OpenAI's Frontier Alliance: Enterprise AI Strategy [2025]

Why OpenAI Is Betting Big on Consulting Partnerships for Enterprise Growth

OpenAI didn't get where it is by accident. The company released ChatGPT, watched it hit 100 million users in two months, and suddenly found itself with a product that captured the world's imagination. But here's the thing: capturing imagination and actually making money from enterprises are two completely different problems.

By February 2025, OpenAI made a strategic move that signals something crucial about how the company sees its future. It announced the "Frontier Alliance," a multi-year partnership with four of the world's largest management consulting firms: Boston Consulting Group (BCG), McKinsey, Accenture, and Capgemini. This isn't just another partnership announcement designed to look good in a press release. This represents a fundamental shift in how OpenAI plans to penetrate the enterprise market.

The consulting alliance model is actually brilliant when you think about it. Most enterprise customers don't want to buy software. They want to solve business problems. They need help understanding how AI fits into their existing processes, where it creates real value, and how to implement it without blowing up their operations. That's exactly what consulting firms do. They already have relationships with Fortune 500 companies, they already speak the language of enterprise transformation, and they already have the trust that comes with years of successful engagements.

OpenAI's move addresses what's been a growing frustration in the enterprise AI space: adoption has been surprisingly slow. Companies have spent billions on AI initiatives, but the return on investment remains fuzzy at best. Forrester research shows that only about 35% of enterprises have successfully scaled AI beyond pilot projects. That's not good enough for OpenAI if it wants to become the infrastructure layer for enterprise AI.

What makes this particularly interesting is that OpenAI isn't just handing over a product and hoping consultants figure out how to sell it. The company's Forward Deployed Engineering team will work directly with the consulting firms. This means OpenAI engineers will be embedded in customer implementations, helping design solutions and ensuring that enterprises get real value from the Frontier platform.

The announcement came just after OpenAI launched its Frontier platform in early February. For anyone unfamiliar with what Frontier actually does, think of it as a no-code environment for building and deploying AI agents. These agents can work with OpenAI models or other models entirely. They can handle tasks like data analysis, customer service automation, process optimization, and complex decision-making. The no-code aspect means that enterprises don't need to hire specialized AI engineers to build agents. Business users can design workflows, define agent behaviors, and deploy them to production.

This is where the consulting partnership makes even more sense. Consultants can help enterprises figure out which processes should be automated first, how to measure success, and how to change internal workflows to accommodate AI agents. They can also help with change management, which is often the harder part of implementing new technology.

Understanding the Frontier Platform and What It Actually Does

Before diving deeper into the alliance strategy, it helps to understand what enterprises are actually buying here. OpenAI Frontier is the successor to a long line of AI tools that tried to make building AI applications easier for non-technical users. But it's worth noting that most of those previous attempts failed because they either oversimplified the problem (making it impossible to build anything real) or they just provided a thin UI layer over complex technology (making it no easier than coding).

Frontier takes a different approach. At its core, it's built on the idea that AI agents—software that can perceive situations, make decisions, take actions, and learn from outcomes—are going to be the primary way businesses extract value from AI. Rather than building one-off chatbots or prediction models, enterprises increasingly need flexible systems that can handle variable, complex tasks across different departments.

The platform includes several key capabilities that matter for enterprise use cases:

Agent Builder: A visual interface where business users can define how agents should behave. You describe what you want the agent to do, what information it should have access to, what constraints it should follow, and what outcomes you're trying to achieve. The system generates the underlying logic rather than requiring users to write code.

Model Flexibility: Enterprises don't want to be locked into OpenAI models. Some companies have contracts with other model providers. Some want to use open-source models. Frontier allows agents to work with any model, which means enterprises can make economic decisions about which models to use without changing their agent logic.

Integration Ecosystem: Agents need to connect to actual business systems. Frontier integrates with common enterprise software: CRM systems like Salesforce, data warehouses like Snowflake, communication platforms like Slack, and hundreds of other tools. This means agents can actually interact with real business data and systems.

Monitoring and Governance: Enterprises care deeply about what their systems are doing. Frontier includes detailed logging, audit trails, and controls for reviewing agent decisions before they take actions. This is non-negotiable for regulated industries like finance, healthcare, and pharmaceuticals.

Multi-Step Workflows: Business processes rarely involve a single action. Frontier supports complex workflows where agents coordinate with each other, wait for human approval, branch based on conditions, and escalate issues when they hit limitations.

