Nearly nine in ten respondents to McKinsey’s 2026 global survey said their organizations regularly use AI in at least one business function. Yet only 44% said they were scaling AI across the enterprise. The difference is the point: trying AI in a function, putting it into production, embedding it in core workflows, and scaling it across an organization are distinct stages—not interchangeable measures of adoption.
Why are so many companies using AI but so few scaling it across the enterprise?
AI can be useful to an individual or team before an organization has the data, integrations, controls, skills, and redesigned workflows needed to operate it repeatedly across departments. A pilot can demonstrate promise without proving that the system works reliably across different teams, connects to existing tools, meets security requirements, or produces measurable business results.
McKinsey’s August 25, 2026, State of AI survey found that nearly nine in ten respondents reported regular AI use in at least one business function. Forty-four percent reported scaling AI across the enterprise, compared with 38% in the prior year’s survey. The survey also found that 56% reported AI use in at least three functions, up from 51%. These are respondents’ reports, not a census of all companies; “scaling” is McKinsey’s survey measure and should not be read as proof of uniformly mature or effective deployment. McKinsey’s 2026 State of AI
Scale also varies with company size in McKinsey’s findings: 54% of respondents at organizations with at least $1 billion in revenue reported enterprise scaling, compared with about one-third at smaller organizations. That difference describes survey responses, not a guarantee that larger organizations achieve better results.
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What do the adoption and scaling numbers actually measure?
It helps to separate five stages that are often compressed into the word “adoption”:
- Use: People use an AI tool in at least one task or business function.
- Production: An AI system is deployed for real work somewhere in the organization.
- Workflow integration: AI is part of an end-to-end process, with defined roles, handoffs, and oversight.
- Enterprise scaling: AI is deployed across the organization rather than remaining isolated in a team or use case.
- Business impact: Results are assessed against defined measures, such as cost, quality, speed, revenue, or financial performance.
These stages do not automatically follow one another. A company may have widespread experimentation or a few production systems without having integrated AI into core workflows or established consistent measurement.
McKinsey’s outcome findings are separate from its use and scaling figures. In the 2026 survey, 80% of respondents said AI improved their individual productivity; 37% attributed at least some EBIT impact to organizational AI use. About 6% met McKinsey’s “high performer” definition: attributing at least 5% of EBIT to AI and describing AI’s impact as significant. These survey responses do not establish that adoption caused the reported outcomes. McKinsey’s 2026 State of AI
Why the gap persists: barriers to moving beyond pilots
Different surveys point to recurring execution problems, but their percentages should not be combined: the studies cover different populations, geographies, dates, and questions.
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In RSM’s March 5–16, 2026, survey of 1,030 U.S. and Canadian middle-market leaders, data quality or availability was the most frequently cited deployment inhibitor across all respondents (34%). Security or privacy was cited by 30%, legacy integration by 28%, and talent or skills by 28%. Among respondents reporting moderate or limited pilot success, the barriers they cited for scaling included data quality (53%), integration (47%), unclear return on investment (33%), and security or compliance (33%).
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Those findings help explain why a tool that works in a bounded test may not transfer cleanly to live operations. Data may be incomplete or inconsistent, and AI systems may need to connect with older software and multiple sources of information. RSM’s survey measures respondents’ reported barriers; it does not show that any single one caused a particular company’s failure to scale. RSM’s middle-market AI analysis
Governance and visibility can lag behind deployment
IBM’s 2026 survey, conducted with Oxford Economics from January through April, covered 2,000 C-level technology executives across 33 geographies and 19 industries. Seventy-seven percent said AI adoption was outpacing their current governance capabilities, and 70% said teams deploy technology faster than IT can track. Only 11% said their organizations were fully ready for expected AI agent deployment.
In IBM’s analysis, organizations that embedded controls directly into AI systems experienced 25% fewer incidents. This is an IBM-reported analysis, not a universal estimate of the effect any organization should expect. The findings underscore the operational challenge: when teams adopt tools faster than an organization can see and govern them, consistent oversight becomes harder. IBM’s 2026 study on the AI control gap
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Netrio’s June 2026 survey, conducted by Censuswide, included 401 U.S. IT leaders at organizations with 200–5,000 employees. Respondents most often identified security, privacy, or compliance (19%), data readiness (17%), integration complexity (16%), and lack of internal expertise (10%) as barriers.
