Key takeaways
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- OpenAI and Anthropic are partnering with major consulting firms to help companies move from AI experimentation to enterprise-wide deployment.
- Adoption remains shallow: many companies still have not scaled AI meaningfully, and many CEOs report limited financial benefit so far.
- This is creating a new revenue pool for consultants, especially those that can combine strategy, workflow redesign, and technical deployment.
- The long-term implication is mixed: consulting demand may rise near term, but AI could still compress traditional consulting work over time.
What Happened?
OpenAI and Anthropic are increasingly working with large consulting firms such as McKinsey, BCG, Accenture, Capgemini, and Deloitte to accelerate the use of AI inside big companies. OpenAI is pairing its engineers with consultants to help clients identify use cases, integrate AI systems, and redesign internal workflows. Anthropic has taken a similar approach through industry-specific partnerships and customizable agents. The push reflects a growing realization that businesses are not yet using AI deeply enough to generate meaningful financial returns, even after heavy industry hype and investment.
Why It Matters?
This is an important signal about where the AI value chain is shifting. The next monetization wave is not just about selling models or tokens — it is about embedding AI into real business processes. That requires change management, systems integration, and workflow redesign, which are classic consulting capabilities. For investors, that helps explain why consulting firms are seeing a boost in AI-related demand and bookings, even though AI was initially framed as a threat to their business model. At the same time, it also suggests that enterprise AI adoption is harder and slower than the market first assumed. If customers need outside advisory help just to operationalize use cases, then AI revenue at model providers may scale more gradually and depend heavily on ecosystem partners.
What’s Next?
Watch whether these partnerships actually convert pilot programs into measurable enterprise outcomes such as productivity gains, lower costs, or higher revenue. That will determine whether consulting-led AI deployment becomes a durable profit pool or just a temporary bridge. Investors should also monitor pricing models, especially the shift toward outcome-based fees, because that could reshape the economics of consulting itself. Over time, AI may still reduce demand for junior consultant-heavy work, while increasing the premium on senior advisory talent, engineers, and implementation specialists who can make AI useful inside real businesses.
