As OpenAI accelerates toward an ambitious goal of reaching US$100 billion in revenue by 2027, a quieter but telling shift is unfolding inside the company. Beyond improving its models, the ChatGPT maker is reportedly building a large team of AI consultants aimed squarely at enterprise customers. The move highlights a growing reality across the industry: selling AI is easy, but making it work inside large organizations is not.
Recent hiring trends and industry data suggest OpenAI is investing heavily in go-to-market and implementation roles just as its enterprise business surges. The company reached an estimated US$20 billion in annualized revenue in 2025, up sharply from US$6 billion a year earlier, with more than one million organizations now using its tools. That growth has brought new pressure to move customers from experimentation to measurable, long-term value.
Why enterprise AI adoption remains difficult
The challenge OpenAI is responding to is well known among CIOs and digital transformation leaders. AI systems often perform impressively in demonstrations and pilot programs, yet stall when rolled out across an entire company. Research cited by Second Talent shows that while 87% of large enterprises are implementing AI in some form, only 31% of AI use cases reach full production.
An industry analyst familiar with enterprise deployments summed it up simply: early adoption was driven by fear of missing out, but the current phase is about execution. That requires skills well beyond model performance, including systems integration, data governance, change management, and staff training.
Surveys from multiple research groups point to consistent obstacles. In 2025, enterprises most frequently cite data privacy risks, integration complexity, and reliability concerns as their top barriers. Each of these issues touches business processes and organizational structures, not just software.
A crowded and competitive market
OpenAI is not the only AI provider grappling with these realities. Rivals are experimenting with different strategies to close the gap between technology and deployment.
Anthropic, for example, is leaning heavily on partnerships rather than building a large internal consulting force. The company has announced major collaborations with firms such as Deloitte, Cognizant, and Snowflake, effectively outsourcing much of the implementation work. Anthropic is on track to reach about US$9 billion in annualized revenue by the end of 2025 and has signaled even higher targets for 2026.
Microsoft continues to capitalize on its long-standing enterprise relationships, embedding AI into familiar products while relying on a global network of consulting partners. Google is bundling AI capabilities into its Workspace and Cloud offerings, and Amazon is positioning AWS as the default infrastructure layer for enterprise AI workloads.
Each approach reflects a different bet on how enterprises prefer to buy and deploy AI, but all acknowledge the same underlying issue: tools alone are not enough.
What OpenAI’s hiring strategy suggests
OpenAI’s reported consultant hiring wave points to a belief that closer, hands-on engagement with customers can provide a competitive edge. Job listings across platforms show recruitment for enterprise account leaders, AI deployment managers, and solutions architects, all focused on helping organizations move from proof of concept to production.
The timing is significant. Market research indicates OpenAI’s share of the foundation model market has slipped, while competitors have gained ground. In that context, helping customers succeed operationally may be as important as releasing the next technical breakthrough.
This mirrors a broader pattern in enterprise software, where vendors increasingly provide domain expertise and advisory services rather than acting solely as technology suppliers.
What this means for enterprise leaders
For companies adopting AI, the growing presence of vendor-led consulting teams offers both promise and pause. On one hand, it can provide access to deep technical and operational expertise, easing complex deployments. On the other, it raises questions about the maturity of AI products if large support teams are required to make them effective.
Industry reports suggest many organizations still treat AI as a tactical add-on rather than a strategic capability. This leads to fragmented initiatives, internal power struggles, and slow progress. A recent survey found that 42% of C-suite executives believe AI adoption is causing internal friction due to unclear ownership and competing priorities.
Successful deployment, experts argue, depends less on choosing the “best” model and more on preparing the organization to use it well. That includes redesigning workflows, clarifying decision rights, and rethinking how knowledge work is done.
A shift from selling AI to making it work
As the competition intensifies, the AI market appears to be entering a more practical phase. The emphasis is moving away from flashy demonstrations and toward the harder task of embedding AI into everyday business operations.
OpenAI’s consultant push underscores this shift. The real race is no longer just about who builds the most capable models, but who can guide enterprises through the complex human and organizational changes that AI demands.
