Build the Program: A 90-Day Implementation Playbook

Start an AI-enabled acquisition program with one complete workflow that the team can operate and measure. Carry it from permitted inputs to a reviewed decision, including failures, costs, and handoffs.

The 90-day sequence below is a planning template. Adapt timing to data readiness, existing systems, and approvals. It does not promise that a full acquisition program or every integration can be delivered in three months.

Choose a first workflow with a clear owner

Target qualification is often a useful starting point because the team can review the result before any external action. Another team may have better evidence and a clearer need in diligence request tracking or approval-pack comparison.

Choose using four questions: is the decision well defined, are inputs available, can qualified people judge the answer, and can the team contain errors? Avoid beginning with a workflow whose output cannot be checked until years after a deal closes.

Set the baseline before automation. Measure how long the current process takes, how much review it needs, and which mistakes matter. Use the same acceptance definition when comparing the new workflow.

Days 1–30: define and connect the work

The program sponsor approves one thesis and target rubric. The deal team selects representative cases, including ambiguous and rejected targets. Finance and the integration leader define the constraints that screening must respect.

Engineering maps the existing systems of record. Establish company identities, source permissions, provenance, and versioned outputs. Agree the tool permissions and the review path.

At the end of this stage, a reviewer should be able to inspect a complete sample result, follow its citations, and explain every decision. Missing evidence should remain visible.

Days 31–60: run beside the existing process

Run the workflow in shadow mode: it produces recommendations while the team continues its current decisions. Compare results without letting automation silently change live records.

Test sparse evidence, conflicting sources, incorrect entities, partial extraction, provider failure, duplicate events, and revoked access. Measure reviewer effort and total cost per accepted output.

Resolve the most important failure causes and rerun the held-out evaluation. Have the business owner and engineering owner sign off on different questions: whether the output supports the decision, and whether the workflow behaves as designed.

Days 61–90: release with explicit boundaries

Introduce the approved workflow to a defined team and scope. Provide an owner for exceptions and a way to stop the workflow. Monitor outcomes and review a sample of accepted and rejected results.

Expand to the next connected step only when the first handoff works. For example, a qualified target can produce a draft diligence brief using the same thesis and source records. It should not become an approved investment simply because the earlier stage passed.

This readiness table defines outcomes, rather than a calendar-based declaration of success.

Buy common infrastructure; build your decision logic

Reuse established document storage, identity, workflow, search, and model services when they meet the requirements. Build the acquisition-specific pieces: thesis criteria, evidence relationships, approval contracts, evaluation cases, and links to integration outcomes.

Evaluate vendors with your own cases. Ask for a source-to-decision walkthrough, a permission-change test, and an export of the decision history. Check data terms, retention, portability, and the operational cost of leaving the service with the relevant owners.

Return to the complete guide, or develop the detailed AI architecture and evaluation plan.