The workforce map: which roles in your organization change, which grow, and which end
The short answer
A role is a bundle of tasks, and that is the unit of analysis
AI does not replace job titles; it automates some tasks, augments others, and leaves the rest alone. Decompose every role into tasks before drawing any conclusion about the role, or you will both overcount and undercount the impact.
What you will be able to do
- Decompose any role in your organization into its core tasks, because AI acts on tasks, not on job titles, and a role is a bundle of tasks that AI touches unevenly.
- Classify each task by what AI actually does to it (automate, augment, or leave unaffected), using evidence about the specific system, not a general feeling that "AI is coming for this job."
- Roll up task-level classifications into a defensible fate for each role: it ends, it changes, or it grows, and state the threshold logic that decides which.
- Detect the roles that AI grows, which is the category leaders miss most often because growth is quieter than loss and shows up as new oversight, judgment, and exception-handling demand.
- Produce a workforce map: a named, inspectable artifact that lists each affected role, its task-level evidence, its fate, the headcount and timing, and what the organization owes each person, feeding the rest of Module 9 and the Module 13 dossier.
- Defend each entry on your map against the challenge it will face from a hostile board, an employee representative, or a works council, because a map that has not survived attack is a wish list. (see Topic 9.5)
- Distinguish an honest workforce map from a dishonest change narrative, using the BT disclosure as a positive anchor and knowing where the dishonest version collapses. (see Topic 9.6)
- Assign each ended role a path (attrition, redeployment, or redundancy) and a timing band, because the fate word alone hides the most important human decision and the sequence in which the organization must act.
- Record the jurisdiction of the people on each row, because the same fate carries different legal obligations in different countries and a global map that ignores this understates what the organization owes where the law binds hardest. (see Topic 9.5)
The lesson
On May 18, 2023, BT Group, the UK's largest telecommunications company, announced it would cut up to 55,000 jobs by the end of the decade. Buried inside that announcement was a sharper figure. The chief executive stated the company expected around 10,000 of those roles replaced by AI.
Two years later, the projection shifted. By June 2025, new chief executive Alison Kirkby told reporters that as the company learned more about AI, it might end up even smaller. BT named a specific function, a precise scale, and a hard date.
Most organizations avoid this clarity, hiding the impact of AI deployments inside press releases about transformation and synergy. Obscuring workforce impact behind euphemisms guarantees failure when facing a regulator or a works council. It is a decision to let the hardest organizational change happen in the dark.
To lead through this transition, you must build a concrete, inspectable artifact, the workforce map. The primary barrier to drawing this map is the executive instinct to reason at the level of the job title, asking if AI will replace accountants or paralegals. To survive an audit and maintain trust, you have to look past the title.
You must build an honest map from the ground up. This approach rests on the task-based view of automation, a framework by economist David Autor. This framework holds that technology substitutes for labor in specific tasks while complementing labor in others.
Therefore, the effect of AI depends entirely on the mix of tasks inside a role. Decomposing a role reveals its constituent daily actions. For a service agent, that means resetting passwords, drafting explanations, and de-escalating angry callers.
Once isolated, you can see that AI touches these fragments uneasily. A system might handle the routine password resets while leaving the human to manage the 20% of cases involving complex judgment or distress. If you fail to map at the task level, you will overcount job losses by cutting roles you still need and undercount them by keeping headcount for roles that have quietly hollowed out.
Every task is forced into three strict categories. Automate means the system handles work end-to-end with no human on the routine path. Augment means the human stays in loop to handle exceptions.
Unaffected tasks require physical presence, trust, or legal accountability that AI cannot reach. These classifications must rest on deployed behavior inside the organization, not on vendor marketing claims. In recent enforcement actions involving Presto Automation and NAIT, the SEC found products marketed as autonomous AI relied heavily on hidden human labor.
Labeling a task automate based on a software demo rather than actual deployment commits you to cutting staff the system still requires to operate. Rolling up task labels determines the overall role's fate. A role ends when automated tasks were its core and remaining work cannot justify a position.
A role changes when the task mix shifts substantially, but a real job remains. Leaders face immense pressure to relabel an end's fate as changes to avoid difficult conversations. A dishonest role fate will collapse under the scrutiny of a works council.
It destroys workforce trust by starting consultations on a false premise. Organizations routinely miss the grows fate because job loss is concrete and immediate, while role growth is diffuse and deferred. Deploying AI creates new human work in oversight, exception handling, and model monitoring.
If AI makes a service cheaper and faster, the resulting expansion in demand can also grow the underlying human roles. Macroeconomic projections show that displacement and creation happen simultaneously. This chart contrasts the 92 million roles projected to be displaced globally by 2030 against the 170 million new roles expected to be created.
A map showing only cuts is analytically incomplete. It removes the very capacity the organization needs to manage its new AI infrastructure. The mapping process concludes by attaching a specific timed commitment to every fate.
We record this in the obligation column. For roles that end, the organization must choose between three distinct human paths. Attrition ends a role naturally by not refilling positions when people leave.
