AI and Org Restructure – AI proof your career ladder

Organisations restructure around Artificial Intelligence. So yeah, be prepare.

We’ve seen this before. Centralised typing pools disappeared and became distributed admin assistant roles: a floor of typists became individual admin assistants spread over the building using wordprocessing on personal computers! Telex operators became IT departments. Each wave of technology compresses certain functions and expands others. AI is doing the same thing now: faster, and with more impact on how careers actually progress. The career ladder changes.

What’s different this time is where the pressure hits home. It’s not just entry-level work being automated. Entire layers of management – coordination, reporting, scheduling – are being compressed, while new roles emerge around AI handling, oversight, and leadership judgement. The traditional career ladder is being reshaped from underneath us.

This is NOT just a story about job loss. It’s a story about role redesign. About moving from doing tasks to owning outcomes. About understanding where human judgement still matters: and I encourage you to step into it deliberately.

In this video, I walk through how organisations are restructuring in response to AI, what’s actually changing in the career ladder, and how you can prepare without needing to become a technical “AI Vibe Coder blah blah” specialist. The goal isn’t to keep up with AI; it’s to stay relevant in a system that is reorganising itself around it. I truly hope this helps.

TLDR -what restructure due to AI looks like:

• AI agents doing frontline work
• Human “AI handlers” managing and auditing systems
• A compressed management layer
• Expanded leadership roles focused on judgement and accountability

Laurel Papworth on how organisations restructure around AI and how to manage that expansion and compression.

Transcript of AI Proof Your Career – Career Ladder and Organisation Restructure due to Artificial Intelligence

[00:00:03.24]
Hello, my name’s Laurel Papworth. It’s pretty late in the afternoon, early evening here actually. Um, but I, so my voice is a bit croaky, but I want to get this video out. It’s another one in the series of how to AI-proof your career. And this specific one is about how the organisation’s going to restructure. And the career ladder changes and how you are to prepare for that. You’ve got a little while, but you should know this is going on. We all love a restructure, don’t we? Um, no, we don’t. So it’s not just for employees.I would encourage anybody in HR to consider these key points and maybe also change management consultants, people like that who are affecting change within an organisation, restructuring and things like that.

[00:00:55.10]
So the first thing to recognise, I’m going to talk about what the issues are and then how to overcome them. With emergent tech, major restructures happened. So think about the typing pool that was the whole floor of the office was typists using typewriters, and then they reskilled and upskilled to use personal computers and then word processing packages like, um, WordPerfect and Microsoft Word, and then they were deployed in a in a distributed way. So not a central typing pool, but distributed throughout the whole organisation. Every floor had a personal assistant, an executive assistant, administrative officer.And in some cases there’d be one computer in that department at the beginning, and it was on his or her desk. And they were the people who entered everything into the computer. So you can see the whole organisation restructured. Instead of tonnes, dozens and dozens of typists, These people were redeployed into every office and eventually everybody was reskilled and upskilled. Telex people, the ones, the one or two people that looked after the telex machines, that was how interoffice communication took place. That got shifted into an IT department with mail servers. You get the idea. Every emergent tech has this change that occurs. And I call it compression and expansion. So the typing pool compressed, but what expanded was reskilled people placed within departments. In-house librarians that would find physical documents for people were replaced by databases, database engineers, internet searches, things like that. And you have this compression, expansion, compression, expansion. So when you know that, it’s a good idea to consider how much of your work is going to be compressed, how much would be expanded. In the last video, I talked about taking task bundles, breaking them down into tasks, and then putting them into columns. This one’s AI ready and this one needs human non-negotiable so that you can actually see which tasks you have there. The layoffs have been pretty horrendous.I know there’s AI washing where companies are saying we are laying off staff because of AI, but it’s actually because of poor management of resources and they’re blaming AI. Poor old AI. Who wants to blame AI? But yeah, AI is causing some of it too. So I’m talking here Intuit that make QuickBooks and Mailchimp. You’ve got Cisco Systems. Shout out to Cisco. You’ve got Oracle, IBM. Oh yeah, Dell. And Amazon— so Amazon laid off or is laying off 14,000 corporate office staff. And I specifically want to talk about the type of staff that are being laid off and then the other type of staff that are being hired in place of them. And it’s not technical staff, so I’ll come to that in a moment. You don’t have to go out and get an AI degree. That’s not what this is about. So the compression that’s mostly occurring is in the structural career ladder. It’s the structure of the department, and I just need to kind of explain how this works. So let me take a customer service department.

