The One Practice That Is Separating The AI Successes From The Failures

The One Practice That Is Separating The AI Successes From The Failures

Somewhere between 60-80% of AI projects are failing according to different news sources, analysts, experts, and pundits. However, hidden among all that doom and gloom are the organizations who are succeeding. What are those 20%+ of organizations doing that are setting themselves apart from the failures, leading their projects to success?

One of the biggest insights from these AI successes is that they don’t see AI projects as application development or functionality-driven projects. Rather, they see them as data projects, or sometimes even data products. A data project doesn’t start with an idea of what the functionality needs to be, but rather focuses on what insights or actions need to be gleaned from the data in whatever current shape it’s in.

The most popular methodology for application development is Agile, which focuses on short, iterative sprints tied to the immediate needs of the business user versus long development cycles. However, Agile falls flat when dealing with AI because it doesn’t tell you how to deal with data, the core asset of an AI system. This is where the CPMAI Methodology comes into play. It updates the traditional CRISP-DM (Cross-Industry Standard Process for Data Mining) framework with Agile and AI-specific details, providing a more comprehensive approach to AI project management.

When AI fails, it is important to analyze the reasons behind the failure. According to Ronald Schmelzer, principal analyst at AI research firm Cognilytica, the failure rate of AI projects is around 80%. However, not all of this is AI's fault. Flaws in design and methodology often contribute to the failure of AI projects, indicating that humans involved in the training process may not be fully knowledgeable about AI development.

To make the most of artificial intelligence gone wrong, organizations need to adopt a different mindset. Instead of viewing failures as setbacks, they should see them as learning opportunities. By analyzing the reasons behind the failure, organizations can identify areas for improvement and make necessary adjustments to their AI projects.

Actionable Advice:

1. Shift the focus from functionality-driven projects to data projects: Instead of starting with preconceived ideas about the functionality of an AI system, focus on the insights and actions that can be derived from the available data. This shift in mindset can lead to more successful AI projects.

2. Incorporate the CPMAI Methodology: When dealing with AI projects, the traditional Agile methodology may not be sufficient. By incorporating the CPMAI Methodology, which combines Agile principles with AI-specific details, organizations can better manage their AI projects and overcome the challenges associated with data management.

3. Embrace failure as a learning opportunity: Instead of being discouraged by AI failures, organizations should embrace them as opportunities for growth. By analyzing the reasons behind the failure and making necessary adjustments, organizations can improve their AI projects and increase their chances of success in the future.

In conclusion, the key to separating AI successes from failures lies in adopting a different mindset and approach. By treating AI projects as data projects, incorporating the CPMAI Methodology, and embracing failures as learning opportunities, organizations can increase their chances of success in the ever-evolving world of artificial intelligence.