This path is available only when the timing is slow enough for natural turnover to absorb the reduction. IBM used this strategy for roughly 7,800 back office roles. Implementing a hiring pause for positions, it expected AI and automation to absorb over time.
The second path is redeployment, where people move into the growing oversight and support roles created by the AI deployment. The final path is redundancy. This carries the heaviest legal requirements for notice and consultation, which vary drastically by jurisdiction.
A fate without a stated obligation is a layoff plan wearing a governance costume. The map exists so the organization can act defensively and humanely. Consider a leader mapping an operations department.
They might find that routine processing roles end while the need for skilled model reviewers and exception handlers grows. By decomposing every role and demanding deployed evidence, they produce a plan that is both rigorous and humane. This map is the document an auditor, a board, or a works council will demand to see when the transition is scrutinized.
The workforce map turns technical data into a leadership plan. It ensures you never lead people through a change you haven't honestly mapped.
The ideas, one by one
Every task gets one of three strict labels
Automate (no human on the routine path), augment (human stays in the loop), or unaffected. The strictness of the automate-versus-augment line is what makes the map survive the accusation that you exaggerated.
Roles have three fates, decided by a stated rule
A role ends when its automated tasks were the core and the residual does not justify a job; changes when the task mix shifts but a real job remains; grows when demand rises or new oversight work attaches. Write the roll-up reasoning down.
Growth is the category you will miss
Loss is loud and growth is quiet. Run the growth question on every role: oversight work, concentrated exception work, and demand expansion. A map that shows only cuts produces a plan that removes the capacity you are about to need.
Build the map from deployed behavior, never vendor claims
"The system can do this" is about the frontier; "our system reliably does this without a human" is about your organization. Only the second belongs on the map, and the gap between them is where overstated cuts come from.
The honest picture beats the palatable one
Softening "ends" into "changes" is a lie that fails at the first attack and pushes the organization into consultation on a false premise. Put the fate honestly and put the humanity in the obligation column.
The obligation column is what makes it leadership, not accounting
A fate without a stated obligation to the person is a cut list in a governance costume. Every "ends" and "changes" carries what the organization will do for the person, which the rest of Module 9 then executes.
The map is a dated forecast, and it is a chained artifact
Date it, state its assumptions, review it, and expect to be wrong, as BT's own revision of its AI-impact estimate shows. It feeds the hard conversation, the literacy rollout, the consultation, and the change narrative, and it is audited in the Module 13 dossier.
Every fate has a date, and sequencing is a moral choice
A role ending next month and one ending in five years demand different responses. The humane sequence usually stands up the growing roles and reskilling pipelines first, so displaced people have somewhere to go before they are displaced, not after. Add a timing band to every row.
An "ends" is not one thing
It plays out as attrition, redeployment, or redundancy, each with different obligations. Name the path on every ended role, because "ends by attrition over three years" and "ends by redundancy next quarter" are different commitments wearing the same word.
The same fate is a different obligation in different jurisdictions
Record where the affected people sit, because a group-level fate can trigger mandatory consultation in some countries and not others, and a global map that ignores this understates its obligations where the law binds hardest.
Build the map from deployed behavior, and follow the failure path
When unsure between automate and augment, ask who gets the call when the task goes wrong; if a human catches every routine failure, the task is augmented. Accountability for failure is the task that leaves a role last.
Growth must be evidenced, not asserted
A "grows" row names its mechanism (a specific oversight duty, a measured escalation increase, a demand elasticity), held to the same evidentiary standard as an "ends." Hand-waved growth deserves the skepticism it gets; evidenced growth is what makes the map credible.
The map is the hinge from systems to people
It is where AI governance stops being technical and becomes leadership. A truthful map gives the rest of Module 9 a foundation; a distorted or skipped one poisons every conversation, training, and consultation that follows.
You read it. Now prove it.
Explain this lesson in your own words, the way you would to a colleague, without looking back at it. It is graded against the lesson itself, by the same grader our learners face. One free try a day, no account needed.
The conversation
The same lesson, talked through at length by two hosts: the full transcript of the audio deep dive.
Listen to it as episode 68 of the podcast.
Read the full conversation
So on May 18th, 2023, BT Group, which is, you know, the largest telecom company in the UK, they put out this announcement that just, it completely shattered the standard corporate playbook. Oh, absolutely. It was a massive shock to the market.
Right. Because normally when a company does a reorg, the CEO puts out this glossy memo. It's all about synergy and unleashing human potential or, you know, some digital transformation journey.
Yeah. The classic strategic ambiguity. Exactly.
But the CEO at the time, Phil Jansen, he didn't do that. He went on the record and just flat out stated that BT Group would cut up to 55,000 jobs by 2030. Which was what, over 40% of their global workforce? Over 40%.
But the really arresting part, the part we are focusing on today, was this smaller, much sharper number buried inside that aggregate. Jansen explicitly attributed around 10,000 of those cuts directly to artificial intelligence. He actually named the specific cause.
