The AI ​​shadow economy is not a rebellion, it is a signal of $ 8.1 billion that CEOs do not measure correctly

Meanwhile, employees using personal AI tools succeed at a rate of 40%.

Disconnection is not technological – it is operational. Companies are struggling with an AI measurement crisis.

Three questions that I invite each management team to answer when they ask questions about the return on investment of AI pilots:

1. How much do you spend on AI tools on a business scale?
2. What commercial problems do you solve with AI?
3. Who is dismissed if your AI strategy does not provide results?

This last question generally creates uncomfortable silence.

As CEO of Lanai, an EDGE-based AI-based AI detection platform, I deployed our AI observability agent in Fortune Societies 500 for CISO and CIOs who want to observe and understand what AI does in their businesses.

What we have found is that many are surprised and ignorant of everything, from the productivity of employees to serious risks. In a large insurance company, for example, the management team was convinced that they had “enclosed everything” with a list of approved suppliers and security exams. Instead, in just four days, we found 27 unauthorized AI tools that cross their organization.

The most revealing discovery: an “unauthorized” tool was in fact a workflow Salesforce Einstein. It allowed the sales team to exceed its objectives – but this has also violated state insurance regulations. The team created appearance models with customer postal codes, stimulating productivity and risks simultaneously.

This is the paradox for companies that seek to exploit the full potential of AI: you cannot measure what you cannot see. And you cannot guide a strategy (or operate without risk) when you don’t know what your employees are doing.

“Governance theater”

The way we measure AI is back.

Currently, most companies are measuring the adoption of AI in the same way that they make a deployment of software. They follow the licenses purchased, completed training and accessible applications.

It’s the wrong way to think about it. AI is an increase in workflow. The impact of performance lives in the interaction models between humans and AI, not only on the selection of tools.

The way we do so can create a systematic failure. Companies establish approved supplier lists that become obsolete before employees ended the training in compliance. Traditional network surveillance is missing AI integrated into approved applications such as Microsoft Copilot, Adobe Firefly Slack AI and the Salence Einstein aforementioned. The security teams implement policies that they cannot apply, because 78% of companies use AI, while only 27% govern it.

This creates what I call the problem of the “theater of governance”: the initiatives of the AI ​​that have succeeded on executive dashboards often provide no commercial value. Meanwhile, the use of AI that stimulates real productivity gains remains completely invisible to leadership (and creates risks).

Shadow Ai as systematic innovation

The risk does not escaped the rebellion. Employees try to solve problems.

The analysis of millions of AI interactions through our detection models based on the edges has proven what most operational leaders instinctively know, but cannot prove. What seems to write rules is often that employees simply do their work in a way that traditional measurement systems cannot detect.

Employees use unauthorized AI tools because they are eager to succeed and because the sanctioned corporate tools only succeed 5% of the time, while consumption tools like Chatgpt reach the production of 40% of the time. The “Shadow” economy is more effective than official. In some cases, employees may not even know that they become thugs.

A technological company preparing for an IPO shown “Chatgpt – approved” on safety dashboards, but missed an analyst using Personal Chatgpt Plus to analyze confidential projections of date pressure income. Our rapid visibility revealed that the risks of dry violation have been completely missed by monitoring the network.

A health system has recognized that doctors using EPIC clinical decision -making assistance, but have lacked emergency doctors in patient symptoms in an integrated AI to accelerate diagnostics. While improving the flow of patients, it violated the HIPAA using AI models not covered by business associate agreements.

Measurement transformation

Companies crossing the “Genai divide” identified by MIT, whose NANDA project has identified the remarkable difficulties with the adoption of AI, are not those which have the greatest budgets of AI; These are the ones who can see, secure and evolve what really works. Instead of asking: “Do employees follow our AI policy?” They ask: “What flows of work on AI lead the results, and how to make them in conformity?”

Traditional metrics focus on deployment: purchased tools, trained users, created policies. An effective measurement focuses on the results of the workflow: what interactions stimulate productivity? What creates a real risk? What models should we standardize on the organization’s scale?

The insurance company that discovered 27 unauthorized tools understood.

Instead of stopping postal code workflows stimulating sales performance, they have built compliant data paths preserving productivity gains. Sales performance has remained high, the regulatory risk has disappeared and they have set the workflow guaranteed to the company’s level – reducing compliance violation to a competitive advantage of a value of millions.

The bottom line

Companies spending hundreds of millions for the transformation of AI while remaining blind at 89% of the real use confronted with aggravating strategic disadvantages. They finance failed pilots while their best innovations occur in an invisible, uncompromising and unreaded manner.

The leading organizations now deal with AI as the greatest work decision they will take. They require clear profitability analyzes, return on investment projections and success measures for each investment in AI. They establish the names named where performance measures include the results of AI linked to the remuneration of managers.

The AI ​​market of $ 8.1 billion will not provide productivity gains through traditional software deployments. This requires visibility in terms of work work by distinguishing innovation from the violation.

Companies that establish a workflow -based performance measurement capture that productivity gains are already generating. Those who stick to measures based on applications will continue to finance failing pilots while competitors exploit their dead angle.

The question is not to know whether to measure the ia of the shadow – it is whether the measurement systems are sufficiently sophisticated to transform the productivity of the invisible workforce in sustainable competitive advantage. For most companies, the response reveals an urgent strategic gap.

The opinions expressed in the Fortune.com comments are only the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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