AI Strategy

Aug 8, 2025

The Great AI Gold Rush: Why Most Enterprises Are Flying Blind

The Great AI Gold Rush: Why Most Enterprises Are Flying Blind
The Great AI Gold Rush: Why Most Enterprises Are Flying Blind
The Great AI Gold Rush: Why Most Enterprises Are Flying Blind

Jim Larrison

From Larridin

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The Emperor’s New Algorithms

Let’s be brutally honest: most enterprise AI adoption today resembles the Wild Wild West — lawless, chaotic, and with a lot of fool’s gold being peddled as the real thing. Companies are throwing obscene amounts of money at AI tools without the slightest idea how to measure their effectiveness or ROI. It’s the SaaS boom of 2020 all over again, but with potentially more devastating consequences.

Remember the pandemic-fueled SaaS explosion? Companies panic-purchased software solutions while employees worked from mountain lodges and beach villas. The aftermath? Crippling SaaS bloat, astronomical expenses, and the painful realization that untangling this web of redundant tools would cost even more than the initial spending spree.

We’re watching history repeat itself — but this time with AI, and the stakes are exponentially higher.

The Dangerous Illusion of AI Progress

Corporate boardrooms across America echo the same hollow demands: “What’s our AI strategy?” The pressure from Wall Street, investors, and competitors has created a toxic environment where the appearance of AI adoption matters more than actual results.

The dirty secret? Most executives have absolutely no idea if their AI investments are delivering value.


Jim Siders, CIO of Palantir

Jim Siders, CIO of Palantir

As Jim Siders, CIO of Palantir, aptly put it, companies are just “a bunch of hammers looking for nails.” They’re stockpiling AI tools without clear use cases, coherent strategies, or — most critically — any way to measure success.

Adding to this problem is the clear and present fear among executives I speak with that they are lagging the market, and more importantly, their competitors, in AI deployment and adoption. They worry about this regardless of whether AI is built into their existing systems or offered as a dedicated solution. A wealth of these solutions exists, and their collective aim is unambiguous: to innovate for massive, previously unimaginable corporate efficiency gains.

Why Your Company’s AI Initiative Is Probably a Disaster

Let’s examine the uncomfortable reality of enterprise AI adoption today:

  1. Rampant Shadow AI: Employees across departments independently adopt AI tools — some approvedothers not — creating massive security vulnerabilities and data leakage risks. Your corporate secrets are being fed into public LLMs while leadership remains oblivious.

  2. Financial Hemorrhaging: The fragmentation of AI tooling means companies are paying for redundant capabilities across departments. We’ve seen enterprises with over 15 different AI writing assistants deployed across various teams — each with its own contract, security profile, and learning curve.

  3. False Metrics: The few companies attempting to measure AI impact are tracking vanity metrics like “number of employees trained” or “prompts generated” — meaningless figures that say nothing about business outcomes or ROI.

  4. The Elon Fallacy: Despite the perception and news about Twitter/X’s dramatic headcount reduction while maintaining operations and driving true profitability, most companies lack the understanding of which functions can truly be automated or augmented by AI. This leads to unrealistic expectations and inevitable disappointment.

The Tragic Gap: Why Existing Solutions Fall Short

Some functions have historically been easier to measure — SDRs have calls & leads, AEs have close rate metrics, support has ticket deflection rates — but these represent a tiny fraction of potential AI use cases. For the vast majority of knowledge work, companies have no established frameworks to measure productivity improvements from AI adoption.

What’s the ROI on a marketing team using AI for content creation? How do you quantify the value of engineers using GitHub Copilot? What’s the business impact of executives using Claude for strategic planning?

Most organizations don’t know, don’t measure, and frankly, don’t want to know — because the answer might reveal their AI initiatives as expensive failures.

The Choice Ahead: Measured Success or Expensive Chaos


The Larridin Bus to AI Successful Measurement and ROI

The Larridin Bus to AI Successful Measurement and ROI

The enterprise AI space is approaching an inflection point. Companies can continue the current path of uncoordinated, unmeasured adoption — guaranteeing massive waste and eventual painful correction — or they can embrace accountability and strategic measurement now.

The reckoning is coming, one way or another. The question is whether your organization will be ahead of the curve with data-driven AI governance or caught flat-footed when the board finally asks for concrete evidence that AI investments are paying off.

I am convinced the future belongs to organizations that measure what matters. The time for AI accountability is now — whether the AI industrial complex is ready for it or not.

This post was previously published on Medium

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Larridin is the complete platform for enterprise Al — from discovery to adoption to impact.

Brand logo

Larridin is the complete platform for enterprise Al — from discovery to adoption to impact.

Brand logo

Larridin is the complete platform for enterprise Al — from discovery to adoption to impact.