My angel investment in Botkeeper has been one of the most influential in my thinking on how AI strategy is evolving. When new high impact technologies come along, they often shake up status quo business models and because no one understands what business models might emerge on the other side, it’s a wonderful time to make a few bets on some startups. As an investor, these initial bets help me learn how the space is evolving, which means when the real wave of startups comes that are embracing this new tech, I’m much more educated than most people who sat out the initial round. And on top of that, sometimes you get lucky on the early bets too.
An example that I give some times during talks is how the web changed graphic design. Pre-Internet, most companies had an established graphic design process to crank out brochures and flyers and such. When the Internet first arrived, they used that same process. The designer created a graphic of a web page, which was then turned over to a developer to turn into HTML. But the Internet isn’t paper, it has hyperlinks and dynamic text and so you can’t just tack on Internet to a paper-centric graphic design process and get a good outcome. Yet companies do that today with AI. They take their existing workflow and try to tack some AI on at one step and thing that’s how it works.
Botkeeper is one of the first companies to really incorporate machine learning in a more economically viable business model. The CEO is a phenomenal entrepreneur who realized before almost everyone else that there were many work tasks you could automate if you had an example data set to do it. At inception, it had a bunch of accountants who did all the tasks, and most investors I spoke to who looked at the company passed because, in their words “it’s just services.”
What they missed was that, by watching what the accountants did, Botkeeper automated away more and more of the tasks. Gross margins moved from services gross margins to software gross margins, and they grew revenue without adding operational headcount because of increased automation efficiency from machine learning.
The services-as-software business model has a lot of interesting characteristics and some of them may surprise you. I expect this to be a type of business model that grows quite a bit in this decade, as machine learning systems make it possible to tackle types of task automation that weren’t possible before.
The defining characteristic of a services-as-software business model is that you take a task previously done by humans and keep the interface the same, but strip out everything behind it and make it an algorithm (if the workflow is digital) or robotic (if the workflow is physical). Most robotic companies that are going to follow this model today are actually software and model training companies using off the shelf robotic parts so, if the business use robots but resembles a software business economically, I still consider it to fit into this model.
When people first hear about the services-as-software business model, they miss a lot of nuance. They typically say “oh so it’s about cost savings via automation.” No. What makes it such a powerful business model is that historically, companies had to choose between quality and price when building their business models. If you want better performance at some step, you have to spend more on that step. Services-as-software business models have the rare ability to improve quality while lowering costs.
Here are some properties of the services-as-software business model:
Consistency – Right now, your experience of a haircut, for example, varies place to place because it is done by humans. Having a robot do it means that it could be done exactly the same way in NYC and in Tokyo if the robots/algorithms are connected to each other. Consistency is enabled by the network that software can build that humans can’t. Contrast that with something like CafeX, where you can get the exact same cup of coffee made the exact same way even if you are on the other side of the world at a CafeX machine.
Accuracy – For many tasks currently done by humans, the human can only absorb so many examples and anomalies in their work lifetime. A machine can absorb many times more. So Botkeeper, for example, seeing the expense classification of so many more companies than a human bookkeeper ever could, has a 2% error rate versus an 11% error rate for humans.
Personalization – A machine providing a service can know and learn more about you than a human can, in most cases, meaning a much more personalized service.
TAM Expansion and Abundance – By lowering the cost of a service using machines, we make it available to more people, thus improving quality of life and expanding the market opportunity simultaneously.
These characteristics imply that services-as-software businesses drive both cost savings and performance improvements simultaneously, which is unusual.
Here are a few possibly non-obvious points about services-as-software.
- Companies miss the opportunity because the change in workflow usually filters up and down the value chain more than they anticipate, and requires them to change other steps in the process, which they don’t want to do. It isn’t as simple as “insert software at step 3 in a 7 step process.” It usually requires you to revamp all steps so that step 3, a machine learning step, has the biggest impact. It requires employees at other steps to label data, evaluate model outputs, and correct the system.
- In general, I believe you want to leave the initial interface to the consumer the same, and strip out everything behind that for automation, but there are exceptions. CafeX, for example, is different than having a Starbucks that is mostly robots but 1 cashier and 1 person filling the bins the robots use to make and serve coffee. The choice of whether or not to keep the existing services interface in tact will have other business model implications. For example, CafeXs can be put in more places, at smaller price points, than automated Starbucks can if the rest of the Starbucks store remains the same.
- Customer acquisition in services industry is often based on geo, and could lead to expensive switching costs. It may be more cost effective to buy a business than acquire a customer. Going back to our haircut example… say a robot does a haircut for $20, but acquiring a consumer who has a regular hair stylist costs $60. You could probably buy a hair salon for not much more than 1x revenue, possibly less, an acquire the customers that way, instead of 3x revenue it takes to steal them away.
- Service-as-software businesses may require full stack business models. For example, Squirrel AI, the educational software startup in China, opened learning centers instead of selling their software into existing learning centers. It’s probably because of point #1 above – the existing learning centers wouldn’t adapt their other workflow steps to make best use of AI automation.
The trick here for an entrepreneur is to figure out which spaces are ripe for services-as-software and which aren’t. My guess is haircuts are not a good one, but accounting is. I think you have to look for situations where the value provided is not at the interface level of the service, and where there is maximum complexity behind the interface that could benefit from automation. You also have to maximize the opportunities for personalization and consistency of service because relying solely on cost cutting will be of limited market value compared to true services-as-software models.
I’ve been thinking about this services-as-software model for a few years now, and have invested in several companies pursuing it, some of which I can’t yet mention. I finally had enough clarity to write about it, but it is still being proven out by the market so, feel free to challenge these ideas or iterate on them – I’m sure there are many key insights I am still missing. And of course if you are building a services-as-software company and are looking for investment, I hope you will reach out.