Direction is about having a clear AI strategy aligned with business objectives. It answers: What role should AI play in our organization, and how will we get there?
This dimension covers:
- • AI vision and ambition level
- • Strategic priorities and use case selection
- • Leadership alignment and governance
- • Investment decisions and portfolio management
Cost of getting it wrong:
- • Wasted investment on disconnected pilots
- • Competitor advantage while you deliberate
- • Organizational confusion about priorities
- • Inability to scale what works
Maturity Levels
Find your current level, then see what it takes to progress.
1 Ad Hoc No strategy, individual experiments
"We have no AI strategy. Experiments happen when individuals take initiative, disconnected from business goals."
You recognize this when:
- No strategy: No documented AI strategy or roadmap exists
- Individual-driven: Experiments depend on personal enthusiasm, not business direction
- Leadership gap: Executives are unaware of or indifferent to AI activities
- No funding: No budget allocated specifically for AI initiatives
- No replication: Success stories remain isolated and can't be repeated
To reach Level 2
- Make AI a leadership-level conversation
- Assign someone to own the AI topic
- Start connecting experiments to business problems
2 Opportunistic Multiple pilots, no cohesion
"We're actively piloting AI in multiple areas, but each department is doing its own thing. No cohesive strategy."
You recognize this when:
- Scattered pilots: Multiple AI experiments running in parallel across the organization
- Siloed efforts: Each department pursuing its own use cases independently
- Case-by-case: Business cases built project-by-project with no portfolio view
- No standards: No common governance, tools, or quality standards
- Resource conflicts: Budget fights over which projects get funded
Common trap: Pilot purgatory — Constantly experimenting but never scaling. Need portfolio thinking.
To reach Level 3
- Document an AI strategy (even a simple one)
- Create a prioritization framework for use cases
- Get leadership aligned on what you're doing and why
3 Defined Documented strategy, leadership aligned
"We have a documented AI strategy with prioritized use cases. Leadership is aligned on what we're doing and why."
You recognize this when:
- Written strategy: A documented AI strategy exists and is communicated
- Prioritization: A framework decides which use cases get investment
- Leadership buy-in: Executives are aligned on AI priorities and direction
- Governance: Clear decision-making model for AI investments and projects
- Metrics: Basic KPIs defined to track progress and success
Watch out: Paper strategy — Level 3 on paper but Level 2 in practice. Strategy exists but doesn't guide decisions.
To reach Level 4
- Implement rigorous ROI tracking across initiatives
- Develop a Build/Buy/Partner decision framework
- Start systematic post-mortems to capture learning
4 Managed Portfolio view, ROI tracking
"We manage AI as a portfolio with investment criteria, ROI tracking, and systematic Build/Buy/Partner decisions. Strategy drives resource allocation."
You recognize this when:
- Portfolio management: AI investments managed as a balanced portfolio, not individual projects
- ROI discipline: Rigorous tracking of return on investment across all initiatives
- Make/buy decisions: Build/Buy/Partner framework applied consistently to new opportunities
- Learning loops: Systematic post-mortems capture lessons and feed future decisions
- Talent pipeline: Proactive development of AI skills and hiring strategy
To reach Level 5
- Make AI a consideration in every major business decision
- Continuously refine strategy based on results
- Evolve organizational structure for AI-native operations
5 Optimizing AI integral to business strategy
"AI is integral to our business strategy. We continuously refine based on results and landscape changes."
You recognize this when:
- Strategic integration: AI is woven into the core business strategy, not a separate initiative
- Continuous refinement: Strategy evolves based on results, market changes, and new capabilities
- AI-first thinking: Every major business decision considers AI implications and opportunities
- Industry leadership: Organization is recognized as an AI leader in its sector
- AI-native structure: Organizational design has evolved to support AI-enabled operations
Strategy-led organization — Direction ahead of other dimensions. Focus on execution catching up.
Maintaining excellence: At Level 5, the challenge shifts to sustaining leadership. Monitor the landscape, adapt quickly, keep AI aligned with evolving business strategy.