Over the last few years, AI board room discussions have move from an interesting topic to an absolute imperative. In early 2023, generative AI was often covered after reviewing the financials, department updates, strategic planning, etc. Initially, many discussions evaluated the technology's potential as a game changer with measured enthusiasm colored by the fading buzz around RPA and blockchain. By 2024, however, the focus had shifted decisively to how AI could be incorporated into products and workflows to drive productivity. AI is now a major part of the financial conversation, especially around productivity gains, people want to understand how different teams are using AI, and AI is a part of almost all corporate strategy, at least in the tech world. 

But here’s the critical gap between strategy and reality: while boards discuss top-down AI initiatives, a massive, unmanaged AI ecosystem is growing from the bottom up. Most of the AI used in an organization is brought in by employees themselves. A recent Workvivo study tracking 3 million workers found that overall AI usage grew by a staggering 485% year-over-year. More than 90% of this growth occurred not within enterprise-sanctioned systems, but in personal "shadow AI" accounts.

This creates a dangerous blind spot. Most AI reporting focuses on the approved apps, while much of the real AI usage goes unreported because it takes place on the web.  I’ve been working with many of the leaders tasked with the difficult job of building AI reporting, and the challenge is immense. A tremendous amount of innovation happens in these web-based AI interactions, but they are also a significant source of risk. You can’t govern what you can’t see, and you certainly can’t improve what you can’t measure.

At Larridin, we illuminate this blind spot by showing companies what their employee AI engagement actually looks like. Visibility is the foundation of strategy. CFOs cannot assess the utilization of their AI spend without measurement. Measurement requires visibility. As AI is heavily browser based, there are unique challenges in discerning what AI people are using, how much and for what purpose. Then leaders can assess what is driving productivity versus just creating noise. Leaders can quickly identify pockets of innovation, discover where your teams are getting the most value, and make data-driven decisions about technology investments while right sizing their spend and scaling their successes. This is how the leading companies are transforming the chaos of shadow AI into a measurable competitive advantage.