AI Is Labour Technology, Not Information Technology

Why This Distinction Changes Everything About Your Workforce Strategy

For the past forty years, we’ve treated new technology as tools that make people more productive.

Software helped accountants work faster.
CRM systems helped salespeople manage more customers.
Cloud platforms helped teams collaborate better.

We called this “Information Technology” because that’s what it was. Technology that processed information to help humans do their jobs.

AI is different.

AI doesn’t just process information faster. It does the work instead of people. Entire tasks, entire roles in some cases and people are talking about even more in the future – perhaps whole departments.

This isn’t Information Technology. It’s Labour Technology.

And if you’re still treating AI like IT, you’re making decisions that will haunt you in 18 months time.

What’s the Difference?

Information Technology makes people 10% more productive at their existing jobs.

A spreadsheet doesn’t replace the accountant. It makes the accountant faster at calculations so they can focus on analysis and judgement.

Labour Technology replaces 30% of the work people used to do.

An AI system doesn’t make your legal researcher faster at finding case law. It does the research. The researcher either finds something else to do, or there’s no longer a role for them.

That’s the fundamental difference. And it requires a fundamentally different response.

The Numbers Tell the Story

By 2030, McKinsey projects that AI will automate up to 30% of work hours across the economy. Not 30% faster. Thirty per cent of hours simply gone, replaced by algorithms and automation.

That’s not a productivity gain. That’s a workforce restructuring.

The World Economic Forum estimates 85 million jobs will be displaced globally over the next few years. They also predict 97 million new jobs will be created. But here’s the uncomfortable truth: those won’t be the same jobs, done by the same people, in the same organisations.

The 85 million job losses will be concentrated in roles performing routine cognitive tasks. Data entry, basic research, report generation, first-line customer service, junior analysis.

The 97 million new jobs? We don’t know what half of them look like yet. They’ll be tasks we can’t imagine until AI makes them possible. Just as “social media manager” didn’t exist before social platforms, and “data scientist” wasn’t a role before we had data at scale.

Why Most Organisations Are Getting This Wrong

Walk into most organisations implementing AI and you’ll hear the same language:

“We’re rolling out Copilot to boost productivity.”

“We’re using AI to augment our teams.”

“This will free people up to focus on higher-value work.”

All true. All missing the point.

Because whilst IT departments are implementing tools and HR departments are running “AI adoption workshops,” nobody is answering the harder questions:

What happens to the junior associates when AI does their research?

Not “how do they use AI to research faster.” What do they actually do when the research task disappears entirely?

What happens to the customer service team when AI handles 60% of enquiries?

Not “how do they handle the remaining 40% more efficiently.” What roles do those people transition into? Do those roles exist yet? If not, who’s creating them?

What happens to your workforce composition when the pyramid collapses?

Professional services firms have built their business models on leverage: partners supervise managers who supervise associates who do the volume work. When AI does the volume work, what’s your business model? How do you train future partners if there are no junior roles to learn in?

These aren’t IT questions. They’re workforce transformation questions.

The Three Questions Leaders Should Be Asking

If AI is labour technology, not information technology, then you need to stop asking IT questions and start asking labour questions:

1. Which tasks should remain human?

Not “can AI do this?” but “should it?”

Some tasks AI could perform but shouldn’t. Client relationship building. Strategic decision-making where wrong answers are costly. Work where human judgement, creativity, or ethical reasoning is essential.

Some tasks AI can’t perform yet but will soon. Complex analysis. Creative problem-solving. Even elements of strategic planning.

Map every role in your organisation against these questions. You’ll be surprised how much work falls into the “AI should do this” category.

2. What new tasks should we create?

This is the question nobody’s asking. Everyone focuses on what AI will eliminate. Few organisations are systematically identifying what new value only humans can create when AI handles routine work.

Example: When AI automates financial reporting, what should your analysts do instead? Not “review AI outputs” (that’s checking work, not creating value). But genuinely new work:

Predictive scenario planning (AI provides data, humans model futures)
Strategic business partnering (AI handles numbers, humans drive business decisions)
Process innovation (AI frees time to redesign how work flows)

If you’re only eliminating tasks without creating new ones, you’re just doing more with fewer people. That’s cost reduction, not transformation.

3. How do we transition our people rather than replace them?

Here’s the uncomfortable economics: it’s often cheaper to make someone redundant and hire fresh talent with AI skills than to reskill existing employees.

It’s also short-sighted.

You lose institutional knowledge. You damage culture and morale. You create an employer brand problem. And you still need to find and hire people with AI skills, which is expensive and slow in a tight talent market.

