OpenAI, Anthropic, and Google’s enterprise push with private equity giants threatens commoditised IT services work

On Wall Street, the announcements sounded like the next phase of the AI boom: frontier model companies moving beyond infrastructure and into enterprise execution alongside private equity heavyweights.

On May 4, Anthropic unveiled a $1.5 billion enterprise AI venture backed by investors including Blackstone, Goldman Sachs, Hellman & Friedman, and Sequoia Capital. Hours earlier, reports surfaced that OpenAI was raising more than $4 billion for its own initiative, “The Development Company,” at a reported $10 billion valuation.

The moves followed a burst of similar activity just weeks earlier, when Google Cloud announced strategic partnerships with Vista Equity Partners and CVC. It is also reportedly exploring arrangements with Blackstone, KKR, and EQT.

Inside India’s $300-billion outsourcing industry, however, the reaction was far less enthusiastic.

Because beneath the financing headlines lies a more consequential shift: AI companies are no longer positioning themselves merely as model providers selling APIs to enterprises. They are increasingly moving closer to the implementation, orchestration, and transformation work long dominated by IT services firms.

For the outsourcing industry, that raises an uncomfortable question: if companies like OpenAI and Anthropic begin participating directly in enterprise transformation and execution, what happens to the commoditised layers of IT services built around supplying human labor at scale?

AI firms are moving closer to the enterprise wallet

Anthropic’s own description of the new venture revealed how far the AI labs are now willing to go into enterprise workflows.

The company spoke about engineers sitting alongside clinicians and IT teams to build tools directly into operational systems already being used inside enterprises.

This is no longer just a software licensing business.

The model increasingly resembles the “forward-deployed engineer” approach popularised by Palantir, where software companies move beyond selling technology and start embedding themselves deeply inside enterprise operations.

OpenAI’s reported plans point in a similar direction, with firms effectively building dedicated enterprise AI deployment engines backed by institutional capital.

While Google is not building a separate consulting business, the model is largely the same: deploying forward-deployed engineers to work side by side with portfolio companies of private equity firms, helping them build and optimize AI solutions on top of its models and broader AI stack.

Karthik Narain, Chief Product and Business Officer at Google Cloud, said while announcing the partnerships that they would allow the tech giant to accelerate AI adoption across these companies in different sectors and help drive industry-wide digital transformation.

For analysts tracking the outsourcing sector, the concern is that they are beginning to occupy higher-value portions of the enterprise transformation stack while AI simultaneously automates parts of the labour-heavy work that powered outsourcing for decades.

“This is not another cloud cycle,” said Phil Fersht, CEO of HFS Research “GenAI and agentic AI fundamentally attack the labour-centric economics that Indian IT services firms have relied on for three decades.”

That distinction matters because cloud adoption, despite all the disruption it caused, still created massive implementation opportunities for IT services firms.

Enterprises needed engineers to migrate infrastructure, modernise applications, integrate systems, and manage increasingly complex digital estates.

AI threatens to behave differently because it directly compresses parts of the human delivery layer itself.

Implementation work, coding, testing, support, maintenance, and orchestration are increasingly becoming automatable.

According to Fersht, if frontier AI firms successfully combine models, developer tooling, agentic platforms, and enterprise execution ecosystems, “they can compress large portions of the traditional systems integration stack.”

The bigger fear for incumbents lies in what comes after that compression.

“The long-term risk for incumbents is becoming subcontractors to AI platforms rather than strategic transformation partners,” Fersht warned.

Why commoditised IT work looks vulnerable

The anxiety is emerging at a time when the outsourcing industry is already under pressure from slowing growth, delayed deal ramp-ups, and changing commercial models.

Growth across India’s top IT firms slipped to low single digits in FY26 despite healthy deal pipelines, while companies increasingly flagged weaker visibility and slower conversion of signed deals into revenue.

At the same time, AI-led productivity improvements are beginning to reshape pricing structures themselves.

Clients now increasingly expect vendors to pass on AI efficiency gains upfront.

Also Read: AI cuts deal values, compresses IT revenue growth in FY26

HCLTech recently flagged a 2-3 percent annual deflationary impact from AI-led efficiencies across parts of its services business, while analysts estimate broader AI-driven pricing pressure across the sector could reach 3-3.5 percent annually over the next few years.

That shift strikes directly at the economics of commoditised IT services work.

For decades, large parts of the outsourcing industry operated on a relatively straightforward equation: more engineers meant more revenue.

The model depended on large delivery pyramids filled with junior programmers, testers, support staff, and maintenance teams handling repetitive enterprise work at scale.

