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Does Agile Work for Machine Learning?

Up to 80% of AI projects are failing, even in tech-forward firms that have implemented Agile in its most advanced and deepest forms. So, does Agile work for Machine learning?

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Does Agile Work for AI And ML?

In the dynamic world of technology, the buzzword “Agile” often pops up, especially in software development. 

Agile methodologies, which emphasize flexibility, rapid iteration, and collaboration, have become a staple since the early 2000s for tech teams. They’re like the Swiss Army knife of project management, adaptable for a wide range of development projects. 

However, Agile is failing Hard with AI and ML Projects

Agile methodologies, celebrated for transforming software development, are facing a formidable challenge in the AI and ML landscape. Up to 80% of AI projects, even in Agile-versed firms, are not meeting success​​​​.

Agile Just Isn’t a Great fit for AI and ML Projects

It’s like trying to fit a square peg in a round hole. Agile, which revolutionized software development with its four core values – prioritizing individuals, working software, customer collaboration, and responsiveness to change​​, seems to be at odds with the unique demands of AI and ML projects.

AI and ML projects are a different beast compared to traditional software development. They are heavily reliant on data availability and quality, and simply being quick on your feet (Agile’s forte) isn’t enough if the data isn’t up to par​​. 

Imagine trying to bake a cake but only focusing on the speed of mixing without considering the quality of the ingredients. The result? A cake that nobody wants to eat.

Agile focuses on the wrong things for AI and ML Projects

The nature of what is being “delivered” in an AI or ML project adds another layer of complexity. Is it the algorithm, the training data, the model, or all of the above? The answer is akin to a riddle wrapped in a mystery inside an enigma​​.

What is the Agile Methodology in AI?

Agile in AI prioritizes rapid iteration and flexibility. But this approach struggles in AI due to the critical role of data quality and availability​​.

So, what’s the solution? The Agile approach can form a foundation for an AI-specific methodology but can’t be the sole player. It’s like needing both salt and pepper for the perfect seasoning; Agile needs to be supplemented with something more. 

That’s where the Cognitive Project Management for AI (CPMAI) methodology comes in, combining Agile’s iterative approach with a focus on data, making it more suited for the unpredictable and data-centric world of AI and ML​​.

But, let’s not forget the human element. Introducing a new methodology like CPMAI can sometimes meet resistance, like trying to convince a cat to enjoy a bath. The key is to blend the new with the familiar, using approachable concepts and terminology for smooth sailing​​.

In conclusion, while Agile might be struggling to keep up in the AI playground, with the right tweaks and an emphasis on data-centric approaches, it can still be a valuable player in the game. Think of it as giving Agile a pair of data-centric glasses to see the AI world more clearly!

Is Agile Outdated?

Agile is not outdated, but Agile requires adaptation for AI’s unique demands. It needs to be more data-centric to align with AI and ML project requirements​​.

How Can Agile be Used for AI Projects?

To suit AI, Agile must evolve. The CPMAI methodology supplements Agile with a stronger focus on data, enhancing its effectiveness in AI and ML projects​​.

What is the Difference Between AI and Agile?

AI projects are driven by data quality, whereas Agile emphasizes rapid product iterations and customer collaboration. This fundamental difference creates a challenge for Agile in AI and ML projects​​.

Adapting Agile for AI

Integrating Agile with AI requires a delicate balance. CPMAI offers a pathway by merging Agile’s iterative nature with a data-centric approach, addressing the unique needs of AI projects​​.

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