SCAN AI AI PROJECT PLANNING PART 2 OF 7 - SETTING EXPECTATIONS

An AI project is a complex undertaking, involving a lot of preparation and planning, often accompanied by a steep learning curve and significant investments in both compute infrastructure, new skillsets and time.

In the first part of this guide we looked at how to get started, the need for a clearly defined problem statement and project scope, and how to begin setting timeframe estimationsand high-level ROI expectations. Here we consider the various stages of an AI project so your organisation can be fully prepared for the journey

Understanding the journey

The journey from conception to deployment through an AI projects involves several stages - each addressed in detail in the remaining parts of this guide - however we summarise these stages here in order to put them in context of each other and their relative weight and bias to the whole process. The flow chart below lays out the stages and the order they need to be approached in.

Although the stages must be tackled in this order, as each one is significantly impacted by the one that precedes it, it shouldn’t be assumed that each will take an equal degree of time or involve even expenditure. The below table shows how both time and costs are likely to be distributed across a typical AI project.

Stage	% Time	% Cost
Problem Statement	5	5
Data Preparation	60	10
Model Development	10	10
Model Training	10	60
Model Integration	10	10
Governance	5	5

You can clearly see that by far the greatest time and cost commitments are the Data Preparation and Model Training stages respectively. The former due to the work required to ensure data is adequately ready for the development of your model and the latter due to the specialist infrastructure required to effectively train an AI model. This will be explained further in parts three and five of this guide - however, that isn’t to say that stages with lesser time or cost commitments are not as important.

Setting realistic expectations

Although producing the problem statement is the initial stage, the accuracy of its details will be informed by understanding the relevant costs and timeframes expected at later stages of the project. Expect AI projects to be long - organisations should allow at least a year to see some results, assuming parameters, goals and objectives aren’t changed within this period. Also avoid the temptation to rush any stages - skimping on your data preparation or under-optimising your model prior to training will undoubtedly incur additional costs further down the line and potentially impact the project’s expected outcomes. Similarly, under-investment in hardware at the training stage may reduce costs in the short term, but timeframes will lengthen, adding other expenses and potentially compromising entire project goals.

It is worth reiterating the four out of five failure rate of AI projects at this stage, as although some projects may not really get off the ground due to lack of understanding of the challenges and costs at the problem statement stage, there will be many more that come to nothing after significant time and money has been invested but later stages such as training and integration were not executed well enough.

Read our 7 part AI Project Planning Guide

• Part 1 - Where do I start?
• Part 2 - Setting Expectations
• Part 3 - Data Preparation
• Part 4 - Model Development
• Part 5 - Model Training
• Part 6 - Model Integration
• Part 7 - Governance