

I’m told interview rubrics at several firms are being rewritten to weight three things more heavily: problem framing, risk spotting, and curiosity under uncertainty. That’s a meaningful shift. It says something about where firms think AI is actually heading — not toward a world where lawyers know less, but toward a world where the ability to ask the right question becomes the scarce skill.“Here Are All the Buttons” Is Dead

The third theme that kept recurring is the death of feature-based training. I don’t think this one is controversial anymore, but it’s worth saying clearly: if your AI training programme starts with “Here’s the interface,” it’s failing.

The model that’s working — the one that came up in almost every adoption-focused session — starts with a specific legal task. Not a tool walkthrough. Not a prompt engineering workshop. A realistic, time-pressured legal task that mirrors actual work.

One session described it this way: the goal is not to teach a lawyer how to use Harvey. The goal is to teach a lawyer how to draft a first-pass asset purchase agreement under time pressure, using whatever tools are available — including Harvey, but not limited to it.

That reframing matters. When you train to the task, the tool becomes incidental. When you train to the tool, you’ve built a dependency that breaks every time the vendor ships an update.

I’ve been thinking about this a lot in my own work. The task-based model isn’t just better pedagogy — it’s better strategy. It forces you to identify the twenty or so repeatable legal tasks that actually drive your practice, and then build training, tooling, and measurement around those. Everything else is noise.

Next in this series: How knowledge teams are quietly becoming the most important function in the modern law firm — and why vendor onboarding is now a strategic bottleneck.

Andrew is a Director of AI and Innovation at a large Canadian law firm. He writes about what AI adoption actually looks like from inside the institution.

Everyone’s asking the wrong question right now. “Will AI take my job?” “Will AI replace engineers?” “Is marketing dead?”

That question is broken — because AI doesn’t see your job the way you do. You see a job title. AI sees a stack of individual tasks. It’s coming for some of them, it’s going to supercharge others, and there are some it can’t touch at all.

Today I’m going to walk you through a framework to figure out which is which — and give you a prompt you can run right now to get your personal AI career strategy in five minutes.

Jobs are task collections

Think about your actual workweek. Not your title — what you actually do, hour by hour.

If you’re a software engineer, some of your time goes to writing boilerplate code. Some goes to architecture decisions. Some goes to code reviews. Some goes to negotiating requirements with stakeholders.

Those are all different tasks. And they have wildly different relationships with AI.

It’s like looking at a toolbox. You wouldn’t throw out the whole thing just because one wrench is worn out. You’d swap the worn tools and keep the ones that still work.

The wrong question: “Will AI replace software engineers?”

The right question: “Which tasks inside software engineering are automatable — and which ones become more valuable?”

A job title is just a container. AI doesn’t replace containers. It transforms what’s inside them.

The framework: Automate, Augment, Double Down

Every task in your job falls into one of three categories.

1. Automate

These are tasks you hand off to AI entirely. It’s faster than you, the output is good enough, and your time is better spent somewhere else.

2. Augment

AI is your co-pilot here. You’re still driving, you’re still making the calls — but you’re moving two to five times faster than before.

3. Double Down

These are your moat. Pure human value. The tasks where no AI can replace what you bring to the table.

Automation frees your time. Augmentation multiplies your output. Doubling down is how you become irreplaceable.

How to categorize your tasks

A task is automatable when it’s repetitive, pattern-based, and low-stakes. Status reports. Data entry. Boilerplate drafting. Here’s the gut check: if it bores you and a minor mistake wouldn’t cause a fire, hand it off.

A task is augmentable when it requires your judgment, but the legwork leading up to that judgment is slow. Think legal analysis, market research, drafting proposals. AI gets you to 80% in 10% of the time. You bring the last 20% — the context, the taste, the strategic direction. It’s like having a research assistant who never sleeps but still needs you to make the final call.

A task is a “double down” when it involves high-stakes judgment, deep relationships, or institutional context that no model has. The client dinner. The negotiation. The decision that requires knowing the politics and the people. These are the tasks people pay a premium for — and here’s the key — they become more valuable as everything else gets cheaper.

The goal is not to protect every task you do today. It’s to ruthlessly focus on the ones where you deliver irreplaceable value. Let the rest go.

The AI Task Audit

Here’s the process.

Step 1 — List every task that eats up your work week. Aim for at least ten. Be specific. Not “engineering” — break it down. “Writing unit tests.” “Reviewing pull requests.” “Attending sprint planning.”

Step 2 — Rate each task on AI automation potential. 1 means it absolutely requires a human. 10 means AI can do it today, no supervision needed.

Step 3 — Rate each task on a human edge score. 1 means it’s generic and commoditized — anyone could do it. 10 means it requires your irreplaceable judgment or relationships.

Step 4 — Categorize. Automate, augment, or double down.

You can absolutely do this manually with a spreadsheet. But I’ve also built a prompt that does the whole analysis for you.

The prompt

Paste the following into Claude, ChatGPT, or whichever AI assistant you prefer. Replace the bracketed sections with your details.

You are a career strategist specializing in AI workforce transformation.

I'm going to give you my role, industry, and a list of tasks that make up my
typical work week. For each task, I want you to:

1. Rate it on AI Automation Potential (1–10, where 1 = absolutely requires a
   human and 10 = AI can do this today with no supervision)
2. Rate it on Human Edge Score (1–10, where 1 = generic/commoditized and
   10 = requires irreplaceable judgment, relationships, or context)
3. Categorize it as: AUTOMATE, AUGMENT, or DOUBLE DOWN

Then sort the table by automation potential (highest first) and give me a
three-sentence career strategy brief telling me where to focus my energy.

