Founders in computer vision often worry about the cost of required hardware like cameras. For high-value industrial applications, this cost is a commodity. The focus should be on delivering an ROI so compelling that the minor, one-time hardware expense is an afterthought for the customer.
The belief that manufacturers are slow to move is a misconception stemming from their resistance to large, risky "rip and replace" projects. They are quick to scale solutions that demonstrate clear, immediate value in a small-scale pilot, making a land-and-expand sales motion highly effective.
Many industrial tech solutions fail because they are designed as standalone engineering fixes. True success requires embedding the technology into daily operations, like shift meetings and handovers, making it a time-saver for workers rather than an additional analytical burden to drive behavioral change.
In the industrial sector, the most critical signal of success is not initial sales but customer expansion (NRR). A high NRR proves the solution delivers tangible value, prompting clients to roll it out across more production lines and facilities, which is the key to scaling in a fragmented market.
Sirian validated its market by securing five paid pilot agreements from large manufacturers based on its vision and understanding of customer pain points. This approach proved market demand and de-risked the venture before significant engineering investment, a powerful strategy for enterprise-focused founders.
Companies focused on ML before the GenAI boom built robust platforms and workflows around their models. When new, more powerful models emerged, they could integrate them as an upgrade, leveraging their existing battle-tested infrastructure to scale faster than new, AI-native competitors starting from scratch.
While sectors like legal AI receive intense media and investor attention, the global manufacturing market represents a vastly larger, greenfield opportunity at $20 trillion versus legal's $1 trillion. This makes industrial AI one of the most attractive yet underserved problem spaces for founders.