A friend who has worked with logistics companies recently built a set of tools to bring together data from across a complex supply chain and surface dashboards and signals to support business decisions. The technical work would previously have required a huge investment of time, skills and money. With AI, he did it in a few weeks.

They can also adapt the tools as the business strategy and bets evolve. As the questions change, they can change the data they bring in and the signals they look for.

To understand what AI made possible here, let’s borrow the Motivation, Opportunity and Ability (MOA) framework from consumer research (MacInnis et al., 1991) and apply it to what people can achieve with AI.

The framework describes three conditions that help someone achieve a goal:

• Motivation: Do they want to do it? Do they have a reason to put in the effort?

• Opportunity: Do they have the time, resources, access and authority to act and influence the outcome?

• Ability: Do they have the knowledge and skills to do it?

Someone can want to solve a problem but lack the skills to do it. They might have the skills but lack the time, resources or access. All three conditions matter.

How does this apply to my friend’s experience?

The motivation was already there. They understood the business need and wanted better information to support decisions. That motivation could be extrinsic, such as achieving business goals, advancing their career or increasing revenue. It could also be intrinsic, driven by a personal mission, a passion for the work or the satisfaction of solving a problem.

Without AI, the effort involved in learning the technical skills, finding support or funding the work could have delayed the project or made it difficult to justify. AI made getting started more achievable. Seeing progress can also help someone stay motivated and continue.

The opportunity changed because the project became feasible with fewer resources.

Without AI, they would likely have needed much more time to build it themselves or the budget to bring in technical support. Depending on the business, that could also mean getting approval and waiting for someone to become available.

AI reduced the time and cost involved. They still needed access to the data and permission to use it, but they had a more practical way to move forward.

The biggest change was in ability.

My friend understood what they wanted to achieve. They knew which business questions mattered. But bringing data together and building tools to answer those questions required technical work.

Without AI, they would have needed to learn the missing skills, work with someone who had them or limit what they built. With AI, they could take on more of that work themselves, with help understanding technical concepts, building the tools and solving problems along the way.

This does not mean they became an expert in everything involved. There is a difference between learning a skill and being able to complete a task with assistance. Both can increase what a person is able to achieve.

Their business understanding still mattered. They needed to decide what to build, which signals were useful and whether the results made sense.

The benefit also continues beyond the first version.

As the business strategy changes and new bets emerge, the tools need to evolve. There may be new data sources to connect, different calculations to use or new signals to monitor.

Without AI, these changes could require more budget, another development request or further learning. With AI, my friend can make changes more readily and test whether they help answer the next business question.

That ability to adapt needs to come with evaluations and verification. Changes to the data, calculations or signals need to be checked against known results and business expectations. A dashboard can look right and still give the wrong answer.

Data access rules also need to apply throughout. People should only see information they are authorised to access, and AI systems should only use data they are permitted to process. Bringing sources together should preserve those rules.

For the individual, AI creates more opportunity to act on their ideas and business knowledge. For the business, it creates more capacity to experiment, learn and adapt.

This is how AI can democratise capability. It can make work that previously required significant resources achievable for more people.

When motivation and opportunity are also present, that expanded ability helps people turn their understanding of a problem into a useful solution, and keep improving it as the business changes.

The company might deliver the same amount of software with fewer engineering hours—the same with less. It might use its existing engineering capacity to build more products—more with the same. Or it might redirect that capacity from feature implementation towards product management, architecture and testing.

The same productivity improvement can therefore lead to very different gains. Which one the company realises depends on how it responds.

In economics, the relationship between inputs and output is represented by a production function:

where Y is output, K is capital, L is labour and T represents the available production technology. AI changes that technology, which can change how productive labour and capital are and how the company chooses to combine them.

What the technology changes: technological bias

Technological bias describes which inputs a technology makes relatively more productive. Economists measure this by comparing the inputs’ marginal products: how much extra output comes from adding a little more of one input while keeping the others unchanged.

If a technology increases the productivity of labour more than capital, it is labour-biased. If it increases the productivity of capital more than labour, it is capital-biased. Here, capital means productive assets such as compute, GPUs, software systems and AI infrastructure, not simply money.

