Most companies assume AI projects need long timelines and big budgets. That used to be true. Today, with careful scoping, off-the-shelf models, and simple governance, teams can show measurable impact inside one quarter. The trick is to pick narrow, high-volume tasks, measure from day one, and protect the business with a light but real risk framework. Evidence from real pilots proves this is possible. Klarna’s assistant handled two thirds of customer chats in its first month and reduced average resolution time from 11 minutes to under 2 minutes.
Start with three rules. First, tie each pilot to one clear KPI and a baseline on day one. Second, prefer augmentation over full automation so people remain in the loop. Third, set minimum governance so you can iterate without creating new risks. NIST’s AI Risk Management Framework gives practical guidance for fast, safe pilots, and ISO 42001 helps you formalize an AI management posture as you scale.
Here is the 90-day playbook I recommend.
Days 1 to 15: align, guardrail, and shortlist use cases
Week one is about choices, not construction. Gather a small AI working group: a business sponsor, a security lead, a data steward, and a product owner per pilot. Run short discovery sessions to find tasks that are high volume, repetitive, and measurable. Good candidates are customer service replies, knowledge retrieval, meeting summarization, and developer boilerplate.
Document one KPI per pilot and capture a one-week baseline. For example, measure average handle time and repeat contact rate for a support pilot, or task completion time for a developer pilot. Use the NIST playbook actions to define human review points and basic logging so you can move fast without flying blind.
Deliverables for the phase. One-page pilot charters, a tiny risk register, and a decision to run two pilots and keep one on the bench.
Days 16 to 30: build small, ship early, measure daily
Ship lightweight pilots quickly. For support, start with an assisted reply model that drafts responses for agents. For productivity, enable a document or meeting copilot for a named cohort. For engineering, give one team access to a pair-programming assistant and measure task times.
Instrument everything. Track number of assisted tasks, acceptance rate of AI suggestions, time to completion, and human escalation. Those metrics let you know if a pilot is improving the KPI or just creating noise. Real trials show measurable gains when adoption is supported with brief training and clear scope. GitHub and controlled research found that AI pair programmers can speed simple tasks by about 55 percent, a useful benchmark to set expectations for developer pilots.
Deliverables for the phase. Working pilots in users’ hands, a daily KPI dashboard, and a short enablement plan.
Days 31 to 60: tighten the loop and integrate with workflow
Now focus on quality and trust. Add retrieval-augmented generation and limit the knowledge sources to approved repositories and resolved tickets. Require grounding citations where accuracy matters, and add human review thresholds for risky actions.
Where safe, integrate read capabilities with ticketing or CRM so agents can update records faster. Run weekly clinics that teach prompt hygiene and show examples of good and bad outputs. At this stage, you should start seeing directionally positive time savings. The UK government’s Microsoft 365 Copilot trial reported an average saving of 26 minutes per day for participants, mostly on drafting and summarization tasks, which is a sign of what focused workplace pilots can deliver.
Deliverables for the phase. An updated KPI dashboard with four to six weeks of telemetry, a risk mitigation backlog, and an interim value brief that maps hours saved to rough cost ranges.
Days 61 to 90: prove value, scale what works, stop the rest
Use the final 30 days to validate success criteria. Graduate the winning pilot into production, assign an owner, and build monitoring and audit logging. Keep a control group for a short A/B comparison and expand the cohort two to three times for the graduates.
Present a crisp value brief to leadership. Include three lines: hours saved, quality impact, and current risk posture. Anchor claims to logged data, not vendor marketing. McKinsey and other surveys show widespread deployment of generative AI, but many companies report limited earnings impact when pilots lack measurement and disciplined scaling. Use your telemetry to avoid that trap.
Deliverables for the phase. One production AI service with owner and budget, and a two-page QBR-ready value story.
Quick starting pilots that tend to win
- Customer support copilot for the top 50 intents.
- Productivity copilot for drafting, summarization, and meeting notes in a named cohort.
- Developer copilot for a single squad with strict measurement. Real world evidence shows these are repeatable quick wins.
Risks and how to handle them in-line
Hallucination and accuracy. Use retrieval from authoritative sources, include citations, and require human review for high-risk outputs. Shadow AI and data leakage. Offer approved tools and clear policies early. Governance is not a blocker. It is insurance that lets you scale with confidence.
Final note
Narrow scope, daily measurement, and a small governance loop win more often than long, unfocused projects. If you follow this 90-day plan, you will get real data, a clear value story, and a pathway to scale. The evidence from pilots and government trials shows the time to impact is measured in weeks, not years.
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90-Day Roadmap at a Glance
Days 1–15 checklist
- Form AI working group: business sponsor, security lead, data steward, product owner.
- Run 2-hour discovery sessions per function.
- Pick 3 candidate tasks, capture one-week baseline for each KPI.
- Create two pilot charters.
- Publish minimal governance: roles, logging, human review points.
- Approve tools and data access plan.
Days 16–30 checklist
- Ship pilot 1 and pilot 2 to named cohorts.
- Instrument metrics: assisted tasks, acceptance rate, time to completion, escalations.
- Run 30-minute enablement clinics twice per week.
- Create prompt hygiene and escalation policies.
- Collect daily qualitative feedback from users.
Days 31–60 checklist
- Add retrieval-augmented grounding for knowledge sources.
- Integrate with ticketing or CRM for read or limited write.
- Run weekly quality reviews and track hallucination incidents.
- Produce interim value brief translating hours saved into cost ranges.
- Update risk register and mitigation owners.
Days 61–90 checklist
- Graduate winning pilot to production with owner and budget.
- Expand cohort two to three times and keep control group.
- Deliver QBR-ready two-page value brief.
- Decide scale or retire pilots.
- Publish lightweight AI policy aligned to NIST and ISO.
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