First APIs, Now Armies: OpenAI’s $10M Consulting Play Explained
Power, Responsibility, and the Future of Enterprise AI
I’ve been thinking a lot about OpenAI’s latest move: stepping into the world of high-touch consulting. This is a moment that says a lot about where we are in the evolution of AI — and where the power lines in this industry are being drawn.
Here’s what’s happening: OpenAI has launched a consulting arm designed to help enterprises and governments deploy customized versions of GPT-4o. And it’s not cheap. The minimum buy-in is $10 million, and early clients reportedly include the US Department of Defense (a $200M contract) and Southeast Asia’s Grab, which is using GPT-4o Vision to map roadways from 360° imagery.
This isn’t “we’ll sell you access to our API and let your engineers figure it out.” This is OpenAI embedding Forward Deployed Engineers (FDEs) directly inside client organizations, fine-tuning models, building bespoke applications, and weaving their technology into core business processes.
It’s ambitious. It’s bold. And it raises some uncomfortable questions.
Why OpenAI is doing this (and why it makes sense)
If you’ve ever tried to roll out a cutting-edge AI system in a large, complex organization, you know how messy it can get.
On paper, APIs and pre-trained models sound like the ultimate scaling strategy: build once, sell infinitely. But reality is different. Enterprises don’t just want raw models. They want AI that:
✅ works with their proprietary data
✅ integrates seamlessly into legacy workflows
✅ produces measurable business outcomes
This is where OpenAI’s pivot makes sense.
By embedding their own engineers directly into client organizations, OpenAI can tighten quality control, avoiding the inconsistent results that often come from third-party integrators. At the same time, this move opens up a lucrative new revenue stream, with consulting poised to become a multi-billion-dollar business line almost overnight. Perhaps most importantly, it gives OpenAI more influence over customer success, ensuring better implementations that drive satisfaction, retention, and long-term value.
Paul Roetzer from Marketing AI Institute compared this to HubSpot’s early days. Back in 2007, HubSpot resisted building an internal services team, fearing it would drag down valuations ahead of IPO. But they eventually realized that without being hands-on, customer results suffered. OpenAI seems to have reached a similar inflection point.
If this works, they’re not just selling tools — they’re shaping how AI is adopted at the highest levels of government and industry.
⚠️ But here’s where it gets tricky
There’s a deeper tension here that we can’t ignore.
OpenAI isn’t just building frontier AI models anymore. They’re now:
- Deploying them into critical workflows.
- Advising organizations on how to use them responsibly.
- Potentially guiding compliance with regulations like the EU AI Act.
This creates an uneasy overlap of roles: builder, deployer, and advisor.
Can we truly call it governance if the same company develops the tech, embeds it into systems, and tells us how to regulate it?
This isn’t about whether OpenAI means well. The point is, goodwill isn’t governance. Without independent oversight, trust is just a story companies tell about themselves — and that’s not enough when the stakes are this high.
What this signals about the future of AI
For all the hype around cutting-edge models, even the smartest AI won’t magically fix an organization’s tangled workflows, siloed data, or cultural resistance to change. Selling access to GPT-4o is one thing; making it work inside the messy reality of a Fortune 500 company is where the real battle begins.
This is why OpenAI’s consulting move feels less like a pivot and more like an inevitability. By embedding Forward Deployed Engineers directly into client organizations, OpenAI isn’t just selling technology anymore — they’re selling transformation.
The future of enterprise AI isn’t about APIs or models alone. It’s a services business in disguise.
But let’s not pretend they’re breaking new ground here.
Google Cloud Consulting: The Quiet Pioneer
Before OpenAI’s splashy $10M+ consulting program, Google Cloud was already moving decisively in this direction. In April 2023, they formally unified their services into Google Cloud Consulting — a single portfolio designed to guide enterprises through every stage of their cloud and AI journeys.
Google’s strategy was clear: bring world-class expertise under one roof, embed their professional service engineers and consultants alongside client teams, and partner with systems integrators like Deloitte and HCLTech to drive innovation at scale.
This isn’t theoretical. It’s already working in the real world:
- Broadcom tapped Google Cloud Consulting to migrate from AWS and accelerate its transformation into a software-led company. As Broadcom’s CTO put it, “Google’s deep technical skills and its data, security and AI offerings have accelerated our transformation.”
- Kroger collaborated with Google Cloud Consulting and Deloitte to overhaul its technical architecture in record time, boosting productivity across its stores.
- Belk, a US department store chain, partnered with Google to build a generative AI application using Vertex AI. Within a single day, Google Cloud Consulting outlined a solution. Within weeks, Belk had an app in production that could generate high-quality, brand-aligned product descriptions and marketing content — 90% of the work done automatically by AI.
These aren’t pilots. They’re production deployments delivering measurable business outcomes.
The consulting land grab has begun
OpenAI is now following a path that’s been validated by Google Cloud — and to some extent by Palantir, Accenture, and others. But their entry raises the stakes.
We’re entering a new phase of enterprise AI where the fight isn’t about whose model is smartest; it’s about who can integrate those models fastest and most effectively into chaotic, real-world systems.
In this new world, APIs are commodities. The real moat is armies of humans gluing AI into legacy infrastructure and making it usable.
My take: power, responsibility, and the lines we draw
OpenAI’s move isn’t surprising. It’s smart, and in many ways, it was inevitable. But it also marks a turning point.
We’ve crossed into an era where the biggest players aren’t just API providers. They’re full-service transformation partners. Google Cloud knew this early and built a consulting arm around it. Now OpenAI is rushing to catch up, and others — Anthropic, DeepMind, Meta, Amazon — won’t be far behind.
The question is no longer “Who builds the best model?” It’s:
- Who can operationalize AI at scale?
- Who can earn the trust of enterprises to guide their transformation?
- And who will ensure this power is checked by independent oversight — not just goodwill?
💬 Final thought
The future of AI won’t be won by whoever builds the smartest model. It will be won by those who can embed those models into the messiness of real organizations — and do so responsibly.
But responsibility can’t just be a marketing line. It has to be built into the structure of how we deploy and govern this technology.
And that’s a conversation we all need to be part of.
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