Few large organizations have rebuilt their core operations around artificial intelligence, according to the technology chief of one of the world's largest consulting firms. He also said companies should not yet spend much energy assigning blame when AI projects go wrong.

The number of large enterprises or government processes "that have been really, truly reinvented with AI at the absolute core is basically close to zero," said Marc Warner, global chief technology officer at Accenture, during a Fortune Brainstorm AI virtual event. Accenture, a founding partner of Brainstorm AI, sponsored the session.

Warner and three other executives spoke with Fortune AI editor Jeremy Kahn about why many companies report little return from AI despite its widespread use. The panelists were Matthieu Caillat of AXA, Greg Ulrich of Mastercard, and Marjorie Janiewicz of Mistral AI. Their answers pointed less to software than to organizational change.

Edges Versus Core

Most companies, Warner said, have deployed AI in peripheral work such as marketing, meeting notes, and transcription. Coding is "perhaps a little different," he noted, because returns there can be measured. Shifting the economics of an industry, by contrast, means targeting core business processes — which is risky. He likened the current moment to early factories that used electricity only for lighting before redesigning production around electric motors. Economists studying earlier general-purpose technologies have documented the same lag: measured gains tend to arrive only after organizations restructure their operations around the new tool, rather than simply powering old processes with it.

Ulrich argued that the biggest returns come from redesigning entire workflows rather than layering AI onto existing ones. Mastercard, he said, is "very, very advanced" in reworking its product development life cycle, using smaller teams and achieving faster turnaround. He also flagged a common trap: accelerate one step in an 80-step process, and the next step simply develops a backlog.

"The AI agent to automate a piece of this isn't … the long pole in the tent for a lot of this," Ulrich said. "It's the rethinking. It's the remapping."

Warner described AI transformations as "politics projects," saying he had viewed them as technology projects until recently. He still regards the AI builds themselves as technology projects.

Caillat said that as the technology improves, the work becomes less about technology and more about, "How do you bring people along?" Ulrich noted that Mastercard relies on CEO sponsorship, including weekly updates with the CEO's direct reports, while conceding that no one has solved the politics of large organizations.

Accountability

Kahn relayed an audience question about who is responsible when an AI agent goes wrong. Warner said there is no single answer. Broadly, he said, the business owner is accountable for an agent's value, the technologist for its functionality, and the chief information security officer for its security.

Still, he argued, most companies should not dwell on fault at this stage. "I don't think most organizations should be worrying too much about the blame game right now," Warner said, urging companies to give people credit for trying things, knowing many efforts will fail. He carved out an exception for critical incidents such as a major security breach.

Janiewicz, who said she spent 10 years in cybersecurity, agreed that finger-pointing is not useful. The better question, she said, is whether a company controls the technology it adopts and can set its own guardrails. Mistral sells open-weight models that customers can download and run on their own infrastructure rather than access only through a vendor's hosted service.

Keeping Pace

The executives also said their plans have a short shelf life. Ulrich said consulting tools Mastercard built roughly a year ago are now "quaint, and just not that useful today," prompting the company to rebuild them. Caillat said AXA once operated on a "measure twice and cut once" basis but "can't afford that anymore," because conclusions expire before certainty arrives.

Caillat said AXA began with about 400 AI use cases to build enthusiasm, then narrowed its focus. The insurer's plan for 2027 to 2029 targets €500 million to €700 million a year in recurring pretax value from AI by 2029, he said, and AXA has roughly 4 billion unstructured documents it is beginning to put to use.

Warner added that, by Accenture's calculations, the current pace shows no sign of slowing until something approaching 2030. Energy constraints in the West could start to bite in the early 2030s, he said.

Accenture is the founding partner of Fortune Brainstorm AI events and hosted this session. Generative AI was used for research assistance and/or transcription for the original article; the reporter independently reported and verified the factual claims, and a human editor reviewed it before publication.

This story was originally featured on Fortune.com.