AI Implementation Roadmap: Your 90-Day Guide from Strategy to Results

You've heard the AI hype. You've seen competitors adopt it. You know you need to act. But where do you actually start?

This is the practical roadmap we use with clients — a proven 90-day framework that takes you from "we should probably do something with AI" to measurable results.

The 90-Day Framework Overview

Phase	Days	Focus
Discovery	1-15	Audit, identify opportunities, build business case
Foundation	16-45	Select solutions, prepare infrastructure, train teams
Implementation	46-75	Deploy pilot, iterate, measure
Scale	76-90	Expand successful pilots, plan phase two

Let's break down each phase.

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Phase 1: Discovery (Days 1-15)

Goal: Understand your current state and identify high-impact AI opportunities.

Week 1: Process Audit

Before implementing any AI, you need clarity on what you're optimising. Map your core processes:

Questions to answer:

• Where do employees spend time on repetitive tasks?
• What processes involve handling unstructured data (emails, documents, images)?
• Where are bottlenecks and delays?
• What decisions require searching multiple systems?

Output: A prioritised list of 10-15 processes ranked by:

• Time spent (hours/week)
• Error rate
• Strategic importance
• Customer impact

Week 2: Opportunity Assessment

For each process, assess AI feasibility:

Factor	Score 1-5	Weight
Data availability	—	25%
Process standardisation	—	20%
ROI potential	—	25%
Technical complexity	—	15%
Change management risk	—	15%

Target: Identify 3-5 high-scoring opportunities for your pilot.

Deliverables by Day 15:

• Process audit complete
• Opportunity scoring matrix
• Business case for top 3 opportunities
• Executive briefing document
• Go/no-go decision for Phase 2

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Phase 2: Foundation (Days 16-45)

Goal: Prepare your organisation technically and culturally for AI deployment.

Weeks 3-4: Solution Selection

Based on your opportunities, evaluate solutions across categories:

Off-the-shelf SaaS:

• Microsoft Copilot (Office integration)
• Claude for Work / ChatGPT Enterprise (general assistance)
• Industry-specific tools (legal, healthcare, finance)

No-code platforms:

• Zapier AI / Make (workflow automation)
• Notion AI / Coda AI (knowledge management)

Custom development:

• When no existing solution fits
• When competitive advantage requires proprietary capability
• When data privacy demands on-premises deployment

Selection criteria:

• Integration with existing systems
• Data security and compliance
• Total cost of ownership (not just license fees)
• Vendor stability and roadmap
• Implementation support available

Weeks 4-5: Infrastructure Preparation

Technical readiness:

• API access and authentication set up
• Data connections configured
• Security policies updated for AI tools
• Logging and monitoring in place

Organisational readiness:

• Pilot team identified (5-10 people)
• Roles and responsibilities defined
• Success metrics agreed
• Training materials prepared

Week 6: Team Enablement

Your pilot team needs:

1. Tool training — How to use the specific AI tools
2. Prompt literacy — How to get good outputs from AI
3. Critical thinking — How to verify AI outputs
4. Feedback loops — How to report issues and improvements

Pro tip: Make training hands-on. Lecture-style AI training doesn't work. Give people real tasks to complete with AI assistance.

Deliverables by Day 45:

• Solution selected and contracted
• Technical infrastructure ready
• Pilot team trained
• Success metrics defined
• Baseline measurements taken

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Phase 3: Implementation (Days 46-75)

Goal: Deploy your pilot, learn rapidly, and iterate.

Weeks 7-8: Controlled Pilot

Start narrow and focused:

• One process (not three)
• One team (not company-wide)
• One metric (time saved, errors reduced, etc.)

Daily check-ins during week 1 of pilot:

• What's working?
• What's frustrating?
• What's surprising?

Weekly reviews thereafter:

• Quantitative: Are we moving the target metric?
• Qualitative: What's the user experience?
• Technical: Any stability or integration issues?

Weeks 9-10: Iteration Cycles

Based on feedback, improve rapidly:

Common adjustments:

• Prompt refinement for better outputs
• Workflow tweaks to reduce friction
• Integration improvements
• Additional training for edge cases

Document everything:

• What works → Standard operating procedures
• What doesn't → Lessons learned database
• Edge cases → Exception handling guidelines

Week 11: Results Assessment

Before scaling, validate your results:

Quantitative measures:

• Time saved per task
• Error rate change
• Throughput increase
• Cost per transaction

Qualitative measures:

• User satisfaction (NPS or survey)
• Confidence in AI outputs
• Willingness to expand use

Minimum threshold for scaling: 20% improvement in target metric AND positive user sentiment.

Deliverables by Day 75:

• Pilot results documented
• ROI calculated
• User feedback collected
• Scale/no-scale decision made
• Lessons learned captured

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Phase 4: Scale (Days 76-90)

Goal: Expand successful pilots and plan your next phase.

Week 12: Controlled Expansion

If pilot succeeded, expand thoughtfully:

Horizontal expansion: Same process, more teams

• Roll out to adjacent teams
• Train new users (use pilot team as champions)
• Monitor for different use patterns

Vertical expansion: Same team, more processes

• Add related workflows
• Build on existing competence
• Increase AI sophistication gradually

Week 13: Governance & Standards

As AI use expands, formalise governance:

Policies needed:

• Acceptable use guidelines
• Data handling with AI tools
• Human oversight requirements
• Escalation procedures

Standards to establish:

• Prompt libraries for common tasks
• Quality assurance checklists
• Performance benchmarks

Planning Phase Two

Use your learnings to plan the next 90 days:

• Which additional processes to tackle?
• What capabilities to add?
• Where to invest in training?
• What technology upgrades needed?

Deliverables by Day 90:

• Expanded deployment live
• Governance framework in place
• Phase 2 roadmap drafted
• Success story documented
• Executive report delivered

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Common Pitfalls to Avoid

1. Starting Too Big

Wrong: "Let's implement AI across all customer touchpoints."

Right: "Let's automate response drafting for the support team's top 5 query types."

2. Skipping the Baseline

You can't prove ROI without knowing where you started. Measure before you implement.

3. Underinvesting in Training

Technology is 30% of AI success. People are 70%. Budget accordingly.

4. Expecting Perfection

AI tools make mistakes. Plan for human oversight, especially initially. The goal is "better than before," not "perfect."

5. Ignoring Change Management

The team members whose work changes need to be involved, not informed. Make them co-creators of the solution.

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Success Metrics by Phase

Phase	Leading Indicators	Lagging Indicators
Discovery	Stakeholder engagement, process clarity	Opportunity quality score
Foundation	Training completion, technical readiness	Team confidence scores
Implementation	Daily active use, feedback volume	Time saved, error reduction
Scale	Adoption rate, champion emergence	ROI, business impact

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What Happens After 90 Days?

The 90-day mark isn't the finish line — it's the starting point for continuous AI evolution.

Ongoing activities:

• Monthly capability reviews
• Quarterly ROI assessments
• Annual strategy refresh
• Continuous training programs

The companies winning with AI aren't those who implemented fastest. They're the ones who built sustainable capability for continuous improvement.

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Ready to Start Your 90-Day Journey?

Every business is different. This framework provides structure, but the specifics depend on your industry, scale, and goals.

Contact us for a free Discovery Session where we'll help you identify your highest-impact AI opportunities and build a customised roadmap.

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Caversham Digital helps businesses implement AI practically and sustainably. We focus on results, not hype.