Yes. And he named the specific divisions too. He pointed right at customer service and network management.
I mean, the departure from normal executive behavior there, it really can't be overstated. We're so used to seeing leaders just mask these structural changes. Right.
Like calling it natural attrition. Exactly. If you look at the late 90s with the adoption of ERP software or even the shift to cloud computing, all those workforce reductions were just categorized as reorganizations.
BT Group refused to play that game. They were brutally honest. They were.
They named the function, the exact scale of 10,000 roles, and committed to a hard timeline of 2030. They basically gave the market an auditable metric for their AI strategy. And the reality of their deployment actually outpaced that brutal forecast.
If you fast forward to June 2025, the successor CEO, Alison Kirkby, she stood up and noted that as they learn more about these AI systems, the initial estimate might have actually been too conservative. Meaning the cups could be deeper. Right.
She said the company could be, quote, even smaller by the end of the decade. Which is fascinating. Right.
Because they learned that when AI deployment moves off a theoretical spreadsheet and onto the actual production floor, the structural changes, well, they just compound in ways you don't initially see. And that serves as the exact foundation for our deep dive today. Because when you deploy AI, the roles in your organization are going to go through three very distinct transformations.
Some roles will change, some will grow, and some will just end. And the critical failure of most modern leaders is that they just let this happen in the dark. Yeah, they react instead of plan.
Right. They only realize a role has ended when, you know, the support queues are suddenly empty or middle management starts frantically restructuring teams just to justify their own budgets. So our mission today is to dismantle that reactive posture.
For everyone listening, we're going to build what is known as the workforce map. And to be clear, this is not just some conceptual high-level framework for a PowerPoint slide. No, absolutely not.
The workforce map is a rigorous, auditable, row-by-row artifact. It forces you to confront the exact mechanical reality of how technology intersects with human labor in your specific building. But before we get into the actual architecture of the map, we really need to establish the core philosophy driving this whole thing.
The spine of the analysis, which is the honest picture beats the palatable one. Yes. That is non-negotiable.
If you try to soften the edges of this, if you categorize a role elimination as a synergistic transition, just to avoid a tough conversation, the map fractures. You have to be unsentimental about analyzing the work, but deeply humane in your obligations to the people doing the work. And holding those two opposing ideas at once, I mean, that's really the defining test of executive leadership right now.
It really is. So let's get into the mechanics. The primary reason leaders fail this test is because they start with the wrong unit of analysis, right? Exactly.
They walk into a boardroom, watch a slick vendor demo, and they ask questions like, will AI replace our accountants? Or will it replace our radiologists? And that question guarantees an incorrect answer. Totally. Because AI does not replace a job title.
A job title is just an HR abstraction. The AI acts on specific tasks, not the title itself. This is the load-bearing pillar of the whole map.
And to anchor this in actual labor economics, we have to look at the work of David Autor, the MIT labor economist. His 2015 analysis is incredible on this. Right.
He looked at why so many jobs still exist despite decades of automation. And his central thesis is that a role is a bundle of distinct tasks. That is the unit of analysis.
And that framework is heavily used in the U.S. Department of Labor's ONET database, too. Yeah. The core truth is that technology substitutes for labor in some tasks, but it complements and amplifies labor in others.
Which means if you analyze AI's impact by looking at the job title, you are walking into a massive cognitive trap. You're going to get the math wrong. You'll see a role, realize 50% of it is automated, and just assume the whole job vanishes.
You'll wildly overcount your headcount reductions. Let me push back here with an analogy, because this is really hard for a lot of leaders to visualize. The typical way we think about job disruption is way too linear.
Let's use the grocery cart analogy. OK. I like this one.
Imagine a job as a grocery cart. And inside this cart, you have eight specific items. Those are your tasks.
Now, you roll out a new AI system. The AI might take three items completely out of the cart. It might fundamentally change the packaging on two items, and it leaves three items entirely alone.
Right. You cannot look at the whole cart from the outside and say, do I still need this cart? You have to look at the specific heavy items left inside to determine if the human still needs to push it. That is the perfect way to visualize it.
And there's a deeply humane angle to this, too. I would say. Well, when you tell an employee your job is at risk, that feels like a personal attack.
It's an existential threat to their identity. Oh, for sure. People tie their worth to their title.
Exactly. But the workforce map neutralizes that panic. You aren't telling them they're obsolete.
You sit down with them, look at the grocery cart and say, look, these three routine tasks are now done by the system. Let's analyze the complexity of the remaining five items. It's an objective description of the work.
Exactly. It completely separates the human from the mechanical reality of the role. OK, so step one is breaking the role down into, say, five to 10 concrete action verbs.
We have to throw away the vague HR jargon. Yeah. If a job description says owns the client relationship, that is totally useless for the map.
You need the actual actions like resolves billing disputes or negotiates renewals. Right. And once you have those verbs, you move to the classification phase.
And this is where you have to be incredibly strict. Every single task gets one of three strict labels, no middle ground, no partially. It's a forced binary at the task level.