When OpenAI first launched Frontier, the company positioned it as the foundation for enterprise AI. Rather than selling individual tools that do specific jobs, OpenAI was offering a platform that enterprises could build their own tools on top of. The consulting partnership makes sense here because enterprises need help figuring out which tools they should build.

The Frontier Alliance includes an equal partnership among four major consulting firms: Boston Consulting Group, McKinsey, Accenture, and Capgemini. Estimated data.

The Real Problem OpenAI Is Solving

Here's what OpenAI's leadership understands: AI technology alone doesn't create business value. This is why Christoph Schweizer, the CEO of BCG, made that statement in OpenAI's announcement: "AI alone does not drive transformation." That's not just consulting consultant-speak. That's the actual reality of enterprise AI deployment.

Consider a typical large enterprise. It probably employs thousands of people. It has processes that evolved over decades. Those processes are baked into how people think about their jobs. An enterprise sales organization, for example, has defined processes for qualifying leads, running discovery calls, preparing proposals, and closing deals. Those processes exist because they've been optimized (or at least settled on) over many years.

Now you tell that organization: "We can use AI to automate parts of this process." The immediate response from people who've spent their careers perfecting that process is usually skepticism. Will the AI really understand the nuances of our business? Will it make decisions that align with our values? Will it handle edge cases correctly? Will it create problems downstream in the process?

These are all legitimate concerns. And the answer is that sometimes the AI will work great, and sometimes it will fail in subtle ways that cause problems. The key is helping the enterprise understand where AI can genuinely help and designing the implementation so that humans remain in control of high-risk decisions.

This is where consulting firms add enormous value. A good consultant has seen dozens of enterprise transformations. They know what works, what doesn't, and how to manage change so that people actually adopt new ways of working. They can help an organization figure out which AI investments will actually pay off and which ones are just shiny new technologies with no real business case.

OpenAI's partnership announcement specifically calls out this connection. The press release emphasized that the consulting firms will help enterprises link AI to their strategy, integrate it into redesigned business processes, and implement it at scale with proper incentives and culture alignment. This is the language of transformation consulting, not just technology sales.

For OpenAI, this partnership model solves several specific problems. First, it gets around the relationship problem. OpenAI doesn't have centuries of relationships with large enterprises the way consulting firms do. Accenture has worked with the same clients for 20 years. McKinsey has deep relationships across industries. These consultants can introduce Frontier to clients in the context of broader business transformation initiatives, not just as a new software tool.

Second, it solves the expertise problem. OpenAI could try to hire thousands of enterprise sales engineers, but that would be expensive and slow. Consulting firms already have thousands of people who understand how enterprises work, what their pain points are, and how to design solutions. Now they'll have access to Frontier and training from OpenAI's Forward Deployed Engineering team.

Third, it helps enterprises feel less risky. When McKinsey recommends adopting a new technology as part of a broader transformation strategy, enterprises listen. When McKinsey says the implementation will be successful and that they'll personally oversee it, that carries weight. OpenAI's direct sales team making the same claim carries much less weight because they have less reputation at stake.

Only 35% of enterprises have successfully scaled AI beyond pilot projects, highlighting the challenges in AI adoption. Estimated data.

How the Alliance Actually Works in Practice

The mechanics of the Frontier Alliance are important to understand because they tell us a lot about how OpenAI thinks about enterprise software distribution going forward.

The structure is fairly straightforward. OpenAI and each of the four consulting firms (BCG, McKinsey, Accenture, and Capgemini) have signed multi-year agreements. These aren't simple reseller agreements where consultants get a cut of every Frontier license sold. They're deeper strategic partnerships where the consulting firms are becoming implementation partners for OpenAI's enterprise platform.

Here's how it works in practice: A consulting firm is working with an enterprise client on a digital transformation initiative. During the engagement, the consulting team recommends using OpenAI's Frontier platform as part of the solution. The consulting firm then manages the implementation. When they hit questions or technical challenges that require deeper knowledge of the platform, they can tap OpenAI's Forward Deployed Engineering team for support.

The Forward Deployed Engineering team is crucial here. This is a group within OpenAI specifically designed to work with enterprise customers on complex implementations. Unlike traditional support teams that answer questions, the Forward Deployed Engineering team actually gets involved in solving problems. They might help architect a complex agent workflow, debug why an integration isn't working correctly, or advise on how to redesign a business process to better leverage AI.

On the compensation side, the arrangement likely works like this: The consulting firm charges the enterprise client for their consulting services (which now include Frontier implementation). OpenAI gets revenue from Frontier licensing. Both parties benefit if the implementation is successful, which creates aligned incentives. Both parties have reputational risk if the implementation fails, which keeps everyone focused on actual value creation rather than just closing deals.

