5 best strategies for scaling AI in enterprises

Every enterprise on earth is now "doing AI." Fact.

McKinsey's latest global survey confirms it: 88% report regular AI use in at least one business function, up from 78% a year earlier.

Confetti, applause, mission accomplished. Except barely anyone scaled it.

Per McKinsey's own December 2025 breakdown:

Only 7% say AI has been fully scaled across their enterprise.
88% showed up to the party.
7% built something that survived the morning after.

That gap is where most enterprise AI budgets go to die, with only 39% reporting any enterprise-level EBIT impact, per Forbes's March 2026 coverage.

Looking to close that gap? We’ve got you covered,

Here are the five strategies separating that top 7% from everyone else still demoing chatbots to their board.

1. Name one accountable owner

Committees approve projects. A single leader ships them. A 2026 Stanford Digital Economy Lab study of 51 enterprise AI deployments, co-authored with Erik Brynjolfsson, found strategic scalers are typically championed by a dedicated Chief AI, Data, or Analytics Officer, while struggling firms lean on a lone champion working the problem solo.

One operations leader interviewed for the study summed up the real bottleneck in five words: "It always starts with the people." Ownership, in other words, beats org-chart theater every time.

2. Build the data foundation before the model

The Stanford study found strategic scalers are far more likely to hold a large, accurate dataset: 61%, compared with 38% for everyone else.

The same report revisits earlier research showing that for every dollar spent on tangible technology, companies spend up to $10 dollars on invisible work: process redesign, reskilling, and organizational change.

That dip before the rise has a name: the Productivity J-Curve, and pretending it skips your organization is how budgets get cut in year one.

💡

A quick gut check before your next model procurement conversation:

Can your pipeline handle production volume, security review, and an audit trail, all requirements that a pilot conveniently sidesteps?

Is ownership of data quality assigned to a person, rather than left as everyone's part-time responsibility?

Have you priced the reskilling and process redesign work—the 10-dollar side of the J-Curve—alongside the model license?

3. Redesign the workflow end to end

Only 2% of companies have redesigned a process end to end around AI, and that 21%is where the value concentrates, per McKinsey's high-performer data.

Every AI leader has sat through a demo that scored beautifully on a benchmark and collapsed the moment it met a real customer, a messy dataset, or simply a Tuesday afternoon.

Bolting a model onto an unchanged process produces a faster version of the same bottleneck, rather than a genuinely new one.

4. Treat governance as the strategy

McKinsey's 2026 AI Trust survey, fielded across roughly 500 organizations between December 2025 and January 2026, found nearly two-thirds cite security and risk concerns as the top barrier to scaling agentic AI, ahead of regulation or technical limits.

Organizations investing $25 million dollars or more in responsible AI report meaningfully higher EBIT impact, above 5%, than peers treating governance as paperwork.

Writer's 2025 survey of 1,600 knowledge workers found companies with a formal AI strategy succeed 80% of the time, compared with 37% among companies improvising one mid-flight.

The same survey found 68% of executives report friction between IT and the rest of the business during deployment, proof that the hardest part of scaling AI has always lived in the org chart rather than the algorithm.

5. Federate the platform, centralize the standards

Metis Strategy partner Michael Bertha, writing for CIO.com in December 2025, describes leading CIOs building domain hubs, staffed with platform experts and responsible AI advisors, that eventually operate as independent, AI-empowered teams while staying aligned to enterprise governance.

The approach accepts an early productivity dip in exchange for enterprise capability that compounds for years afterward.

This matters even more once agentic AI enters the picture...

McKinsey's 2025 survey found 23% of organizations actively scaling an agentic system in at least one function, with 39% still experimenting and at most 10% scaling agents within any single business function.

Box CEO Aaron Levie has compared today's agent adoption curve to cloud computing around 2010, when boardroom conviction ran years ahead of actual deployment.

💡

History rewarded patience over hype that time around, and the logic transfers cleanly here too.

MJ Smith, CMO at CoLab Software, summed up the wider gap in nine words: "Only 5.5%of companies drive significant value from AI." A federated platform with centralized standards is how the other 94.5% close the distance.

Signs your organization is actually ready to scale

A named executive owns AI as a KPI, reviewed weekly, in the same way they own revenue or churn.
A workflow has been redesigned end to end, with the old manual steps retired rather than kept around as a backup plan.
Governance runs as a live guardrail with an owner and a budget, rather than a policy document gathering dust in a shared drive.

The CIO.com framing for 2026 is blunt: After two years of experimentation, this is the year that separates organizations able to scale AI responsibly from those that stay parked in pilot mode.

The question is: have you adapted?

Want to go deeper?
Scaling AI is one challenge. Understanding what it actually costs to get there is another. Read The hidden costs of scaling AI for the numbers most enterprise AI budgets forget to include.