OpenAI is partnering with Accenture, McKinsey, and the rest of the Big Four. Anthropic is building its own embedded implementation teams, more like Palantir than Microsoft. Both companies have realized AI adoption is an implementation problem, not a software licensing one. The value is moving up the stack toward integration, governance, workflow redesign, and AI Operations. The frontier labs are no longer content being software vendors. They are becoming transformation companies.
Why OpenAI and Anthropic’s New Enterprise Push Feels Familiar
In the early 2000s, I worked at Certicom Corp., a Canadian cryptography company best known for its elliptic curve cryptography patents and mobile security technology.
Certicom sold software SDKs, IP licenses, cryptographic toolkits, and specialized security expertise.
The business model looked straightforward on paper: license the technology and let customers implement it.
Reality was messier.
Customers consistently needed help integrating the SDKs, understanding implementation tradeoffs, tuning performance, designing secure architectures, validating deployments, and translating theoretical capabilities into operational systems.
The software alone was rarely enough.
That created a natural pull toward consulting services, implementation support, architecture guidance, and embedded technical expertise.
Watching OpenAI and Anthropic this week felt strangely familiar.
Both companies have now openly acknowledged something the market has been figuring out for the last two years:
AI adoption is not primarily a software licensing problem.
It is an implementation problem.
The Shift From “Model Providers” to Transformation Partners
Over the past year, most organizations have experimented with copilots, chatbots, prompt engineering, internal GPTs, coding assistants, and retrieval systems.
Many of those deployments stalled after the pilot phase.
Why?
Because enterprises discovered that successful AI adoption requires workflow redesign, data integration, governance, security controls, change management, evaluation systems, operational ownership, employee training, and trust frameworks.
The hard part is no longer getting access to an LLM.
The hard part is integrating AI into how organizations actually function.
That realization is now reshaping the business models of the frontier AI labs themselves.
OpenAI’s Enterprise Consulting Strategy
OpenAI’s recent announcements signal a major expansion into enterprise implementation and transformation services.
The company has formed “Frontier Alliance” relationships with major consulting firms including Accenture, McKinsey, BCG, Capgemini, CGI, PwC, TCS, and Cognizant.
The strategy is clear. OpenAI wants to become foundational enterprise infrastructure while leveraging large consulting ecosystems to help customers deploy AI operationally.
This is a very Microsoft-like approach.
OpenAI provides the models, platforms, APIs, agent frameworks, and enterprise tooling.
The consulting firms provide integration, transformation programs, governance, implementation teams, and organizational change management.
The result looks increasingly similar to ERP implementations, cloud transformation projects, and enterprise modernization programs, except now the “platform” is a reasoning system.
OpenAI is also positioning itself around AI agents, enterprise memory systems, coding transformation, software engineering acceleration, and workflow automation.
This is no longer about employees chatting with ChatGPT.
It is about AI becoming embedded into the operational fabric of the enterprise.
Anthropic’s Approach Feels Different
Anthropic is pursuing a more direct and operational model.
Instead of primarily enabling large consulting firms, Anthropic increasingly appears to be building an AI-native implementation organization itself.
Its recent enterprise announcements emphasized applied AI engineering teams, embedded implementation support, workflow redesign, managed agents, and long-context operational systems.
Anthropic’s model feels less like Microsoft and more like Palantir Technologies.
The company is effectively saying:
“We won’t just provide the model. We will help redesign how your organization works around AI.”
That is a much more opinionated and vertically integrated strategy. Rather than supporting consultants, Anthropic appears willing to compete with them. And honestly, that is the right call. The model providers have a depth of operational knowledge no Big Four consultancy can match in the short term.
The Realization Both Companies Have Reached
Both OpenAI and Anthropic now seem to understand something fundamental:
The value is moving up the stack.
In the early AI phase, value was concentrated in training frontier models, securing GPU infrastructure, and scaling inference.
As models become commoditized, differentiation shifts toward integration, workflows, trust, deployment, governance, operational execution, and enterprise context.
