AIStrategyGuide

AI Implementation Roadmap

From Discovery to Production and Beyond

How to Use This Roadmap

Implementing AI successfully requires following a structured path from discovery to scale. This roadmap breaks down the journey into six phases, each with clear activities, deliverables, timelines, and success criteria. Whether you're implementing Tier 0 automation or Tier 5 agentic systems, these phases provide a proven framework for success.

Key principle: Don't skip phases. Each phase builds on the previous one and validates assumptions before increasing investment.

PHASE 0

Discovery & Assessment

What It Is

Before writing code or buying tools, understand your problem, assess your data readiness, and validate that AI is the right solution. This phase prevents costly mistakes by ensuring clarity before investment.

Key Activities

  • Define the business problem and success criteria
  • Assess current data readiness (using Data Readiness framework)
  • Map problem to appropriate AI tier (using Technology Tiers framework)
  • Identify stakeholders and get executive sponsorship
  • Preliminary vendor research if buying vs building

Deliverables

  • Problem statement document
  • Success metrics defined (what does "working" look like?)
  • Data readiness assessment results
  • AI tier recommendation
  • High-level feasibility assessment

Team Involved

  • Business stakeholders
  • IT/data team lead
  • Executive sponsor

What Success Looks Like

You can articulate: "We need [AI Tier X] to solve [specific problem] because [business case]. Our data is at Level Y, so we need to [close gaps / proceed directly]."

Typical Timeline & Budget

  • Timeline: 2-6 weeks
  • Cost: Mostly internal time (or $5K-$15K if using consultants)
  • Team: Part-time involvement from stakeholders
⚠ Common Pitfall

Skipping this phase and jumping straight to vendor demos or hiring data scientists. Result: Wrong solution, wrong timing, wasted budget. Take the time to understand what you're solving and whether AI is the right approach.

PHASE 1

Proof of Concept (POC)

What It Is

A small-scale technical validation to answer: "Can AI solve this specific problem with our actual data?" Not production-ready, just proving feasibility. Think of it as a scientific experiment to test your hypothesis.

Key Activities

  • Prepare sample dataset (representative but small)
  • Build/configure prototype solution
  • Test against success criteria from Phase 0
  • Document what worked, what didn't, and why
  • Estimate production costs and requirements

Deliverables

  • Working prototype on test data
  • Performance metrics vs. success criteria
  • Technical architecture document
  • Production cost estimate
  • Go/no-go recommendation

Team Involved

  • Data scientist or ML engineer
  • Subject matter expert
  • Data engineer (for data prep)

What Success Looks Like

POC demonstrates accuracy/performance above baseline. You can confidently say: "Yes, this works on our data and we know what it will take to productionize it."

Typical Timeline & Budget

  • Timeline: 1-3 months
  • Tier 0-1: $10K-$30K (mostly data prep and basic modeling)
  • Tier 2-3: $30K-$100K (requires ML expertise)
  • Tier 4-5: $50K-$200K (complex, may need specialized vendors)
⚠ Common Pitfall

Making the POC too perfect. A POC should answer "does this work?" not "is this production-ready?" Over-investing here wastes time and money. Keep it scrappy and focused on the core question.

PHASE 2

Pilot Implementation

What It Is

Deploy to a limited real-world environment with a subset of users. Goal: Validate that the solution works in production conditions with real users and real processes before committing to full rollout.

Key Activities

  • Select pilot group/department (friendly users who will give feedback)
  • Build production-grade version (not prototype quality)
  • Integrate with existing systems (APIs, databases, workflows)
  • Train pilot users
  • Monitor performance and collect feedback
  • Refine based on learnings

Deliverables

  • Production-ready system (for pilot scope)
  • User training materials
  • Integration with key systems
  • Performance monitoring dashboard
  • Pilot results report

Team Involved

  • Full development team
  • MLOps engineer (for Tier 2+)
  • Pilot users
  • Change management lead

What Success Looks Like

Pilot users adopt the tool, report positive business impact, and want to keep using it. Performance metrics meet targets. Issues are identified and addressable.

Typical Timeline & Budget

  • Timeline: 2-6 months
  • Tier 0-1: $50K-$150K
  • Tier 2-3: $150K-$500K
  • Tier 4-5: $300K-$1M+
⚠ Common Pitfall

Choosing a pilot that's too ambitious (high stakes, complex edge cases). Pick a contained scope where success is achievable and measurable. You want to learn, not bet the company on an untested system.

