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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I built a full playbook with the five scoring dimensions, weighted formula, gating rules, score anchors, and a printable worksheet for manual scoring. I also built a Claude Skill if you want to get sophistication scoring within you conversation, along with tips on how to improve.

Ai Sophistication Scoring Spec

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Ai Sophistication Playbook

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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.