How to execute an artificial intelligence project?
A step-by-step guide for business

Irina Kolesnikova
June 9th, 2022

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ai execution plan

The number of artificial intelligence (AI) projects is going to increase by double in leading organizations, Gartner reports. However, for businesspeople, it can be unclear where to start those projects as well as what to consider before taking them on.

Based on our experience in delivering more than 80 AI projects in 20 countries worldwide, and developing the AI strategy for the Estonian government we decided to help decision-makers avoid the pitfalls on their path to AI implementation, and created an actionable plan of what should be done and/or considered while creating and implementing an AI project.

A few nuggets from the detailed step-by-step plan:

how to execute an AI project
  • How to define a problem to solve with artificial intelligence and set up business goals. Describe the desired result as precisely, as possible.

For example, “How can we enhance the tax audition system?” is too vague for AI to solve, while “Can AI flag the issues for tax auditors?” already sounds like a candidate and promotes you to think about necessary criteria. (Read more about how to determine the problem in the guide.)

  • How to frame the problem for the artificial intelligence project using machine learning canvas
  • How to set up a team for an AI project (The list of the roles needed for any AI project is in the guide.)

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What else is included

  • Steps to explore your data. To make the data collection smoother and faster, answer 5 important questions about the data. (Find the checklist for the best data collection in the guide.)
  • Important things to know about the adjustment of the plan for deliverables
  • What you should know about the development, testing, and deployment of the AI model?
  • The step to pay attention to: educate your employees/end-users
  • The basement of the future prosperity: maintain and continuously improve your AI system


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