That strictness is what gives the map its integrity. Let's start with the first label, automate. Let's define it.
When you label a task automate, it means the deployed AI system does the task end to end with absolutely no human on the routine path. The routine path being the key phrase there. Yes.
If a customer needs a password reset and the AI verifies them, resets it and confirms it without a human ever seeing the ticket, that task is automated. But wait, let me play devil's advocate. Yeah.
If I'm an operations director and my AI handles the routine path perfectly, but it completely fails on, say, 5% of the tickets and a human has to clean up the mess. Is that task still automated? Yes. The routine task is automated.
Even with the failures? Yes. Because the management of that 5% failure rate is a completely separate task. That becomes exception handling.
If you refuse to label a task as automated just because of edge cases, your map is going to suggest the human is still doing the routine work. And they aren't. They're just doing error correction.
Exactly. So that's automate. The second label is augment.
Okay. Define augment. This is when the AI changes how the task is done, but the human definitively stays in the loop.
The human handles exceptions or they use the AI to draft something for review, but they own the final outcome. So like a paralegal using an LLM to summarize case law. Exactly.
The AI altered the speed, but the paralegal still has to read the output, check for hallucinations, and submit the brief. The human retains the friction of execution. Right.
And the third label is unaffected. This is the durable human core of the enterprise. It is where AI doesn't meaningfully touch the work.
So we're talking about physical presence, high stakes in-person relationship building. And legal or fiduciary accountability. You can't automate who goes to jail if something breaks.
Wow. Yeah. That's a great way to put it.
Now, this brings us to a massive hazard. The whole integrity of these three labels relies on one rule. You have to build your map from deployed behavior, never from vendor claims.
Never. A vendor telling you in a pitch meeting that their platform fully automates onboarding is an aspiration. It's marketing.
Right. The deployed system running in your actual legacy tech stack is the reality. And the danger of falling for the marketing is huge.
Just look at the enforcement cases with Presto Automation and Nate. Oh, those are perfect examples. Yeah.
Both companies made these really aggressive claims about having autonomous AI. Presto was doing drive-thru orders, and Nate had an app for e-commerce checkout. But when regulators looked under the hood, there was a massive workforce of humans behind the scenes, manually typing in orders and fixing errors in real time.
It was essentially human in the loop pretending to be autonomous. Exactly. If you built your workforce map believing they're marketing, you would have fired your staff, turned on the AI, and watched your whole operation instantly collapse.
There's also the Dukhan case from 2023. The CEO of this Indian e-commerce platform went viral for claiming they replaced 90% of their support staff with an AI chatbot. Which sounds incredibly efficient.
It sounds great in a headline. But to actually replicate a 90% cut on your own map, you have to pretend that deeply complex escalations, emotionally distressed customers, and third-party shipping failures can all just be automated away. Taking those claims at face value just ruins the map.
But let me ask you this as a diagnostic tool. Vendor demos are really good right now. When an executive watches a flawless demo, how do they know for sure if a task is actually automate or if it's going to end up being augment in reality? You apply the ultimate diagnostic test.
You don't ask about the speed or the success rate. You ask one very specific question. When this system inevitably fails, who gets the phone call? Accountability as a task.
Precisely. If the AI hallucinates a contract clause and the human attorney is the one who gets sued for failing to catch it, that human is still in the loop. The task is augmented.
If human catches the routine failure, the task is augmented. Period. Yep.
Automation only happens when the system fails and the organization itself absorbs the blast radius, not an individual human. I love that. Okay, so we've labeled our tasks.
The next step is rolling them up. After labeling, roles have three fates decided by a stated rule, plus a subcategory for totally new roles. Let's define the three fates.
Sure. The first fate is ends. Okay.
A role ends when the automated tasks were the absolute core of the role and whatever residual tasks are left over, the augmented or unaffected ones, they just don't justify keeping a full-time position. Right. If the human work left over equals zero, the role ends.
What's the second fate? Changes. The task mix undergoes a violent shift, but a real job remains. Routine volume is gone, but the unaffected tasks expand.
And honestly, a changed role is almost always a much harder role. And the third fate. Grows.
Demand rises or brand new human work attaches directly to the role. Let's ground this in a real scenario so listeners can really picture it. Imagine Colleen.
She's the director of operations at a mid-sized insurance company. Her CEO mandates a huge AI transformation to cut costs, but Colleen knows a blanket percentage cut will fail. So she builds a bottom-up workforce map.
She looks at two deployed systems, a claims triage model and a customer service chatbot. Let's start with her level one claims processors. So she lists out their tasks, receive claim, check completeness, categorize, pull policy, route anomalies.
Exactly. And when she runs the test against the triage model, every single one of those core tasks gets marked automate. The residual work left for the human is zero.
So she has to classify that role fate as ends. There's no way to soften it. The role ends.
Now she looks at her first line customer service agents, probably expecting the same bloodbath. Their tasks include routine queries, password resets, but also taking first notice of loss and deescalating upset customers. Okay, so the password resets and routine queries are automate.
