The AI Rush In SaaS: How Indian VCs Are Reinventing Their Thesis

By mid-2024, the Indian SaaS landscape reached a turning point. AI was no longer an edge or an innovation layer; it had become a foundational pillar for SaaS and software development.

Inc42 reported that AI startups comprised nearly 40% of new SaaS ventures funded in 2024, a sharp leap from 19% in 2022. In total, Indian SaaS startups raised over $2.1 Bn in 2024, up 31% YoY. A growing chunk of this capital is flowing toward companies that are not just building on AI, but being built by AI.

The shift is clear even in boardroom conversations or pitch meetings. Investors now probe product architecture with surgical precision. The question isn’t simply whether a company uses AI — it’s how deeply AI is embedded, whether it creates a durable moat, and whether it meaningfully improves the startup’s economics and defensibility.

“An AI-native SaaS firm is created with AI at its foundational level,” says Ankur Mittal, cofounder and partner of Inflection Point Ventures, adding, “These businesses constantly train and enhance their algorithms using private data, creating a defensible tech moat that is hard to replicate. In contrast, typical SaaS providers frequently patch AI onto existing systems, providing only incremental gains rather than radical revolution.”

This distinction is becoming crucial to funding decisions. Indian SaaS is no longer chasing AI adoption—it’s being rebuilt around it. And this evolution is rapidly reshaping investor priorities, frameworks, and thesis.

Ashwin Raguraman, founding partner at Bharat Innovation Fund, believes that once the power of AI itself became evident, it compounded, especially as compute power increased.

The capabilities that were there back then at a certain level have dramatically advanced today. Users are more aware. Interfaces are more intuitive. People can describe a task, toss in a prompt, even if they’re not tech-savvy, and get meaningful results.

There’s now a common understanding of how to use AI. That’s made a huge difference.

From Cloud-First To AI-Native SaaS 

Before we go further, it is important to rewind the clock and understand India’s SaaS journey. The SaaS ecosystem has come a long way from making software more accessible to now fundamentally reshaping how intelligence is built into applications.

In the early 2010s, the appeal of SaaS was straightforward: cloud-native tools that were easy to deploy, cost-effective, and didn’t require complex on-premise infrastructure. Businesses of all sizes could access software on demand, paying monthly and scaling as needed.

This ease of use created the perfect environment for workflow applications to thrive — CRMs, billing software, customer support — products built for efficiency. Indian SaaS startups, in particular, found success by tailoring these tools for mid-sized and emerging enterprises.

But things started to shift. With growing public cloud infrastructure and better digital readiness, SaaS began consolidating into unified platforms. Instead of scattered point solutions, companies started offering end-to-end cloud-native experiences.

And then came generative AI.

The rise of GenAI around 2018 didn’t just enhance SaaS — GenAI resulted in reinvented models.

Traditional SaaS solutions became smarter, more adaptive, and more intuitive. And new AI-native platforms emerged, built from the ground up with LLMs, natural language interfaces, and agentic automation.

As Raguraman puts it, “We’re watching the line between software and intelligence blur.”

What made this leap possible? Two enablers: data and compute. Enterprises now have access to structured, well-connected datasets that were previously hard to collect or clean.

Meanwhile, GPU acceleration — originally built for gaming — unlocked the compute power needed to train and deploy AI at scale.

These building blocks have fuelled a rapid acceleration — one where startups can now move from idea to intelligent software in months, not years.

Gauging AI-Native Depth

When a startup claims to be “AI-native,” the first question investors ask is: Is AI core to the product, or just a layer?

Many companies today integrate third-party AI APIs and label themselves GenAI players. But for investors, depth matters — how embedded is the AI, and does it meaningfully enhance the product?

As Raguraman puts it, this is where enterprise buyers and investors often align: they want to see if the solution truly solves a business problem. Large incumbents like Zoho or Freshworks can add AI layers to existing platforms. But newer players need to show real differentiation — through proprietary data, custom models, or vertical-specific applications.

On the customer side, especially in large enterprises, adoption is cautious. Security, bias, and governance remain top concerns. The Bharat Innovation Fund partner shares an example: a SaaS company added GenAI and pitched it to CIOs, who responded, ‘I’m glad you didn’t activate it yet.”

This hesitation is symptomatic of the need for compliance and control in mission-critical environments. That’s why enterprise use cases for AI today are mostly around support functions — report generation, summarisation, or automation — rather than core systems like payments or telecom networks.

Adoption will scale only once AI proves reliable and safe enough for critical workflows.

From the investor’s lens, diligence runs deeper. They evaluate three key aspects:

• Data Quality & Results: What training data was used? What accuracy does the model achieve today?
• Data Flywheel: Is there a mechanism to keep improving via fresh data? Does the system learn over time?
• Technical Rigour: Why was a particular model chosen? Is it truly ML, or just rule-based logic masked as AI?

Investors such as Raguraman also test claims during founder conversations — asking how model accuracy evolved, what trade-offs were made, and how deployment impacted real customers.

“Ultimately, the test is not just in claims, but in proof — where is it deployed, what’s the accuracy, and how much has it changed the customer’s workflow?”

In a market filled with AI branding, these questions help separate foundational AI-native platforms from SaaS products with superficial AI add-ons.

New Chapters In SaaS Investments Playbook 

For investors like Abhishek Prasad, managing partner at Cornerstone Ventures, the AI wave in SaaS is prompting fundamental changes in deal evaluation. His lens has shifted from traditional SaaS metrics like CAC and ARR growth alone to newer AI-driven qualifiers.

