AI-native enterprise services: automating the outsourcing engine
AI-native enterprise services are business offerings designed from the ground up so that artificial intelligence, not human labor, performs most software customization, integration, and maintenance work that large organizations once outsourced to manual IT service teams. Vishal Sikka, former Infosys CEO, has launched Hang Ten Systems as a clear bet that this AI-first model can replace the old delivery engine. The company has raised a USD 32 million (approx. RM150 million) seed round led by Mayfield, with a strategic investment from Aramco Ventures and participation from angel investors, and is already working with customers like Siemens Gamesa Renewable Energy and Fresenius. In other words, this is not a slide deck idea; it is an assault on the assumption that enterprise software work must be powered by armies of people.
The key takeaway is blunt: Hang Ten Systems is an attempt to automate away a big slice of the USD 250 billion-plus IT services market by treating AI as the primary worker rather than a supporting tool. That makes this Vishal Sikka startup less a side project and more a referendum on whether IT services automation can rewrite the economics of global outsourcing.
From OpenAI backer to AI-native disruptor
Vishal Sikka is not a newcomer parachuting into AI hype; he was running Infosys when it gave a grant to help OpenAI get off the ground, long before foundation models were fashionable. Now, with Hang Ten Systems, he is trying to do in practice what many incumbents only talk about: use AI workflow automation to shrink the need for traditional consultants and offshore teams. Hang Ten’s model combines agentic code generation, reusable AI skills, and domain experts to continuously build, modify, and operate enterprise software. That sounds like jargon, but the implication is sharp: codified AI workflows that get better with each project, instead of headcount that gets more expensive every year.
Hang Ten already lists Siemens Gamesa Renewable Energy and Fresenius as customers for AI-native project delivery, covering areas like finance, HR, and new product development. This is a strategic pivot away from the classic IT consulting playbook of time-and-materials billing and multi-year implementation programs. Sikka is betting that accumulated AI skills, not billable hours, become the core asset of AI-native enterprise services. If he is right, the firms that still rely on labor-arbitrage economics will face an uncomfortable margin squeeze.
Why launch Hang Ten now, and why it matters
The timing of Hang Ten is not random. According to one analysis, the technology industry is expected to reach about USD 315 billion (approx. RM1.4 trillion) in FY26, with the software services segment alone estimated at over USD 250 billion and already under AI pressure. Public-market analysts have warned that a severe AI disruption scenario could drive another 30% to 65% valuation hit for major IT service players. At the same time, IT services stocks have been under strain as investors ask how much revenue is exposed to tools that automate coding, testing, documentation, and support. Hang Ten is stepping into that anxiety with an explicit promise: AI can build, change, and run enterprise software with far less human drag than the old outsourcing model.
Incumbents are not asleep. Large service providers are striking partnerships with frontier-model companies and pitching an AI-first services opportunity measured in hundreds of billions. Their argument is that AI will amplify them, not replace them. Hang Ten Systems argues the opposite: that the vulnerable piece is the labor-arbitrage assumption itself, the belief that complex enterprise work naturally requires layers of project managers and long delivery cycles. By building AI workflow automation at the core of its offering, Hang Ten is trying to change the unit economics before the big firms can fully adapt. If it succeeds, the first budgets to move will likely be those tied to repetitive customization and maintenance projects that AI agents can handle faster and cheaper.
An automation-first shot at a USD 250B market
Hang Ten’s strategy rests on a simple but disruptive claim: “AI can build, change, and run large-company software with far less human drag than the old outsourcing model requires.” Instead of selling generic AI transformation workshops, the company is targeting the exact categories of work that global enterprises have long handed to system integrators—customization, integration, and ongoing support. It does this through agentic code generation and a reusable skills library, backed by a bench of forward deployed engineers who step in where automation stops. Over time, each new project is supposed to enlarge the skills library, meaning future work requires less human intervention and delivers higher margins than traditional consulting can manage.
This is what makes Hang Ten Systems more than another AI-native enterprise services pitch. The model is explicitly designed to scale through accumulated project leverage rather than headcount growth. That approach mirrors product companies more than services firms, and it is a direct challenge to the billing structures that defined the last generation of IT. Skeptics will point out that regulated enterprises still prize trust, compliance, and relationship depth—areas where incumbents have decades of advantage. But if even a slice of the USD 250 billion-plus services market shifts to automation-first delivery, the balance of power between clients, vendors, and AI systems will not look the same.
Conclusion: surfing the AI wave, or being pulled under
Sikka likes to describe the current AI moment as a massive wave, and “hanging ten” as the art of mastering it rather than clinging to the board. Hang Ten Systems embodies that metaphor more aggressively than most incumbents are comfortable admitting. By structuring itself as an AI-first services disruptor, it is attacking the core assumption that more revenue in enterprise IT services must mean more people. It may not topple the giants overnight; those firms still command client trust, compliance expertise, and sprawling delivery infrastructures. But Hang Ten does not need to replace them to matter. If its AI workflow automation model proves that large chunks of integration and maintenance work can be automated, the entire sector will be forced to choose between surfing the AI-native wave or being dragged under by its own cost base.
