How do you manage scope creep in an AI project?

Scope creep is the tendency of a project to expand beyond its original goals, often resulting in delays, cost overruns, and reduced quality. It can be especially challenging to manage in AI projects, where the expectations and requirements may change rapidly due to the complexity and uncertainty of the domain. In this article, you will learn some practical tips on how to prevent and control scope creep in your AI projects, using agile methods and best practices.

1 Define the problem and the value proposition

Before you start developing your AI solution, you need to have a clear and shared understanding of the problem you are trying to solve and the value you are creating for your stakeholders. This will help you set the scope and the success criteria for your project, and avoid adding unnecessary or irrelevant features. You can use tools such as problem statements, value propositions, and SMART goals to define and communicate your project vision and objectives.

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Ganes Kesari

2X Founder & Chief Decision Scientist | TEDx Speaker | Contributor to MIT SMR & Forbes | B-School Adjunct Prof. | C-Suite Advisor
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Most AI projects encounter scope creep because of a poorly defined problem. Remember, an AI project isn't just about introducing advanced tech, but solving a business problem. For example, in one of our projects, the initial goal was to "build a customer attrition model AI." Such technical framing can distract teams from the underlying business goals. When we reframed our goal to "improve customer retention by 10%", it helped get clarity on the business approach and then figure out whether AI was the right fit.

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2 Involve your stakeholders and users

One of the main causes of scope creep is poor communication and feedback from your stakeholders and users. You need to involve them early and often in your AI project, to understand their needs, expectations, and pain points, and to validate your assumptions and hypotheses. You can use techniques such as interviews, surveys, personas, user stories, and prototypes to elicit and prioritize their requirements, and to test and iterate your AI solution.

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3 Adopt an agile approach

AI projects are inherently dynamic and unpredictable, and require a flexible and adaptive approach to manage them. You can use agile methods, such as Scrum or Kanban, to deliver your AI solution in small and frequent increments, and to respond to changing requirements and feedback. You can use tools such as product backlogs, sprints, daily stand-ups, and retrospectives to plan, execute, monitor, and improve your AI project.

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4 Manage the technical complexity and uncertainty

AI projects involve a lot of technical complexity and uncertainty, such as data quality, model selection, performance evaluation, and ethical implications. You need to manage these aspects carefully, to avoid scope creep and ensure the feasibility and reliability of your AI solution. You can use practices such as data exploration, model validation, error analysis, and explainability to assess and mitigate the risks and challenges of your AI project.

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5 Document and communicate the scope changes

Despite your best efforts, scope creep may still occur in your AI project, due to unforeseen circumstances, new opportunities, or stakeholder requests. You need to document and communicate any scope changes, and their impact on the project schedule, budget, and quality. You can use tools such as change requests, impact analysis, and scope statements to formalize and approve the scope changes, and to update your project plan accordingly.

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6 Learn from your experience

Finally, you need to learn from your experience, and identify the root causes and the lessons learned from scope creep in your AI project. You can use techniques such as post-mortems, feedback sessions, and knowledge sharing to capture and disseminate the best practices and the improvement opportunities for your future AI projects.

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Ganes Kesari

2X Founder & Chief Decision Scientist | TEDx Speaker | Contributor to MIT SMR & Forbes | B-School Adjunct Prof. | C-Suite Advisor
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"Mistakes are the portals of discovery," said James Joyce. Each misstep in an AI project doesn't signify failure but an opportunity. Embracing this perspective ensures that we gain wisdom from our efforts, whether they succeeded or not.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you manage scope creep in an AI project?

1

2

3

4

5

6

7

1 Define the problem and the value proposition

2 Involve your stakeholders and users

3 Adopt an agile approach

4 Manage the technical complexity and uncertainty

5 Document and communicate the scope changes

6 Learn from your experience

7 Here’s what else to consider

Artificial Intelligence

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How do you manage scope creep in an AI project?

1

2

3

4

5

6

7

1 Define the problem and the value proposition

2 Involve your stakeholders and users

3 Adopt an agile approach

4 Manage the technical complexity and uncertainty

5 Document and communicate the scope changes

6 Learn from your experience

7 Here’s what else to consider

Artificial Intelligence

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