[00:04:22.28]
You have a customer service agent, human being, who takes the calls and responds. They have a knowledge base, they’re trained, and they answer the questions. If a customer is unhappy, and asks to speak to a manager, then it gets escalated to the team leader or the supervisor. The team leader or the supervisor works for kind of like a department manager, and that department manager works within the system and has like maybe 12 dashboards. There’s a staff scheduling dashboard and there’s a resource allocation dashboard and there’s a procurement dashboard and there’s a request toilet paper dashboard. I don’t know. There’s all kinds of dashboards and they know exactly which system to go into in order to look things up and do what they need to do. They do resource allocation stuff, scheduling, and this dashboard stuff. We’ll come to that in a moment. Above them would be, I don’t know, an area manager and above them would be a chief customer service officer who sits on the executive and they’re in the leadership group. They, the leadership doesn’t work in the system, they work on the system. They initiate changes. Managers don’t change the system. They work with the changes as they come down the line. So that’s been the career ladder. If you’re really good at customer service and then you show yourself to be a real people person, you might get promoted into supervisor because you go above and beyond. From supervisor, you get promoted into department, uh, into the group head and then into, you go off and do some post-grad graduate work and you end up being the one of the chief executives in this area. Awesome. That’s the traditional career ladder.

[00:06:09.05]
As the organisation goes through a restructure, the agents that speak to the customer will be AI agents. So AI agents will be first port of call for customers. They’ll be given something called a decision hierarchy.In human terms, it’s an escalation chart. There’s a few differences, but just call it a decision hierarchy, which says if the customer says they’re going to the ombudsman, if the customer says they’re going to the press, if the customer says my brother’s a lawyer, whatever it is, then the agent and the customer service rep both know to escalate this. And in this case, there’s going to be an influx of AI handlers who are like micromanagers for a team of agents. So if you have core skills and you you know the domain knowledge, you know the customer service knowledge or the technical support knowledge or the whatever it is that you’re doing, you’ll be put in charge of the agents so that they can come to you, your mummy or daddy, whatever, however you identify. But they’ll come and ask you questions. Should I respond to this? Because this is going to be written into the agents. You’ve got to put constraints in which says don’t email back automatically, escalate it if you see this problem.

[00:07:34.17]
Once it goes through to this mid-level management, at the moment, the hiring around AI handlers is primarily interns. So maybe you did a graphic design course and you are straight out of graphic design college. You’ll be given charge of the AI agents that are creating the graphic designs for the company, and your job is to make sure that the images don’t have two heads and 10 fingers and are doing something inappropriate. Inappropriate that could walk you into a brand reputation issue or, um, something catastrophic. It’s problematic because it actually means that graphic designers won’t get skills in doing graphic design. They’ll only be given skills in managing AI agents that are doing graphic design.And I think that’s a concern. A replacement there for customer service where it becomes AI agents, and then there’s an AI handler who is somebody who has domain knowledge or specific knowledge, and then just enough information to be able to take on their calculation, decide what to do with it. Human judgement steps in where there’s accountability. So wherever there’s risk or accountability, put a human in. The management layer vanishes altogether. This is the compression. So you’ve got compression at the agent level, expansion at the handler supervisor level, and then the manager level with all the dashboards. The AI Claude can create a dashboard in seconds. Resource allocation, AI can do that. Staff scheduling, AI can do that. So these manager layers are problematic because they build in efficiencies within a system that an AI can be more efficient at. I’m not saying there won’t be an AI handler at a management level, but there’ll be many, many, many less. So there’s really not going to be many. We’ll get on how to fix this in a moment.

[00:09:28.28]
The next level up, which is, um, into the leadership space, that expands and that expands automatically, because what’ll happen is everything that’s outside of the standard operating procedure will be sent through to a human who takes agency. So if you’re the sort of person that sits at work and goes, nah, above my pay grade, oh, I’m not getting involved in that discussion, that’s worth more than my job is worth, anything like that, you have a problem, Houston. But if you’re the sort of person who likes to jump in and resolve those things and find solutions, then you move into what I would call the leadership layer. And that, that area expands, which is one of the reasons why you are seeing OpenAI and Anthropic and others hiring a lot of content creators that have deep experience in delivering great messages because the AI can churn out low-grade stuff, but they need people with real leadership who are willing to put skin in the game to step up. And drive certain areas outside of the system. So you’ve got AI agents replacing customer service, but then you have an intern level coming in as the supervisor. You’ve got the compression of the management layer, and then you’ve got expansion of the leadership area.

