How to Roll Out Enterprise AI Without Losing Control of Your Data

Most enterprise AI rollouts stall in the same place: pilots work, then legal, security, and IT can't sign off on scaling them. The fix isn't another vendor — it's a rollout sequence that puts governance and memory before agents.

This guide walks through the four phases we use with teams standing up AI SNAP Enterprise Platform.

Phase 1 — Decide your model boundary

Before any agent gets built, pick where inference happens. Options in order of control:

• Self-hosted open-source model on your own GPUs or private cloud.
• Dedicated tenancy with a hosting partner under your DPA.
• Shared API with strict data-processing terms (fallback only).

Write the boundary down. Every later decision — which data to ingest, which agents to build — depends on it.

Phase 2 — Build the memory layer first

Agents without memory start cold every time. Before shipping assistants, capture the sources your teams already rely on: internal docs, product screens, research PDFs, meeting notes, and web pages. Pool them into a shared, governed AI web memory with retention rules and access roles from day one.

A good test: can a new hire ask a question and get a cited answer from your memory in their first week? If not, keep ingesting before you build agents.

Phase 3 — Ship narrow agents, not one giant assistant

Build specialists per workflow — legal review, clinical intake, competitive intel, product research — using a no-code Agent Maker so domain owners write the instructions, not engineers. Each agent reads from shared memory but has its own scope, tools, and evaluation set.

Phase 4 — Set policy at every layer

Once agents are live, lock in:

• Data policy — what can be ingested, from where, and for how long.
• Model policy — which models are approved for which sensitivity tiers.
• Access policy — who can read memory, who can edit agents, who can publish.

Governance written after rollout is theater. Written before Phase 3, it's a moat.

Do the phases in this order and enterprise AI stops being a compliance risk and starts compounding.