Why Do AI Initiatives Tend to Fail? | Pecan AI

Why Do AI Initiatives Tend to Fail?

Failure is more frequent than success when companies try to implement AI. Let's explore the reasons and some solutions.

‎Why Do AI Initiatives Tend to Fail?

In this video, I wanted to tackle an interesting and counterintuitive topic: why AI initiatives tend to fail more often than succeed. Some estimates suggest that as many as 80% of projects fail — a massive waste of time and resources.

This is an important issue to address as organizations increasingly invest in AI/ML to drive innovation.

I boil it down to two main reasons:

1. Data Issues Derail AI Initiatives

The first big reason AI projects fail is data problems. If the input data itself is of poor quality or irrelevant, the models built on top of it will be meaningless.

The saying "garbage in, garbage out" has persisted for a long time for good reason. Ensuring high-quality, clean data is critical for any successful AI/ML project.

2. The Disconnect Between Technical Teams and Business Users

The second major cause of failures is a talent/communication gap. Specifically, technical teams building AI models need to deeply understand the business context, requirements, and use cases. Otherwise, they end up solving the wrong problems that don't move the business forward.

That's why we've focused at Pecan on making AI accessible for data and business analysts, who are closer to the business problems and better understand how to connect them to AI solutions.

How Pecan Overcomes These Pitfalls

Pecan has been designed to directly address these two main challenges.

On the data side, Pecan includes automated data preparation and feature engineering capabilities that help users rapidly move from raw data to AI-ready data sets. They don't have to take hours or days to clean and tidy data. Features used in models are built and evaluated automatically, eliminating human bias in feature engineering. Both of these kinds of automation also reduce the risk of human error.

These powerful, time-saving capabilities together enable higher-quality inputs for downstream AI/ML initiatives. Models better represent all the available data and perform better with clean, well-prepared data.

Additionally, Pecan lets users speak directly to the platform using natural language via our Predictive GenAI features. You can simply explain your business challenges to our Predictive Chat and fine-tune the question you want to answer with predictions.

That means users don't have to try to communicate their needs and requirements to external teams, but can instead have a chat conversation to make sure models match the business's real-world needs.

Move Forward Confidently With AI

Pecan can help organizations avoid these AI pitfalls. Try a free trial to start building a model today, or get in touch if you'd like a personal tour.