[00:10:54.19]
What are you going to do? Because your normal career ladder is to take each one of those, and now they’re— some of those steps are disappearing. And that’s what happens with AI. It compresses the whole organisation, takes 10 steps person makes it through. I would suggest that you work on specific skills and you don’t worry too much about others.

[00:11:17.18]
Let me explain. First of all, be really honest with yourself and say where you actually sit within the three tiers of an agent, intern supervisor, and leadership. Are you an agent just doing day-to-day work? Are you the supervisor who tries to problem solve, or are you leadership where you take responsibility and accountability? Which one are you most comfortable with? If you are going to stay in the handler area, I suggest you deepen your domain knowledge. So when I worked in, um, troubleshooting, like technical support, there were certain people that could answer lots and lots of calls and resolve them very quickly. I was always the person who went and chased up the answer later on with the vendors or with subject matter experts because I hated not knowing the next time somebody rang what the answer was so that I could tell them. And that paid off very well for me later on because I would be called into very senior meetings because I was the person who understood more about how these pieces went together. So people who quickly churn out answers, that’s going to be a problem for them. AI can do that. But the outliers, the questions that people don’t have a quick answer to, the ones that make you say, oh, lemme just stop and think about that for a moment. They’re the ones that you want to deepen your knowledge on. So that means going very, very deep on that.

[00:12:53.23]
If you want the leadership layer, you have to understand accountability and be willing to take accountability. And this is what accountability looks like. Yep, I’ve listened to everybody. I I think you’ve all got great points. We’re going to have to make a decision here, and my decision is we’re going to pursue this path. And if that doesn’t work out, it’s on my head, not on your head. I will back you and protect you in this. Are we all in agreement? Okay, Jim, you’re never in agreement. Everybody else is awesome. Let’s go. So it’s about taking accountability. It’s about making a decision under pressure. It’s about not letting the AI— the— well, the system said not to do that, like That’s not leadership. You need to take back the reins. You’re the pilot. AI is the co-pilot. Make sure you pilot this ship. So if you feel like you can do that, then awesome. But otherwise you might want to stay a handler. I think middle management, there’s no chance. All those tasks are compressed. They’ll be farmed out. They’ll become— the executive will run their own dashboard and press a button to get the report that used to take a week to write. That’s, I think that’s a no-brainer. So I wouldn’t be looking for a manager within the system. I’d be looking for leadership to change the system or handling very specific, very deep knowledge on specific topics. Okay, so another area to look at would be seam knowledge. I would call this the branches and it’s systems integration. Not just knowing your department, but knowing how your department impacts operations. IT, finance, HR, knowing how all the pieces come together. AI is really good at deep, one subject domain, but it’s not so brilliant across the board. I would recommend developing seam knowledge. Understand how your department integrates with other departments, like really become a business person here. The person who knows how marketing breaks operations and impacts IT is invaluable. It’s not parameters at the moment that AI can manage as well as a human with deep knowledge can. So single domain experts are fully exposed because I can train an AI. A domain just means subject matter. So if you have, I don’t know, deep knowledge on billing and fraud systems, I can chuck all the manuals and all the trouble tickets for the last 10 years and everything at an AI and go, learn this, and it will learn it. So that kind of deep knowledge is important. The troubleshooting side or the outlier side is more interesting. That’s what I mean by the edges. But if you have cross-domain knowledge, so you know how the billing system interacts with the fraud system, interacts with the I don’t know, data collection systems with something else, then that, that’s very valuable at the moment.

[00:15:54.20]
You also want to practise your escalation skills and be ruthless. When does a human have to be involved here? And if there’s a temptation to say, oh, well, the system’s recommending this, I’ll just do that, really put the brakes on and ask yourself the difficult, uncomfortable question: why? Why are we doing it? Why is this happening? Why? What are, what are the outcomes if we don’t do this? What are the outcomes if we do this and it’s wrong? I would get comfortable owning the edge cases, these outlier cases, and say, put your hand up for them. I know it feels like work, but I’ve got, I think it’s fulfilling. I think you’ll find it fulfilling, certainly more than just doing the same thing every, and answering the same question all the time. The handler needs to know how to escalate up into leadership, and then the leaders need to know how to delegate amongst a team of humans to resolve the issue. So there’s still the old management skills there. It’s just the management layer itself has gone.