For enterprises, the value proposition is clear: you get access to proven AI technology, implementation expertise from firms with decades of experience, and ongoing support from OpenAI engineers who helped build the platform. You're not trying to figure this out on your own.

For OpenAI, the value is that it now has four channel partners with combined annual revenues exceeding $300 billion and employees exceeding 1.5 million. Each of these firms has direct relationships with the largest enterprises in the world. When a McKinsey team that's already embedded in a Fortune 500 company's transformation initiative recommends Frontier, that recommendation carries weight that OpenAI's direct sales team could never achieve.

Competition and Market Context

OpenAI isn't alone in recognizing that consulting partnerships are crucial for enterprise AI. Competitors are making similar moves, which tells you something about where the market is heading.

Anthropic, which many consider OpenAI's most credible competitor in the large language model space, has already inked deals with consulting firms including Deloitte and Accenture. Google has partnerships with Deloitte and Accenture. Microsoft leverages its own consulting arm plus partnerships with firms like Deloitte. This pattern across the industry confirms that the consulting partnership model is becoming standard for enterprise AI distribution.

What's interesting is that these partnerships don't prevent consultants from working with multiple AI providers. Accenture has partnerships with both OpenAI and Anthropic. McKinsey works across multiple AI vendors. This means that consultants will recommend the technology that makes sense for each specific client, not necessarily push one vendor's solution over others.

For enterprises, this is good news. It means consultants have incentive to recommend the right tool for your problem, not just push their favored partner. For OpenAI, Anthropic, and other vendors, it means they need to make genuinely great products and support, because recommendations are based on merit.

The broader market context is that enterprise AI adoption has been slower than anyone expected. Everyone predicted that ChatGPT would immediately transform enterprise workflows. In reality, most enterprises struggled to find high-impact use cases that justified the change. Many pilot projects got stuck in "proof of concept" stages and never scaled to production. Some organizations spent millions on AI initiatives that generated little measurable value.

The consulting partnership model is partly a response to this reality. It says: "We understand that AI requires more than just good technology. It requires strategy, implementation expertise, change management, and ongoing support. We're partnering with firms that provide all of these services."

OpenAI's enterprise sales strategy in 2025 focuses heavily on building dedicated enterprise products and forming direct partnerships, with significant attention also given to consulting partnerships and demonstrating ROI. Estimated data.

OpenAI's Enterprise Sales Strategy in 2025

OpenAI has been building out its enterprise sales operation throughout 2024 and 2025. In January 2025, the company named Barret Zoph to lead enterprise sales. Zoph came from Google Brain, where he spent years working on AI research and scaling AI technologies within Google. His appointment signals that OpenAI is serious about enterprise but also that the company understands enterprise sales requires different skills than building AI.

Under Zoph's leadership, OpenAI has pursued several complementary strategies:

Direct Partnerships with Key Technology Vendors: OpenAI inked deals with Snowflake and ServiceNow in early 2025. These are strategic partnerships where the AI company's technology gets deeply integrated into these vendors' platforms. Snowflake customers can now use OpenAI models for data analysis and insights. ServiceNow customers can use OpenAI for automating workflows and customer service. This model gets OpenAI's technology in front of massive enterprise customer bases without OpenAI having to hire thousands of sales engineers.

Consulting Firm Partnerships: The Frontier Alliance is the most visible manifestation of this strategy, but it's one part of a broader consulting strategy. OpenAI recognizes that consultants are often the ones who actually recommend technology to enterprises, so building strong relationships with consulting firms is crucial.

Dedicated Enterprise Product Teams: Frontier itself is a signal that OpenAI is building products specifically designed for enterprise use cases, not just adapting consumer products for enterprises. Frontier includes features like audit trails, governance controls, and integration capabilities that wouldn't exist if OpenAI was just trying to sell ChatGPT to businesses.

Demonstration of Concrete ROI: OpenAI has been increasingly vocal about the business value of AI. Rather than just talking about AI capabilities, OpenAI points to specific customer outcomes: cost reduction, time savings, quality improvements. This shift from "AI is powerful" to "AI generates measurable business value" reflects an understanding that enterprises care about ROI, not just capability.

OpenAI's CFO Sarah Friar said in a January blog post that enterprise is a major focus area for 2025. The company has built out enterprise-specific sales and implementation teams, created product features that address enterprise needs, and now is channeling those capabilities through consulting partnerships. This is a coordinated, multi-faceted approach to building an enterprise business.

