The Digital Cognitive Labour Market Is Here. How Ready Are You?

Context

The business is a group of talent agencies in the UK.

Until recently, one of my team members, Katie, spent 30 minutes reviewing and reformatting a candidate’s CV before it was ready for client submission.

It was careful work. Necessary work. Entirely manual.

Now we have AI and it takes two minutes.

Katie was six years old when one of our subsidiaries first opened its doors. More than thirty years later (forgive me, Katie), she leads a business that has survived recessions, Brexit, and COVID-19. We’ve seen disruption before.

But this is different.

Change isn’t new. The speed of change is.

When AI reduces a 30-minute task to two minutes, most leaders focus on the time saved. Yes, it costs less. Yes, it’s more efficient. But cost reduction is the least interesting part of the story.

We redesigned our KPIs so those reclaimed 28 minutes didn’t disappear. They were redirected toward winning clients, strengthening relationships, and shaping strategy. In other words, we expanded capacity. And in business, capacity creates opportunity.

The real gain isn’t time saved. It’s more output — and better output. AI increases both the volume and the quality of what we produce. It multiplies productivity.

This isn’t just efficiency. It’s a force multiplier for growth.

What Was Human — And What Is Now Machine

For decades, professional work was limited by how much people could process in a day.

Drafting. Reviewing. Summarising. Screening. Reconciling. Documenting. Productivity increased only when human effort increased. The work was necessary and billable.

However, the constraints were simple: Time. Fatigue. Headcount.

Today, organisations are buying something new. Not just software.

They are buying:

• Reasoning time

• Agent autonomy

• Workflow execution

• Decision support

• Multi-step task orchestration

This is digital cognitive labour.

Across law, healthcare, insurance, recruitment, finance — structured AI workflows now absorb tasks once handled by humans.

This is not just automation. It is machine-performed cognitive work. And when cognitive work scales, revenue can grow without increasing headcount.

The Digital Cognitive Labour Market at a Glance

The digital cognitive labour market is starting to build in layers.

Foundation model providers like OpenAI, Anthropic, and Google DeepMind provide raw reasoning capacity — general digital cognition delivered via a set of rules allowing different software applications to communicate called Application Programming Interface (API) or enterprise cloud.

Agent infrastructure providers such as LangChain and AutoGPT enable organisations to orchestrate multi-step task execution — turning digital reasoning into workflows.

Vertical digital cognitive labour providers such as Harvey (legal), GitHub Copilot (software development), and Nabla (clinical documentation) act as specialised digital knowledge workers.

Individually, these appear to be tools.

Collectively, they behave like labour supply offerings.

The Transition from Software Tools to Digital Cognitive Labour

What makes this different from past software transformations is not automation.

It is measurability.

With tools like LangChain and LangGraph, organisations can trace AI tasks step by step:

• Which model was used

• How many tokens were consumed

• What decisions were made

• Which tools were called

• What the task cost

For the first time, digital thinking can be measured at the level of a single task. That changes everything.

It allows organisations to:

• Predict margin before scaling

• Cap agent labour in order to manage cost escalation

• Compare models for quality-to-cost trade-offs

• Optimise prompts to reduce token usage while improving output

• Justify machine OpEx

• Build usage-based pricing

AI stops being a black box. It becomes measurable work.

This is an exciting development because it allows us to move beyond measuring productivity solely through the traditional lens of the human Full-Time Equivalent (FTE).

Through my research, I’ve been developing and testing this under the Pignatelli Framework (PF). Traceability turns machine execution into structured labour inside the production function:

• hFTE — human judgment and accountability

• mFTE — machine execution capacity

• aFTE — algorithmic cognitive capacity

• dFTE — directed and orchestrated digital workflows

The core idea is simple:

Total Productive Capacity = hFTE + mFTE + aFTE + dFTE

Productivity is no longer just about human hours. It becomes the coordinated sum of human and digital capacity.

As Harvard Business Review research suggests, AI reshapes work when it augments people rather than replaces them (Dell’Acqua et al., 2023). The advantage lies in redesigning work — not removing workers.

