Analyst(s): Mitch Ashley, Keith Kirkpatrick, Fernando Montenegro, Nick Patience, Brad Shimmin
Publication Date: February 9, 2026
OpenAI has introduced Frontier, an enterprise platform designed to operationalize AI agents as digital coworkers across business systems. The move highlights a widening gap between AI model potential and enterprise execution, and intensifies competition to define the agent platform layer.
What is Covered in this Article:
- How OpenAI Frontier aims to move enterprise AI from isolated pilots to integrated, production-scale agent deployments
- Why shared business context, identity, and governance are becoming prerequisites for AI coworkers
- Competitive implications for vendors racing to define the agent execution and control layer
- Governance that will win enterprise customers
- Whether Frontier narrows the enterprise AI opportunity gap or shifts it to a new layer of complexity
The News: OpenAI has launched Frontier, an enterprise platform designed to help organizations build, deploy, and manage AI agents that function as digital coworkers rather than standalone bots. Frontier combines shared business context, agent identity and permissions, onboarding and learning workflows, and evaluation and optimization tooling intended to support long-lived, production-grade agents.
Early customers include HP, Intuit, Oracle, State Farm, Thermo Fisher, and Uber, with pilots underway at BBVA, Cisco, and T-Mobile. Frontier is designed to integrate with existing enterprise systems, operate across on-premises and cloud environments, and support collaboration between in-house and third-party agents, including those built on models from Anthropic, Google, and Microsoft. OpenAI is also working with a select group of AI-native partners to expand the Frontier ecosystem.
OpenAI Frontier: Close the Enterprise AI Opportunity Gap—or Widen It?
Analyst Take: The launch of Frontier marks a clear inflection point in OpenAI’s enterprise AI strategy. The market conversation is shifting away from how capable models are, toward how AI work is operationalized, governed, and trusted inside real organizations. That shift exposes a growing opportunity gap. Enterprises increasingly believe AI can deliver transformational value, yet struggle to move beyond pilots due to fragmented systems, unclear ownership, and governance requirements.
Frontier is OpenAI’s attempt to address that gap by treating agents less like tools and more like employees. The platform’s emphasis on shared business context, explicit identity, and continuous evaluation mirrors how enterprises onboard, manage, and improve human workers. In doing so, OpenAI is positioning Frontier as AI infrastructure, not simply an extension of model access.
That positioning raises the stakes. If Frontier succeeds, OpenAI moves closer to becoming a central coordination layer for enterprise AI execution. If it falls short, the opportunity gap may widen further, shifting from model limitations to platform complexity and misses on what enterprises are looking for in AI agent governance.
Both OpenAI and its main rival, Anthropic, are preparing for public offerings, intensifying pressure to demonstrate enterprise revenue traction and platform stickiness. Enterprise contracts provide the recurring revenue and expansion potential that public market investors value. Frontier represents OpenAI’s bid to close the perception gap with Anthropic, which has built its reputation on enterprise adoption and draws a significant share of its revenue from business customers.
From Experiments and Pilots to Integrated AI Coworkers
A core problem Frontier targets is the proliferation of disconnected agents that solve narrow tasks but fail to scale to larger workflows and business problems. Without shared context, agents duplicate effort, make inconsistent decisions, and introduce operational risk. Frontier’s semantic layer approach, connecting data warehouses, CRM, ticketing, and internal applications, is designed to give agents a unified understanding of how work flows through the business.
This reflects an emerging enterprise reality. Organizations need AI systems that can operate across functions, respect permissions, and improve over time. Frontier’s built-in evaluation loops and memory mechanisms address this requirement by making agent quality observable and governable rather than assumed.
Governance – The Race to Enterprise Trust
As AI agents move into production, governance is the primary determinant of which platforms enterprises trust first. Model capability may attract attention, but governance determines deployment. Enterprises will back the platforms that can clearly define who an agent is, what authority it has, how its actions are reviewed, and how risk is contained at scale.
The early advantage will go to vendors that make governance operational rather than treat it as a product roadmap placeholder. Identity, permissions, auditability, and evaluation must be embedded into the execution layer so that every agent action is observable, attributable, and reversible. Platforms that deliver this natively reduce approval cycles and accelerate time-to-production. Those that do not will see adoption stall under compliance and security review.
This creates a race dynamic. The first platforms to convince enterprises they can safely delegate real authority to agents will gain disproportionate traction, even if their agent capabilities are not the most advanced. Trust compounds. Once governance frameworks are established, organizations are more likely to expand agent scope, onboard additional teams, and standardize rather than re-evaluate risk for every deployment.