The AI model itself is becoming only one layer of the overall solution.
This mirrors what happened repeatedly in enterprise technology history. Databases became ecosystems. Cloud became managed transformation. Cybersecurity became continuous operations. APIs became platforms.
Now AI is following the same pattern.
Why This Matters More Than People Realize
This transition has major implications for consulting firms, enterprise IT departments, software vendors, CIOs, product leaders, and governments.
Traditional consulting firms now face an uncomfortable possibility:
The AI vendors themselves may increasingly own the customer relationship.
Historically, software vendors sold software and consultants implemented it.
AI changes this dynamic because the vendors themselves often possess the deepest operational understanding of the models.
That creates enormous incentives for the labs to move closer to implementation.
And unlike traditional enterprise software, frontier AI systems evolve monthly, change behaviour dynamically, require ongoing evaluation, require governance tuning, depend heavily on prompt and workflow design, and introduce new operational risks continuously.
This creates recurring implementation demand.
Not one-time deployment projects.
Continuous AI operationalization.
The New Enterprise Discipline: AI Operations
A new enterprise function is emerging in real time: AI Operations.
Not merely MLOps. Not simply prompt engineering. Something broader:
- AI governance
- model evaluation
- agent orchestration
- workflow reliability
- hallucination management
- retrieval quality
- security alignment
- cost optimization
- human oversight
- organizational adoption
Organizations are discovering that deploying AI responsibly requires entirely new operational muscles.
That is exactly the kind of complexity that historically creates massive consulting markets.
The Irony of AI Consulting
There is an interesting irony here.
For years, Silicon Valley promoted AI as something that would reduce dependence on expensive human expertise.
The frontier labs are now effectively saying:
“To implement AI successfully, you need even more specialized expertise.”
And they are right.
The challenge was never simply generating text. The challenge is integrating reasoning systems into human institutions.
That turns out to be extraordinarily difficult.
Back to Certicom
Looking back, the Certicom experience feels like an early preview of what is happening in AI.
The SDK was important. The IP mattered. The cryptography was valuable.
But customers ultimately needed help operationalizing the technology safely and effectively.
AI is following the same trajectory, just at a vastly larger scale.
The models alone are not enough.
The real value increasingly lies in implementation, trust, integration, governance, workflow redesign, and operational execution.
That is why the frontier AI labs are no longer content being software vendors.
They are becoming transformation companies.
Frequently Asked Questions
Why are OpenAI and Anthropic moving into consulting?
Because enterprise AI adoption is an implementation problem, not a software licensing one. Pilots stall on workflow redesign, data integration, governance, change management, and evaluation. The labs have realized that the hard part is operationalization, and they have the deepest knowledge of how to do it well.
How is OpenAI’s strategy different from Anthropic’s?
OpenAI is partnering with major consulting firms (Accenture, McKinsey, BCG, Capgemini, PwC, CGI, TCS, Cognizant) through its “Frontier Alliance,” much like Microsoft’s enterprise model. Anthropic is building its own AI-native implementation teams that look more like Palantir, going directly into customer environments rather than enabling third-party consultants.
What is “AI Operations”?
AI Operations is the emerging enterprise discipline of running AI systems reliably in production. It includes governance, model evaluation, agent orchestration, workflow reliability, hallucination management, retrieval quality, security alignment, cost optimization, human oversight, and organizational adoption. It is broader than MLOps and broader than prompt engineering.
Does this threaten traditional consulting firms?
Yes and no. Firms partnered into OpenAI’s ecosystem benefit from the demand. Firms competing with Anthropic for direct enterprise transformation work face a vendor with deeper model knowledge and tighter feedback loops. The customer relationship is the strategic question, and the AI labs are increasingly positioned to own it.
What’s the Certicom parallel?
Certicom sold cryptography SDKs and IP licenses but customers consistently needed help with integration, architecture, and deployment. Pure software wasn’t enough, so a consulting pull emerged. AI is following the same arc at vastly larger scale: powerful technology, complex implementation, and natural gravity toward services.