The key point is that technological bias describes what the technology makes possible, not what the company ultimately chooses to use. A labour-biased technology, for example, does not necessarily mean the company will employ more labour.

What the company chooses: factor use

Factor use describes how much labour, capital or another input the company actually uses after adopting a technology.

A technology is labour-saving if it leads the company to use less labour, and labour-using if it leads the company to use more.

But this depends on whether we mean labour used per unit of output or in total. A technology can reduce the labour required to produce each unit while total labour use moves in the opposite direction.

The substitution and scale effects

Two forces help explain this: the substitution effect and the scale effect.

The substitution effect describes how a company changes the mix of inputs used to produce a given amount of output. For example, AI may allow capital to perform work that previously required human time, reducing the labour required for each unit of output.

The scale effect describes what happens when lower costs make it worthwhile to produce more. The company may build more products, serve more customers or undertake activities that were previously uneconomic.

Whether total labour use rises or falls depends on which effect is stronger. If the scale effect outweighs the labour saved through substitution, a technology can be labour-saving per unit of output while still being labour-using in total.

How technological bias and factor use fit together

Technological bias and factor use answer different questions:

• Technological bias: Which input does the technology make relatively more productive?

• Factor use: Does the company end up using more or less of that input?

Applying the framework to generative AI

Consider a generative AI coding assistant that helps software engineers write, test and review code more efficiently.

Suppose it reduces the engineering time required per feature by 30%, but the lower cost of software development leads the company to double the number of features it builds. The company also uses more compute and AI infrastructure to support the tool.

Under these assumptions, the technology could be:

• Labour-biased, if it raises the marginal product of engineering labour relative to capital.

• Engineering-labour-saving per feature, because each feature requires fewer engineering hours.

• Engineering-labour-using in total, because the company expands software production enough to use more engineering hours overall.

• Capital-using in total, because it uses more compute and AI infrastructure.

These are not contradictions.

Applying the framework to agentic AI

Generative AI often helps people perform individual tasks. As AI systems become capable of completing longer sequences of work, however, their economic impact can extend from improving individual tasks to reorganising entire workflows.

An agentic system might choose a task, produce an output, test it and escalate uncertain decisions to a person. This makes it useful to distinguish between two types of labour:

• Execution work: carrying out tasks such as writing code, testing, documentation and routine coordination.

• Complementary work: setting objectives, exercising judgement, reviewing results, handling exceptions and taking responsibility for important decisions.

These are types of work, not fixed categories of workers. The same software engineer may do both.

Suppose an agentic system automates much of the execution work in a software workflow while allowing experienced engineers to oversee a larger amount of AI-generated activity. The company also invests in more compute and agent infrastructure.

Under these assumptions, agentic AI could be:

• Biased away from execution labour and towards AI capital and complementary labour, if AI increases the relative productivity of compute and human judgement.

• Execution-labour-saving, because less human execution time is needed for each workflow.

• Complementary-labour-using, if the expansion in AI-enabled activity creates more demand for direction, evaluation and exception handling.

• Capital-using, because the company uses more compute and agent infrastructure.

The important point is that agentic AI can affect different kinds of labour differently.

The important shift may be in the composition of work

The impact of AI is often framed as whether companies will use more or less labour. But the more important change may be what people spend their time doing.

AI may affect tasks within the same job differently. As some activities are delegated to AI, people can spend more time setting direction, applying judgement, resolving ambiguity and evaluating results.

Its economic impact may therefore come not only from doing existing work more cheaply, but from changing how work itself is organised.

What this means for AI and work

A productivity gain alone cannot tell us whether AI will increase or reduce the use of labour. We need to understand which inputs become more productive, how companies reorganise production, and how much additional activity becomes worthwhile as costs fall.

As AI progresses from assisting with individual tasks to carrying out longer workflows, one of its most important impacts may be less about reducing labour and more about expanding what people can achieve. Human work may shift towards the activities where people add the most value, with people playing a more active role in shaping, guiding and taking responsibility for what AI does.