What problem are you trying to solve, and is artificial intelligence the right solution

You should start with the problem you’re trying to solve. Once you’ve identified the problem, can you explain why you are choosing AI? What additional intelligence do you need and why is AI the solution? You should consider whether:

• the problem you’re trying to solve is associated with a large quantity of data which an AI model could learn from 
• analysis of that data would be on a scale so large and repetitive that humans would struggle to carry it out effectively
• you could test the outputs of a model for accuracy against empirical evidence
• model outputs would lead to problem solving in the real world
• the data in question is available even if disguised or buried - and can be used ethically and safely

If you can’t satisfy these points, a simpler solution may be more appropriate.

You should consider the appropriate scale for addressing your challenge, organisational, system, regional or even national. Organisations may experience specific challenges in common, making collaboration valuable.

Working at a system-level, for example, may offer economies of scale through ‘doing things once’ and increased buying power. Key to this decision is the data required for the AI solution and at what scale the dataset is sufficiently large to ensure a minimum level of viability. It may be that data needs to be pooled across several organisations to achieve this.

Like any investment, you’ll need to produce a business case to justify expenditure. But owing to the experimental nature of many AI projects, this is not straightforward. 

Testing hypotheses on historical data, and then through a pilot project, may help you through some of the uncertainty.