AI weakens that equation because many of those repetitive layers are exactly where automation is accelerating first.

“The traditional fresher-heavy pyramid model is under severe structural stress,” said Shubham Rathore, Principal Analyst, Gartner.

He argued that commoditised delivery areas such as routine application maintenance, low-value BPO and KPO work, and junior associate-heavy services face growing deflationary pressure as AI agents become more capable.

Also Read: AI deflation could wipe $10 Billion off Indian IT revenues

IT analyst firm NelsonHall’s Principal Research Analyst Gaurav Parab similarly said highly repetitive ADM (application, development, and maintenance) work appears most vulnerable because those areas are easier to automate and AI-assist.

Former Cognizant CEO Francisco D'Souza has gone even further, describing AI as “the biggest operating model shift since offshoring.”

In a recent interview to Moneycontrol, D’Souza argued that the traditional outsourcing pyramid would gradually evolve into a “diamond-shaped workforce,” where the bottom layers historically occupied by large numbers of junior engineers would increasingly be handled by AI agents.

“The bottom of the pyramid, which was historically made up of large numbers of junior engineers, will increasingly be handled by AI,” he said.

The shift is already becoming visible in hiring patterns.

India’s top IT firms collectively reduced headcount by 7,000 in FY26 even as AI spending accelerated, while staffing firms increasingly reported rising demand for specialised AI, cybersecurity, analytics, and orchestration talent rather than transactional coding roles.

The battle may shift from labour scale to orchestration

Yet even amid the disruption narrative, several executives believe markets may be underestimating how difficult enterprise AI deployment actually is.

Cognizant Chief AI Officer Babak Hodjat recently argued that enterprises are moving faster in expectations than in operational reality.

“AI is an engineered discipline,” Hodjat said, highlighting that deploying AI safely inside large organisations still requires architecture design, governance layers, process understanding, domain expertise, and deep integration with fragmented enterprise systems.

That complexity may ultimately preserve a major role for IT services firms, although the nature of that role may look very different from the traditional labour-arbitrage model that defined the industry’s rise.

Instead of billing for large engineering teams, future opportunities may increasingly revolve around AI governance, orchestration, security, model monitoring, retraining, AI-ready data pipelines, and outcome-driven transformation work.

Coforge CEO Sudhir Singh recently argued that enterprises will continue needing firms capable of monitoring AI systems, governing models, building agentic AI harnesses, and managing enterprise-grade AI operations.

In other words, AI may not eliminate services demand. But it may fundamentally change which parts of the services stack remain valuable.

Krishna Rao, chief financial officer at Anthropic, said last week that the company’s partnerships with the world’s leading systems integrators remain central to how Claude reaches large enterprises, and that it continues to “invest deeply” in those relationships.

The new firm extends that delivery capacity further, particularly across mid-sized companies in sectors like healthcare, manufacturing, financial services, retail, real estate, infrastructure. It will become a member of Anthropic’s growing Claude Partner Network, which includes system integrators and other partner firms helping enterprises adopt Claude.

“Demand for hands-on AI implementation in sectors is significantly outpacing what is available across the industry today," he said.

Boosting AI adoption among enterprises is a strategic necessity for AI research firms like Anthropic and OpenAI as both firms face to shore their revenue growth and justify their sky-high valuations ahead of their highly anticipated initial public offerings, which could arrive as soon as this year.

For Google, the move is equally crucial to maintain the rapid growth momentum at Google Cloud, which is emerging as a key growth engine for the tech giant's revenues and profits.

During the company's earnings call last week, Alphabet CEO Sundar Pichai said that enterprise AI solutions became the primary growth driver for Google Cloud for the first time in the past quarter.

The outsourcing industry’s centre of gravity is shifting

Nevertheless, the bigger shift underway may ultimately be about who controls enterprise transformation itself.

Frontier AI firms are moving downward into implementation and execution.

Enterprises are simultaneously expanding GCCs, rebuilding internal engineering capabilities, and reassessing earlier outsourcing arrangements.

Vanguard’s recent decision to partially reverse an earlier outsourcing arrangement with Infosys and bring some functions back in-house reflects that broader reassessment happening across global enterprises.

That leaves traditional outsourcing firms facing pressure from both sides.

AI-native companies increasingly want to own more of the enterprise workflow layer, while enterprises themselves want greater control over strategic technology capabilities. The middle of the outsourcing stack, especially commoditised implementation and maintenance work, risks becoming the most exposed layer in that transition.

The next battle in enterprise technology may therefore not be decided solely by who builds the most powerful models.

It may be decided by who ultimately owns enterprise execution, governance, and accountability in an AI-first world.