When frontier AI labs start acting like consultants

Last week, two of the most powerful AI labs on the planet both announced they were getting into professional services. OpenAI launched a $4 billion Deployment Company, acquired a consulting firm, and started embedding engineers directly inside client organisations. Anthropic launched an enterprise services company backed by Blackstone, Hellman & Friedman, and Goldman Sachs. In the same week.

That is not a coincidence. It is a thesis about where the value in enterprise AI actually lives — and it is not where most executives have been looking.

The model was never the hard part

OpenAI’s new unit deploys what it calls Forward Deployed Engineers — practitioners embedded inside client organisations to redesign workflows around AI systems. The $4 billion in backing came from 19 investment firms including TPG, Bain Capital, Brookfield, and SoftBank. To seed the unit with immediate capacity, OpenAI simultaneously acquired Tomoro, an applied AI consulting firm, adding approximately 150 deployment specialists from day one.

Anthropic’s parallel move targets mid-sized businesses. Its applied AI engineers will work alongside the new company’s team to identify use cases, build custom systems, and support customers over time — a long-cycle engagement model, not a product sale.

Both labs are making the same bet: that getting AI to work inside a real organisation is a professional services problem, not a software problem. The implication is direct. The model — the thing both labs have spent years and billions developing — is increasingly a commodity input. The configuration, integration, change management, and governance work is where the actual value concentrates.

Traditional system integrators have held that position for decades. Accenture, Deloitte, and their peers built multi-billion-dollar practices reselling enterprise software and managing the implementation complexity on behalf of clients. What OpenAI and Anthropic are signalling is that they intend to own that layer themselves — or at least take a significant share of it.

The ROI data enterprises don’t want to publish

While the labs were announcing their services arms, Gartner published a finding that should sit uncomfortable alongside most AI strategy decks currently circulating in boardrooms.

Approximately 80% of enterprises piloting autonomous business capabilities have reduced headcount. The workforce reduction rates among companies reporting high AI ROI and those with modest or negative outcomes were nearly identical. The cuts are happening. The returns are not following.

The companies with the highest AI gains used the technology for what Gartner calls people amplification — making workers more productive rather than replacing them. A second Gartner prediction sharpens the stakes further: 50% of enterprises without a people-centric AI strategy will lose their top AI talent by 2027. The practitioners who know how to make these systems work in production will leave for organisations that give them room to grow alongside the technology.

Across the technology sector, more than 92,000 workers have been laid off in 2026 through mid-May. Meta and Microsoft cut 20,000 jobs in April. Coinbase cited AI workflow consolidation for a 14% workforce reduction. PayPal plans to cut 20% of staff over two to three years. In most cases, the public attribution points to AI-driven efficiency. The Gartner data suggests the efficiency gains are not materialising at the rate the announcements imply.

What the labs understand that most enterprises don’t

The labs are not launching professional services businesses because they have spare engineering capacity. They are doing it because they can see, at scale, where their own products fail inside enterprise environments — and they understand that failure is structural, not technical.

Enterprise AI deployment fails at the integration layer. It fails when automated workflows don’t connect to core systems of record. It fails when governance is absent and agents proliferate without oversight — a problem Microsoft’s Agent 365, which went generally available in May, is explicitly designed to address. It fails when the workforce reduction narrative runs ahead of the change management required to make the new model work.

The Forward Deployed Engineer model is a direct response to this. You embed practitioners with the client, you redesign the workflow from the inside, and you carry accountability for the outcome rather than handing over a licence and a user guide. It is expensive. It does not scale like software. But it works in a way that self-service AI deployment, for complex enterprise environments, demonstrably does not.

What this means for enterprise IT leadership

Three things follow from this for anyone managing AI strategy in a large organisation.

The buy vs. build decision has a new variable. If the frontier labs are prepared to embed engineers inside your organisation, the question is no longer just whether to buy a platform or build on an API. It is whether to engage the lab directly as an implementation partner — and what that means for data governance, model dependency, and negotiating leverage over time.

The headcount-reduction narrative deserves more scrutiny than it is getting. Gartner’s data is not arguing that AI cannot generate efficiency gains. It is arguing that cutting people to fund AI, without a clear account of where the productivity improvement is going, is not a strategy — it is cost accounting dressed as transformation. The organisations generating real AI ROI are treating it as a capability multiplier, not a replacement programme.

Governance is no longer optional. Colorado’s AI Act takes effect June 30, 2026 — the first major US state law imposing requirements on algorithmic employment decisions, including impact assessments and employee notification obligations. Illinois has been in effect since January. The regulatory surface area for enterprise AI is expanding in real time, and compliance is now a core operational requirement, not a future consideration.

The signal in the timing

It is worth sitting with the fact that OpenAI and Anthropic made the same strategic move in the same week. Both read the same enterprise feedback. Both concluded that the deployment and integration problem is large enough, and sticky enough, to justify building a services capability from scratch — or acquiring one outright.

That is a clear message about where enterprise AI value is concentrating. The model is a commodity input. The configuration, integration, and governance work is the moat. Most enterprise AI strategies are still organised around the former. The labs just told you, with $4 billion and a press release, that the latter is what matters.

If this is relevant to where your organisation is in the AI journey, I share observations from the enterprise technology frontline regularly on LinkedIn. Connections and follows welcome.