India’s AI startup ecosystem has grown significantly. Given the high impact of machine learning technology, startups working in deep learning and ML have attracted the biggest funding. According to a NASSCOM Research report, AI funding in India has gone up from $44 million to $73 million in a year. The report cites that investors are bullish to back “mature” ventures. The country has significantly stepped up in the AI market with a funding of $73 million and more than 50 percent of the companies say that they are working on advanced analytics and computer vision-based AI technologies, among others. Some of the driving forces behind their rapid adoption are tech incubators, accelerators and Government initiatives.
Even then, 2017 saw some promising ML startups shutting down. One of the sad cases was Bengaluru-based fintech startup Finomena which shut its shop after it failed to raise series-A funding. It was founded by IIT-Delhi graduate Abhishek Garg and Stanford graduate Ridhi Mittal in 2015. Another startup to bite the dust last year was PropheSee Solutions, a Delhi-based analytics startup that gave brands data-driven insights. The startup shut down since it failed to raise funding and failed in customer acquisition. Another data analytics startup that went in red was Kaarya, launched in 2014 by Nidhi Agarwal. The startup was funded by Mohandas Pai and Ratan Tata, and it shut down to funding issues in December 2017.
As AI and ML applications mature, and we see more vertical focused startups entering the pipeline, there are also reports of the number of enterprise product startups seeing a decline. The NASSCOM report hinted that there was a 13 percent decline in the number of startups from 2016. By and large, NCR is considered to be India’s startup death-valley.
Top Reasons Why Enterprise Product Startups Shut Down:
Lack Of Scalability: With the hype around ML and AI growing, it is a wonderful time for tech enthusiasts to become entrepreneurs and set up ML-focused startups. The cloud computing infrastructure helps startups compete with enterprises offering similar solutions and capture a segment of the market. Even as the data availability and talent issues in ML are resolved, startups lack the sales support of big tech firms like IBM or Microsoft. Despite being more vertical-focused and nimble in their approach with quick turnarounds, startups are vulnerable to low product demand.
Product Development Issues: In their early stages, startups are usually bootstrapped in the product development stage. The potential investment increases once there is a working prototype. However, the product development journey can be tough for founders as sometimes they fail to keep the scalability of the product in mind. Due to a lack of bench strength, early-stage startups don’t have a product manager to oversee the development and define the product vision. Failure to patent technology and IP protection in a bid to market the product soon can also lead to stunted growth.
Lack Of Usable Design: Usable design plays a key role in customer acquisition and failure to understand the customer’s pain points has always been one of the key reasons to gain critical mass. According to a startup founder, product managers should not just display tech fluency but also be mindful about customer’s pain points and objectives such as usability and user experience.
Lack Of Customer Acquisition: Companies trying to concentrate entirely on the product itself without taking sales process under consideration lead to low product demand which causes a cash crunch. In fact, 43 percent of advanced tech companies in India have failed to expand due to low demand.
According to a study by Oxford Economics and the IBM Institute for Business Value Indian startups are able to exploit a range of attributes such as the domestic market readiness and skilled workforce. Even then, a key attribute that threatens the startup ecosystem in India is a lack of pioneering innovation. The report hints that VCs believe Indian startups lack innovation based on their business models or technologies. Startups in the enterprise product space are fostering deeper collaboration with IT bellwethers, accelerate the time-to-market of new products, provide new skills and new technologies, but in the process fail to scale at a global level.
Another reason why ML-focused or deep tech-focused startups flounder is because of a lack of mentorship from tech incubators, investors or experienced leaders. Even though the last few years have seen extensive collaboration between IT giants and established companies who wish to exploit new technologies, the engagement is limited to developing new go-to market strategies for their products. During these collaborations, there is an increased focus on tech underpinning of the product rather than customer centricity.