The enterprise AI landscape has reached a pivotal moment. While 75% of enterprise workers report that AI helps them accomplish tasks they couldn't do before, organizations face a critical challenge: the gap between what AI models can do and what enterprises can actually deploy continues to grow. OpenAI's answer to this challenge is Frontier, a comprehensive platform designed to help enterprises build, deploy, and manage AI agents that perform real work across their organizations.

The AI Opportunity Gap

AI has transformed how work gets done, with impact visible across every department, not just technical teams. The results speak for themselves: at a major manufacturer, agents reduced production optimization work from six weeks to one day. A global investment company deployed agents end-to-end across the sales process, freeing up over 90% more time for salespeople to spend with customers. At a large energy producer, agents helped increase output by up to 5%, translating to over a billion dollars in additional revenue.

These achievements demonstrate what's possible for AI leaders across industries. However, most organizations struggle to replicate this success. The bottleneck isn't model intelligence—it's how agents are built and deployed within organizations.

The Fragmentation Problem

Companies already grapple with disconnected systems and governance spread across clouds, data platforms, and applications. AI has made this fragmentation more visible and, in many cases, more acute. Agents are being deployed everywhere, but each one operates in isolation, unable to see or do what it needs to be truly useful. Every new agent risks adding complexity rather than value because it lacks sufficient context to perform effectively.

As agents have become more capable, the opportunity gap between what models can do and what teams can deploy has widened. This gap isn't purely technological—teams are still developing the knowledge to move agents beyond early pilots and into production work as quickly as AI capabilities improve. At OpenAI alone, new capabilities ship roughly every three days, and this pace is accelerating. Keeping up requires balancing control with experimentation, a challenge that's difficult to navigate.

OpenAI Frontier: An End-to-End Solution

Frontier takes lessons from how enterprises scale human teams and applies them to AI agents. Organizations create onboarding processes for new employees, teach institutional knowledge and internal language, enable learning through experience, and establish clear permissions and boundaries. AI coworkers need exactly the same things.

For AI coworkers to be effective, several elements must come together:

- Understanding how work actually gets done across systems
- Access to computers and tools for planning, acting, and solving real-world problems
- Clarity on what good performance looks like, with quality improving as work evolves
- Identity, permissions, and boundaries that teams can trust

All of this must work across many systems, often spanning multiple clouds. Frontier integrates with existing systems without forcing organizations to replatform. Teams can bring together their existing data and AI where it lives, and integrate the applications they already use, all through open standards. This means no new formats and no abandoning agents or applications already deployed.

Shared Business Context

Every effective employee understands how the business works, where information lives, and what constitutes good decisions. Frontier connects siloed data warehouses, CRM systems, ticketing tools, and internal applications to give AI coworkers this same shared business context.

AI coworkers understand how information flows, where decisions happen, and what outcomes matter. Frontier becomes a semantic layer for the enterprise that all AI coworkers can reference to operate and communicate effectively. With shared context in place, agents gain the foundation they need to do actual work.

Agent Execution Environment

Teams across organizations—technical and non-technical alike—can use Frontier to employ AI coworkers who take on many computer-based tasks that people currently perform. Frontier provides AI coworkers with the ability to reason over data and complete complex tasks, including working with files, running code, and using tools, all in a dependable, open agent execution environment.

As AI coworkers operate, they build memories, converting past interactions into useful context that improves future performance. Once deployed, AI coworkers can run across local environments, enterprise cloud infrastructure, and OpenAI-hosted runtimes without forcing teams to reinvent workflows. For time-sensitive work, Frontier prioritizes low-latency access to OpenAI's models, ensuring responses remain quick and consistent.

Learning and Improvement

For agents to remain useful over time, they must learn from experience, just as people do. Built-in evaluation and optimization capabilities make it clear to both human managers and AI coworkers what's working and what isn't, enabling good behaviors to improve continuously.

Over time, AI coworkers learn what good performance looks like and become better at the work that matters most to the organization. This continuous improvement transforms agents from impressive demonstrations into dependable teammates who deliver consistent value.

Security and Governance

Frontier ensures AI coworkers operate within clear boundaries. Each AI coworker has its own identity, with explicit permissions and guardrails. This makes it possible to deploy them confidently in sensitive and regulated environments. Enterprise security and governance are built in from the ground up, enabling teams to scale without losing control.

Technology Meets Expertise

Closing the opportunity gap requires more than technology alone. OpenAI has worked closely with large enterprises on complex AI deployments for years, learning what works and what doesn't. Now the company is helping teams apply these lessons to their most challenging problems.

OpenAI pairs Forward Deployed Engineers (FDEs) with customer teams, working side by side to help develop best practices for building and running agents in production. The FDEs also provide a direct connection to OpenAI Research. As teams deploy agents, OpenAI learns not just how to improve systems around the model, but also how the models themselves need to evolve for specific use cases.

This feedback loop—from business problem to deployment to research and back—accelerates progress for both parties.

Opening the AI Ecosystem

AI works best in enterprises when platforms and applications work together. Because Frontier is built on open standards, software teams can integrate easily and build agents that benefit from shared context.

This matters because many agent applications fail for a simple reason: they lack necessary context. Data is scattered across systems, permissions are complex, and each integration becomes a custom project. Frontier makes it easier for applications to access the business context they need (with appropriate controls), enabling them to work inside real workflows from day one. For enterprises, this means faster rollouts without lengthy integration cycles.

Availability and Next Steps

Frontier is available today to a limited set of customers, with broader availability coming over the next few months. Organizations interested in exploring Frontier can reach out to their OpenAI team.

The question facing organizations today isn't whether AI will change how work gets done, but how quickly they can transform agents into a genuine competitive advantage. Frontier provides the platform, expertise, and ecosystem support to make that transformation happen at enterprise scale.

Source: Introducing OpenAI Frontier - OpenAI Blog