How Enterprises Actually Buy AI Solutions

Understanding the consulting partnership strategy requires understanding how large enterprises actually buy software and services. It's not like buying cloud infrastructure or SaaS tools, where you can sign up online and start using the product immediately.

Enterprise software purchasing typically involves these stages:

1. Problem Recognition: Someone in the enterprise recognizes a significant business problem. Maybe it's that customer service costs are too high, or that data analysis is taking too long, or that sales processes are inefficient. This problem needs to be significant enough to justify investment in solving it.

2. Solution Exploration: The enterprise explores potential solutions. This is often where consultants come in. A consulting firm might recommend a technology as part of a broader solution approach. Alternatively, enterprises might search for solutions themselves, but this is slower and less likely to identify cutting-edge options like Frontier.

3. Vendor Evaluation: The enterprise evaluates different vendors who claim to solve the problem. In the AI space, this means comparing different platforms, different model providers, different implementation approaches. This evaluation is complex because enterprises often lack internal expertise to accurately assess technical capabilities.

4. Proof of Concept: Large enterprises rarely buy enterprise software without first running a pilot. They'll implement a limited version of the solution with a specific department or use case to validate that it actually works and delivers promised value. This stage is crucial because it's where many initiatives fail. Pilots work in theory but don't translate to production environments, or they work but the ROI doesn't justify the investment.

5. Implementation and Scaling: If the pilot succeeds, the enterprise scales the solution across more departments, more use cases, or the entire organization. This stage requires change management, training, integration with existing systems, and ongoing support.

6. Optimization and Evolution: After the initial implementation, the enterprise optimizes the solution, adds new use cases, and potentially expands across more of the organization.

Consultants are valuable at almost every stage of this process. They help recognize problems by identifying opportunities that the enterprise may not have considered. They suggest solutions based on their experience with similar problems at other companies. They help evaluate vendors by assessing technical capabilities and likelihood of success. They often lead the proof of concept, which increases the chance of success because consultants know how to structure pilots for learnable results. And they manage implementation to ensure the scaling phase actually works.

OpenAI's Frontier Alliance leverages this entire process. The consulting firm has relationships with the enterprise from previous engagements. They introduce Frontier as part of a broader transformation initiative. They help evaluate whether Frontier is the right choice. They implement the pilot. They manage the scaling. Throughout this process, they have OpenAI's Forward Deployed Engineering team to tap for technical expertise.

Estimated data shows that consulting services, implementation support, and OpenAI licensing each play significant roles in the Frontier Alliance, highlighting a balanced partnership approach.

Real-World Enterprise Use Cases for Frontier

To understand why the consulting partnership model makes sense, it helps to think about what enterprises are actually building with Frontier.

Customer Service Automation: This is probably the most common enterprise use case for AI agents. A large financial services company might implement AI agents that handle initial customer inquiries, troubleshoot common problems, and escalate complex issues to human agents. The agents can access the customer database to understand account history and previous interactions. They can review common troubleshooting steps and follow them systematically. The consulting firm helps redesign the customer service process to optimize for AI: what should be automated, what should escalate to humans, how should agents be trained to work alongside AI. OpenAI's engineering team helps architecture the agent workflows and integrate with the bank's systems.

Data Analysis and Insights: A retail company might implement AI agents that analyze sales data, identify trends, spot anomalies, and generate reports. Instead of data analysts spending hours pulling data and creating visualizations, agents do this automatically. The consulting firm helps the retail company define what insights matter most, how to measure success, and how data analysts' roles should evolve. OpenAI's team helps connect the agents to data warehouses and define appropriate guardrails.

Process Automation: A healthcare organization might use agents to automate appointment scheduling, insurance verification, and claim processing. These are high-volume, repetitive processes that are ideal for automation. But they're also processes where errors have consequences (wrong appointment, claim denied improperly). The consulting firm helps redesign the processes for automation, define where human oversight is required, and manage change management so staff understands how their jobs are changing. OpenAI's team helps architect the agents and integrations.

Sales Process Automation: A B2B software company might implement agents that qualify leads, run discovery calls, prepare proposals, and handle basic contract negotiation. The consulting firm helps rethink the sales process for AI: what makes a good lead?, what information does the AI agent need?, how do salespeople transition from doing everything to managing AI agents? OpenAI's team helps build the agents.

Each of these use cases requires the combination of technology expertise and business transformation expertise that the consulting partnership provides.