The organisations that will win are those that build internal capability to transition people from shrinking roles to growing or newly created ones. That requires:

Honest assessment of who can transition (not everyone will)
Rigorous reskilling programmes (not generic training, but role-specific capability building)
New role creation (designing jobs that need doing)
Internal mobility infrastructure (making it easy to move people between roles)

This is hard. It takes 18-36 months. It requires investment. But the alternative is worse.

What This Means for Your Organisation

If you’re treating AI as Information Technology, you’re likely:

Measuring success by tool adoption rates (how many people have access to Copilot)
Running training programmes on how to use AI tools
Prototyping new AI tools
Expecting productivity gains without workforce changes
Leaving workforce implications to HR to figure out later

If you recognise AI as Labour Technology, you’re asking different questions:

Which roles will shrink or disappear?
What new roles should we create?
How do we transition people ethically and effectively?
What does our organisation look like in 3-5 years?

These are Board-level questions. CEO and CFO questions. Not IT department questions.

The Window Is Closing

Here’s why this matters now: you have a choice between proactive transformation and reactive cuts.

Proactive transformation means:

Analysing your workforce composition before the market forces you to
Redesigning roles around what humans should do
Transitioning people whilst you have time and budget
Creating new sources of value

Reactive cuts mean:

Waiting until competitors move first or investors demand action
Scrambling to reduce costs quickly
Losing good people who could have transitioned
Destroying morale and employer brand

The organisations doing proactive transformation now will emerge stronger. Better workforce, higher productivity, preserved culture, competitive advantage.

The organisations waiting will face brutal, reactive restructuring when the market makes the decision for them.

Where to Start

If this resonates, here’s what to do:

1. Audit your workforce through a labour lens

Which roles contain tasks AI can substitute for? Which roles can be augmented? Which must remain human?

Don’t do this as an IT capability assessment. Do it as a workforce composition analysis.

2. Calculate the full cost

Not just “what could we save by automating task X” but “what value are we leaving on the table by not transforming?” Most organisations obsess over automation cost savings whilst ignoring the bigger opportunity: what could your workforce create if they weren’t doing work AI should handle? Factor in the full cost (reskilling, transition support, redundancies, new roles) AND the full opportunity (new value, competitive advantage, capabilities you don’t have today).

Most organisations dramatically underestimate transition costs and therefore make poor decisions.

3. Create new tasks, not just eliminate old ones

For every hour of work AI automates, where should that hour go? What new value can your workforce create that they don’t have time for today?

If you can’t answer this, you’re not doing transformation. You’re just cutting.

4. Design new role types for displaced workers

Creating new tasks within roles helps, but it’s not enough when entire roles disappear or shrink by 60%. Where do those people go? Not “find another job externally” but “transition to new roles we intentionally create.” This requires active role design, not passive absorption:

What roles become necessary when AI handles routine work? (AI operations specialists, quality assurance analysts, human-AI workflow designers)

What roles become viable that weren’t before? (Customer intelligence architects, predictive business planners, innovation catalysts)

What roles have you always needed but never had capacity for? (Process optimizers, knowledge curators, cross-functional integrators)

For example: a regional financial services company faces automation of 40% of customer service roles as AI handles routine enquiries. Rather than redundancies, they create “Financial Wellbeing Advisors.” These advisors use AI to identify customers at financial risk (missed payments, unusual spending patterns, life events) and proactively reach out with support. The AI handles the data analysis; humans provide the empathy, judgement, and trusted advice that vulnerable customers need.

The reskilling programme takes 4 months:

Month 1: Financial analysis and AI tool training

Month 2: Counselling and sensitive conversation skills

Month 3: Regulatory compliance and advice boundaries

Month 4: Supervised practice with real customers

Result: 75% of displaced customer service staff successfully transition. Customer satisfaction increases (proactive support vs reactive problem-solving), and the bank meets regulatory expectations around vulnerability support whilst reducing overall headcount by only 15% instead of the 40% that pure automation would require.

4. Build governance for labour decisions

Who decides when to automate versus augment? What criteria matter beyond cost? How do you balance efficiency with employee welfare?

Without a framework, you’ll make inconsistent decisions that undermine trust and create legal risk.

The Bottom Line

AI is labour technology, not information technology.

That means your AI strategy isn’t a technology strategy. It’s a workforce strategy.

And workforce strategies belong in the boardroom, not just the IT department.

The question isn’t whether AI will transform your workforce. It will. By 2030, 30% of work hours will be automated. Roles will disappear. New ones will emerge.

The question is whether you’ll manage that transformation proactively and ethically, or whether it will happen to you reactively and brutally.

Choose wisely. The window for proactive transformation is open, but it won’t stay open forever.