The First 100 Days of AI Work After an Acquisition

The first 100 days after an acquisition give a sponsor and management team a short window to turn the investment thesis into operating priorities. AI belongs in that plan when it can move a specific revenue, margin, cash, service, or risk measure. Start with the company's work and data, then fund the small number of tests that can change a decision.

Days 1–20: link work to the deal thesis

Start with the value-creation plan, the operating baseline, and the people who run the process. If the thesis depends on faster order fulfilment, map order intake, exceptions, approvals, and rework. If it depends on cash conversion, inspect collections and invoice disputes. Measure volume, time, error rates, and the cost of each step before anyone claims a saving.

Bain's post-acquisition guidance calls for an actionable value-creation plan agreed by management and sponsor. Use that shared plan to rank AI opportunities. A task can be easy to automate and still have little effect on the deal thesis.

Days 21–45: check data and ownership

For the top candidate workflows, name a business owner and a technical owner. Find the source system for each required record, who can grant access, how current the data is, and which fields staff correct by hand. Record the exceptions that the formal process map misses. Give the pilot only the permissions it needs.

Decide which actions need a person to approve them. A draft reply, a proposed purchase order change, and a payment instruction carry different consequences. NIST's AI Risk Management Framework asks organisations to map context, measure outcomes, assign responsibility, and manage risks throughout the system's life. Put those decisions into the workflow design before launch.

Days 46–75: test two workflows

Choose two bounded tests with different value levers. A service team might use AI to draft answers from approved policy documents; a finance team might use it to sort invoice exceptions for review. Keep a human decision at the point where a wrong action could affect a customer or move money.

Build a test set from real cases, including difficult and rare cases. Record the old process's time and quality, then compare the pilot on completed tasks. Count review and correction time, software cost, and the number of tasks that still need manual work. OpenAI's evaluation primer recommends testing a workflow under realistic conditions and reviewing errors with subject-matter experts. A shorter draft time means little if the reviewer spends those minutes repairing it.

Days 76–100: decide what scales

Give each pilot a clear decision: expand, redesign, or stop. Expand only when the result meets a stated quality bar and improves the business measure after the full cost of review and operation. Redesign when the underlying data or process creates the errors. Stop when the benefit is too small or the risk cannot be controlled at a reasonable cost.

For a pilot that earns wider use, fund the integrations, monitoring, support, staff training, and named owner it needs. Check that workers know when to rely on the system and how to flag a wrong result. BCG's 2026 private equity survey links early digital work after acquisition to later AI deployment; a shared system foundation should follow a proven use case rather than lead it.

What the board needs to see

1. Value: The deal-model lever, baseline, pilot result, and the assumption behind a larger rollout.
2. Quality: The cases tested, failure severity, human review load, and the threshold for pausing the workflow.
3. Ownership: The executive accountable for the outcome, the team maintaining the system, and the person who can stop it.
4. Capital: The cost of data work, integration, licences, monitoring, and training before expansion.

Day 100 should end with a funding decision and an owner for each next step. A board can then compare AI work with every other use of capital in the value-creation plan.