Dive Brief:
- Companies are struggling to transition from smaller, generative AI use cases to widespread agentic systems within their organizations, according to a Deloitte report published Wednesday. The professional services firm surveyed more than 500 tech leaders and conducted interviews with 20 executives and data science leaders for insights.
- Although roughly half of technology leaders say they have a clear view of their future AI operating models, only 15% of organizations have actually been able to scale multiagent systems. Most said they are unprepared when it comes to their workforce, business processes, ecosystem partnerships, security and governance policies.
- Tech leaders should realize that the shift isn’t about adding more agents, but changing the way work happens within their organizations, China Widener, vice chair and U.S. technology, media and telecommunications industry leader at Deloitte, told CIO Dive. “It’s a multiyear journey because it’s going to continue to evolve, frankly, as the technology around the agents continues to improve.”
Dive Insight:
Enterprise spending has trended more toward agentic systems in 2026, but readiness for full-scale deployment is still far off for most organizations.
Tech leaders surveyed by Deloitte said that most organizations were at least three or four years away from having half of business processes redesigned around AI agents and from having the agents work autonomously and collaboratively with each other.
Currently, many organizations are applying AI agents on top of existing processes instead of redesigning processes from the ground up, which can take years. It’s a quick path to implementing the technology, but it doesn’t reap the true benefits of the technology, Deloitte’s report said.
A lack of a unified and accessible data foundation, inability to trust and govern agents and the cost and complexity of integration were among the technical challenges companies faced with agentic AI adoption. Organizational challenges included workforce readiness and underprepared business processes, according to Deloitte.
Because of AI’s breakneck pace, CIOs are in a constant state of evolution, Widener said. AI strategies should be developed parallel to the changing technologies, she said.
“You have the technical components, the governance components, the data components, and the human components all evolving at the same time,” Widener said. “That is what contributes to the difficulty in the transformation.”
Agentic AI is providing most companies some positive impact, according to a recent Accenture report, but few organizations say they’re seeing sustained business value — impact that can be reported to a company’s board — from their AI investments.
CIOs looking to deepen their agentic AI use in a meaningful way should realize that it’s a drastically different type of technology than the linear, generative AI models that most companies have deployed over the last few years, Widener said.
With automated models, a task or query will follow the same steps each time, but agentic models work less predictably. It’s a user’s job to validate the outcome, which is a very different way of working than most are used to, Widener said.
“We’re entering a different time where the technology and the agentic solution has the ability to think and take those outcomes and arrive at the best path of travel to get there without being told all the steps,” Widener said.
