SCAN AI AI PROJECT PLANNING PART 1 OF 7 - WHERE DO I START?

AI is in the news, tech press and business publications on an almost daily basis, often citing claims of ground-breaking social or financial impacts, revolutionary performance or ground-breaking results. Equally, there are many articles from naysayers, counteracting with warnings and limitations of the technology.

What never gets mentioned from either side of the debate is that four out of five AI projects fail. AI is a relatively new discipline, so perhaps the governance knowledge and technical skills required are still thin on the ground, but whatever you or your organisation’s starting point or strategy, there are many considerations you need to be aware of and pitfalls to avoid in order to increase the chances of success. This seven-part guide will take you through every step from initial project planning through to ongoing governance requirements once projects are deployed.

What can AI do?

AI and its closely related disciplines, machine learning and deep learning are very powerful tools, however it must be stated from the outset that AI isn’t suitable for every challenge and it can’t do everything. For example, not every problem is complex enough to require an AI solution - many can be solved with simpler data analysis tools or if-then rule based applications. Similarly, as we’ll see later in the seven stages, many of the latest AI applications are based on pre-existing frameworks and models, however unique application development requires expensive and experimental cutting-edge research, so it may just be that AI isn’t capable of solving your particular challenge - yet.

How do I apply AI in my business or organisation?

An AI project needs to start with a problem statement. A problem statement is a concise and focused description of an issue that the project aims to address. It should identify the problem, explains its impact and set the stage for proposing solutions. A well-crafted problem statement is specific, measurable, achievable, relevant and time-bound (SMART), however in the context of AI, there are a few things to avoid as described below.

Don’t frame the problem in the context of a specific solution

e.g. ‘Design a better recommendation engine’

Avoid framing the problem in terms of a specific technology

e.g. ‘Use an LLM to create product descriptions’

Don’t frame the problems by a set of desired features

e.g. ‘Our assistant needs to recognise 20 accents’

Avoid classifying your problem in the context of what already exists

e.g. ‘It should the Siri of financial advice’

Don’t presume the behaviour of users

e.g. ‘Why would you do it manually if we could automate it?’

Try not to solve problems that don’t yet exist

e.g. ‘If we do it like that, we’re going to have to do A, B, C also’

Your problem statement should also address:

Who - who are the stakeholders that are affected by the problem?
What - what is the current state, desired state, or unmet need?
When - when is the issue occurring or what is the timeframe involved?
Where - where is the problem occurring?
Why - why is this important or worth solving?

A clearly defined problem statement, will help advance your progress through the remaining stages of your AI projects.

What are the risks?

Leadership teams are likely to be the ones putting together a problem statement. However, few business leaders have a background in data science, so any project objectives need to be clearly understood and translated into distinct goals that can be achieved by an AI model. Be aware that this process may involve compromises to project goals or timeframe impacts, and technical teams need to be able to communicate any changes to the project scope effectively. Similarly, leadership teams need to be available to discuss options and amend their expectations in accordance with a revised scope.

Diagram showing AI project risks and considerations

During this scoping phase between leadership and technical teams, it is also paramount that the resulting problem statement is clear. Business leaders may state they need an AI model to automatically set prices across a range of products, when what they actually want is a model that sets prices to maximise profit. As the technical team lacks this context, data scientists could work hard for months to deliver a trained AI model that ultimately makes little impact on the business. Transversely, the technical team may be aware that data, in its present state, is not suitable for an AI project - leaders may think detailed daily ecommerce customer activity reports equate to large amounts of data to train an AI model, however it may be that they see what a user clicked on, but not the complete customer journey, so they have no context of why the customer made the decision they did, impacting the usefulness of any resulting model.

How can I get buy-in for an AI project, estimate costs and understand ROI?

Even with a well-defined problem statement and great communication between leadership and technical teams, it can be difficult to accurately assess complete project costs and analyse ROI. Your statement should have addressed the ‘Why’ - why is the problem worth solving? - this can be viewed as either the value gained, the experience delivered or the profit realised as a result of implementing the project.

However, unlike the return on traditional investments where gains are compared against costs for immediate financial returns, the return on AI investments may not necessarily have a financial impact in the short term. Instead, benefits arising from self-service, automation of tasks and predictive analytics have a far-reaching impact on employee productivity and robust decision making. These may be quick wins, differentiating use cases and transformational initiatives that tend to accumulate over time and contribute to long-term business success.

AI promises unprecedented productivity improvements and business transformation opportunities, but calculating the value of new investments in AI requires you to build a business case by simulating potential cost and value realisation across a range of AI activities.

—Gartner, 2023

Although estimated expenses and timeframes are likely to shift and evolve as the project progresses and scales, this isn’t to say you have no visibility or control - the later parts of this guide address how to prevent costs spiralling and timeframes slipping at the later stages of an AI project. Additionally, given that we stated at the start of this guide that four out of five projects fail, it is clear that a well-defined problem statement and project understanding will vastly increase chances of stakeholder buy-in and success.

Similarly, as the benefits of AI are realised over longer periods of time it is also true that small, focused project scopes offer less room for ambiguity, less data to prepare and work with, and fewer chances to go wildly off-piste from the original problem statement. It may be that the overall scope is broken down into a number of smaller mini-projects, as one may require completing prior to building on it for the next goal.

Read our 7 part AI Project Planning Guide

Contact the Scan AI team to learn more about our AI solutions.