The risk for vendors is overcorrection. Governance that is too rigid slows execution and drives teams toward shadow AI. Governance that is too permissive limits agents to low-impact tasks. Winning platforms will strike a narrow balance, providing strong guardrails without constraining autonomy. In this market phase, governance is not a checklist item. It is the competitive weapon that determines who reaches production first.
The challenge for OpenAI Frontier is that it must now demonstrate that it can secure agents, not API calls. Until now, OpenAI had to contend primarily with how it uses customer data and how it controls access to its own platform. Agentic is a new ballgame: now OpenAI must provide, at a bare minimum, security capabilities for autonomous access to customer data and integration with customer identity stores that support these agentic workloads.
Besides references to supporting agent identity and access management, privacy, security, compliance, and observability, the initial release of Frontier documentation doesn’t provide information about agent-specific security functionality. The main OpenAI security and compliance landing page, for example, does not yet even list OpenAI Frontier as a product.
This can present a significant hurdle to adoption, as OpenAI needs to quickly demonstrate a deep understanding of enterprise security needs.
Platform Competition Shifts Up the Stack
Frontier also intensifies competition with established enterprise AI and application platform providers, including Anthropic, Microsoft, Google, and Salesforce. Each is pursuing its own strategy to embed agents into workflows, data platforms, and productivity environments.
OpenAI’s differentiation is its attempt to position Frontier as a neutral execution and coordination layer rather than an extension of a single cloud or application suite. For enterprises operating across hybrid and multi-cloud environments, that neutrality is attractive. At the same time, neutrality raises execution risk. Integration depth, operational simplicity, and ecosystem scale will determine whether Frontier reduces friction or becomes another platform enterprises must rationalize.
Another challenge for OpenAI is that it lacks the level of domain expertise and years of experience working with large enterprises that incumbent software vendors have. Simple agentic workflows can benefit from a horizontal approach to AI, powered by customer data, but as complexity increases, so does the need to incorporate nuances around processes, existing technologies, and workflows, and, in many cases, regulations and standard operating procedures.
The use of Forward Deployed Engineers to co-develop production agents underscores how early this market remains. An early success metric will be how quickly OpenAI can translate on-the-ground learnings into actionable workflows while constraining costs. The long-term signal will be whether those deployments become repeatable and scalable as Frontier expands beyond its current customer base.
Redefining the Semantic Layer as Critical Infrastructure
To achieve meaningful scale, AI needs to evolve from a tool that enterprises sporadically prompt into a coworker that autonomously navigates functional silos. One of the most daunting technical hurdles to this lies in corporate data and how it serves as context for AI workflows. OpenAI’s Frontier hopes to unify this business context. Most current agents operate in a vacuum because they lack a unified view of the enterprise. Frontier’s strategy is straightforward. The company wants to weave data warehouses and internal applications into a cohesive semantic layer. The objective is to provide agents with the institutional knowledge required for consistent decision-making.
This, Futurum believes, is the ground where the battle for the enterprise stack will be won or lost. If an agent fails to recognize that “Gross Margin” in the CRM is identical to the metric in the ERP, it will either hallucinate or stall. Our 1H 2025 Data Intelligence, Analytics, and Infrastructure Decision Maker Survey confirms this priority: 29% of data teams are focusing on building AI capabilities as their primary objective, while 24% cite “trust in data” as their North Star. Frontier hits at this intersection squarely, promising to give agents the memory and context needed to solve problems dependably over the long haul.
This sounds good on paper. And so far, OpenAI has not provided much detail about Frontier. Therefore, the presumption is that this platform will build upon the company’s existing tooling and API portfolio. That will make for a great head start. However, the trick will be in how OpenAI manages the overall governance of solutions built on Frontier. That capability alone will dictate the ceiling of any deployment. You can deploy the most brilliant model imaginable, but if legal and compliance teams can’t identify an agent, audit its authority, or reverse its actions, that agent will never leave the sandbox.
Frontier’s focus on agent identity and granular permissions is a pragmatic response to this bottleneck. But despite its leadership position in the frontier model space, OpenAI is far from the only vendor with a vested interest in bringing data to agentic systems in a highly governed manner. Pure-play providers like Dataiku, Databricks, and Snowflake, along with Hyperscalers including Google, AWS, and Microsoft (all partners of OpenAI), have been working to build this functionality for years. To serve as more than a model maker, OpenAI will have to demonstrate it can manage, secure, and govern agents at scale and do so affordably.
