AI moats shift to workflows—adviser urges dependence over features

Tech adviser Itay Sagie argues that the next durable moats in AI will be built inside customer workflows, not on model performance or feature sets. Writing for Crunchbase, Sagie advises founders to prioritize measurable dependence—how deeply and frequently users rely on an AI tool—while investors and acquirers should evaluate integrations, trusted relationships, and access to workflows as key drivers of retention and growth.

The framing arrives as startups like Kanu AI, Siena, and Kore.ai raise capital to automate or optimize enterprise routines. Kanu’s $11.7 million round targets converting undocumented employee workflows into custom software, while Siena’s $17 million Series A funds autonomous agents handling customer interactions. Kore.ai’s Autoloop, launched last week, continuously tunes live AI agents to meet performance targets, suggesting workflow integration is becoming a product category of its own.

Sagie’s emphasis on dependence over differentiation echoes recent investor sentiment. Leo AI’s CEO Maor Farid recently noted that unit economics now trump growth metrics in AI funding, implying that stickiness—how embedded a tool is in daily operations—could become the new benchmark for valuation. The shift aligns with AMD’s reported move to run AI agents locally, signaling workflow optimization may also shape hardware and platform strategies.

Sources: news.crunchbase.com