Managing AI projects requires a different approach from traditional IT project management. What are these differences and how can you manage an AI project for success?
In 2019, the number of AI projects that ultimately failed was around 85%, with 96% of organizations reporting that they were experiencing issues with data quality, data labeling, and model reliability. Figure. It is also reported that senior management lacks understanding of artificial intelligence and the value that it can deliver.
Today, AI (and AI projects) are still in the early stages of implementation. If companies use AI, they are using it in prefabricated systems from external vendors, where the vendors developed the AI, not their client companies.
In the future, however, more companies will find reasons to develop their own in-house AI — and that means defining a project management methodology that works with AI.
How is an AI project different from traditional projects?
In traditional project management, even when it is done with methodologies like Agile, the success of the project is determined by the software created and a well-understood process. Even if project development is not done linearly like in Agile, the basic steps are still define, design, develop, test, and deploy. The data on which these applications operate is almost always a structured record data system that has been quality tested and is quite complete in its appearance and content.
Because the data on which traditional software development works is reliable, and because everyone understands the development steps used in the project, there is significantly less uncertainty in the projects. Traditional software development projects. This makes it possible to reliably attach project deadlines based on past project history.
Unfortunately, AI projects do not have this stability and do not easily set deadlines for project completion.
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Uncertain navigation in AI projects
There is no absolute “end” to an AI project, unless it’s one you’re working on.
If you’re an AI project manager, you have to live with that “never-ending” reality — so should your project’s management and sponsors.
Why is there no end?
Because AI questions the data it analyzes based on the data it operates on, and that data is constantly changing. As you add new data sources, the results change. The AI itself will also contain machine learning (ML) that recognizes data patterns and learns from those patterns. This can also change the results.
Your management and users should understand (and expect) that when data changes, so can results. Part of this process includes accepting uncertainty as part of the AI system development process.
Identify your AI project is doable
To some extent from a project perspective, an AI project should be considered finished.
The goal of most AI projects is to achieve at least 95% agreement of the AI results with what the subject experts would conclude. When this threshold of 95% is reached, the project is considered accurate enough to go live. At this point, the project should be declared complete.
That does not mean that all activity on the resulting AI system or application is over. Will have
“Drift” over time can cause the AI to lose some accuracy. At these points, the AI will need to be recalibrated to deliver again at optimal quality — but this is software maintenance.
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Do AI project delivery always go according to plan?
The answer is no reverberation!”
Sometimes the data being used by AI is not properly prepared, especially when new and unfamiliar data sources are introduced. Dirty data will skew AI results.
Second, if your business case changes (and the value users want to derive from it), then AI will no longer align with what the company wants. to try on. That possibility should be discussed with management in advance — and everyone should be ready to “unlock” as soon as an AI project shows it can’t succeed.