My role: [Your role]
My industry: [Your industry]
My tasks:
1. [Task 1]
2. [Task 2]
3. [Task 3]
...

Here’s what catches people off guard: the output is often surprising. Tasks you thought were safe show up as highly automatable. And tasks you’ve been rushing through — the ones you thought were low-value — turn out to be your highest-value work. That’s the whole point. It forces you to see your job the way AI sees it. Not through your title, but through your tasks.

The shortcut

If your AI assistant is already connected to your email and calendar — Copilot, Claude, whatever you’re using — it already knows what your tasks are.

Just ask it: “Based on my last two weeks of meetings and emails, what tasks consumed most of my time?”

Then run the audit on that list. The data is already sitting there. You just need to ask the right question.

Your homework

Your career strategy is not about learning to use AI tools. Everyone’s going to learn the tools — that’s table stakes. Your strategy is knowing which of your tasks to hand to AI, and which ones to never let go.

Here’s what I want you to do this week:

1. Run the task audit. List your tasks, rate them, categorize them.

2. Identify your top three “double down” tasks. That’s your career moat.

3. Find two tasks you can automate immediately and reclaim that time.

4. Redirect those freed-up hours into your highest-value work. That’s where you win.

Your job is not a job. It’s a collection of tasks. And the ones worth keeping are the ones only you can do.

Drop your role in the comments — I’m curious what tasks you’d put in the “double down” column.

In 1865, England figured out how to burn coal more efficiently. The natural assumption was that the country would use less coal. Instead, it used more. Way more.

That one fact explains why the dominant narrative about AI and jobs has a fatal flaw built into its foundation.

Right now, there’s a panic spreading through every office, every Slack channel, every LinkedIn feed. The fear is straightforward: AI makes knowledge work so efficient that companies won’t need as many people. Fewer engineers. Fewer analysts. Fewer lawyers.

But what if making work faster doesn’t shrink the workforce — it explodes it?

There’s a 160-year-old economic rule that predicts exactly what’s coming. And once you see it, you can’t unsee it.

The Paradox

Here’s the story. It’s 1865. A British economist named William Stanley Jevons notices something that makes no sense.

Steam engines have gotten dramatically more efficient. Everyone assumes England will use less coal. The math seems obvious — better engines, less fuel.

But the opposite happens. Because the engines are so efficient, it suddenly becomes cheap to use steam power for everything. Factories that couldn’t afford steam engines before? Now they can. Industries that never used steam? Now they do. Total coal consumption doesn’t drop. It skyrockets.

That’s the Jevons Paradox: increased efficiency doesn’t reduce demand. It creates it.

This isn’t just a cute story about coal. We’ve already seen this exact pattern play out in your lifetime.

The Spreadsheet Proof

The 1980s. Electronic spreadsheets hit the market — VisiCalc, Lotus 1-2-3, eventually Excel. A single accountant could suddenly do in an hour what used to take a room full of clerks an entire week.

The panic was immediate. Computers are going to eat accounting.

But here’s what actually happened. Because complex math was suddenly so cheap and fast, companies didn’t fire their finance teams. They asked for more math. They wanted daily forecasts. Risk modeling. Scenario analysis. Deep analytics on every product line.

The number of financial analysts and accountants didn’t shrink. It multiplied.

The spreadsheet didn’t kill finance. It made math so cheap that companies wanted ten times more of it. The Jevons Paradox, playing out right in front of us.

The Cognitive Boom

Now let’s talk about right now.

Today, cognitive labor — coding, writing contracts, doing market research, designing campaigns — is expensive and slow. So companies only do what is strictly necessary. You only build software for mass markets. You only have lawyers review the most critical contracts. You only run marketing campaigns for your biggest audiences.

But what happens when AI makes a programmer ten times more efficient? The company doesn’t fire ninety percent of its engineers and build the same app. The cost of creating software plummets. And when the cost drops, the demand doesn’t stay flat.

It erupts.

Think about a law firm. Fifty lawyers. Today, they can only take on high-value cases because legal work is so labor-intensive. But with AI handling contract review, research, and first drafts? Suddenly, thousands of cases that were previously too small or too expensive to touch become viable. That firm doesn’t lay off forty lawyers. It hires two hundred more to manage the flood of newly accessible work.

The same pattern hits every field:

• Marketing campaigns become viable for niche audiences that were previously too small to justify the spend.

• Custom software becomes economical for markets that couldn’t afford it before.

• Legal services become accessible to small businesses for the first time.

And in every single case, you still need humans to direct the AI, manage the projects, make the judgment calls, and ensure quality.

When the cost of intelligence drops, we don’t consume the same amount of intelligence with fewer people. We consume radically more intelligence. We start tackling projects that were too expensive or too complex to even attempt.

We don’t shrink the economy. We scale it up.

Your Move

This is not the automation of existing work. This is the creation of entirely new categories of work that were previously too expensive to exist.

So here’s the exercise worth doing: look at your current role. What work does your company skip because it’s too expensive or too slow? That skipped work is your expansion zone. That’s where the new jobs, the new teams, the new opportunities are going to come from.

Don’t position yourself as the person AI replaces. Position yourself as the person who directs AI into that latent demand.

The professionals who thrive in this next decade won’t be the ones who fear the efficiency. They’ll be the ones who see the rebound.

AI isn’t the end of white-collar work. It’s the beginning of the cognitive boom.

What work does your company currently skip because it’s too expensive? That’s your Jevons Paradox moment waiting to happen. I’d love to hear it in the comments.