Why This Matters for the AI Industry

The Frontier Alliance announcement might seem like a typical partnership between a technology company and consulting firms. But it actually signals something important about the maturation of AI as a business technology.

When ChatGPT was released in November 2022, the focus was all on what AI could do technically. Could it write essays? Could it code? Could it have conversations? The enterprise AI narrative was that businesses would buy this technology and integrate it into their existing workflows.

Three years later, the narrative has shifted. The focus is now on business outcomes: improved efficiency, cost reduction, faster decision-making. And the recognition that technology alone doesn't deliver those outcomes. You also need business strategy, change management, and implementation expertise.

This shift suggests that AI is moving from the "exciting new technology" phase to the "business infrastructure" phase. Exciting new technologies get sold by salespeople who emphasize what the technology can do. Business infrastructure gets sold and implemented by consultants who focus on business outcomes.

For OpenAI, this positioning is smart because it acknowledges that OpenAI is really a technology company, not a consulting company. Rather than trying to become expert at solving enterprises' business problems, OpenAI partners with firms that already are. OpenAI provides the technology and some implementation support. Consultants provide the business expertise and client relationships.

For enterprises, this should mean better outcomes. Rather than buying technology from a vendor trying to dress itself up as a consultant, you're getting technology from the technology experts plus consulting from actual consultants.

The chart illustrates the estimated importance of various success metrics for the Frontier Alliance, highlighting customer retention and business outcome achievement as top priorities. Estimated data.

The Competition for Enterprise AI Partnerships

OpenAI isn't the only technology company pursuing the consulting partnership strategy. Google, Microsoft, and Anthropic have all made similar moves. Understanding the competitive landscape helps clarify why this strategy matters.

Microsoft has a somewhat different approach because of its relationship with OpenAI. Microsoft invested billions in OpenAI and has exclusive licensing rights to GPT models. Microsoft then embeds OpenAI's models into its own products: Azure OpenAI, Copilot for Microsoft 365, etc. Microsoft also has its own consulting arm and partnerships with major consulting firms. For enterprises using Microsoft products, the path to using OpenAI models is often through Microsoft partnerships with consulting firms.

Google is pushing its own large language models (like Gemini) and has partnerships with Deloitte, PwC, and others for enterprise deployment. Google's strategy emphasizes Google Cloud services integrated with Google's AI models.

Anthropic positioned itself as the "safer" alternative to OpenAI and has emphasized this positioning in consulting partnerships. Anthropic's Claude models are now available through Deloitte, Accenture, and others.

The fact that all major AI companies are pursuing consulting partnerships confirms that this is how enterprise AI will be distributed and implemented. Direct sales of AI technology to enterprises is proving less effective than going through trusted intermediaries with existing customer relationships.

This competition is actually good for enterprises because it means consultants will recommend the technology that makes sense for each situation, not push one vendor's offering across the board.

Challenges and Potential Issues with the Consulting Model

While the consulting partnership model has clear benefits, it's worth acknowledging potential downsides.

Consultant Incentive Alignment: Consultants are paid for their time and expertise, not for generating AI-driven value for clients. So there's a potential incentive misalignment: a consultant might recommend a complex implementation that generates consulting fees, even if a simpler implementation would be better for the client. This isn't unique to AI partnerships, but it's worth being aware of.

Slower Innovation Cycles: When technology is sold through consultants, there tends to be a lag before enterprises adopt new features or improvements. A consultant is usually in an engagement for 6-12 months, so they're not constantly evaluating new versions of the technology. By contrast, direct sales of SaaS products can see rapid adoption of new features because customers evaluate them immediately.

Lock-in Risk: If you implement Frontier with a consulting firm and the implementation becomes closely integrated with that firm's approach, it can create lock-in. If you later want to switch to a different vendor or different consultant, it might be expensive and disruptive. Enterprises should be aware of this and explicitly design implementations to be portable.

Consultant Knowledge Gaps: Consultants are generalists. Even with training from OpenAI's Forward Deployed Engineering team, consultants may lack the depth of knowledge of AI specialists. This could lead to implementations that aren't optimized for the technology.

Scaled Implementation Issues: Large enterprises sometimes struggle when consultants try to scale successful pilots to the entire organization. The pilot might have succeeded in a favorable environment with a favorable group of people. Scaling to the whole organization means dealing with more diverse circumstances, more entrenched resistance, and more edge cases.

None of these issues are dealbreakers, but they're worth understanding when evaluating whether the consulting partnership model is right for your organization.

