How to transform a business case into an AI use case

Irina Kolesnikova
June 21st, 2022

The latest hype around AI hides that this is a complex but, if executed properly, rewarding way to enhance business. The journey toward beneficial AI implementation starts with a business problem. However, since machines speak their own language, business leaders naturally find it a challenge to describe this problem in a way that is clear enough for the AI.

To transform a business case into an AI use case, business leaders will need the help of an AI team, who judge if and how to proceed with the problem enhancement; the problem owner who knows the most about the issue; and technical specialists who understand how the problem described by the problem owner can be interacted with in the technical world.

Having experience in setting up machine learning and AI systems for more than 80 projects in 20 countries worldwide, MindTitan shares this guide on how to formulate an AI use case based on existing business challenges and how to quickly analyze which solution will be the most convenient and bring the most benefits.

Part 1. Choose the right use cases using the AI gap analysis

Discovering the right process to be enhanced with AI may be an unusual task for business people. A handy tool for this goal is the AI gap analysis. Having all the possible cases written down briefly with the most important pieces of information will soon help you put a plan together. It’s the first step to make sure you connect what AI can do to your organization’s objectives as well as assess its feasibility. Collect and describe ideas with the AI gap analysis: the main goal is to find out and sum up the info concerning current processes, future processes, how to close the gap between these two states, and setting minimal limits on the tools and procedures to be used.

Below you will find the seven steps of the gap analysis to determine the best use case for AI implementation.

  1. Define the use case
  2. Define the business processes gap
  3. Determine your data gap (including data collection)
  4. Assess an application gap
  5. Revise an infrastructure gap
  6. Estimate the use cases
  7. Define the first steps

Points 1 and 2 should be the task for business people as the first round to define the right process to enhance. Points 3-6 should be the results of discussions between business and technical people, such as data scientists, to describe the process to enhance in more detail. Point 7 should be a result of collaboration between all parties to prioritize use cases, think about the next steps, and create a master plan. To be more efficient, all steps should be supervised by AI experts.

Part 2. Specify the use case in detail using a machine learning canvas

After finishing the gap analysis, you have the project (or projects) that is the most suitable for your business goals and worth implementing (no point in trying to solve a problem with AI if it can be solved more easily), define them in the machine learning canvas in even more detail. The best version of the machine learning canvas results from collaboration with a good in-house or outsourced machine learning team. The machine learning canvas lets you lay down your vision for your ML system and communicate it with your team. This is a tool that can be updated during the project and can help when the team needs to refresh their heads about the project, and specifically is very useful if new members join the ML project.

Fill in the form to download the full AI project execution plan

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

  • The AI gap analysis reveals the big picture of different use cases, helping to build a long-term AI strategy. Place all answers into the table to make it easier to grasp.
  • Specific descriptions for each project from the ML canvas help to communicate and transfer the ideas and vision of the AI use case between teams and team members. As you fill it in, you’ll be able to identify key constraints of your ML system, which have an impact on the technology to choose.

To get the best results, remember: it is important to coordinate and discuss the answers with a good AI team.


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