The sweet spot for their transformational AI platform wasn't the largest corporations, which are too rigid to adopt new tech. Instead, it was mid-market companies (100-1,000 employees) that had budget and pain but were agile enough to implement new workflows successfully.
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Spreading efforts across startups, SMBs, and enterprises created confusing signals. A deep dive into metrics revealed enterprises, despite being a smaller revenue portion, showed the highest expansion potential, prompting a decisive focus that unlocked growth.
Most companies are not Vanguard tech firms. Rather than pursuing speculative, high-failure-rate AI projects, small and medium-sized businesses will see a faster and more reliable ROI by using existing AI tools to automate tedious, routine internal processes.
Investor Stacy Brown-Philpot advises that to win large enterprise deals, an AI startup must create a solution so compelling it beats the customer's internal team vying for the same budget. The goal is to access the core 15% budget pool, not the 1% 'play money' budget.
Incumbent companies are slowed by the need to retrofit AI into existing processes and tribal knowledge. AI-native startups, however, can build their entire operational model around agent-based, prompt-driven workflows from day one, creating a structural advantage that is difficult for larger companies to copy.
Stop targeting the ambiguous "mid-market." Your strategy, hiring, and ACV must align with either a marketing-led SMB motion or a sales-led enterprise motion. Blending them leads to failure as they are distinctly different games.
Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.
Small firms can outmaneuver large corporations in the AI era by embracing rapid, low-cost experimentation. While enterprises spend millions on specialized PhDs for single use cases, agile companies constantly test new models, learn from failures, and deploy what works to dominate their market.
StackAI found the bulk of enterprise revenue comes from expansion, not the initial deal. They operationalized this by creating a team of "AI strategists" who work with customers post-sale to proactively identify and build new use cases, driving deep account penetration and growth.
Jumping to enterprise sales too early is a common founder mistake. Start in the mid-market where accounts have fewer demands. This allows you to perfect the product, build referenceable customers, and learn what's truly needed to win larger, more complex deals later on.
Initially building a tool for ML teams, they discovered the true pain point was creating AI-powered workflows for business users. This insight came from observing how first customers struggled with the infrastructure *around* their tool, not the tool itself.