What are the signs of AI project failure, and why?
How can you mitigate and find a better solution?
Signs of ai failure begin with a failure to establish what we’ll call the AI triangle. There are multiple triangles, but the major triangle is infrastructure, application, and data. Within each of those triangles, there are more triangles. Looking at infrastructure, we see the sub-triangle of platform, hardware, and software. Where we see failure is where those AI triangles are not working in functional independent units then rolling up correctly into their respective parts of the larger AI triangle.
We begin to see project failure at the data layer, at the infrastructure layer, and at the application layer of the database. We see failure as the inability to align the right data set at the infrastructure layer, we see failure as the inability to provide the proper hardware, software, and human infrastructure. And at the application layer, we find that organizations need to be set up to accept failure – you’re going to fail as much as you’re going to succeed when beginning to apply machine learning. Most organizations are not set up to fail. Companies that understand the value of machine learning understand that failure is really success, and they celebrate it.
Better Data Resolution
When people don’t collect data with the right level of resolution, with the right level of fidelity, and the right level of frequency to be useful for AI, they fail. Often, people do not store data in manner both for the efficiency of the AI and for the and for cost effectiveness. People fail because they don’t know how to store the right data. They don’t know what data to store to make AI successful. when the wrong data is stored it makes the project experience cost failure.
Infrastructure that Scales
People also fail because they anticipate that there are there is an infrastructure pre-built for them to buy. When they scale their project, they incorrectly assume that the scaling mechanisms are out there to purchase, and they’re not. On the platform front, people often can’t find the interdisciplinary talent they need then get that talent to work together. When you can’t hire all of the skills you need then have all people work together under one roof you experience a lack of cross-functional collaboration. These functions have never had to interact before in the organization which becomes a challenge. They fail because they don’t have an enterprise platform. If an organization succeeds in proving the concept works, they may still fail, because they don’t have a platform to scale it.
When it comes to the infrastructure, which consists of the hardware, the software, and the storage, it gets even more complex. This is the infrastructure you need change the organization to adapt to this new way of using technology. They fail because they fail to change their business processes to take advantage of the insights from their AI project. They fail because they don’t have a culture that allows them to fail fast.
We see failure at the application level when the organization hasn’t changed their mentality to understand that to move fast and break things is to get closer and closer to the right applications for the business value of this technology. The final thing on the application front is adoption. Understanding a fundamental shift in how AI should be received inside the organization to truly affect change: AI projects should be transparent.
We can help solve this problem. Get a demo today.
- Introduction to MLflow for MLOps Part 3: Database Tracking, Minio Artifact Storage, and Registry
- Believe the Hype: Gartner, Noodle.ai, and Decision Intelligence
- Introduction to MLflow for MLOps Part 2: Docker Environment
- Atlas, Noodle.ai’s Machine Learning (ML) Framework Part 3: Using Recipes to Build a ML Pipeline
- Supply Chain Leaders Struggle with Unpredictability. Noodle.ai has the Cure.