Is Agile Framework The Reason Why Most AI Projects Fail

Agile methodology is a proven method for delivering software. However, in artificial intelligence (AI) projects, the development stack appears more like a pyramid with a large base supporting fewer user-visible outputs. Hence the standard Agile approaches fail in AI projects.

Research in data science demands extensive data collection and processing. Therefore, data initiatives tend to focus on research and learning. However, there are two in particular that can cause issues with AI attempts. First, there are a few things to keep in mind when it comes to software development. Second, the developers presume that the solution design is efficient and will produce a well-coordinated and worthwhile outcome. 

What distinguishes AI projects from software development?

Projects involving AI typically contain several additional levels of unpredictability and unknowns. The following are a few of the issues that AI teams need to investigate:

• Does the data contain enough relevant information to produce meaningful results?
• Is there enough data from which to conclude the future?
• What is the quality of the data?
• Is there any source data bias that could lead to immoral predictions?
• Which algorithmic biases could potentially influence the predictions the same way?
• When integrating data from many sources, how much time and effort is needed to make entity resolution?
• What algorithmic combination is going to offer us the best results?
• The developers need to get the model to handle the data quickly enough to get timely and useful results.
• Can the developers rely on the model's usefulness to last long enough to see an ROI based on the current state of the data?

As a result, AI initiatives are rarely linear and predictable. Rather than that, they necessitate continuous investigation and testing based on validated knowledge and proven (or disproven) hypotheses.

The Possibility of Agile in Artificial Intelligence

Here are three essential factors for doing Agile work for AI:

1. Build on Agile principles, not implementation frameworks.

2. Take a top-down and bottom-up approach to AI data initiatives.

3. Include and adapt related principles such as lean startup and development operations.

Developing an AI-centric methodology

There are various aspects of AI project development, including the following:

• Developing conversational applications and assisting in the creation of conversational models
• The difficulties associated with bias in model creation and repeated de-biasing
• Difficulties associated with the implementation of hardware-centric models and repetitive loops around them
• Simultaneous evaluation and assembly of AI algorithms, which introduces extra technique issues

CPMAI, based on techniques such as CRISP-DM, does not replace Agile but rather adds another vital tool to the toolbelt of best practices for conducting AI projects.  

Benefits

The following benefits are achieved from an agile approach to AI development:

• Manages the uncertainty involved in AI projects and mitigates the risk that results.
• Rather than spending the resources on unsuitable solutions, the researchers may routinely validate and, if required, quickly dismiss one proposal in favour of another.
• Delivery and market entry times are reduced.

Conclusion

In AI projects, the development stack appears more like a pyramid. As a result, AI projects fail not due to a lack of technology or AI expertise but rather because organisations miss key processes, shorten data lifecycle operations, misalign business needs and data capabilities, and other easily addressed factors through data-centric approaches. Iterations in classic agile approaches are time-boxed and deploy vertical pieces of functionality across the full technology stack. If the solution is evolving as expected, it will be easier to track progress. For data-science solutions, this method is less effective.