Right. But taking the first notice of loss is augment. The AI services the policy, but the human explains it.
And deescalating a traumatized customer whose health just burned down, that is unaffected. Because routing a traumatized customer to a chatbot is a great way to destroy your brand. Exactly.
So for the service agents, three tasks are automated, two are augmented and two are unaffected. The role does not end. The fate is changes.
But let's talk about what that actually means for the human in the chair. Well, it means their cognitive load just spiked off the charts. Right, because they lost their cognitive breather.
Exactly. Before AI, 60% of their day was answering easy password resets. It let their brain rest.
Now the AI blocks all the easy stuff. And every single call that actually reaches the human is, by definition, a severe escalation. It's back-to-back trauma and complex edge cases.
So a changed role is a heavily taxed role. Now let's explore some of the harder cases. What if a role is heavily augmented, but it's a highly credentialed role, like a radiologist? Ah, this leads into the illusion of safety and a concept we call hollowing.
I really want to dig into hollowing. Because on paper, the radiologist looks safe. The hospital's map says the role is augmented.
The AI flags the anomalies, but the human doctor still legally signs the report. The headcount stays the same. To the CFO, nothing changed.
Right. But operationally, what happens? Operationally, the hospital administration sees the AI as fast. So they radically increase the volume of scans the radiologist has to process per hour to justify the software cost.
So the doctor isn't doing deep interpretive cognition anymore. No, they're staring at a screen where the AI has already drawn red boxes around the tumors, and they have like 45 seconds to just click approve before the next scan loads. So they are legally on the hook, but their actual influence on the decision has plummeted.
They are just a rubber stamp. That is hollowing. The tell is the gap between formal accountability and real influence.
And it's incredibly dangerous because it masks systemic risk. When the machine finally makes a massive error, the hollowed out human is just going to rubber stamp it because of automation bias. Wow.
OK, so that's a phantom role. But let me challenge the framework from another angle. Yeah.
What about roles where the tasks are entirely physical or high trust? Like a plumber or an enterprise sales director. 100% of their tasks are unaffected. Do we just ignore them on the map? If you ignore them, you fall straight into the most critical blind spot in this entire analysis.
Which brings us to our next major rule. Growth is the category you will miss. Loss is loud and concrete.
You see the empty desks. But growth is diffuse, quiet, and deferred. Let's look at the macro data to prove this.
The World Economic Forum put out their future of jobs report 2025, projecting out to 2030. They projected 92 million roles would be displaced by automation. But they also projected 170 million new roles created.
Right. A net increase of 78 million jobs globally. Fast declining roles were clerical, data entry.
Fast growing were tech, frontline care, complex logistics. Now, obviously, a CFO isn't going to let you put a WEF macroeconomic stat on your internal workforce map. Of course not.
It doesn't balance the internal payroll. Right. But it proves the law of technological shifts.
Displacement and creation happen at the exact same time. You have to run a strict growth check on every single role in your company. So let's give the listener the three sources of growth they need to look for.
Source number one, oversight work. This comes directly from regulatory reality. High-risk systems, especially if you're operating under the EU AI Act, they legally require proportional human oversight.
You have to review, approve, and audit the AI. And that work lands on the exact teams whose routine tasks were just automated. So going back to Colleen, her junior claims processor role ended.
But now she has a legal mandate to audit 5% of the AI's claims. So she has to create a brand new role, claims model auditor. Exactly.
Growth source number two is exception concentration. We talked about this with the customer service agents. If you automate the routine 80% of a workload, you cannot just cut 80% of your headcount.
Right, because the relationship isn't linear. The 20% of tasks left over are the hardest, most stressful problems. A routine password reset takes 40 seconds.
A multi-party fraud investigation takes four hours. If you execute an 80% cut, your remaining team will drown in the exception backlog in a week. A changed role is a harder role.
And the third source of growth, this is my favorite one-demand expansion, the ATM effect. It's the perfect historical parallel. When the ATM came out, everyone thought human bank tellers were going extinct.
It automated the core tasks of dispensing cash. But the opposite happened. Right.
Because the ATM made it so much cheaper to run a single branch, banks opened thousands of new branches. The cost of the service plummeted, so demand exploded. And the total number of human tellers actually grew.
They just shifted to relationship banking. Exactly. If AI makes your legal review 10 times faster, you might not fire your lawyers.
You might just pursue 10 times as many contracts, which requires more humans to execute the un-automatable parts of those deals. Now I'm playing the skeptical CFO again. Growth sounds like a really soft, squishy word consultants use to make cuts palatable.
How do we prove it on the map? The CFO is right to be skeptical. Unevidenced growth is a lie. If you claim an oversight role is needed, you have to cite the exact compliance mandate.
You need receipts. You need receipts. If you claim exception concentration, show me the time and motion study proving the edge case takes four hours.
If you claim demand expansion, show me the elasticity in the sales pipeline. Growth must be justified with task-level evidence. Period.
Okay, so we have the map. We have the tasks, fates, and growth. But a map without timing and obligations is just a spreadsheet of cuts in a governance costume.