The same survey found that 42% of respondents reported a confirmed AI-related security incident or exposure in the preceding 12 months, while 31% reported a near miss. These are sponsor-reported results for that defined U.S. mid-market sample, not rates for businesses generally. Netrio’s 2026 mid-market survey
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- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
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Training and workflow redesign determine whether tools become routine
Canadian findings illustrate the difference between experimentation and embedding AI in day-to-day operations. KPMG Canada’s March 2026 analysis of its 2025 data said 93% of surveyed Canadian business leaders reported using or piloting AI, while 31% said they had embedded generative AI across core operations and workflows. Only 2% said they were realizing measurable returns on generative AI investment.
KPMG also identifies employee literacy and training needs. The practical implication is that supplying a tool is not the same as changing how work gets done: employees need role-relevant skills, clear responsibilities, and processes that define when AI is used and where human review belongs. These figures describe Canadian survey respondents and should not be generalized globally. KPMG Canada’s analysis of AI adoption and returns
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How does AI readiness vary across organizations and regions?
Survey results can look very different depending on who was asked and what counted as adoption. For example, Netrio’s U.S. mid-market survey found that 82% of respondents said AI was in production somewhere or in widespread use, while 26% said it was scaled and governed enterprise-wide. The 82% combines production use and widespread use; the 26% describes the narrower condition of enterprise-wide scale and governance. They illustrate a gap, but should not be treated as two measurements of the same thing.
Dun & Bradstreet’s Q3 2026 India findings show another view of the adoption-to-scale journey. Its quarterly global survey covers 10,000 businesses across 32 countries; the figures below are for surveyed Indian businesses:
- 100% reported AI-related projects underway.
- 44% were planning or piloting projects.
- 30% were scaling AI into production.
- 19% had operationalized AI across multiple core processes.
- 7% reported deploying agentic workflows.
- 4% said enterprise data was fully ready for AI at scale.
These are distinct reported deployment stages, not a universal progression or a forecast. The low share reporting fully ready enterprise data alongside broad project activity highlights that starting AI work and preparing the organization for repeatable scale are separate conditions. Dun & Bradstreet India’s Q3 2026 AI Momentum Survey release
What does it take to move from AI pilots to production at scale?
Leaders can assess readiness across five practical dimensions. This is a decision framework synthesized from recurring survey barriers, not a validated scoring model.
- Workflow depth: Identify whether AI is embedded in an end-to-end workflow, with roles and handoffs redesigned, or remains an optional tool or isolated pilot.
- Data and integration: Check data quality, access, legacy connections, and interoperability. Establish whether the system can operate repeatedly with the information and applications the workflow actually uses.
- Governance and security: Determine whether the organization can see which AI tools are in use, apply appropriate controls, and respond to incidents. Put oversight into the way systems operate, rather than relying only on informal guidance.
- People and operating model: Give employees role-specific training, define who is accountable for AI-assisted work, and establish where human review or escalation is needed.
- Measurement: Set a baseline and business measures before expansion. Track results against those measures rather than treating user activity or positive anecdotes as evidence of financial impact.
Expansion is more defensible when a use case works within its real workflow, has reliable data and integrations, can be governed, and demonstrates results against a baseline. A successful pilot alone does not establish those conditions across the enterprise.
What the surveys can—and cannot—tell you
The sources here are surveys and analyses published by McKinsey, IBM, Netrio, RSM, KPMG Canada, and Dun & Bradstreet. Their respondents and questions are not interchangeable: IBM surveyed C-level technology executives globally; Netrio surveyed U.S. IT leaders at mid-market organizations; RSM surveyed U.S. and Canadian middle-market leaders; KPMG’s figures concern Canadian respondents; and the cited Dun & Bradstreet figures concern India.
Use the McKinsey result as a global benchmark for what respondents reported, and the other findings as context about particular populations and barriers. None of the percentages alone establishes how every company is performing, or proves that one factor caused a company to scale—or fail to scale—AI.
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