Beyond horizontal AI: Why Indian enterprises need vertical intelligence

Indian enterprises do not have a software problem. They have a relevance problem. There is more software being sold into Indian companies today than at any point in history. AI-powered, beautifully designed, backed by serious capital. And yet, renewal rates are poor, adoption is shallow, and the graveyard of failed enterprise pilots keeps growing.

The technology is not the issue. The issue is that most of these products were not built for the specific, often messy, reality of how Indian businesses actually operate. The "AI-first horizontal platform" thesis is the most common culprit.

Why horizontal fails in India specifically

Horizontal software succeeded in the US because it was layered on top of decades of operational standardisation. American enterprises had already converged on common workflows, clean CRM data, structured HR processes. Salesforce did not teach companies how to run a sales pipeline. It digitised a pipeline model that already existed.

India's mid-market is at a different point in that journey. A ₹400 crore auto components manufacturer in Pune is coordinating supplier deliveries over WhatsApp. A cooperative bank in Nashik is doing loan collections through field agents whose entire workflow lives in their head and a paper register. A 200-bed hospital in Indore is losing 18% of its revenue to claim rejections it does not have the bandwidth to appeal.

These are not problems a horizontal AI platform solves by being powerful and flexible. They are solved by products that arrive already understanding the domain, already knowing where the failure points are, already speaking the operational language of that specific industry.

Asking an Indian mid-market enterprise to first standardise its processes and then adopt your AI tool is asking them to solve two hard problems in sequence. They will start the pilot. They will not finish it.

What genuine vertical depth looks like

In healthcare revenue cycle management, the problem is not that hospitals lack billing software. It is that their billing software does not know that a particular TPA rejects claims with a specific diagnosis code unless a supporting document is attached on page one, not page three. It does not know that CGHS reimbursements for a certain procedure have a shadow rate that differs from the published rate. It cannot tell a billing executive, before submission, which claims are going to come back and why.

Building that requires someone to have worked through thousands of actual Indian claim rejections, mapped the behaviour of 20-plus TPAs, understood the internal staffing constraints of a hospital billing department. No horizontal platform makes that investment for one vertical. A focused vertical company has no choice but to make it.

In manufacturing, the insight is that downtime and quality problems are intensely local. The vibration signature that precedes a bearing failure on a 10-year-old machine running cotton yarn in Surat is not in any global training dataset. The informal coordination between a store manager and a contract maintenance crew, which is how most Indian factories actually prevent unplanned downtime, is not visible to any ERP. A vertical AI product built for Indian discrete or textile manufacturing has to encode this. It has to integrate with the specific PLCs and SCADA versions actually installed on Indian floors, not the ones in vendor brochures. It has to earn the trust of a shift supervisor who has watched three software rollouts fail.

In BFSI, the most interesting whitespace is in the segments large institutions have always found structurally difficult to serve. Over 1,500 NBFCs in India are underwriting credit in markets where bureau data is thin, where the real signal is in GST filing patterns, utility payment history, and local market intelligence that no centralised model is calibrated for.

A vertical AI company building specifically for agri lending or MSME equipment finance is not competing with anyone at scale. It is filling a gap that has existed for decades and will not be filled by a generalist platform.

The compounding advantage

Every customer a vertical AI company closes generates domain-specific workflow data that makes the product meaningfully better for the next customer. A horizontal platform accumulates generic interaction data. A vertical platform accumulates proprietary operational intelligence. Eighteen months in, the difference is not a feature gap. It is a knowledge gap that cannot be purchased or reverse-engineered.

The switching cost dynamic is different too. When software is embedded in clinical workflows or factory floor operations, replacing it is not a procurement decision. It is an operational risk decision. That is a fundamentally different renewal conversation.

What I look for

The founders building durable vertical AI companies in India share something specific: they understood the industry before they designed the product. They know why the previous implementations failed. They have an informed view on which workflows need to change and which need to be preserved exactly as they are.

That kind of domain depth is not a feature of the pitch deck. It shows up in the product, in the customer conversations, and eventually in the retention numbers.

India does not need more AI platforms. It needs more founders who know one industry well enough to make AI irreplaceable inside it.

(Abhishek Srivastava is the General Partner at Kae Capital)