Yes, every fate has to have a date. Timing changes everything. BT Group didn't cut 10,000 people overnight.
They set a seven-year horizon. A role ending next month is a redundancy crisis. A role ending in three years is a redeployment opportunity.
There are three distinct paths for a role marked ends. The first is attrition. Meaning you just don't refill the role when someone naturally leaves.
IBM did this in 2023, pausing hiring for roughly 7,800 back-office roles. It's gentle, but it takes time. And you have to monitor it for equity.
If natural turnover disproportionately hits older workers or minorities, passive attrition can create a discriminatory pattern. Great point. Path two is redeployment.
Moving people from the ends, roles into the grows, and changes roles. Which is why mapping growth is non-negotiable. You can't redeploy someone if you haven't identified where the new work is.
And path three is redundancy. The person leaves the organization. The honest last resort.
And this is where jurisdiction really matters. It's everything. A map that ignores where people physically sit is legally indefensible.
Because in much of the U.S. private sector, employment is at will. The floor is relatively low. Right.
But take that same role, fate to France or Germany. Works councils require mandatory formal consultation for group-level changes or ends. You have to prove the economic necessity and negotiate a social plan before anyone leaves.
So why wouldn't a company just speed up the timeline? Why not capture the cost savings immediately and do mass redundancies? Because rushing forecloses attrition and redeployment. It forces massive redundancies, which carry huge hidden costs. Severance, lost trust, plummeting productivity.
Plus, you probably underestimated the exception work, so you'll end up panic rehiring expensive contractors six months later. Exactly. It's a disaster.
Which brings us full circle to our core philosophy. The honest picture beats the palatable one. The pressure from the C-suite to soften ends into changes is going to be immense.
Nobody wants to announce job eliminations. But if you go to a works council or a union to, quote, unquote, consult on a role that you secretly decided to eliminate, you were consulting on a false premise. And when the truth comes out, trust and your legal standing completely collapse.
So let's summarize the actual physical artifact listeners need to build. The workforce map is an auditable table. You need role, core tasks, task fates tied to specific systems, role fate with mathematical reasoning, headcount, timing, and crucially, the final column.
What we owe the person. Is it reskilling? Redeployment? Consultation? If the change narrative you tell the public diverges from the reality on the ground, you get the robodebt scandal in Australia. Exactly.
The government claimed the AI was just an augmentation tool, but on the floor, it was automating aggressive debt notices with zero human oversight and frontline staff were stripped of their ability to intervene. A truthful map is the only foundation for a change narrative your workforce will actually believe. But what if we finish the map, lock it in, and a year later, the tech changes? Did we fail? No, absolutely not.
The map is a dated forecast. BT's CEO updated her estimate two years later. You timestamp the map, state your assumptions, and update it when the tech advances.
It's a chained artifact. Incredible. We have covered a massive landscape today.
We really have. And I want to leave the listener with one final provocative thought about the deepest test of AI leadership. We hear it.
True mastery in this era is the ability to hold two opposing disciplines at once. You must be clear-eyed, unsentimental, and absolutely ruthless about the role, while being genuinely humane and deeply obligated to the person. Ruthless about the work, humane about the person.
I love that. And that brings us to the Monday morning move. The single most valuable action you can take when you get to your desk.
Please do not try to write a sweeping 50-page AI strategy for the whole enterprise. No. Monday morning, pick one role you understand well.
Break it down into 5-10 action verbs based on what they actually do, not the HR description. Put it in front of a skeptic, label each task against a real deployed system, and assign it an honest fate. Start there, in the light.
Real cases
These are documented cases used to show the analysis, not to predict your organization. Each is cited; each is used for a specific analytical point.
A note on how to read these: in each case, look past the headline to the analytical move it illustrates. The point is never "here is what happened to a famous company"; it is "here is the reasoning step you must be able to perform on your own organization." A learner who collects the anecdotes but not the moves has missed the topic.
Example 1: BT Group's honest disclosure (this topic's anchor). In May 2023 BT announced up to 55,000 job cuts by 2030, with the then-CEO Philip Jansen attributing about 10,000 to AI, concentrated in customer service and network management (Forbes; CNN Business, both 18 May 2023). The analytical value is the specificity: BT named a function and a scale rather than hiding behind transformation language. It is also a lesson in humility about numbers: by June 2025 the successor CEO Allison Kirkby indicated AI might drive the company "even smaller" than the original plan (The Register, 16 June 2025). A workforce map is a dated forecast, and BT's own revision proves it.
Example 2: Clerical and data entry roles at the macro scale. The World Economic Forum's Future of Jobs Report 2025 (January 2025, survey-based projection) identified clerical and secretarial workers as the largest projected absolute decline, with bank tellers, postal clerks, and data entry clerks among the fastest-declining roles. The analytical point is task composition: these roles decline fastest because a very high share of their core tasks (transcription, routine lookup, standardized processing) fall cleanly into the "automate" bin, leaving little irreducibly human residual. Roles decline in proportion to how automatable their task bundle is, which is exactly what the task-based view predicts.
