The State of AI in Business: Are We on the Wrong Side of the Divide?

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VARATO’s Matt Harris recently discussed a fascinating article from NANDA, the MIT Networked Agents and Decentralized AI group. The piece, titled The State of AI in Business 2025, The Gen. AI Divide, highlights a surprising disconnect: high AI adoption but low business transformation.

The Problem: Massive Investment, Minimal Results

Companies are pouring unprecedented amounts of money into AI, yet the returns are often invisible. The article reveals that only 5% of AI initiatives have shown any measurable value. This means 95% of these projects have yet to prove a measurable benefit, creating immense pressure on businesses to see results from their massive investments.

The Two Sides of the Divide

The core of the issue is a fundamental divide in how AI is being used.

• AI in a silo: This is the current state for most companies. Individual employees are using tools like ChatGPT for personal efficiency—to draft emails or generate content. These efforts, while helpful on a small scale, don't create any enterprise-wide value or intelligence.

• AI as an integrated component: This is the future. It's about AI agents that can work collaboratively, remember context, and build a proprietary knowledge base for an entire organization. This allows AI to make the whole business smarter, not just one person.

The Shadow AI Economy

Another key finding from the article is the "shadow AI economy." When leaders were asked how many employees use AI, the average answer was 40%. However, when employees were asked directly, the number jumped to 90%.

This gap highlights a lack of awareness at the management level. While employees are actively using AI to be more productive, these siloed, individual efforts aren't generating system-wide benefits because the data and learning aren't being centralized and shared across the organization.

The Looming AI Bubble

The article suggests that this mismatch between investment and results could lead to an "AI bubble," similar to the dot-com boom of the 90s. With companies spending billions on projects that aren't paying off, a market correction may be on the horizon. This isn’t to say AI lacks value—it’s absolutely transformative—but we may be in a period of "irrational exuberance."

A Smarter Path to AI Success

For businesses that can't afford to waste millions on failed experiments, there’s a smarter, more deliberate approach:

• Start with Data Fundamentals: Before jumping into complex AI models, focus on your data. Centralize your data into a single repository and ensure it is clean and normalized.

• Visualize Your Data: Once your data is in one place, create a visualization layer to generate reports. This gives you immediate insights and a foundation for more advanced analysis.

• Crawl, Walk, Run: Instead of a massive, all-at-once investment, use a sprint-based approach. Work with a trusted partner to build guardrails and guide the project. The article notes that working with partners leads to a significantly higher success rate than internal projects.

By focusing on these fundamentals, businesses can learn from the mistakes of others and build a solid foundation for enterprise-wide intelligence. Avoid over-investing and instead, invest in the groundwork that will make your AI projects truly transformative.

To learn more, check out the full conversation on the VARATO podcast, available on Spotify and other major podcast platforms.

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