Hello, Ashish! Please, tell us who you are and what your business is about.
I'm driven by the belief that data can drive efficiencies and improve the experience; the need to align with sustainable development goals, making a world a fairer place with opportunities for all; and a belief in the quadruple helix where corporations, society, universities, and governance needs to align towards creating a better world.
Before starting my venture, I worked in a large bank for almost two decades and am currently obtaining my Doctorate from two top European business schools. I was a part of market entry strategies and ran transformation programs with $25million plus budgets across multiple markets in the professional world.
I was recently a part of the Durham University research team that worked on Smart City as a Social contract, a project that was launched in the House of Lords, UK. Ztudium, citiesabc, indexdna, and Durham University, in partnership with the University of Surrey, Big Innovation Center, and World Smart Cities Forum, led this research.
Also, my poster on exploring data techniques for determining the funding of AI startups was featured at the 4th Sophia AI summit at Sophia Antipolis, France, in late 2021.
My business is central to my belief. I believe machine learning can help startup investors avoid losses and, at the same time, help founders improve the probability of getting funded. The three main issues affecting startup investments are lack of predictability of startup success, liquidity in startup investments, and many good ideas lacking funding. These are the challenges I am solving.
You have had a very long journey coming to the idea of starting your current business. Can you tell us more about the backstory of your startup?
The idea originated in the Emlyon campus in Paris because of two questions: Will work in AI lead to job creation, and whether data drives everything that we do? To answer these questions, I looked at whether AI investment was perceived as market growth signals and whether the capabilities led to job creation.
What better way to look at these than to look at AI startups as they are at the cutting edge, especially as they are expected to be the next engines of growth?
A closer look helped me to validate what most of us know. The decision on what gets funded is still primarily perception and heuristics driven and often influenced by the social contracts. Also, while data could drive significant improvements in the process, most investors lacked a data-driven tool to screen requests.
Investors are also receiving more requests than they can manually screen and rely on visual signals, social contracts, and hypothesis-based preferences. All these indicated a genuine need for a solution. I did also run proof of concepts, though, before I turned the idea into a venture.
Can you tell our readers more about AI and how it can be used by founders to raise money?
Frankly, I still haven’t closed my round, even though I have significant interest. I believe the question of fundraising is not based on whether it is AI or any other industry. It is whether you are passionate about what you are working on, whether you are building it correctly, and whether you are creating the right capabilities. Building the right support and ecosystem to scale the venture also helps in this context.
Remember, at the end of the day, investors place bets where they want returns but don't want to lose the money. Given the nature of the business, investors understand that this is not a perfect market and there will be losses. However, they are also trying to understand the best bets that help them minimize the losses and maximize the returns.
A good example of this is the restaurant investments on crowdfunding platforms in the US. Many successful funding campaigns have used debt as the funding instrument. Investors understand that scalability may be limited, but there is a greater surety of returns that is improved by debt as an assurance.
AI, and the other growth areas, fit well with the investor perception of scaling and returns. Statistically, only a tiny percentage of startups will ever reach unicorn status. The probability of these increases with investments in growth areas. Hence, it may be easier to get funded as an AI startup if you solve the right problem and build the capabilities correctly.
Is it necessary to have any IT background to understand all the processes connected with AI?
That is an interesting question. I have always looked at IT as an enabler, not an end state. IT background does help in the AI use cases. It helps with deciding a practical and implementable use case and helps to keep the cost of building it in check.
It enables a logical thought process and looking at possibilities that can turn the ideas into reality. While there are founders that may succeed without an IT background, the logical grounding helps to define implementable use cases and avoid the risk of over-engineering it. One is aware of what is real. Keeping it real is crucial, especially in AI, where product development possibilities are unlimited.
The other aspect critical for AI is the cost of enabling the use cases. Every product is excellent at a particular price. Often what will help you win is not what you build but the cost.
The cost of the build is where IT training can help significantly. The modular building approach, external partnerships, infrastructure costs, and cost-effective platforms can help distinguish between a winner and a loser.
What excites you the most about AI and your entrepreneurial journey connected with it?
AI is challenging the way we think and work and unravelling possibilities to improve the quality of life significantly. In addition to this, I am also very excited about the efficiencies it can bring to the process. Whether it is to optimize energy consumption, thus reducing greenhouse gasses and helping the sustainability goals, or enabling greater opportunity for all people to up-skill and improve earnings, AI can be a game-changer.
Being a data fanatic, I am enthralled by the capabilities that AI, especially machine learning, allows. Coming from a research background, I understand that the limits and boundaries have still not been tested. There is a need to look at new use cases, new statistical and research techniques, and a capability to work with nonstandard data. That is where I want to make a mark.
I believe in the age-old quote from Deming, “In God we trust, all others bring data”. Data is pervasive, and I can get it for everything I want to drive. It is what I do with that data that will help define my venture going forward.
What are your most current plans?
The first priority is to get the basics right. My current focus is on organizational capabilities around the right talent that I need, the leadership positions that are still to be filled and fine-tuning the product.
I am looking at exponential growth, focusing on product fine-tuning, and improving the data use cases and predictability for my customers. With an intense focus on these, in 6 months, I should be ready for a massive ramp-up. That is when the fun starts.
Have you always felt that you want to be an entrepreneur and that the corporate world is not your path?
Interestingly, I have spent two decades in corporate, many of them in leadership positions. It is a recent urge to become an entrepreneur. Both worlds have their own charm. If stability is your driving force, then you are best suited for the corporate world.
Research has proven that corporations are challenged in innovation due to resource inflexibility and principal-agent conflict. The aim is always to find incremental growth opportunities with lower risk, whereas entrepreneurs, especially in the innovative startup market, are fraught with uncertainties but are driven by a passion for radically improving a process.
I became an entrepreneur because of a drive to find a solution to a problem that was bothering me, and that I thought needed to be solved.
What difficulties are you facing when working on your own business?
Coming from a corporate background, mindset change was probably the first and the biggest challenge for me. I had to realize that I don’t have a dozen people waiting for my call but am now the thirteenth person waiting for someone else’s call. Once I got over this mindset, things became a lot easier. There are still challenges.
While resources in a startup are more fungible as compared to a large corporation, they are still limited. Often the need to scale up rapidly and the urge to solve a real-life problem need to be tapered with reality. Whether it is funding, talent, or the ability to create credibility in the market or refine the product rapidly, there are limitations that you need to operate within. I am facing some of the same challenges.
Can you give any advice to people who are just starting their own business?
Startups are glorified by the exceptions, the one person that made billions in an M&A deal or an IPO. Remember, these exceptions are not the rule. If money drives you, research has proven that you are much better in a corporate world where there is a lot more surety on earnings.
Don’t get into entrepreneurship unless there is a genuine passion, a problem that you want to solve, and you feel you are incomplete unless you work towards solving it, and you are motivated by something more than money. Don’t get into it unless you can roll up your sleeves and get your hands dirty.
And don’t get into it unless you can keep yourself motivated, knowing fully well that the odds are more in favor of your not making it.