Not everyone has to have gone to Harvard Business School to identify a killer use case
Teach yourself some basic top-down market analysis and layer on some common sense
The size of the market will drive who you should be looking to raise from
For all those of you who can identify with this… Forward Partners are here to help. We’re investors in Applied AI businesses. Taking technical know-how and applying it to a big use-case can seem like a daunting task. Hopefully this article can provide you with a 5 minute MBA, at least in regard to finding a market for your work. We’re going to do a ‘top-down’ investigation of a potential use-case for computer vision and, at a later stage, machine learning.
If you’ve been building, for example, technology or a set of algorithms - you’ve possibly been ‘going from the bottom up’. You’ve been applying your set of skills to a problem that you personally know a lot about. That may, or may not, be applicable for a large amount of people or a big market: the use-case. We’ll come to the importance of market size later.
Finding and validating that killer use-case will probably take some top down thinking. The best place to start is to identify industries and verticals where there are big problems yet to be solved by technology in any real way. That sounds a bit abstract but it’s fairly easy to interpolate. A good assumption as to the degree of tech substitution or advancement in a consumer market is the rate of inflation in a given category.
You can see, from this graph, that education and care are areas that are ripe for technology to come in, solve some problems and release some value. Given that 1-on-1 or in-person education is assumed to be the best way to learn for the time being, and thus hard to substitute technology into that equation meaningfully, let’s take healthcare forward.
Now it’s time to do a little bit of common sense validation. What are some possible macro-trends driving increasingly expensive health care? We all know that we are living longer and therefore there are more elderly people. Our environment is also changing, contributing to a wider range of potential health problems that we may suffer from. Knowing this, it’s a decent assumption to think that the price rises in healthcare have been driven by job creation and an increase in manual tasks. I just typed in “rise in number of healthcare workers” into Google and this next barchart is the 4th image result.
This is a really interesting result. We’re getting somewhere with identifying a killer use case: we’ve got a massive market and price rises likely being driven by increased employment in relatively-low skilled jobs. This is an almost perfect use-case for software.
That’s where you come in. If you’re an technical expert, you’ll know best about what is a tractable problem that you could help to solve. Talking to a nurse or medical assistant or two should reveal a couple of insights about what they spend large swathes of time on. I’d guess that you could drive huge efficiencies by helping to solve for the amount of paperwork that has to be done e.g. using computer vision to transcribe physical, handwritten records to digital. That’s no easy task. Nor is deriving insight from the data that you’ll end up with. Though these are the kind of tough problems in markets ripe for disruption that talented founders go after and that VCs love to back.
One important thing to know, regardless of what you’re working on, is that if you’d like to attract institutional funding you’re going to need to go after a big market. At Forward Partners, we need to be able to be convinced that every investment *could* return our fund. We have a £60m fund so that means that if we were to own 10% of your business we’d need to see your business have an exit value of £600m. There aren’t that many markets where that’s achievable, so that should help to narrow it down. The healthcare markets are massive and so the value that can be released by streamlining processes and improving outcomes is often well above the minimum market size bar. If you land on a slightly more niche area, this is something to bear in mind.
The final point is that, much like we’re not expecting the classic MBA-style founder to possess in-depth technical knowledge about computer vision, we’re not expecting founders with highly specific skill sets to come in and hit us with a pitch deck and business plan a la Harvard. There’s a minimum bar though, and hopefully we’ve been able to demonstrate that it’s pretty easy to overcome