Timing Your Business AI Adoption Strategy

Every company is under pressure to adopt artificial intelligence (AI). Boards ask about it, investors ask about it, and executives who aren’t talking about it are increasingly asked why they have not started using it in their companies. This binary framing of using AI or not misses what matters. The question isn’t whether a company adopts AI. It’s when, and what adoption actually does inside a firm.

A New Way to Measure AI Adoption

To answer that question, you first need to know precisely when a company started using AI — as opposed to just talking about it. That turns out to be harder than it sounds. Existing approaches all share the same limitation: they capture talk, not action. Scanning job postings, counting patents, or searching for keywords in corporate filings picks up discussion, aspiration, and preparation, but not deployment. A company can post AI job listings for two years before a single system goes live. Phrases like “artificial intelligence” appear in earnings calls long before anything operational happens.
The solution was to use AI to read over 187,000 corporate transcripts that included earnings calls, investor conferences, and analyst days, which covered more than 4,300 U.S. public firms from 2011 to 2024. Rather than counting mentions, the large language model (LLM) distinguished concrete deployment language from generic discussion. The result is a precise timeline of when each firm crossed from talking about AI to operationalizing it.

How AI Adoption Impacts Firms

A common assumption is that AI helps companies charge more. The data says otherwise. Measured markups were essentially flat around adoption. What changes is how costs are organized internally.
AI is expensive to implement well. It requires data infrastructure, software, reorganized workflows, and new skills. These are investments in intangible assets: things that don’t show up as physical equipment on a balance sheet but are real and valuable. Firms that adopt AI are restructuring around a new capability, and that process costs money before it saves money.

The data also shows where AI lives inside companies. Product development and operations dominate, followed by IT and sales. Human resources, legal, and finance see considerably less activity. This is useful context for any executive deciding where to start: AI is being deployed where it most directly shapes what the firm sells and how it runs, not in supporting functions.

Timing Matters

So far, these effects apply broadly across adopters. Where timing becomes decisive is in how financial markets respond.

Firms that adopted AI early, when few competitors had done so, experienced substantial gains in market value. Firms that adopted later, once AI had become standard across their industry, saw much smaller gains. And firms adopting purely defensively, scrambling to catch up after rivals had already moved, may not see the market value gains they expect — the costs of transition arrive without the competitive upside that early movers captured.

By 2024, more than 60 percent of Software & Services firms had adopted AI. In pharmaceuticals, banking, and metals and mining, adoption rates remained in the single digits. In other words, the competitive window has effectively closed in some sectors and is still wide open in others. Where a firm sits on this map should fundamentally shape its AI strategy.

The pace is also compressing. General AI ( machine learning, predictive analytics, and automation tools that existed before the generative AI wave) diffused gradually across the late 2010s. Generative AI (AI that creates text, images, or other content) did not: it went from under one percent of firms in 2022 to more than eleven percent by 2024. For executives, this means the timing window is shorter than it used to be. The cost of waiting is rising.

What This Means When Considering AI Adoption

The data points to several concrete lessons for business leaders:

Locate yourself on the diffusion curve. The competitive value of AI adoption depends on where your industry stands. If you are in a sector where adoption is still low, the case for acting now is strong. If you are in a sector where most peers have already moved, the question shifts from “how do we get a competitive edge?” to “how do we deploy efficiently without overspending on something the market has already priced in?”

Budget for reorganization and the technology. The highest hidden cost of AI adoption is not software licenses or compute, which is the processing power, memory, and hardware required. It is the redesign of workflows, the hiring of new skills, and the rebuilding of internal processes around the new capability. Firms that underestimate this and treat AI as a plug-in product systematically underperform those that treat it as a structural change.

Expect to spend money early to make money later. Successful AI adoption costs money before it saves money. Boards and investors should understand this and be prepared for a period in which profits look soft even as the long-run trajectory is improving.

Watch what investors reward. Market valuations respond to perceived competitive position, not to AI announcements in the abstract. A credible, differentiated AI strategy is key for investors.

Don’t wait for clear best practices. The companies that captured the largest gains moved before the playbook was settled. By the time best practices emerged, the premium for adopting was already being competed away. That said, speed without proper oversight carries its own risks — early adoption and careful implementation are not mutually exclusive.

The Bigger Picture

AI is not a switch that gets flipped uniformly across firms. Its consequences depend on timing, on what happens inside the firm during adoption, and on whether leaders correctly understand where they are putting their money.

The companies pulling ahead aren’t simply the ones that said yes to AI. They are the ones that moved while the window was still open and structured the move around the deep reorganization that the technology actually demands.

Further reading

1. Akbari, Parand, When Firms Adopt AI Matters: Diffusion, Intangible Investment, and Firm Value — Evidence from Corporate Disclosures (February 15, 2026). Available at SSRN: https://ssrn.com/abstract=6245818 or http://dx.doi.org/10.2139/ssrn.6245818