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.

Your job is not a job. It’s a collection of tasks. And AI doesn’t treat all of them the same way.

Once you see it like that, everything changes.

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.

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.