Strategy
When to build custom AI—and when not to
Custom work fits when the constraint is how your company runs—not when a mature SaaS category already solves it with configuration. A practical discovery frame: evidence, phased delivery, honest “buy” when that is right.
Signals toward custom
Deep integration across systems you will not replace soon—ticketing, identity, ERP fragments, proprietary queues. Shelf tools hit walls when routing, roles, and audit needs do not match reality.
Differentiation is in how work gets done: approvals, risk, client-specific policy. If the workflow is your ops product, a thin wrapper on a generic model rarely lasts.
You can name success metrics—throughput, errors, cycle time, compliance—and will instrument them. Custom without measurement tends toward shelfware.
Signals to buy or configure first
Mature category, happy-path fit, risk posture allows vendor-hosted data—often faster when you need outcomes this quarter, not a platform program.
Bottleneck is policy or ownership, not software. Unclear approvers and SLAs are not fixed by a tool.
No named business-side product owner. Internal tools need operators who prioritize against live incidents—IT alone is not enough.
Phasing either way
Even when custom is justified, phase one stays narrow: one workflow, one audience, clear guardrails. Prove value with real volume before org-wide assistants.
Unsure? A workflow audit can separate “buy with integration help” from “build.” Both are valid; funding a big build before the workflow is understood is not.
Discuss this for your team
If these patterns match what you are seeing internally, we can help prioritize workflows and a first delivery slice.
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