The process of purchasing AI solutions in enterprises involves multiple stages, each varying in time and complexity. Solution exploration and vendor evaluation are particularly complex, while implementation and scaling take the longest time. (Estimated data)

What Success Looks Like for the Frontier Alliance

How will we know if the Frontier Alliance is actually working? What metrics matter?

For OpenAI, success would be measured in several ways:

Revenue Growth from Enterprise: The company wants to see significant revenue from Frontier and other enterprise products. This is probably already happening given partnerships with Snowflake and ServiceNow, but the consulting partnerships should accelerate growth.

Implementation Volume: OpenAI wants to see many enterprises implementing Frontier, not just a few high-profile customers. Volume matters because it proves the model scales.

Customer Retention and Expansion: Initial implementation is just the start. Real success means enterprises keep using Frontier and expand to new use cases over time.

Competitive Positioning: OpenAI wants to establish Frontier as the go-to platform for enterprise AI agents, ahead of competitors from Anthropic, Google, and others.

For consulting firms, success is measured in:

Consulting Revenue: Implementing Frontier should create new consulting revenue. This is straightforward—help more customers adopt Frontier, earn more consulting fees.

Client Satisfaction: The consulting partnerships only work if clients actually achieve their business objectives. So consulting firms need to ensure implementations drive real value.

Differentiation: The partnership helps consulting firms differentiate from competitors. They can point to successful Frontier implementations as proof of their ability to execute on AI strategies.

For enterprises, success is measured in:

Business Outcome Achievement: Did the implementation actually reduce costs, improve quality, speed up processes, or achieve whatever outcome you were targeting?

Time to Value: How long did it take from starting the implementation to actually delivering business value?

Adoption and Usage: Did the organization actually start using the implemented solution, or did it sit unused after the consultant left?

Team Capability Building: Did your team develop the capability to manage and evolve the solution independently?

The consulting partnership model creates the best environment for all parties to achieve success because incentives are reasonably well aligned. If the implementation fails, the consulting firm's reputation suffers. If the implementation succeeds but takes forever and costs a fortune, the enterprise won't hire that consultant again. And if the technology doesn't actually work, nobody's implementation will succeed regardless of consultant quality.

The Broader Trend Toward Partnership-Driven Distribution

The Frontier Alliance is part of a broader trend in enterprise software toward partnership-driven distribution. This trend has implications beyond just OpenAI and consulting firms.

Traditionally, enterprise software companies built large direct sales forces. They hired thousands of salespeople, trained them on the company's products, and sent them out to sell. This model worked reasonably well for decades because there weren't that many enterprise software options. If you wanted to buy ERP software, there were maybe 5-10 serious options. Salespeople could develop expertise in those options.

But the software ecosystem has exploded. There are thousands of enterprise software vendors. No sales organization can have deep expertise in all of them. So enterprises increasingly rely on consultants and systems integrators to recommend which software to buy and how to implement it.

For software vendors, this creates a challenge: how do you reach customers when your direct sales force can't possibly have relationships with all potential buyers? The answer is partnerships. Build relationships with consulting firms and systems integrators who already have customer relationships. These partners can recommend your software as part of their solution approach.

This model has implications for how enterprises buy software. Rather than evaluating software directly and buying what they want, enterprises increasingly ask consultants what software they should use. This means the consultant's recommendation carries enormous weight. It also means consultants become gatekeepers between vendors and customers.

For OpenAI and other AI companies, this partnership model makes sense. The consulting firms have established relationships and trust with enterprises. Those relationships are probably more valuable than anything OpenAI could build directly.

Looking Ahead: How Enterprise AI Will Evolve

The Frontier Alliance announcement is a snapshot of where enterprise AI is heading in 2025 and beyond. Several trends seem likely to continue:

Increased Focus on ROI: Enterprises will demand clear, measurable return on investment from AI initiatives. The days of "let's experiment with AI to see what we can do" are ending. The focus is now on "which AI investments will generate profitable outcomes?"

Rise of AI Agents: OpenAI's Frontier and similar agent platforms suggest that AI agents (autonomous systems that can perceive situations, make decisions, and take actions) will become the dominant form of AI in enterprises, replacing one-off chatbots and prediction models.

Deeper Consultant Integration: As enterprises invest more in AI, consulting firms will deepen their AI expertise and capabilities. Consulting firms like BCG and McKinsey are probably hiring AI specialists and building proprietary AI methodologies.

Competition Among Platform Providers: We'll see increasing competition between platforms like Frontier, Google's Vertex AI, and others for enterprise dominance. This competition will drive better products and prices for enterprises.

