As AI Goes Into Production, VC Bets Turn Disciplined

India’s enterprise AI narrative is entering a phase that is far less about experimentation and far more about execution. Over the past two years, much of the conversation around AI adoption in enterprises revolved around pilots, proofs of concept, and exploratory budgets. 

That phase is now giving way to something more consequential: production-scale deployment, where AI systems are embedded into core workflows and are expected to deliver measurable business outcomes.

“Enterprise adoption of agentic AI is decisively moving from pilots to production, and investors in India are responding with sharper discipline, shifting from broad AI-led narratives to outcome-driven evaluation, says Dr. Apoorva Ranjan Sharma, Co-founder & MD, Venture Catalysts. 

According to Gartner, 33 per cent of enterprise software applications will include agentic AI by 2028, up from less than 1 per cent in 2024, with global AI spending forecast to touch $2.52 trillion in 2026.

“This is a clear signal that enterprise demand is now real and at scale. The preference today is firmly for startups that can show measurable ROI and tangible efficiency gains, with focus returning to fundamentals like unit economics, real enterprise use cases, and deep workflow integration,” said Dr. Sharma. 

His firm actively backs AI and enterprise deeptech like Wiom and Garuda Aerospace, where the technology directly drives productivity, automation, and cost outcomes. 

Nasscom estimates that India’s GCC market could grow to over USD 100 billion by 2030, with a significant portion of that growth tied to advanced analytics, automation, and AI-led transformation mandates. 

These centres are not just back offices anymore; they are increasingly responsible for building and deploying mission-critical AI systems for global enterprises. This creates a fertile environment for startups that can plug directly into enterprise workflows and deliver immediate value.

Dr Sharma feels that India’s GCC ecosystem, which is projected to reach USD 110 billion by 2030, is a powerful structural tailwind for Indian founders building for global enterprise buyers.

“This is a broader shift from speculative growth to long-term value creation. In FY26, capital will flow decisively to founders who can prove execution in real-world environments, not in demos, not in decks, but in production,” said Dr. Sharma/.

A clear shift in global enterprise technology trends backs the transition. According to Gartner, enterprise software is rapidly evolving to integrate autonomous and semi-autonomous systems, with agentic AI expected to be embedded in roughly one-third of applications by 2028, up from negligible penetration just a few years ago. At the same time, IDC projects global AI spending to cross USD 2.5 trillion by 2026, driven largely by enterprise adoption rather than consumer-facing use cases. These projections indicate that AI is becoming foundational infrastructure.

The implications for venture capital are significant. During the previous funding cycle, many AI startups were evaluated on narrative strength, and broad claims around “AI-powered” transformation often sufficed. That threshold has now tightened considerably. Investors are demanding evidence of real deployment: integrations into enterprise systems, demonstrable efficiency gains, and clear unit economics. In effect, the market is moving from valuing potential to valuing proof.

Supria Dhanda, Co-founder and Managing Partner, Wyser Capital, said that the Indian enterprise landscape is entering the execution phase of Agentic AI, where outcomes, not experimentation, drive value. 

“In FY26, this is sharpening capital discipline, with more selective, conviction-led investment decisions. At Wyser Capital, we see a clear shift from generalist AI to specialized, vertical platforms solving deep enterprise problems, anchored in rigorous technical diligence and deployment readiness. Investor participation is becoming distinctly operator-led, with seasoned leaders engaging through advisory roles at the fund level and actively with portfolio companies. This is creating a more aligned capital base, informed, and execution-focused, backing long-cycle, IP-led innovation built in India for global enterprise markets,” said Dhanda.

Another notable shift is in the composition of capital itself. Venture funds are increasingly bringing in operators, former enterprise leaders, CTOs, and domain experts into their investment processes. 

Firms like PeakXV, Accel, and Lightspeed Venture Partners actively leverage an extended network of operators (ex-founders, CTOs) for technical diligence and go-to-market validation.

This operator-led approach changes how startups are evaluated. Instead of relying solely on financial projections or product demos, investors are conducting deeper technical diligence, assessing deployment readiness, scalability within enterprise environments, and the robustness of underlying IP.

McKinsey’s research on AI adoption consistently highlights that the highest-performing companies are those that move beyond isolated use cases and embed AI into end-to-end business processes. Close to 88 per cent of organizations use AI in at least one business function, yet only about one-third have actually scaled AI across the enterprise.

Similarly, BCG’s studies show that companies generating the most value from AI prioritize operational integration over experimentation. Indian startups building in this space are increasingly mirroring these principles, designing products that are not just innovative but deployable at scale.

Just 26 per cent of companies have built capabilities to move beyond proofs-of-concept, while 74 per cent struggle to generate tangible value, according to BCG. Also, only 4 per cent of companies consistently generate significant value from AI across functions, indicating how hard end-to-end integration is.

What emerges from all of this is a more mature, disciplined ecosystem. Capital is still available, but it is being allocated with greater precision. Startups are still being built, but they are being held to higher standards. And enterprises are still adopting AI, but with clearer expectations around outcomes.