AI Adoption: Observations from traditional industries
Our business model is unique. We build ventures in partnerships with midmarket companies in regulated and asset-heavy industries. But these ventures don't start as ventures on Day 1. They usually start as co-build/co-sell motions or consulting projects that we start for them. Occasionally our pre-built IPs come into play. (Related read: Buying Vs. Becoming AI)
Through all these motions, we meet executives from a wide range of industries. In the last year, we have spent time with executives from biotech, paper, finance, HVAC, agri, healthcare, municipal tax, energy, and a few other industries. This gives us a rich perspective on how AI diffuses in each domain. (Related read: Building on Boring Strength)
The short of it: Each industry moves at its own pace, for several reasons.
The finance function is being automated at a speed second only to software engineering. Counterintuitive as it may sound, accounting for instance, is a set of rules with a predictable set of exceptions. The complexity is human-level but not so complicated that AI will fail. I anticipate that within the next 12 months several accounting functions will be automated by at least 70%. Almost no one writes code in startups these days. They simply watch AI write code. Posting transactions and closing books will follow this pattern. We anticipate that foundational models will include these capabilities out of the box.
Let's take two asset-heavy industries: HVAC and Agri.
I was at a recent HVAC conference with over 50,000 professionals. It was chilling to see just 50 (out of 1000) booths occupied by tech. The biggest tech vendor booths belonged to aggregators and marketplaces. So, not really tech. HVAC technology isn't software; it involves heat pumps and similar equipment. That's what I understood. The loudest announcement about AI compared purchase orders (POs) with invoices—a problem the insurance industry solved 2 years ago. AI may be magical, but diffusion absorbs the shock and propagates the innovation over the years.
Agriculture is a different beast. The product is a living organism. Living things are complex and complicated. There is already significant innovation in this area, including activating lights of specific wavelengths, targeted nutrient delivery, and moisture control. However, none of these systems communicate with each other or recommend holistic interventions.
Moative has built interesting intellectual property in this area (‘State-Space Models') that understands how underlying systems of confounding variables behave, by observing other visible variables. It's applicable in Agri but diffusion plays a role. An AI engine that recommends holistically is also a weapon that can destroy the entire system if its not understood well. In agriculture, the failure of one crop batch can lead to the failure of the entire business.
Moative's business model is malleable to the effects of diffusion. Is everyone rushing to do AI in an industry? Consulting might be a good idea but launching a startup is not. If an industry is two years behind, now is the right time to invest in data infrastructure and engineering to harness the flow of data and context. That's what we would do. Is an industry technically ready, but held back by past scars from moving forward? We will seek a visionary company that thinks from first principles and experiments with us. Potentially this would mean a co-build motion. (Related read: Don’t Sell to Private Equity)
If you are thinking of where to start with AI for your business, the right first step is to understand the state of tech, how it is diffusing in your industry, and assess whether accelerating that diffusion provides a competitive advantage. Measure the risks you are taking and choose a partner willing to partake in that endeavor, through models that proportionately distribute the risks and rewards. (Related read: Don’t Sell to Private Equity)
If you have questions, write to me. I am on ash@moative.com
Enjoy your Sunday!
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