Continued Talent Shortage: Building and implementing AI solutions will remain talent-constrained. There simply aren't enough AI specialists to go around. This means partnership models (where consultants manage implementation and technology companies provide engineering expertise) will remain important.

Ethical and Governance Frameworks: As AI becomes more central to enterprise operations, we'll see more emphasis on governance, ethics, and compliance frameworks. The next generation of enterprise software will include built-in governance features that enterprises currently have to layer on top.

FAQ

What is the Frontier Alliance?

The Frontier Alliance is a multi-year partnership between OpenAI and four major consulting firms (Boston Consulting Group, McKinsey, Accenture, and Capgemini) to drive enterprise adoption of OpenAI's Frontier platform. Under the alliance, consulting firms help enterprises implement Frontier for building and deploying AI agents, while OpenAI's Forward Deployed Engineering team provides technical support for complex implementations. This model combines consulting expertise with AI technology and implementation support.

How does the Frontier platform work?

Frontier is a no-code platform that allows enterprises to build, deploy, and manage AI agents without requiring specialized coding knowledge. Users define agent behaviors and workflows through a visual interface, specify what data and systems the agents should access, and the platform generates the underlying logic. Frontier works with OpenAI models or other AI models and integrates with enterprise business systems like CRMs, data warehouses, and communication platforms. This enables enterprises to automate complex business processes using AI agents.

Why does OpenAI partner with consulting firms instead of selling directly?

Consulting firms offer several advantages for enterprise AI adoption. They already have established relationships with large enterprises and understand those companies' business challenges. They can recommend AI solutions within the context of broader business transformation initiatives, making the recommendation more credible. Consultants have expertise in change management, business process redesign, and implementation that technology companies typically lack. By partnering with consultants, OpenAI can reach more customers and help ensure implementations are successful, since both parties have reputational risk if initiatives fail.

What is the Forward Deployed Engineering team?

The Forward Deployed Engineering team is a specialized group within OpenAI focused on working directly with enterprise customers and their implementation partners on complex Frontier deployments. Unlike traditional support teams that answer questions, the Forward Deployed Engineering team proactively solves problems, helps design agent workflows, advises on business process optimization, and ensures that implementations successfully deliver business value. This team bridges the gap between consultants' business expertise and the technical complexity of building advanced AI solutions.

What types of business problems can Frontier solve?

Frontier is designed for automating complex business processes where AI agents can perceive situations, make decisions, and take actions. Common applications include customer service automation (where agents handle inquiries and escalate complex issues), data analysis and insights (where agents analyze data and generate reports), process automation (appointment scheduling, insurance verification, claims processing), and sales process automation (lead qualification, discovery, proposal generation). The key requirement is that the business process involves decisions and actions that don't require specialized human judgment or emotional intelligence.

How should enterprises approach implementing Frontier through a consulting partner?

Enterprises should start by identifying a high-impact business process that causes real pain, is relatively contained, and has clear success metrics. Work with a consulting partner to design a pilot implementation that's large enough to prove value but small enough to contain risk. Explicitly define what success looks like, including both technical metrics (accuracy, speed) and business metrics (cost savings, time savings, quality improvements). Plan for change management alongside technology implementation, recognizing that people adoption matters as much as the technology itself. Ensure that your team builds capability to manage and evolve the solution independently after the consultant's engagement ends.

What advantages does Frontier offer compared to building custom AI solutions?

Frontier significantly reduces time to value by providing pre-built infrastructure for common enterprise needs rather than starting from scratch. The no-code interface allows business users to contribute to development rather than requiring specialized AI engineers for every change. Built-in governance, audit trails, and monitoring address enterprise requirements that custom solutions would need to implement separately. Integration with common enterprise systems is pre-built rather than custom. The platform receives continuous improvements and security updates from OpenAI rather than requiring enterprises to maintain custom solutions. These factors combine to make Frontier substantially faster, cheaper, and lower-risk than building comparable capabilities internally.

How does pricing work for Frontier through consulting partnerships?

Pricing typically involves separate components: the consulting firm charges for their services (business consulting, implementation, change management, etc.) based on time and scope, while OpenAI charges licensing fees for Frontier based on usage and features. The exact pricing varies depending on the scope of implementation and the consulting firm, but enterprises should expect that total cost (consulting plus software) will be substantial. The key is that both parties benefit from successful implementations, creating aligned incentives to deliver real business value rather than just closing deals.

