The Stages of an AI Project

There are four distinct stages in an AI project that it’s important to be mindful of. Each requires a different skillset and attitude to make it successful.

#1 – Business Framing

Making the business case for an AI solution starts with understanding the problem your business is experiencing and the pain that it results in for them. Too often, IT teams are eager to play with new tech, jump straight to solutioning, and fail to ask themselves whether the new tech is “worth it” for the business. Build your business case. Show the problem or opportunity in terms of time and money. Explain the solution in terms of business results. Understand the costs to build it – at each of the next three stages.

#2 – AI Skunkworks

To quickly attack the risk of your AI project, you need to start with an iterative proof-of-concept that builds the AI model. This will involve repeated short attempts to gather your relevant data, augment it with interesting external data, select your algorithms, train your model and test your results.

#3 – Operational Buildout

Connecting your successful proof-of-concept to your existing workflows and data pipelines will be the bulk of the effort for most organizations. How quickly you can do this depends on the state of your software architecture, DevOps and data operations and how flexible they are to change.

#4 – Business Rollout

Don’t wait to the end to think about how your business will successfully use the new features and data insights you provide for them. Plan for this and expect to spend time supporting them through the rollout, including making adjustments when necessary, so that your work will truly achieve the business results aimed for at the start.

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