What to Watch:
- How quickly Frontier expands beyond early adopters into broader enterprise availability
- Whether enterprises treat Frontier as a central execution layer or a specialized agent platform
- OpenAI has not disclosed pricing for Frontier. Adoption patterns will be shaped by whether it follows a consumption model, per-agent licensing, or a platform fee structure.
- Competitive responses from Microsoft, Google, Salesforce, and other platform vendors
- Evidence that embedded governance reduces time from pilot to production
- Whether governance and security capabilities scale as agent authority increases
- How will OpenAI move from “consultingware” to a scalable product?
See the complete post about OpenAI Frontier on its website.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
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Author Information
Mitch Ashley is VP and Practice Lead of Software Lifecycle Engineering for The Futurum Group. Mitch has over 30+ years of experience as an entrepreneur, industry analyst, product development, and IT leader, with expertise in software engineering, cybersecurity, DevOps, DevSecOps, cloud, and AI. As an entrepreneur, CTO, CIO, and head of engineering, Mitch led the creation of award-winning cybersecurity products utilized in the private and public sectors, including the U.S. Department of Defense and all military branches. Mitch also led managed PKI services for broadband, Wi-Fi, IoT, energy management and 5G industries, product certification test labs, an online SaaS (93m transactions annually), and the development of video-on-demand and Internet cable services, and a national broadband network.
Mitch shares his experiences as an analyst, keynote and conference speaker, panelist, host, moderator, and expert interviewer discussing CIO/CTO leadership, product and software development, DevOps, DevSecOps, containerization, container orchestration, AI/ML/GenAI, platform engineering, SRE, and cybersecurity. He publishes his research on futurumgroup.com and TechstrongResearch.com/resources. He hosts multiple award-winning video and podcast series, including DevOps Unbound, CISO Talk, and Techstrong Gang.
Keith Kirkpatrick is VP & Research Director, Enterprise Software & Digital Workflows for The Futurum Group. Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
Fernando Montenegro serves as the Vice President & Practice Lead for Cybersecurity & Resilience at The Futurum Group. In this role, he leads the development and execution of the Cybersecurity research agenda, working closely with the team to drive the practice's growth. His research focuses on addressing critical topics in modern cybersecurity. These include the multifaceted role of AI in cybersecurity, strategies for managing an ever-expanding attack surface, and the evolution of cybersecurity architectures toward more platform-oriented solutions.
Before joining The Futurum Group, Fernando held senior industry analyst roles at Omdia, S&P Global, and 451 Research. His career also includes diverse roles in customer support, security, IT operations, professional services, and sales engineering. He has worked with pioneering Internet Service Providers, established security vendors, and startups across North and South America.
Fernando holds a Bachelor’s degree in Computer Science from Universidade Federal do Rio Grande do Sul in Brazil and various industry certifications. Although he is originally from Brazil, he has been based in Toronto, Canada, for many years.
Nick Patience is VP and Practice Lead for AI Platforms at The Futurum Group. Nick is a thought leader on AI development, deployment, and adoption - an area he has researched for 25 years. Before Futurum, Nick was a Managing Analyst with S&P Global Market Intelligence, responsible for 451 Research’s coverage of Data, AI, Analytics, Information Security, and Risk. Nick became part of S&P Global through its 2019 acquisition of 451 Research, a pioneering analyst firm that Nick co-founded in 1999. He is a sought-after speaker and advisor, known for his expertise in the drivers of AI adoption, industry use cases, and the infrastructure behind its development and deployment. Nick also spent three years as a product marketing lead at Recommind (now part of OpenText), a machine learning-driven eDiscovery software company. Nick is based in London.
Brad Shimmin is Vice President and Practice Lead, Data Intelligence, Analytics, & Infrastructure at Futurum. He provides strategic direction and market analysis to help organizations maximize their investments in data and analytics. Currently, Brad is focused on helping companies establish an AI-first data strategy.
With over 30 years of experience in enterprise IT and emerging technologies, Brad is a distinguished thought leader specializing in data, analytics, artificial intelligence, and enterprise software development. Consulting with Fortune 100 vendors, Brad specializes in industry thought leadership, worldwide market analysis, client development, and strategic advisory services.
Brad earned his Bachelor of Arts from Utah State University, where he graduated Magna Cum Laude. Brad lives in Longmeadow, MA, with his beautiful wife and far too many LEGO sets.