Will consulting partnerships slow down AI innovation and adoption?

Consulting partnerships may introduce some lag in technology adoption because consultants typically engage with customers for 6-12 months and don't constantly evaluate new features. However, this lag is offset by higher quality implementations and more successful outcomes. Enterprises often struggle when trying to implement cutting-edge technology without expert guidance. Working with experienced consultants reduces implementation risk and increases the likelihood of success. For enterprises, the balance typically tips toward slower but more successful adoption over faster but riskier adoption.

What should enterprises watch out for when working with consulting partners on AI implementations?

Enterprises should explicitly manage potential incentive misalignment—consultants are compensated for their time, which could incentivize longer or more complex implementations than necessary. Design implementations to avoid vendor or consultant lock-in by ensuring that solutions can be managed and evolved independently. Be explicit about success metrics upfront so there's no ambiguity about whether the implementation succeeded. Invest in building internal capability alongside the consultant engagement so your team can manage the solution after the engagement ends. Avoid trying to transform everything at once; focus on high-impact use cases that can demonstrate clear value. Ensure proper governance and oversight of AI agent decisions, particularly in regulated industries.

How does this partnership model compare to how software has been distributed in the past?

Enterprise software distribution has historically followed one of two models: direct sales (the vendor hires salespeople and sells directly to customers) or through systems integrators (companies like Deloitte or EY that handle implementation). As the software ecosystem has become more complex with thousands of specialized vendors, enterprises increasingly rely on consultants to evaluate which software to use rather than having expertise in all options directly. The Frontier Alliance represents a hybrid approach where OpenAI focuses on technology and implementation support while consulting firms handle customer relationships and change management. This allows OpenAI to reach customers more effectively than direct sales alone could achieve while leveraging consulting firms' existing relationships and expertise.

Conclusion

OpenAI's announcement of the Frontier Alliance might seem like a routine partnership between a technology company and consulting firms. But it's actually a significant strategic move that signals where enterprise AI is heading in 2025 and beyond.

The core insight is that AI technology alone doesn't drive business transformation. Success requires strategy, change management, implementation expertise, and ongoing support. OpenAI recognizes this and has made a strategic bet that consulting firms are better positioned to provide these services than OpenAI itself could ever be. By partnering with BCG, McKinsey, Accenture, and Capgemini, OpenAI gains access to customer relationships, implementation expertise, and change management capabilities that would be impossible to build internally.

For enterprises, the consulting partnership model offers significant advantages. Rather than trying to figure out how to use AI on your own, you get guidance from firms with experience implementing AI at dozens of other companies. Rather than hiring specialist AI engineers, you leverage consulting firms' resources. Rather than managing complex implementations yourself, you get expert project management and change management. Rather than being on your own after implementation, you have ongoing support available.

The consulting partnership model also signals maturation in the enterprise AI space. Three years ago, the focus was on what AI could do technically. Today, the focus is on business outcomes: reduced costs, faster decision-making, improved quality, better customer service. This shift from "AI is amazing" to "AI creates business value" reflects a fundamental change in how enterprises think about technology.

Looking ahead, we should expect this partnership model to become standard across the AI industry. Google, Microsoft, and Anthropic are all pursuing similar strategies. Consulting firms are building deeper AI expertise and capabilities. Enterprises are increasingly expecting consultants to recommend AI solutions as part of broader digital transformation initiatives.

For OpenAI, the Frontier Alliance represents a bet that this partnership-driven model will outcompete direct sales for enterprise AI adoption. For enterprises, it offers a clearer path to successful AI implementation than trying to figure it out alone. For consulting firms, it offers an opportunity to deepen their relationships with enterprise clients by helping them adopt cutting-edge AI technology.

Whether measured by revenue growth, implementation volume, customer satisfaction, or competitive positioning, the success or failure of the Frontier Alliance over the next 12-24 months will tell us a lot about how enterprise AI will actually develop as an industry.

Key Takeaways

• OpenAI's Frontier Alliance partnerships with BCG, McKinsey, Accenture, and Capgemini signal that consulting relationships are crucial for enterprise AI adoption
• Enterprise AI adoption requires more than technology—it demands strategy, change management, and implementation expertise that consulting firms provide
• The Forward Deployed Engineering model combines OpenAI's technical expertise with consultants' implementation capabilities for higher success rates
• Frontier platform enables business users to build AI agents without coding, addressing the talent shortage in enterprise AI development
• Partnership-driven distribution is becoming industry standard as enterprises increasingly rely on consultants to evaluate and implement AI solutions

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