Intuit, Uber, and State Farm trial AI agents inside enterprise workflows

The way large companies use artificial intelligence is changing. For years, AI in business meant experimenting with tools that could answer questions or help with small tasks. Now, some big enterprises are moving beyond tools and into AI agents that can actually do work across systems and workflows, not just answer prompts.

This week, OpenAI introduced a new platform designed to help companies build, run, and manage those kinds of AI agents at scale. The announcement has drawn attention because a handful of large corporations in finance, insurance, mobility, and life sciences are among the first to start using it. That signals a shift: AI may be ready to move from pilots and proofs of concept into real operational roles.

From tools to agents

The new platform, called Frontier, is meant to help companies deploy what are sometimes described as AI coworkers. These are software agents that can connect to corporate systems like data warehouses, customer relationship tools, ticketing systems, and internal apps, and then carry out tasks inside them. The idea is to give the AI agents a shared understanding of how work happens in a company, so they can perform meaningful work reliably over time.

Rather than treating every task as a separate, isolated use case, Frontier is built so that AI agents can function across an organisation’s systems with a common context. In OpenAI’s words, the platform provides the same kinds of basics that people need at work: access to shared business context, onboarding, ways to learn from feedback, and clear permissions and boundaries.

Frontier also includes tools for security, auditing, and ongoing evaluation, so companies can monitor how agents perform and ensure they follow internal rules.

Who’s using this now

What makes this shift newsworthy is not just the technology itself, but who is said to be using it early.

According to multiple reports and OpenAI’s own posts, early adopters include Intuit, Uber, State Farm Insurance, Thermo Fisher Scientific, HP, and Oracle. Larger pilot programs are also said to be underway with companies such as Cisco, T-Mobile, and Banco Bilbao Vizcaya Argentaria.

Having actual companies in different sectors test or adopt a new platform this early on shows a move toward real-world application, not just research or internal experimentation. These are firms with complex operations, heavy regulatory needs, or large customer bases, environments where AI tools must work reliably and safely if they are to be adopted beyond experimental teams.

What rxecutives are saying

Direct quotes from executives and leaders involved in these moves give a sense of how companies view the shift.

On LinkedIn, a senior executive from Intuit commented on the early adoption:

“AI is moving from ‘tools that help’ to ‘agents that do.’ Proud Intuit is an early adopter of OpenAI Frontier as we build intelligent systems that remove friction, expand what people and small businesses can accomplish, and unlock new opportunities.”

That comment reflects a belief among some enterprise leaders that AI agents could reduce manual steps and expand what teams can accomplish.

OpenAI’s message to business customers emphasises that the company believes agents need more than raw model power; they need governance, context, and ways to operate inside real business environments. As one commenter on social media put it, the challenge isn’t the ability of the AI models anymore: it is the ability to integrate and manage them at scale.

Why this matters for enterprises

For end-user companies considering or already investing in AI, this moment points to a broader shift in how they might use the technology.

In the past few years, most enterprise AI work has focused on narrow tasks: auto-tagging tickets, summarising documents, or generating content. These applications were useful, but often limited in scope. They didn’t connect to the workflows and systems that run a business’s core processes.

AI agents are meant to close that gap. In principle, an agent can pull together data from multiple systems, reason about it, and act; whether that means updating records, running analyses, or triggering actions across tools.

This means AI could start to touch real workflow work rather than just provide assistance. For example, instead of an AI drafting a reply to a customer complaint, it could open the ticket, gather relevant account data, propose a resolution, and even update the customer record; all while respecting internal permissions and audit rules.

That is a different kind of value proposition. It is no longer about saving time on a task; it is about letting software take on pieces of the work itself.

Real adoption has practical requirements

The companies testing Frontier are not using it lightly. These are organisations with compliance needs, strict data controls, and complex technology stacks. For an AI agent to function there, it has to be integrated with internal systems in a way that respects access rules and keeps human teams in the loop.

That kind of integration, connecting to CRM, ERP, data warehouses, and ticketing systems, is a long-standing challenge in enterprise IT. The promise of AI agents is that they can bridge these systems with a shared understanding of process and context. Whether that works in practice at scale will depend on how well companies can govern and monitor these systems over time.

The early signs are that enterprises see enough potential to begin serious trials. That itself is news: for AI deployments to move beyond isolated pilots and become part of broader operations is a visible step in technology adoption.

What comes next

If these early experiments succeed and spread, the next phase for enterprise AI could look very different from earlier years of tooling and automation. Instead of using AI to generate outputs for people to act on, companies could start relying on AI to carry out work directly under defined rules and boundaries.

That will raise questions for leaders in operations, IT, security, and compliance. It will also create new roles; not just data scientists and AI engineers, but governance specialists and execution leads who can take responsibility for agent performance over time.

The shift points to a future where AI agents become part of the everyday workflow for large organisations, not as assistants, but as active participants in how work gets done.

(Photo by Growtika)

See also: OpenAI’s enterprise push: The hidden story behind AI’s sales race

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