Info-Tech Research Group has published new findings showing that enterprise AI adoption is shifting from pilots and experimentation toward measurable business outcomes. Its report, AI Adoption and Impact Study: AI in the Enterprise June 2026 Top 10 Insights, examines how senior leaders are approaching AI maturity, strategy, investment, vendor sourcing, data readiness, workforce planning, and value realization.

Drawing on 551 completed survey responses from senior leaders involved in enterprise strategy, the study found that 42% of organizations have reached department-wide AI adoption with measurable impact. But activity alone does not guarantee value: enterprises with a dedicated, governed AI strategy report measurable impact 60% of the time, against just 20% for those with no active strategy — a three-to-one gap.

Data Readiness and Ownership Separate the Leaders

According to Jackson, organizations seeing measurable impact are not managing AI as a scatter of disconnected use cases. They tie it to strategy, data readiness, executive accountability, and defined measures of business outcomes.

The conditions for success remain unevenly distributed. Organizations reporting department-wide adoption with measurable impact are considerably more likely to rate their data quality as excellent, reinforcing the role of data governance and accessibility in AI outcomes. On ownership, CIOs and CTOs lead AI initiatives at more than half of surveyed organizations — but firms with a dedicated chief AI officer currently post the highest rate of department-wide adoption with measurable impact, suggesting IT leaders will need to keep proving value to retain that mandate.

Budgets Are Rising, and Strategy Maturity Drives Confidence

Investment continues to climb. Info-Tech reports that 96% of IT executives expect AI budgets to grow over the next 12 months, with 46% anticipating increases above 25%. Budget confidence tracks closely with strategic maturity: 73% of organizations with a formal, board-governed AI strategy report high confidence in budget increases, compared with 34% of those running ad hoc or department-led approaches.

Sourcing patterns are also clear. Some 80% of organizations prefer to buy AI solutions rather than build in-house — 42% activating AI through existing vendors and 38% selecting new best-of-breed providers. Meanwhile, 78% of IT executives expect AI to disrupt their current SaaS model within two years, with some anticipating outright platform replacement and others expecting reduced reliance on existing tools.

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Cost Reduction Is Rarely the Headline Goal

Among the most impactful AI use cases identified by respondents, only 11% named cost reduction as the primary objective. Productivity and throughput led at 38%, followed by revenue growth, risk reduction, quality and accuracy, customer satisfaction, and regulatory or compliance outcomes.

Jackson argues that value emerges when leaders know which outcomes they are pursuing and have the data, ownership model, and measurement practices to demonstrate progress. Cost savings may follow, but the strongest use cases are being built around productivity, risk reduction, quality, and growth rather than headcount cuts.

What Enterprise Leaders Should Do Next

Info-Tech advises organizations to formalize AI strategy while budgets and adoption momentum are still rising. That means defining ownership and decision rights, linking initiatives to measurable business outcomes, improving data readiness, assessing vendor sourcing models, and framing business cases around productivity, risk, quality, customer experience, and revenue impact.

As AI spending grows and buying outpaces building, technology leaders also face new questions around vendor dependency, SaaS disruption, and data architecture. The firm suggests evaluating sourcing decisions not only on speed of deployment but on long-term fit, governance, integration, and provable value.