Vishal Sikka's New Startup Takes On IT Services With AI-Native Platform

Former Infosys CEO Vishal Sikka has reemerged with a new startup that aims to disrupt the global IT services industry from the inside out. Backed by Mayfield and Aramco Ventures, the venture brings together veterans from SAP, Infosys, and VianAI to build an AI-native platform designed to automate enterprise IT operations at scale. According to the NeuralPress AI Statistics & Trends 2026 resource, enterprise AI adoption reached 78% in 2026, up from 55% in 2023, but most legacy IT service providers still rely on manual processes and outdated delivery models. This new entrant aims to exploit that gap with a fundamentally different approach.

• Vishal Sikka’s startup is backed by Mayfield and Aramco Ventures, signaling deep institutional confidence in an AI-first IT services model.
• The founding team includes veterans from SAP, Infosys, and VianAI, blending enterprise software, IT services, and AI expertise.
• The platform is built from scratch to automate IT operations, a direct challenge to labor-intensive legacy providers like Infosys, TCS, and Wipro.
• The venture targets the $1 trillion global IT services market, where margins have been squeezed by rising wage costs and client demand for faster delivery.
• If successful, this could force every major IT services firm to accelerate their own AI transformation or risk losing market share.
• The involvement of Aramco Ventures suggests a strategic interest in applying AI to oil and gas enterprise IT, a sector with high compliance and data complexity.

How Does an AI-Native IT Services Platform Differ From Legacy Systems?

Traditional IT services firms operate on a labor arbitrage model: they hire large teams of engineers in low-cost locations to build, maintain, and support enterprise software. Margins depend on utilization rates and hourly billing. An AI-native platform flips this equation. Instead of human engineers writing code and troubleshooting manually, the platform uses machine learning models trained on millions of past incidents, codebases, and configuration files to automate the majority of routine tasks. The startup’s platform is designed to handle everything from incident response and system monitoring to code deployment and compliance auditing without human intervention. This is not incremental improvement; it is a structural shift from headcount-driven delivery to algorithm-driven delivery. The key difference is that AI-native systems improve over time as they ingest more data, whereas human teams plateau without continuous training and hiring.

For CIOs evaluating AI-native IT services, start by identifying repetitive, high-volume tasks in your current operations. These are the easiest to hand off to an automated platform, freeing your team for strategic work that requires human judgment.

Why Is Building an AI-Native Platform Harder Than It Looks?

Creating a platform that can reliably replace human engineers across diverse enterprise environments is a monumental engineering challenge. The first hurdle is data quality. Enterprise IT environments are messy: logs are inconsistent, configurations are poorly documented, and incident reports vary wildly in format and completeness. Training models on such data requires extensive preprocessing and domain-specific knowledge. The second challenge is safety and reliability. An automated system that accidentally deletes a production database or applies a misconfigured firewall rule can cause millions in damage. The platform must incorporate robust guardrails, human-in-the-loop validation, and extensive testing before any action is taken autonomously. Third, the platform must integrate with hundreds of legacy systems, each with its own APIs, protocols, and security requirements. The startup’s leadership team, with deep experience at SAP and Infosys, understands these integration complexities better than most, but execution risk remains high.

Aspect	Legacy IT Services	AI-Native Platform	Impact on Enterprise Clients
Delivery Model	Labor-intensive, offshore teams	Algorithm-driven automation	Faster delivery, lower cost, but less human oversight
Scalability	Linear with headcount	Near-infinite with compute	Clients can scale operations without hiring
Quality Consistency	Varies by engineer skill	Uniform, model-driven	Fewer human errors, but model biases may emerge
Cost Structure	Hourly billing, utilization-driven	Subscription or outcome-based	Predictable costs, but vendor lock-in risk
Innovation Cycle	Slow, dependent on training	Rapid, model updates in days	Clients get new features faster, but must manage change

What Should Enterprise Leaders Know Before Adopting an AI-Native IT Services Model?

Adopting an AI-native platform is not a simple swap of one vendor for another. It requires rethinking the entire IT operations workflow. First, leaders must assess which parts of their IT estate are suitable for automation. Highly standardized environments, such as cloud-native applications on AWS or Azure, are easier to automate than bespoke legacy systems running on mainframes. Second, the organization must have a strong data governance framework. The platform will ingest vast amounts of operational data, raising concerns about data privacy, security, and compliance, especially in regulated industries like finance and healthcare. Third, the workforce must be reskilled. As routine tasks get automated, IT staff will need to focus on higher-value activities such as architecture design, vendor management, and strategic planning. The transition can be disruptive, and change management is critical. Finally, enterprises should demand transparency into how the platform makes decisions. Explainability is essential for building trust, especially when automated systems are handling critical infrastructure.

Who Benefits Most From This New AI-Native IT Services Startup?

The primary beneficiaries are large enterprises with complex, multi-vendor IT environments that spend heavily on maintenance and operations. These organizations typically allocate 70-80% of their IT budgets to keeping existing systems running, leaving little room for innovation. An AI-native platform can slash maintenance costs by automating incident response, patch management, and monitoring. The secondary beneficiaries are CIOs and IT directors who are under pressure to deliver more with less. By reducing reliance on large offshore teams, they can reallocate budget toward strategic initiatives like digital transformation and AI adoption. The startup’s investors also stand to benefit: Mayfield and Aramco Ventures are betting that a first-mover advantage in AI-native IT services will capture significant market share from incumbents. However, the biggest winners may be the end customers of these enterprises, who will experience faster, more reliable digital services at lower cost.

• CIOs: Gain cost predictability and faster incident resolution, but must manage workforce transition.
• IT Operations Teams: See routine tasks automated, freeing them for strategic work, but face reskilling pressure.
• Enterprise Vendors: Legacy providers like Infosys, TCS, and Accenture face existential disruption if they fail to adapt.
• Investors: Mayfield and Aramco Ventures get exposure to a high-growth market with potential for outsized returns.

Enterprises should be cautious about over-automating too quickly. A sudden shift to an AI-native platform without adequate testing and human oversight can lead to catastrophic failures. Start with a pilot in a low-risk environment and gradually expand.

Which Warning Signs Predict Problems Ahead for This Startup?

Despite its strong leadership and backing, the startup faces several risks. The first warning sign is customer concentration. If early adopters are limited to a few large, tech-savvy enterprises, the platform may struggle to generalize to the broader market. The second red flag is slow adoption in regulated industries. Banks, insurers, and healthcare providers have stringent compliance requirements that may delay or prevent the use of fully automated IT operations. The third concern is talent retention. The startup’s top engineers and domain experts are in high demand, and competitors will try to poach them. If key personnel leave, the pace of innovation could stall. Fourth, the platform’s performance in real-world production environments will be scrutinized. Any high-profile outage or security breach could set the company back years. Finally, the startup must navigate the competitive response from incumbents. Legacy IT services firms are investing heavily in their own AI capabilities, and they have deep relationships with enterprise clients. The startup’s ability to win deals against entrenched vendors will be the ultimate test of its value proposition.

The launch of Vishal Sikka’s AI-native IT services startup marks a pivotal moment for the industry. It signals that the era of labor-intensive IT services is ending and that AI-driven automation is no longer a futuristic concept but a present-day business reality. For enterprise decision-makers, the message is clear: the window to prepare for this shift is closing. Those who embrace AI-native operations early will gain a competitive edge, while those who delay may find themselves struggling to catch up.

Source: TechCrunch AI