PHASE 3

Production Deployment

What It Is

Full-scale deployment to entire organization or all target users. Goal: Make the AI solution available to everyone it's designed for, with proper change management, training, and support infrastructure in place.

Key Activities

  • Roll out in phases (by department/region) or all at once
  • Comprehensive user training program
  • Establish support channels (helpdesk, documentation, FAQs)
  • Change management communications (why, what's in it for me, how to get help)
  • Monitor adoption metrics and user satisfaction
  • Address resistance and edge cases

Deliverables

  • Fully deployed production system
  • Comprehensive user documentation
  • Training program and materials
  • Support infrastructure (ticketing, knowledge base)
  • Adoption tracking dashboard
  • Rollout completion report

Team Involved

  • Full development and ops team
  • Change management lead
  • Training team
  • IT support team
  • Executive sponsors

What Success Looks Like

Target adoption rate achieved (e.g., 80%+ of users actively using the system). System stability maintained. Support tickets decline over time as users become proficient. Business metrics show positive impact.

Typical Timeline & Budget

  • Timeline: 2-6 months
  • Tier 0-1: $30K-$100K (training, support setup)
  • Tier 2-3: $100K-$300K (more complex change management)
  • Tier 4-5: $200K-$500K+ (extensive training and support)
⚠ Common Pitfall

Declaring victory too early. Just because the system is deployed doesn't mean it's adopted. Budget time and resources for change management, training, and support. Low adoption kills AI projects just as surely as technical failures.

PHASE 4

Monitoring & Optimization

What It Is

Ongoing monitoring, maintenance, and improvement of the deployed AI system. Goal: Ensure the system continues to deliver value, catch performance degradation early, and adapt to changing business needs and data patterns.

Key Activities

  • Monitor system performance (accuracy, latency, uptime)
  • Track business impact metrics (ROI, productivity, quality)
  • Watch for model drift or degradation
  • Collect and prioritize user feedback
  • Implement improvements and bug fixes
  • Retrain models as needed (Tier 2+)

Deliverables

  • Performance monitoring dashboards
  • Regular health check reports
  • Model retraining pipeline (Tier 2+)
  • Incident response procedures
  • Quarterly business impact reports
  • Feature enhancement backlog

Team Involved

  • MLOps/DevOps engineer (Tier 2+)
  • Data scientist (for model updates)
  • Product owner
  • Support team

What Success Looks Like

System maintains target performance over time. Issues are caught and resolved before users notice. Business metrics remain positive or improve. Users continue to adopt and rely on the system.

Typical Timeline & Budget

  • Timeline: Ongoing (as long as system is in use)
  • Tier 0-1: $2K-$5K/month (basic support and maintenance)
  • Tier 2-3: $5K-$15K/month (model monitoring and retraining)
  • Tier 4-5: $15K-$50K+/month (advanced MLOps, continuous improvement)
⚠ Common Pitfall

Treating AI as "set it and forget it." AI systems require ongoing attention—models drift, business needs change, edge cases emerge. Budget for continuous monitoring and improvement, or your AI investment will decay over time.

PHASE 5

Scaling & Integration

What It Is

Expand successful AI implementation to new use cases, departments, or regions. Integrate AI deeply into core workflows and systems. Goal: Maximize ROI by scaling what works and building an AI-enabled organization.

Key Activities

  • Identify adjacent use cases (similar problems in other areas)
  • Build reusable AI components and infrastructure
  • Deepen integration with core business systems
  • Expand to new geographies or business units
  • Build internal AI capability and governance
  • Create AI center of excellence

Deliverables

  • Reusable AI platform or components
  • AI governance framework
  • Internal AI capability roadmap
  • Scaled implementations in new areas
  • ROI analysis and portfolio view of AI initiatives

Team Involved

  • AI/Data leadership
  • Enterprise architecture team
  • Multiple product teams
  • Executive steering committee

What Success Looks Like

AI is embedded in core business processes. New AI use cases are delivered faster by reusing infrastructure and learnings. Organization has clear AI strategy and governance. ROI is demonstrable and growing.

Typical Timeline & Budget

  • Timeline: 6-18+ months (depends on scale of expansion)
  • Budget: Highly variable based on scope
  • Platform investment: $200K-$1M+ for reusable infrastructure
  • Per new use case: Faster/cheaper than Phase 0 (30-50% reduction)
⚠ Common Pitfall

Scaling before proving success. Phase 5 is for winners—systems that have proven value and adoption. Don't scale mediocre AI just because it exists. Also, avoid "scaling chaos"—invest in governance, reusable components, and standardization to scale sustainably.