AI Agents are Only the Beginning: Deloitte Survey Examines the AI Readiness Gap and Reveals How Enterprises Can Prepare for Agentic Success

NEW YORK, August 12, 2026 —

Key takeaways

• A high ceiling for expectations: Within four years, 74% of leaders surveyed expect nearly half of their business processes will be redesigned or rebuilt around AI agents, and 61% expect most AI agents used will be generally autonomous (with humans acting as oversight). In fact, 75% agree that human collaboration with AI agents creates more value than AI agent-powered automation alone.
• Transformation beyond technology: Nearly two-thirds of surveyed executives are reevaluating their business models, recognizing that agentic AI demands reinvention of processes and workflows in addition to technical deployment.
• Challenges to scale: Only 5% of organizations say their business processes are highly prepared for AI agents, and just 15% have scaled orchestrated, cross-functional multi-agent adoption. Even among the more mature AI adopters, challenges persist: Fewer than half (46%) of scaled adopters believe their business processes are prepared for agentic adoption.
• Workforce disruption is accelerating: 43% of leaders expect agentic AI to significantly disrupt their workforces in the coming 12 to 18 months. This disruption includes changing job requirements, the need for additional learning and development, design of new ways of working to leverage human and agentic capabilities, and the creation of new roles. Despite these expectations, half say their organizations aren’t adequately investing in AI-related workforce transformation.
• Layering isn’t enough: Many organizations are increasingly layering AI agents on top of existing processes in pursuit of quick ROI. Despite the widespread expectation for business process redesign, only 1 in 5 leaders surveyed say their organizations are prepared to redesign processes for autonomous, agentic operation.

Why this matters

The difference between expectations and preparedness in agentic AI adoption illustrates a growing imperative for transformation. Deloitte’s latest survey of more than 500 U.S. business and IT leaders finds that many organizations anticipate broad AI implementation will drive growth and productivity while making work more meaningful, yet only a fraction of them have their processes and workflows ready to realize these benefits.

Without decisive action, gains could stall, returns on AI investment may disappoint, and competitive advantage may lag. Near-term efforts may generate localized, quick-win ROI. But realizing agentic AI’s potential requires organizations to treat AI not as a layer atop existing processes, but as an enterprise-wide transformation that enables true human-machine collaboration. This could include redesigning processes and workflows, focusing on workforce readiness and trust, and implementing new operating models and organizational structures.

Key quotes

Scaling remains limited

More than 4 in 10 leaders surveyed say their organizations have tested or deployed AI agents (42%), and about as many have deployed them across more than one function (43%). However, the impact of agentic AI at scale is more limited. Only 15% say they have scaled, orchestrated, multi-agent adoption in place, and many who do are applying those deployments in low-risk, low-ROI applications.

Operating model transformation challenges

Organizations appear to accept that true AI transformation will include business model transformation: nearly two-thirds of surveyed leaders report that wholesale reevaluation is under way. But fewer — about half — say they have a clear view of what an agent-powered operating model will look like. What stands in the way? Seventy-two percent say they lack unified, accessible data; 70% don’t feel they can trust and govern agents; and 67% say integration is too costly and complex.

Preparedness across the enterprise

The survey tested respondent organizations’ preparedness for agentic AI adoption across seven broad areas. Slightly more than half of leaders (52%) say they are “prepared” or “highly prepared” in terms of vision and strategy. No other area topped 50% preparedness: technology infrastructure (48%); data foundation (42%); risk, security and governance (39%); ecosystem partnerships (34%); or workforce (25%). The least-prepared area, business processes, found only 21% of surveyed leaders ready.

New processes, or just newly automated ones?

Most leaders interviewed say their organizations are focusing on adding, or “layering,” AI agents on top of existing processes instead of redesigning those processes. One reason is that layering may lead to earlier returns on an AI investment. Fewer than one-third (31%) of respondents expect the majority of their business processes to be redesigned around agentic AI within the next two years.

Job disruption ahead

More than 4 in 10 of surveyed leaders (43%) say the next year to year and a half is likely to bring significant job disruption to their organizations due to AI agents. Over a two- to three-year span, that number increases to 72%. They expect AI agents to reshape work through changing job requirements, new ways of working, evolving training needs, and the emergence of new roles. Yet despite anticipating these changes, half of surveyed leaders say their organizations aren’t making the necessary investments in AI-related workforce transformation.

About the survey

The insights and statistics included in this report came from a survey that was fielded to 501 senior manager- to C-suite-level respondents, based in the United States, across five industries between April and June 2026. All organizations participating in the survey had to be at least piloting agentic AI solutions. The respondents were all involved in their organization’s agentic AI strategy or implementation. Industries included: Consumer; Energy, Resources and Industrials; Financial Services; Life Sciences and Health Care; and Technology, Media and Telecom. Technology-focused functional leaders (e.g., data, AI, analytics, IT, engineering, digital transformation) made up 55% of the sample and line of business and other functional leaders (e.g., strategy, operations, sales, finance, product management) made up 45%. The survey data was augmented by additional insights from 20 interviews with executives and AI and data science leaders at large organizations across a representative range of industries.