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According to research from IDC, the global spending on AI (Artificial Intelligence) is expected to hit $97.9 billion by 2023, up from $37.5 billion in 2019. This represents a compound annual growth rate of 28.4%.
No doubt, this is an enormous opportunity for startups. “These days, almost every company needs to leverage AI in order to thrive and build a meaningful future,” said Saar Yoskovitz, who is the CEO of Augury. “This is true for younger startups, and it is true for the largest companies, even in the most traditional and nascent industries like manufacturing and insurance. In a sense, AI has become another layer in the tech stack, like databases, and not a business model.”
All this is true. But then again, finding the right product-market fit is extremely challenging. Consider that AI models generally require a lot of customization. In fact, there is often much variation even for companies in the same industry.
And yes, there must be a high-quality dataset. But of course, this is far from easy for a startup to develop.
“Data creates an interesting chicken-and-egg problem,” said Yoskovitz. “Without a customer, you don’t have data, which means you cannot train your algorithms. Without algorithms, you are not able to provide value to your customers and compete in the market. Therefore you will not have customers to provide data.”
Recommended For YouWhat to do? You can look at forming partnerships, such as with companies that may not have strong AI capabilities. Or another approach is to create a free app that collects data.
“What we have found is that to build a successful AI startup, the key is sourcing and building proprietary data,” said Saniya, who is the CEO and co-founder of Pilota. “This is what makes your business defensible and attractive to investors. Almost every investor has asked us where our data comes from and what would stop others who can access this data from replicating what we do. So when creating a business centered around AI, it is extremely important to make sure that your data is proprietary and you are not just building a business on analyzing public datasets.”
But even with a good dataset, this is still not enough. For example, does the AI model make a real difference? Or are the results mostly minor improvements?
“Investors are interested in startups that are building tailored AI solutions for previously unsolvable problems,” said Jay Srinivasan, who is the CEO and co-founder of askSpoke. “So focus on areas where there are many inefficiencies and repetitive human processes, such as call centers and back-office paperwork processing. Investors want successful AI solutions that address specific workflows and problems, such as a legal document review.”
Then there is the issue of adoption. Let’s face it, customers are still leery of the powers of AI. It does not help that the models are often complex and opaque. There are also the nagging problems with bias.
“Know your customer,” said Vasiliy Buharin, who is the Associate Director of Product Innovation at Activ Surgical. “You may have the algorithm to solve the worst LA traffic or shorten airplane boarding time 10-fold. But if the solution requires people to behave like preprogrammed automatons, it will never be adopted, and your company will fail. Your customer has a certain way of doing things. Your product must fit this workflow.”
Given all the challenges, a startup will likely undergo multiple pivots. This is why you need a top-notch team that works well together.
“If you start with a problem and leave yourself open to many different solutions, you will learn from the market and find the best solution to build your business around,” said Sean Byrnes, who is the CEO of Outlier. “Spending six months exploring and selecting the right problem can save you six years of wasted effort trying to build a business around a flawed idea.”
Tom (@ttaulli) is an advisor to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems. He also has developed various online courses, such as for the COBOL and Python programming languages.