AI implementation plan in 30, 60 and 90 days

An AI implementation plan in 30, 60, and 90 days should not promise to transform the entire company in one quarter. The realistic goal is to select a case, build the minimum base, operate with real users and reach day 90 with evidence to scale, adjust or stop.

Days 1 to 30: diagnosis and choice

Map processes, volume, time, data, systems, pain and those responsible. Interview those who perform the work and note exceptions. List use cases and compare impact, feasibility and risk. The method is detailed in how to prioritize AI use cases.

Choose a case with an owner, baseline and verifiable result. Define user, AI action, sources, autonomy limit, human review, integrations and stopping criteria. At the end of the first month, deliver architecture, data plan, risk assessment, and metrics — not just a presentation of tools.

Days 31 to 60: Build and Test

Implement the smallest complete flow. Connect a real source, an interface and the system where the result needs to be recorded. Create an evaluation set with normal, hard, and inappropriate cases.

Run in shadow mode or with a small group. Compare the output with the current process. Record latency, cost, accuracy, fixes and failures. Prepare logs, permissions, fallback, support, and documentation. Train users on the task and boundaries, not a list of prompts.

Days 61 to 90: controlled production

Release to a team or demand category. Frequently review samples and incidents. Fix fonts, rules, integration, and experience. Don't try to resolve every error just by changing the prompt.

Measure impact against the baseline. Calculate total cost per unit and track quality. At the end, make an explicit decision: scale, maintain, redesign or shut down. Document conditions and those responsible for the next step.

Day 90 deliverables

The company must have a working process, metrics panel, risk register, responsible parties, operational playbook, continuity plan and prioritized backlog. The NIST framework helps maintain governance, mapping, measurement, and management throughout the cycle.

The success of the quarter is not about “having AI”. It's learning, with real production and controlled risk, where it improves the work. To guide the initial diagnosis, use the AI maturity assessment.