[00:16:55.18]
Get better at prompting and not generic prompting. And this is a scaffolding prompt and this is an n-shot prompt, but how it specifically applies to your work. So know the constraints for your work. Know where the AI doesn’t know the constraints. So that you have to add them in. Get better at managing the agents. And there’s a whole range of things there. Whenever an AI agent goes off the rails, it’s invariably because it wasn’t given thinking controls and thinking constraints.

[00:17:27.25]
So knowing all the bits and pieces that go into that is, I think, pretty critical. And if you can’t audit the AI agent’s output, If you don’t, somebody else will. So get better at AI so you are the one who’s piloting the AI and auditing it and escalating it and that sort of thing. I want you to reframe your career. If the old ladder was a rep, a representative, and then a manager, and then executive or leader, the new, the new one is AI handler. Demonstrated expertise. Actually, not just demonstrated expertise, but demonstrated judgement. The ability to say, that’s a good decision, that’s a bad decision. I think governance. So accountability and risk and all those things that maybe you don’t normally think about too much. Now you need to. And the other one is you do think about them, but it’s instinctive for you because you don’t have to explain to an AI. But once you’re working with an AI, you realise it’s got no clue. It’s literally like it’s first day on the job and you know, why would you do that? That, that would put things at risk. Oh, okay. It’s, it’s really interesting. I would not at this point in time be optimising for the management rung. And so any courses you’re doing in that area, I think you might want to think about shifting it into a leadership job as opposed to management. Be careful. There’s all sorts of courses out there that call themselves leadership, but they’re management. It’s a bit of a furphy. Managers have to do some leadership and some management, full stop. But that’s splitting out now. Just be aware of that. Take the list you made in the last video with the AI-friendly side and the humans-only non-negotiable side.
Take the AI-friendly side and then make decisions about how you would audit the AI to make sure it was doing each of those tasks correctly. So. So if on the audit side you have that you have to summarise new regulations coming through, how would you audit those AI-generated summaries? What would you have on your checklist? It wouldn’t be enough to go, yeah, it looks okay to me. Make sure that you’ve got a way of going back into the senior leadership team and say, I’ve audited all the work the AI’s been doing. This is a repetitive issue I’m seeing. This is what’s going on. Because now you’re showing leadership and now you are showing that you belong on a team with AI because you are managing the AI, you’re piloting the AI. You’re not just outsourcing to AI and letting it try and do your work for you.

[00:20:15.20]
Anyway, I’ve been talking way too long. I hope that made sense. Specifically focus on the compression and expansion. Just keep thinking to yourself, as the organisation restructures, it will compress certain things like the management layer, Amazon firing 14,000 managers, and then it will expand in expertise area, OpenAI, Anthropic hiring good quality comms people. And then you’ve got other companies that are hiring entry-level graduates and you’ll see a lot of jobs, must know AI to be AI handlers.

[00:20:51.22]
So if you can start to think about the career ladder and proofing your career that way, then you can work with AI and not work for AI. Hope you found this useful. And don’t forget to stay human.

Note: she’s a long one, thank you for sticking with it.

Resources for AI Proof Your Career – Restructure and AI Career Ladder links

• International Monetary Fund (IMF) Gen-AI: Artificial Intelligence and the Future of Work https://www.imf.org/en/publications/staff-discussion-notes/issues/2024/01/14/gen-ai-artificial-intelligence-and-the-future-of-work-542379 (note the distinction between job displacement vs job transformation)
• McKinsey How AI is—and isn’t—changing the future of work https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-organization-blog/how-ai-is-and-isnt-changing-the-future-of-work (note: these are the very companies at risk of compression).
• ABC Australia Amazon cuts 14,000 jobs as it ramps up AI push https://www.abc.net.au/news/2025-10-29/amazon-culls-14-000-jobs-in-ai-push/105945212 (note: AI washing or real compression?)
• IBM restructure and hiring pause due to AI – Bloomberg https://www.bloomberg.com/news/articles/2023-05-01/ibm-to-pause-hiring-for-back-office-jobs-that-ai-could-kill
• OECD AI and Work (papers/topic) https://www.oecd.org/en/topics/ai-and-work.html
• Gartner on delayering middle management. Somewhere in here https://www.gartner.com/en/newsroom/press-releases/2024-11-06-gartner-it-symposium-xpo-2024-barcelona-day-3-highlights (survey: “In a quest to build dynamic leadership capacity, 56% of CEOs said they will use AI to de-layer most middle management roles within the next 5 years.”)