AI Use Is Nearly Universal - Scaling It Is a Different Story
- Steven Wriston
- 4 days ago
- 4 min read
If you've been telling yourself you're "behind" on AI because your company is still testing tools rather than running the whole business on them, the data says otherwise. You're in the majority. You may be surprised at just how low the bar is.
Everyone's using it. Almost no one has scaled it.
According to McKinsey's most recent State of AI survey, 88% of organizations now report regularly using AI in at least one business function — up sharply from 78% the year before. AI use has become the norm, not the exception, across industries and company sizes.
But adoption and transformation are not the same thing. McKinsey found that only about a third of organizations have actually begun scaling AI across the enterprise. The rest — nearly two-thirds — are still in the experimentation or piloting stage: running trials in one department, testing a tool with a small team, or letting individual employees use AI on their own initiative without it being woven into core operations.
Separate research from Publicis Sapient, which surveyed more than 1,500 enterprise AI decision-makers for its 2026 report, puts a finer point on the gap. While 73% of companies say they use AI regularly or across most processes, only 10% describe AI as truly "core" to how their business operates. The report's authors frame this bluntly: AI doesn't have an innovation problem, it has an execution problem. Roughly four in ten leaders in that survey admitted their organizations simply aren't structured to capture the value AI could deliver.
What "using AI" actually means
It's worth pausing on what these adoption numbers actually measure, because the bar is lower than it sounds. A related McKinsey report found that 91% of individual employees say they personally use generative AI for work — and largely through everyday, consumer-facing tools like ChatGPT and Microsoft Copilot. Someone on the marketing team using Copilot to draft an email or summarize a document counts toward that number. There's no requirement that the tool be sanctioned, integrated into a workflow, or even known about by leadership.
That same lightweight bar carries into the organizational figure. When McKinsey reports that 88% of companies are "regularly using AI in at least one business function," it's counting exactly this kind of activity — a team using an AI writing assistant, a support function using a chatbot plugin — alongside more structured deployments. The survey doesn't distinguish between a handful of employees drafting documents with Copilot and AI being genuinely built into how a function operates. Both count as "use."
That's precisely why the scaling numbers fall off a cliff. Individual employees experimenting with off-the-shelf tools is not the same thing as an organization restructuring its processes around AI — and McKinsey's own analysis makes that point directly, noting that most adoption so far is "functional, not end-to-end." Only about 6% of companies qualify as true AI "high performers" with measurable bottom-line impact from deep, cross-functional integration.
Why the gap exists
None of this is surprising once you consider what "scaling" actually requires. Trying out a chatbot or an AI writing assistant takes an afternoon. Rebuilding workflows, retraining teams, restructuring decision rights, and integrating AI into the systems that run a company day to day takes years — and a level of organizational change most businesses are still working through. McKinsey's data backs this up directly: larger companies scale faster. Roughly half of organizations with more than $5 billion in revenue have reached the scaling phase, compared to just 29% of companies under $100 million in revenue. Scale takes resources, and most businesses simply haven't had the runway yet.
Why it matters for you
If you feel behind because your business is still testing AI tools rather than running everything on them, you're actually standing right where most companies are standing. The businesses that have fully scaled AI and made it central to their operations are still a small minority — even among large enterprises with deep pockets and dedicated AI teams.
There's no prize for rushing this. A hasty, top-down AI overhaul that outpaces your team's readiness tends to create more friction than value — half-adopted tools, confused workflows, and employees who don't trust the output. Steady, practical adoption, where you test AI in a real part of the business, learn what actually works, and expand from there, is not a slower path to the same destination. It's usually the only path that actually gets you there.
So if your AI strategy right now is "we're trying a few things and seeing what sticks," that's not a sign you're falling behind. That's what nearly everyone else is doing too.
Ready to move from experimenting to executing?
Closing the gap between using AI and scaling it doesn't require a rushed overhaul — it requires the right roadmap. PPT Consulting Services helps businesses turn scattered AI experiments into a practical, structured plan for real impact.
Visit www.pptconsultingservices.com to schedule your free consultation.
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