Indian IT companies sold scale for decades. Larger teams meant larger contracts, more engineers meant more revenue, and labor arbitrage drove the $300 billion outsourcing sector.
Both sides are now challenging that equation. While frontier AI companies are getting closer to enterprise execution itself, AI is automating portions of the delivery pyramid.
From muscles to models
A bigger portion of enterprise transformation is what the AI labs want.
Recent actions by Google, Anthropic, and OpenAI indicate that these businesses are no longer satisfied with just selling models and APIs.
They are moving closer to the implementation layer, which has historically been dominated by IT services companies, integrating themselves more deeply into enterprise workflows, deploying engineers alongside clients, and assisting in the direct integration of AI systems into operations.
The model is becoming more and more similar to the “forward-deployed engineer” model made popular by Palantir, in which software companies actively participate in execution rather than just selling tools.
There is pressure on the middle, which is vulnerable to commoditized IT work.
This is significant because AI targets the labor-intensive layers that drove outsourcing economics for many years.
Automation is becoming more and more common in application maintenance, repetitive coding, testing, support, and services that heavily rely on junior associates.
The traditional fresher-heavy pyramid model is under structural stress, according to analysts, as businesses demand upfront productivity gains from AI while pricing pressure increases throughout the industry.
Strategic fear is the greater concern.
A significant portion of the conventional systems integration stack may be condensed if AI companies are able to successfully integrate models, agentic platforms, developer tooling, and enterprise implementation ecosystems.
The new moat is orchestration.
However, the industry might change rather than vanish.
Enterprise AI deployment, according to a number of executives, is much more complicated than just plugging in a model. Deep operational knowledge is still needed for governance, security, orchestration, monitoring, compliance, and integration across dispersed enterprise systems.
Although it would be very different from the labor-arbitrage era that defined their rise, that could maintain a significant role for IT services firms.
Businesses that can coordinate AI systems instead of just providing engineering capacity may be rewarded in the future.
The center of gravity is moving.
There is more going on here than just another technological cycle.
AI startups are descending into execution. Businesses are reevaluating their reliance on outsourcing while also rebuilding their internal capabilities.
This puts traditional IT companies in a precarious position, right where commoditized implementation work used to flourish.
It’s possible that developing the best AI model won’t be the next big battle in business technology.
In an AI-first world, it might be about who owns enterprise execution in the end.
By: B.D.Bagati