“We give the most importance to the business model and the impact being created by the startup for its customers,” says Prasad. “With AI becoming core to SaaS, we are interested in how AI is being leveraged by these companies, what is the impact it is making to the value proposition, is it creating new moats, and is the cost of leveraging AI capabilities delivering the right ROI to both the startups and their customers.”

This evolving lens has consequences for early-stage evaluation. Investors are digging deeper into the architecture of the product stack, the flexibility and efficiency of AI models, and how core AI is to the startup’s evolution and scalability.

IPV’s Mittal echoes the shift: “We now evaluate data strategy, model flexibility, and AI-driven defensibility. Startups that capitalise on proprietary data and thoroughly integrate AI into their value offering outperform those who only use AI as an add-on.”

Take vertical SaaS, for instance. Once considered niche, it’s now center stage in investor pitch decks. From financial risk platforms to HR tech, legal automation, and precision healthcare tools, domain-specific SaaS models with embedded AI are drawing attention for their ability to deliver tailored solutions and faster ROI.

“The biggest disruptions will come in horizontal SaaS, where several features and functionalities could become commoditised with AI accelerating the ability to build these capabilities. But vertical SaaS, with deep domain capabilities, will benefit the most from AI. Speed of innovation and operational efficiencies will change dramatically,” Cornerstone’s Prasad claimed.

AI is also informing how investors assess the founding team. While technical depth remains essential, investors now look for domain fluency, product-founder fit, and a nuanced understanding of AI ROI.

A good AI team consists of more than simply technical talent; it also comprises deep domain knowledge, research-backed AI capabilities, and a clear vision for AI-driven value generation. Mittal said, “We seek entrepreneurs who have not only created AI models but also effectively implemented them at scale, resolving real-world challenges with demonstrable results.”

Even the AI model choices are scrutinised. Investors are keen to see if founders are making strategic decisions — using lightweight, specific language models (SLMs) instead of large language models (LLMs) where efficiency and cost justify it. It’s not just the sophistication of the AI but its relevance to business goals that matters. For example, there are SLMs for AI in radiology or imaging. Now, other people can build on top of those using the data they have access to, for example, in diagnostics.

What’s Changing In SaaS And What India Gets Right

AI is reshaping how SaaS products are built and scaled. It’s not just about features anymore — it’s about driving new economics through smart, efficient AI deployments.

In India, this shift is visible across CRM, ERP, cybersecurity, and process automation, where AI is already powering predictive insights and operational gains. “AI-powered CRM and ERP platforms are revolutionising organisational decision-making by enabling customisation and predictive analytics,” says Mittal of IPV.

Startups here are embracing a constraint-driven approach, building narrow, efficient models suited for low-compute settings and multilingual use cases. Proprietary data loops are also becoming long-term moats, as each user interaction improves the model.

India’s edge lies in three things: frugal innovation, a 7M+ strong tech workforce, and massive digital adoption. It’s really the technology workforce that gives India the advantage to be a global SaaS leader, according to the investors we spoke to.

But challenges persist. Access to high-quality talent, annotated data, and reliable compute infrastructure become critical barriers as startups scale. Startups building with regulatory readiness, especially around privacy, explainability, and compliance, are better poised for enterprise deals.

The playbook for Indian SaaS founders is now two-fold:

• India for the world: Competing on enterprise-grade solutions globally.
• India for India: Solving uniquely local problems with contextual, high-ROI AI

The Road Ahead: Where SaaS Investing Is Going

The next five years will test whether Indian SaaS can shift from smart experimentation to sustained, AI-led leadership. For investors, this moment calls for a reset in how defensibility is defined, particularly in a landscape where open-source models are abundant, features are easy to replicate, and compute is commoditised.

In this new environment, the spotlight is shifting toward proprietary data, AI-native product architecture, execution speed, and the depth of integration across the value chain. Startups showing strong sales efficiency, high expansion revenue, and well-tuned AI deployments are setting the new benchmarks.

Multimodal AI is changing how users interact with software. Emerging use cases like AI copilots, synthetic data generation, and verticalised automation layers are moving from theory to execution.

But the road ahead is also about safeguarding trust. As digital footprints grow and AI-generated content becomes harder to distinguish from the real, startups are under pressure to bake responsible AI into their systems from day one. Security, explainability, and provenance tracking are no longer optional—they’re becoming table stakes in enterprise conversations.

Investors expect that regulation will catch up eventually, but many believe that the first line of defense will be technological. Just as antivirus and encryption became foundational to internet adoption, detection and traceability tools will become essential to the AI stack. Some even liken it to the early cybersecurity playbook, where one threat sparks an entire ecosystem of solutions.

At the same time, questions are emerging around AI adoption itself: how much is real, how much is hype, and what metrics actually indicate long-term impact. For SaaS founders, that’s creating a different kind of execution dilemma. Should they move fast and build on fast-evolving tools or wait for more stable ground before scaling critical infrastructure? It’s a tension that shows up both in product roadmaps and in boardroom conversations.

Looking further ahead, infrastructure shifts like scalable quantum computing and decentralised architectures and others could change the game again. SaaS companies may soon need to redesign not just their tech stacks, but their entire GTM strategies, around new computing models and user expectations.

In this broader context, Indian SaaS startups have a distinct opportunity in the AI-first world.