Creating an AI Roadmap

An AI roadmap translates strategy into action. It sequences initiatives, allocates resources, and creates accountability for results. Without a roadmap, AI efforts become scattered experiments rather than strategic investments.

Roadmap Design Principles

Balance Quick Wins and Transformational Bets

Quick wins (0-6 months):

Demonstrate AI value early
Build organizational confidence
Generate momentum and learning
Lower risk, faster payback

Transformational initiatives (6-24 months):

Deliver significant competitive advantage
Require more resources and change management
Higher risk, higher potential reward
Build on quick win foundations

The 70-20-10 Portfolio Approach

Consider allocating AI investments across three horizons:

Horizon	Focus	Investment	Risk
H1 (70%)	Improve existing operations	Core business AI	Lower
H2 (20%)	Extend into adjacent areas	Growth AI	Medium
H3 (10%)	Explore new possibilities	Experimental AI	Higher

This ensures you're improving today's business while preparing for tomorrow.

Roadmap Structure

Phase 1: Foundation (Months 1-3)

Objectives:

Establish AI governance structure
Launch 1-2 quick win pilots
Begin data quality initiatives
Build initial AI team or partnerships

Key activities:

Form AI steering committee
Select pilot use cases from high-value, high-feasibility quadrant
Assess and address critical data gaps
Define success metrics for pilots

Success criteria:

Governance framework approved
Pilots launched with clear objectives
Baseline metrics established

Phase 2: Validation (Months 4-8)

Objectives:

Prove AI value with pilot results
Expand successful pilots
Launch next wave of initiatives
Develop internal capabilities

Key activities:

Measure and communicate pilot outcomes
Scale pilots that meet success criteria
Begin medium-term strategic initiatives
Train business teams on AI tools

Success criteria:

Demonstrated ROI from pilots
Organization-wide awareness building
Clear path to scale identified

Phase 3: Scaling (Months 9-18)

Objectives:

Operationalize proven solutions
Embed AI in business processes
Build sustainable AI capabilities
Launch transformational initiatives

Key activities:

Transition pilots to production
Integrate AI into standard workflows
Develop AI center of excellence
Begin larger strategic AI projects

Success criteria:

Multiple AI solutions in production
Measurable business impact
Self-sustaining AI operations

Phase 4: Optimization (Months 18+)

Objectives:

Continuously improve AI solutions
Explore advanced AI applications
Position for emerging opportunities
Build AI-native culture

Key activities:

Monitor and optimize existing solutions
Evaluate new AI capabilities
Expand AI across the organization
Share learnings and best practices

Roadmap Components

A complete AI roadmap should include:

Initiative Details

Use case description
Business value expected
Resource requirements
Dependencies and risks
Success metrics

Timeline and Milestones

Start and target completion dates
Key decision points
Review checkpoints
Go/no-go gates

Resource Plan

Team requirements
Budget allocation
Technology needs
Partner involvement

Governance Structure

Steering committee membership
Decision rights
Escalation paths
Reporting cadence

Managing the Roadmap

Regular Reviews

Monthly progress reviews
Quarterly strategic assessments
Annual roadmap refresh

Adaptation Triggers

Update your roadmap when:

Business priorities shift
New AI capabilities emerge
Pilots reveal unexpected learnings
Resource availability changes
Competitive landscape shifts

Communication

Executive dashboard for leadership
Team-level project tracking
Organization-wide progress updates
External stakeholder briefings

Common Roadmap Pitfalls

Too many initiatives:

Spreading resources too thin
Nothing gets proper attention
Team burnout and cynicism

No quick wins:

Long wait for any AI value
Stakeholders lose patience
Momentum dies before results

Rigid planning:

Failing to adapt to learnings
Missing emerging opportunities
Outdated priorities persist

Missing dependencies:

Data not ready when needed
Integration blockers surprise teams
Change management overlooked

Key Takeaway

An effective AI roadmap balances ambition with pragmatism. Start with quick wins to build credibility and momentum, then progressively tackle larger opportunities. Build in flexibility to adapt as you learn, and ensure governance keeps initiatives aligned with business strategy.

Next: Learn how to calculate AI ROI and build compelling business cases. :::

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