Sizing the emerging AI market for Series B

An accurate projection of market growth and size was critical in unlocking funding to scale. But how do you size something brand new, in a volatile space?

The challenge Our ability to scale hinged on securing Series B investment and a well-informed growth strategy; for that we needed robust market sizing and growth projections. But how do you size something that is still in creation; an emerging technology in a volatile market with very little track record? It wasn't going to be with a cookie-cutter sizing model, that's for sure.
Insights & mind shifts 🔮 No corporate fortune-telling, thanks. Market projections from credible research companies varied wildly, with little explanation of rationale. That ruled out a top-down approach, and unfortunately - the easier option. No surprise there.
🔎 A standard TAM won't give us enough! After talking both to previous investors and the Growth and Operations teams, I realised that even if developed a solid sizing, they didn't have enough context to make meaning from it. This exercise wasn't just about arriving at a figure - it was about giving real people enough information to make good decisions.
🔦 Approach it like a speculative futures project? In the practice of futures, we identify stable drivers and behviour patterns that help us understand what happens in future contexts. Where possible using proven patterns and behavioural economics Identified the key-dependent factors influencing both demand and supply; alongside cultural analysis of technology adoption. Interviewed investors and researched previous investment decisions to understand decision factors.
Results ✅ The funding round came in at over US$14 million.
💬 Feedback from investors was that the wider-lens approach allowed them to relate their current experience to a new scenario.
🌎👀 Meanwhile, the internal Growth and Operations teams could set a geographic expansion strategy, whilst being aware of some critical dependencies to keep an eye on, mitigating future risk.

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