Example 3: The care and frontline growth the same report projects. The same WEF report projected some of the largest absolute growth in nursing, social work, personal care, delivery, and construction roles. These grow because their core tasks (physical presence, in-person judgment, human trust, bodies in space) sit heavily in the "unaffected" bin, and because AI-driven productivity elsewhere can expand demand for them. The lesson for your map: the roles AI leaves alone can grow precisely because the technology cannot reach their core, and a map that only looks at what AI touches will miss them entirely.
Example 4: The IBM back-office hiring pause (referenced, owned elsewhere). In 2023 IBM's then-CEO said the company would pause hiring for roughly 7,800 back-office roles it expected AI and automation to absorb over time. This is a workforce decision expressed as a hiring freeze rather than a cut, and it is the anchor for the 90-day priorities decision in Module 0 (see Topic 0.4), so it is only referenced here. The analytical note worth carrying: pausing hiring is itself a workforce-map action (a role fate of "ends by attrition"), and honest maps distinguish attrition from redundancy because the human obligations differ.
Example 5: Augmentation that looks like automation. The Presto Automation drive-thru case, where the United States Securities and Exchange Commission found that offshore humans handled a large majority of orders behind an "autonomous AI" claim, is owned by the vendor-interrogation topic (see Topic 3.3). It appears here as the cautionary example for the automate-versus-augment line: a task marketed as automated was in fact heavily human. If you had built a workforce map from that vendor's claims, you would have ended roles that the system could not actually cover. The map is only as honest as the task-level evidence under it.
Example 6: The oversight role that AI creates. High-risk AI systems under the EU AI Act carry a legal human oversight obligation (owned in the risk-classification topic (see Topic 5.3)). The analytical point for the map is that this obligation is new, ongoing human work: someone must be positioned to understand, monitor, and override the system. That work is a growth entry on the workforce map, frequently attaching to the very team whose routine tasks the system automated, which is how a single deployment both ends old tasks and creates new ones on the same desk.
Example 7: The skills-transformation finding as a map input. The World Economic Forum's 2025 report projected that about 39 percent of workers' existing skill sets would be transformed or become outdated over 2025 to 2030 (a projection, so treat it as emerging). The analytical value is that this is a "changes" statement at the macro scale: it says the dominant fate is not roles ending but roles transforming, which reskilling has to serve. A map that shows mostly "ends" and little "changes" is out of step with the broad evidence and is worth re-examining, because for most organizations the transformation of surviving roles, not their elimination, is the larger and harder workforce challenge. The number is not your organization's number, but the shape of the finding (transformation over elimination) is a useful sanity check on the balance of your map.
Example 8: Attrition as a fate, expressed as a hiring pause. The IBM back-office hiring pause of 2023, in which the company said it would pause hiring for roughly 7,800 roles it expected AI and automation to absorb (owned by Topic 0.4 (see Topic 0.4)), is the clean real-world example of an "ends by attrition" fate rather than an "ends by redundancy" one. No one was dismissed; the reduction was designed to come through natural turnover and a hiring freeze over time. The analytical lesson for the map: the same role fate ("ends") can be realized through radically different human paths, and an honest map names which path applies, because "ends by attrition over several years" and "ends by redundancy next quarter" are different commitments with different obligations even though the fate word is identical.
Example 9: The multi-year horizon as a deliberate choice. BT set its workforce reshaping over a seven-year horizon (2023 to 2030) rather than compressing it, and the successor leadership continued to treat the pace as a variable rather than a fixed cut (The Register, June 2025). The analytical point is that horizon is a decision, not a given: a longer horizon opens attrition and redeployment and phased consultation, while a compressed one forces redundancy. Reading the BT case as a lesson in timing, not just in scale, is what separates an analyst who copies the headline number from one who understands why the number was spread across years.
Example 10: The macro shape as a sanity check, not a source. The WEF 2025 projection of simultaneous large displacement and larger creation (about 92 million and 170 million by 2030, a projection) is worthless as a source for any specific role on your map, because your organization is not the world economy. Its analytical use is as a sanity check on the balance of your map: if your map shows only elimination and no transformation or growth, it is out of step with the broad evidence that transformation dominates and creation exceeds displacement, and that mismatch is a prompt to re-examine whether you ran the growth and change analysis honestly or stopped at the losses.
Where people go wrong
- "Will AI replace [job title]?" as the framing. This is the foundational error, because the job title is the wrong unit of analysis. AI acts on tasks, and every role is a bundle of tasks that AI touches unevenly. Reasoning at the title level produces both false losses (killing roles whose residual tasks still justify a person) and false safety (keeping headcount for roles that have quietly hollowed out). Always decompose to tasks first.
- Confusing automation with augmentation. A task where a human still verifies, handles exceptions, or owns the outcome is augmented, not automated. Counting augmented tasks as automated is the single largest source of overstated job-loss numbers, and it is exactly the gap that the Presto and Nate enforcement cases exposed between "autonomous AI" claims and human-in-the-loop reality (see Topic 3.3) (see Topic 4.1). Be strict: automated means no human on the routine path.
