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Build on existing systems of record (NetSuite, Salesforce, SAP, ServiceNow)

2026-07-31 · 17:39 UTC ·@vasuman ·1 min read

Brief

Vasuman outlines three enterprise-AI principles: integrate agents on top of existing systems of record (NetSuite, Salesforce, SAP, ServiceNow) rather than replacing them; create one orchestration layer unifying finance, sales and procurement agents; and automate roughly 90% of repeatable work while keeping humans for the 10% of high-judgment exceptions.

Why it matters

Build on existing systems of record (NetSuite, Salesforce, SAP, ServiceNow): run AI agents inside those tools to eliminate manual handoffs—no migration, no new logins, no retraining.

Key details

  • Provide a single 'pane of glass' orchestration layer so finance, sales, procurement agents share context and avoid 12 disconnected AI tools, extra licenses, and one-off workflows.
  • Let agents handle ~90% of repeatable work while humans handle the ~10% judgment-heavy exceptions (e.g., vendor quirks, custom invoice formats, quarterly exceptions that can cost $200K); the 90/10 split can shift as agents learn from corrections.
Source evidence

There are 3 core philosophies that we believe in when it comes to implementing AI for a large company:

  1. Build on top of the existing systems of record.

Your company spent years and millions of dollars building its stack. NetSuite, Salesforce, SAP, ServiceNow, whatever it is. The problem was never the systems themselves.

The problem is the manual work your people do between them. Agents should run inside your existing tools, not replace them. No migration, no new logins, no retraining your team on a new platform.

  1. Create a single pane of glass that unifies systems, that all agents live on top of.

AI should be the last piece of software that you integrate, not the reason you add 50 more licenses and one-off workflows. One orchestration layer that connects your systems and gives agents a unified view of your operations.

Finance agents, sales agents, procurement agents, all living on the same layer, talking to each other, sharing context. Not 12 disconnected AI tools that each solve one problem and create three new ones.

  1. AI can do a lot, but can't do everything.

Your team knows things that no model ever will. The vendor who always pays late. The client who needs a custom invoice format. The exception that happens once a quarter but costs $200K when someone misses it.

Agents handle the 90% that's repeatable. Your people handle the 10% that requires judgment. Over time, that ratio shifts as agents learn from your team's corrections, but certain humans stay in the loop on what matters.