Most people think building AI for enterprise is just building AI but bigger. It’s not.
It’s harder. Much harder. Here’s why:
1/ Data complexity.Our agents don’t just pull from one or two sources. They have to ingest, reconcile, and act on data from multiple systems - many of them legacy, many with quirks you only discover deep in deployment.
2/ Workflow integration.In consumer AI, you can create a neat standalone app. In enterprise, agents have to live inside existing workflows, often ones that have evolved over decades. Break the workflow, you break the business.
3/ Domain depth.A consumer app can get away with being “mostly right”. In regulated financial services, our agents have to understand the nuance of credit policy, lending criteria, and regulatory constraints and get it right every time.
4/ Accuracy and security aren’t optional.If you’re serving banks, “good enough” accuracy isn’t good enough. Nor is anything less than enterprise-grade data security. These are table stakes.
5/ Buying cycles are strategic, not impulsive.In consumer AI, someone can swipe a card and try you for a month. In enterprise, contracts can run for years. You’re not just selling the value today, you’re asking the client to bet on your ability to execute your vision three years from now.
That’s why building AI for enterprise isn’t about chasing trends.
It’s about earning trust, proving value today, and de-risking the future.