- Building the map from vendor claims instead of deployed behavior. "The system can do this" is a marketing statement about the frontier; "our deployed system reliably does this without a human" is a statement about your organization. Only the second belongs on the map. A map built on capability claims ends roles the system cannot actually cover.
- Softening "ends" into "changes" to avoid the conversation. This is the most damaging mistake because it is a lie that fails at the first attack and sends the organization into consultation on a false premise. If a role's core tasks are automated and the residual does not justify a job, the honest label is "ends," and the humane response is in the obligation column, not in relabeling the fate.
- Only mapping the losses. Growth is quiet and loss is loud, so first-draft maps almost always miss the roles AI grows: oversight work, concentrated exception work, and demand expansion. A map that shows only cuts produces a plan that removes the capacity the organization is about to need. Run the growth question on every role.
- Treating the map as a one-time slide instead of a dated forecast. A workforce map is a forecast and forecasts age. BT's own 2023 estimate of about 10,000 roles to AI was later signaled as possibly understating the impact (The Register, June 2025). Date the map, state its assumptions, and set a review cadence, or it becomes a quiet lie as the technology and business move.
- Skipping the obligation column. A fate without a stated obligation to the person is a cut list wearing a governance costume. The "what we owe the person" column is what makes the map a leadership instrument rather than a spreadsheet, and it is what Modules 9.2 through 9.5 execute.
- Assuming automation is neutral to the survivors. Automating the routine 80 percent leaves humans doing the hard, high-risk 20 percent, which is more stressful and more skilled, not easier. A changed role is often a harder job than the one it replaced, and a map that treats "changed" as "no big deal" underestimates the reskilling and support burden.
- Collapsing the three ways an "ends" plays out. "Ends" can mean attrition (not refilling as people leave), redeployment (the person moves to a growing role), or redundancy (the person leaves the organization). These carry completely different obligations, and a map that writes "ends" without naming the path hides the most important human decision. Name attrition, redeployment, or redundancy for every ended role.
- Ignoring where the people sit. The same fate carries different legal obligations in different jurisdictions: a "changes" affecting a group in Germany or France may trigger mandatory works-council or committee consultation before you may act, while the floor is lower elsewhere. A single global map that ignores jurisdiction understates the obligations exactly where they bind hardest (see Topic 9.5).
- Decomposing roles from job descriptions instead of observed work. A job description is an aspirational document, often years out of date, written for hiring rather than for accuracy. Building the task list from it alone produces a map that is confidently wrong. Decompose from what people actually do, verified with the people who do it, not from what the role was once written up to be.
- Reasoning from the incumbent's skill instead of the task mix. "This person is too talented to be affected" confuses a property of the person with an analysis of the tasks. A talented person can hold a highly automatable role; the fate follows from the tasks, and the person's talent belongs in the redeployment conversation, not in the fate.
- Mapping only the present state. A role can be stable today and hollowing on a two-year view because deployments roll out over time. A map built only against how the systems behave today will keep discovering fates after the window to handle them humanely has closed. Decompose the role against the deployment roadmap, and mark a role trending toward "ends" while there is still time to reskill.
- Treating "changes" as automatically kind. A role can change so far that it becomes a job the current incumbent cannot or will not do. Heavy change is a real transition needing real support, and for some people the honest answer is redeployment to different work rather than forcing them through a change they did not choose. The obligation column must be honest about changed roles, not only ended ones.
Questions people ask
- What is workforce map?
- A dated, inspectable artifact that lists each role affected by an organization's AI systems, decomposed into tasks, with each task classified by what AI does to it, each role assigned a fate (ends, changes, or grows), and each fate carrying the organization's obligation to the person. Produced in this topic, consumed across Module 9, and audited in the Module 13 dossier.
- What is task-based view of automation?
- The settled expert framework (associated with the economist David Autor, 2015) that technology substitutes for human labor in some tasks and complements it in others, so the effect on any occupation depends on the mix of tasks inside it, not on the job title. The reason a role is the unit that gets a fate but a task is the unit of analysis.
- What is task decomposition?
- Breaking a role into the five to ten concrete tasks that actually make up the job, stated as action verbs (answer, check, calculate, de-escalate, approve), so that AI's uneven effect on different tasks can be seen. The foundational step of building a workforce map. More on Task decomposition
- What is automate (task fate)?
- The label for a task the deployed AI system performs end to end with no human on the routine path. The strict test is deployed behavior, not vendor claims, and not tasks where a human still verifies or handles exceptions (those are augment).
- What is augment (task fate)?
- The label for a task where AI changes how the human does it but the human stays in the loop, doing the judgment, the exception, the sign-off, or the relationship. Augmented tasks change a role rather than end it and are where reskilling concentrates.
Keep going
This lesson builds Workforce transition and role redesign, and that page shows the roles that hire for it. Every Certified AI Governance Professional (CAIGP) lesson.