State of AI in Business 2025

This post looks at the state of play for corporate AI deployment and bottom line impacts based on a recent MIT report. I would categorise the result as AI represents a net cost to most business with no consequential ROI nor improvement in Net Income or future cost reduction.

Tools like ChatGPT and Copilot are widely adopted. Over 80 percent of organizations have explored or piloted them, and nearly 40 percent report deployment. But these tools
primarily enhance individual productivity, not P&L performance. Meanwhile, enterprise grade systems, custom or vendor-sold, are being quietly rejected. Sixty percent of organizations evaluated such tools, but only 20 percent reached pilot stage and just 5 percent reached production. Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.

The over-riding key is corporate attempts, particularly from Microsoft who must be called out here to lock in customers already on their enterprise plan by provide the tools to staff. Certainly this will benefit training by osmosis for employees but with no direct benefit to the corporation. This approach in particular is holding back real growth in productivity based initiatives. An therein lies the ‘industrial revolution’ side of AI not really being touched on any scale.

I am deliberately excluding any efforts with chatbots which have a customer impact of broadly negative and a most deployments representing a weak attempt at freezing customer direct contact with the corporation. I admit this is a pet peeve.

Four patterns emerged that define the GenAI Divide:

• Limited disruption: Only 2 of 8 major sectors show meaningful structural change
• Enterprise paradox: Big firms lead in pilot volume but lag in scale-up
• Investment bias: Budgets favor visible, top-line functions over high-ROI back office
• Implementation advantage: External partnerships see twice the success rate of internal builds

Here is the key takeaway in the report:

Takeaway: Most organizations fall on the wrong side of the GenAI Divide, adoption is high, but disruption is low. Seven of nine sectors show little structural change. Enterprises are piloting GenAI tools, but very few reach deployment. Generic tools like ChatGPT are widely used, but custom solutions stall due to integration complexity and lack of fit with existing workflows.

The GenAI Divide is most visible when examining industry-level transformation patterns.
Despite high-profile investment and widespread pilot activity, only a small fraction of organizations have moved beyond experimentation to achieve meaningful business transformation.

….

One mid-market manufacturing COO summarized the prevailing sentiment:
“The hype on LinkedIn says everything has changed, but in our operations, nothing
fundamental has shifted. We’re processing some contracts faster, but that’s all that has
changed.”

Transformation ought to be the objective: I will stop there and let you study the report, and how your company could view transformation as a target, not satisfying the vendor motivation of lock in. Business must look to operational processes directed at customer satisfaction, onboarding, and retention.. Thats the hard part; highest risk but greatest potential for genuine productivity benefit through increased revenue per dollar spent on operations.