Technology insights: 2026 AI Impact Survey Report

AI adoption runs ahead of controls

Eighty-one percent of technology firms are scaling or fully integrating agentic AI, compared to 38% across industries. Those that govern AI and prove results are pulling away from those that do not. That is the AI proof gap.

This report explains why this gap exists — and what firms need to do to move from disconnected solutions to enterprise value.

Speed without control

The technology sector leads all industries in AI adoption, but companies have left something out. Grant Thornton’s 2026 AI Impact Survey of 950 business leaders shows that many technology companies are stuck in the gap between deploying AI and achieving measurable results.

Governance serves as the operating architecture that enables returns on AI investments. Our survey found AI governance is lacking across all industries, but the technology industry has a unique combination: the highest maturity, the fastest agentic scaling and an internal governance architecture that has not kept pace with either. External regulation is not the constraint — the pressure is coming from inside the enterprise.

This report examines three pressure points specific to the technology industry: whether integration claims hold up under the performance tests executives increasingly face, why the absence of enterprise governance architecture is costing the industry in ways the adoption numbers do not show and what strategic leadership will mean as AI disrupts the industry deploying it.

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Scaling without control creates exposure

81%

are scaling agentic AI across multiple business units or fully integrating it into workflows

24%

of tech companies say agentic AI is fully integrated into enterprise workflows, nearly three times the 9% average across all industries

In the technology industry, almost every department has the skills to build and deploy AI without waiting for organizational alignment. That is why scaling happens faster than anywhere else in this survey. It is also why autonomy without governance compounds risk rather than just scaling performance.

When teams run their own agents without shared data definitions or enterprise standards, two agents working from two different definitions of the same business metric can produce two confident but contradictory answers. That is a leadership failure. Technology executives are responsible for the architecture that prevents it. At 57% scaling across multiple business units, which is double the survey average, and 24% fully integrated, technology is scaling agentic AI faster than any other industry in this survey. The governance architecture has not kept up.

Organizations that are building the governance, controls and proof record have treated integration as an operating claim requiring evidence: documented workflows, measurable performance and defined ownership across functions. Those treating deployment as the destination are accumulating proof debt that agentic scaling will surface, not resolve. That absence of enterprise governance is costing organizations in ways the adoption numbers do not show.

“The real risk in technology right now is that companies have moved so fast that few built the enterprise architecture to hold it together. Governance is the structure that converts a portfolio of pilots into an enterprise performance engine.”

Andrea Schulz

Head of the Technology Industry,

Grant Thornton Advisors LLC

AI coherence is the new enterprise performance gap

55%

of technology leaders cite regulatory or compliance uncertainty as a top scaling barrier, and that is often internal: Governance architecture is absent, not external regulation

48%

say governance or compliance barriers have contributed to AI underperformance in their organization

AI deployments that outrun governance produce outputs no one can trace and decisions no one owns. External AI regulation for technology companies remains limited. So when 55% of technology leaders cite regulatory or compliance uncertainty as a top scaling barrier, they are identifying the absence of internal governance clarity: which use cases are approved, which data standards govern which teams and who is accountable when an agent produces the wrong answer. Teams that lack those definitions proceed in silos and absorb the coherence cost when it is harder to unwind.

Technology organizations are built with builders — almost every department has the skills to deploy AI without waiting for organizational alignment. The result is a landscape of point solutions with no common architecture: agents that work well within their own logic but carry no shared data standard, no shared definition, and no traceable accountability structure. For technology leaders, that is not just an engineering problem. It is a strategic one, and the industry’s own data shows they know it.

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AI is disrupting tech faster than tech can govern it

57%

of technology leaders name strategic decision-making under uncertainty as a top leadership attribute, the highest industry rate in the survey

43%

name a risk, controls and compliance mindset as a top leadership attribute — the next most common response, even above creativity and innovation in third place at 39%

No other industry in this survey is being disrupted by the same technology it is deploying. Companies that built differentiation around software features have seen those features become table stakes within 12 months. Investor sentiment has questioned SaaS valuations as AI commoditizes product functionality faster than development cycles respond. The 57% of technology respondents who name strategic decision-making under uncertainty as the essential leadership attribute are not describing a management skill. They are describing a survival requirement.

The market test for AI-washing is already in progress. Buyers are distinguishing vendors that deliver AI substance from those delivering AI positioning. Technology leaders who establish measurable, defensible AI performance at the enterprise level will carry an advantage that acquirers, investors and customers recognize. Those who wait for the market to force the question will find the answer considerably more expensive than the preparation.

Three actions to turn AI scale into AI success

Firms that move early and with precision in these areas will move from back-office efficiencies to performance-building use cases that enhance customer experiences and drive sustainable revenue growth, to capture a higher tier of value from AI adoption.

Step 1: Require evidence for integration claims.

If teams describe AI as fully integrated, require the operating proof: which workflows changed, which performance metrics moved, which controls are in place. Deployment without workflow change, performance measurement and defined ownership is not integration. Closing that definitional gap before boards and buyers apply their own test is considerably cheaper than defending the claim under pressure.
Step 2: Assign enterprise ownership to AI coherence.

The absence of governance architecture in technology is not about external regulation. It reflects the lack of a shared operating model: shared data definitions, shared standards and shared accountability across agents and functions. Technology organizations do not have a deployment problem. They have a coherence problem. The firms closing the gap have not slowed their builders down. They have given them shared constraints to build within common data definitions, documented standards, traceable accountability across every agent and function. That architecture has to be designed deliberately. In a company full of builders, it will not emerge on its own.
Make the substance decision before the market does.

The AI-washing correction is already removing companies from consideration at the investor, acquirer and buyer level. Technology leaders who establish measurable, defensible AI performance will carry a durable advantage. Those who wait for the correction to force the question will pay for preparation under pressure rather than ahead of it.

These are the capabilities Grant Thornton helps organizations build. We designed this research to identify barriers to achieving AI business value and we help organizations overcome the barriers it identified by designing governance infrastructure across front-, middle- and back-office workflows, providing AI compliance readiness and aligning leadership teams, building measurement frameworks and preparing for the governance demands of agentic AI. The insights in this report reflect what Grant Thornton sees and builds every day.

In technology, the gap between fast adoption and slow control has a market price. SaaS valuations are already reflecting investor skepticism about AI substance versus AI positioning. Buyers are making the same distinction. Organizations that establish measurable, defensible AI performance now will carry that advantage when acquirers, investors and customers apply their own tests. Those that wait will pay for preparation under pressure rather than ahead of it.

Methodology

Between Feb. 23 to March 18, 2026, Grant Thornton surveyed 950 business leaders, a group restricted to CFOs, CIOs/CITOs, COOs, and VPs, department heads, and directors who report directly to the C-suite. The technology-specific subgroup comprises 100 respondents. Findings specific to the subgroup of the data are directional only.

Contact:

Andrea Schulz

Head of Technology Industry
Grant Thornton Advisors LLC

Partner, Audit Services, Grant Thornton LLP

+1 408 346 4335

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Technology industry: 2026 AI Impact Survey Report