Most people think about AI as a way to improve business productivity. But let’s see how AI can benefit businesses and individuals in other ways, by lowering barriers and making capabilities accessible to more people.
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.