Cognitive labour is no longer invisible software. It is now:

• Deployable.

• Traceable.

• Costed.

• Governable.

And once something is measurable, it becomes manageable. Human capability moves upward toward judgment, creativity, accountability, and leadership.

Digital cognition absorbs structured execution at scale. This is not labour replacement. It is labour re-composition.

The Global Dimension

At my Harvard Business School reunion in 2025, professors spoke about shrinking working-age populations across advanced economies. Demographics are tightening labour supply, but digital cognitive labour changes the equation.

Unlike human labour, digital cognition:

• Requires no healthcare

• Requires no visa

• Pays no payroll tax

• Has no relocation costs

• Has no language barriers

• Has no geographic limits

It scales instantly.

It operates globally.

It does not fatigue.

This isn’t about replacing people. It’s about working differently in a world where there are fewer human workers. Machine cognition can support a clinician in New York, triage claims in Singapore, and draft contracts in London, all simultaneously.

Labour without borders.

However, (disclaimer alert!) not governance without borders.

Governance – The Hard Part

Digital cognitive labour may operate globally. Regulation does not. To provide context:

• The EU enforces GDPR and the AI Act.

• The UK applies sector oversight.

• The US regulates by sector and state.

• China enforces strict data localisation.

• India applies its Digital Personal Data Protection Act.

Execution may be borderless. Compliance is not.

If governance is weak, trust erodes. If regional alignment is ignored, borderless AI becomes a liability. With governance, scale multiplies capacity. Without it, scale multiplies risk.

Three Executive Steps for AI Labour Readiness

If digital cognitive labour is part of your workforce, readiness is leadership.

The organisations that win in the digital cognitive labour market will not be those that deploy the most tools. They will be those that redesign capacity, govern intelligently, and measure relentlessly.

Expanding capacity isn’t just about growth — it’s about building organisational resilience.

Back to Katie

Katie’s 28 reclaimed minutes were never about saving time. They were about what we chose to do with it.

Katie didn’t lose part of her role. She gained part of her future.

We redirected that capacity toward clients, strategy, and growth. She spends less time formatting and more time leading.

That is the shift.

Digital cognitive labour doesn’t replace human value. It elevates it.

The question is no longer whether AI works. The question is whether your organisation is ready to work with it.

References

Anthropic (2024) Claude API documentation. Available at: https://www.anthropic.com 

Brynjolfsson, E., Li, D. and Raymond, L.R. (2023) ‘Generative AI at work’, Harvard Business Review, November–December.

Dell’Acqua, F., McFowland, E., Mollick, E.R., Lifshitz-Assaf, H., Kellogg, K.C., Rajendran, S., Krayer, L. and Lakhani, K.R. (2023) ‘Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality’, Harvard Business Review, September–October.

European Parliament and Council (2016) Regulation (EU) 2016/679 (General Data Protection Regulation – GDPR). Official Journal of the European Union.

European Parliament and Council (2024) Artificial Intelligence Act (EU AI Act). Official Journal of the European Union.

Google DeepMind (2024) Vertex AI and Gemini documentation. Available at: https://cloud.google.com/vertex-ai (Accessed: 17 February 2026).

Government of India (2023) Digital Personal Data Protection Act, 2023. Ministry of Electronics and Information Technology.

Harvey (2024) Harvey AI product overview. Available at: https://www.harvey.ai 

LangChain (2024) LangChain documentation. Available at: https://www.langchain.com 

LangChain (2024) LangGraph documentation. Available at: https://docs.langchain.com

Microsoft (2024) GitHub Copilot documentation. Available at: https://docs.github.com/copilot 

Nabla (2024) Nabla Copilot product documentation. Available at: https://www.nabla.com 

OpenAI (2024) OpenAI API documentation. Available at: https://platform.openai.com/docs 

People’s Republic of China (2021) Personal Information Protection Law (PIPL).

People’s Republic of China (2021) Data Security Law (DSL).

State of California (2018) California Consumer Privacy Act (CCPA).

UK Parliament (2018) Data Protection Act 2018. London: The Stationery Office.