AI Giants Spend $8 Billion to Fix Enterprise Adoption

The money for artificial intelligence companies is in enterprise, but the challenge isn’t building the model. It’s getting it to work inside a company that wasn’t designed for it.

AI model makers are racing to address the gap between ideation and integration. Microsoft launched Frontier Company with $2.5 billion and roughly 6,000 engineers, technical consultants and industry specialists whose job is to sit inside enterprise client organizations and build AI systems that produce measurable results. Amazon also committed $1 billion to a parallel effort.

PYMNTS Intelligence’s “The Enterprise AI Benchmark Report” found that 71% of executives at companies with at least $1 billion in annual revenue identified organizational readiness as the primary barrier to AI performance. Only 11% cited the technology itself.

Forward-Deployed Engineers Become the Default Enterprise AI Model

Monthly job listings for forward-deployed engineers, or technical specialists who embed directly inside client organizations to customize AI systems and integrate them with existing operations, increased more than 800% between January and September 2025. A wave of vendor commitments followed.

The challenge is no longer model capability, OpenAI Chief Revenue Officer Denise Dresser said in a May 11 press release. It’s helping companies integrate AI systems into the infrastructure and workflows that power their businesses.

“Forward-deployed engineers can sit with an organization, sit with their users, understand the workflow, and then help them take that capability from their back-office applications, connecting it to the model, and then really building intelligence in terms of each of the workflow,” Dresser said in a May 11 CNBC report.

OpenAI and Anthropic both launched enterprise deployment ventures.

OpenAI’s Deployment Company launched May 11 as a majority-owned subsidiary, raising more than $4 billion from 19 investors, including TPG, Bain Capital and Goldman Sachs, and acquiring AI consulting and engineering firm Tomoro to add 150 deployment engineers.

Anthropic formed a parallel $1.5 billion joint venture May 4, backed by Blackstone, Hellman & Friedman and Goldman Sachs, and focused on mid-sized companies that lack in-house resources to run frontier deployments.

Both ventures use private equity backing to reach portfolio companies directly, creating a sales channel that bypasses the traditional enterprise software procurement cycle.

Social media giant Meta is also forming a new unit called Enterprise Solutions, designed to place engineers and product managers directly inside large corporate clients to deploy its AI tools. Meta Head of Product Naomi Gleit detailed the structure in an internal memo. Product managers will lead client engagements, data engineers will prepare corporate data for Meta’s AI systems, and software engineers will embed Meta’s products into existing client operations.

When announcing Microsoft Frontier Company, Microsoft Commercial Business CEO Judson Althoff said in a Thursday (July 2) company blog post that customers have moved beyond experimentation.

“They are now concentrating on delivering measurable business outcomes and demonstrating a return on their AI investments…,” Althoff said in the post.

Early Frontier Company clients include the London Stock Exchange Group, Unilever and Land O’Lakes.

Deployment Wins Do Not Resolve the Underlying Data Problem

The concurrent announcements put the largest AI labs in direct competition with the firms that have historically owned enterprise technology implementations. Accenture, Deloitte, TCS and Infosys have each built substantial AI services practices. By deploying their own models through embedded engineering teams backed by private equity capital, OpenAI and Anthropic gain preferred access to portfolio companies of their backers. The arrangement creates a parallel channel that can route around traditional integrators in those accounts, Forbes reported May 28.

For enterprises, the considerations run in both directions. Deployments built on a single vendor’s tooling deepen infrastructure dependence over time, even when contracts permit the use of competing systems. Microsoft said customer data will not train its models and that clients can continue running rival AI systems, GeekWire reported Thursday.

The PYMNTS Intelligence report “The Enterprise AI Readiness Gap: What Company Data Reveals About the Real Barrier to Scale” found that data quality, governance processes, budget constraints and unclear process ownership each rank as primary barriers for between 46% and 63% of executives at large companies. The report revealed that 85% of those companies reported that their data remains fragmented or only moderately integrated.

Vendor-embedded engineering teams address the workflow and integration layer. The data and governance gaps underneath require a different kind of work.

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