Enterprise AI functions more like middleware than SaaS, integrating with entrenched ERP systems rather than inserting simple APIs. The lengthy procurement process and the intricacy of customizations slow product development. Small deals do not provide sufficient financial incentives relative to the workload they require. Large systems integrators like Accenture and Deloitte often overshadow startup contributions. Enterprises prioritize stability, seeking reliable outcomes over cutting-edge features. Companies such as Palantir illustrate that enterprises seek practical, outcome-driven solutions rather than raw AI technology itself.
Enterprise AI sells like middleware, not SaaS. You're not dropping an API into Slack; you're rewiring 20-year-old ERP systems. Procurement cycles are long and bespoke scoping kills product velocity.
Small deals are just as much work as larger deals, but are just way less lucrative. By the time Accenture or Deloitte finishes the rollout, your startup's software is a rounding error on the services bill.
Maintenance is greater than innovation. Enterprises don't want models that drift; they want uptime, and AI's non-deterministic 'feature' is very much a bug for the enterprise.
Enterprises don't buy AI platforms; they buy outcomes. Palantir succeeds when it shows an oil company how to shave days off planning the site for a new well.
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