Execution is about actually deploying AI and getting people to use it. It covers the journey from individual experiments to organization-wide adoption to autonomous systems.
This dimension covers:
- • How AI is being used day-to-day
- • Level of automation and integration
- • Coordination and shared practices
- • Progression from Help → Automate → Rethink
Cost of getting it wrong:
- • Expensive shelfware (tools deployed but unused)
- • Adoption stalls after initial enthusiasm
- • No scaling from pilots to production
- • Value captured by individuals, invisible to org
Maturity Levels
Find your current level, then see what it takes to progress.
1 Individual Personal use, no coordination
"People use AI individually for basic tasks with no coordination. There are no shared practices or standards."
You recognize this when:
- Scattered adoption: People trying AI on their own with no organizational support
- No coordination: Nobody knows what others are doing or what's working
- Inconsistent results: Quality varies wildly depending on individual skill
- No knowledge sharing: Practices leave when enthusiasts leave the team
- Manual only: Copy-paste workflows with no automation
To reach Level 2
- Surface and share what's working across teams
- Create basic training programs
- Build prompt libraries and templates
2 Guided Training and coordination, still manual
"We have structured training and shared prompts. Teams coordinate on how to use AI, but it's still mostly manual."
You recognize this when:
- Training exists: Structured programs teach people how to use AI effectively
- Shared resources: Prompt libraries and templates circulate across teams
- Team coordination: Groups coordinate on practices and share what works
- Power users: Champions emerge who help and coach others
- Still manual: Work still requires copy-paste between AI and other systems
Common trap: Manual handoff bottleneck — Stuck at Level 2 because outputs require manual copy-paste into other systems. Need to invest in integration.
To reach Level 3
- Identify manual handoff bottlenecks
- Build basic integrations with business systems
- Define quality checkpoints for AI outputs
3 Connected Basic automation and integrations
"AI outputs flow into other systems. We have basic automations and integrations with business processes."
You recognize this when:
- System integration: AI outputs flow into CRM, ERP, or other tools without copy-paste
- Basic automation: Automations reduce manual steps in repetitive workflows
- Clear handoffs: Defined points where work passes between AI and humans
- Quality gates: Checkpoints catch errors before they reach customers
- Process redesign: Some workflows redesigned around AI capabilities
To reach Level 4
- Embed AI in core applications via APIs
- Build dedicated agents for specific tasks
- Implement automated quality validation
4 Integrated Embedded AI, orchestrated workflows
"AI is embedded in our core applications via APIs. We have orchestrated workflows and dedicated agents for specific tasks."
You recognize this when:
- Native integration: AI embedded in core applications—users access AI inside existing tools
- Automated QA: Quality checks run automatically on AI outputs
- Orchestration: Organization-level coordination across multiple AI components
- Dedicated agents: Specific agents handle specific tasks end-to-end
- Strategic oversight: Humans oversee at key decision points, not every step
Watch out: Automation without trust — Built integrations but humans still double-check everything manually. Need to build confidence through quality validation.
To reach Level 5
- Develop multi-agent coordination
- Enable dynamic task delegation
- Build continuous learning loops
5 Autonomous Multi-agent systems, minimal oversight
"We have multi-agent systems with dynamic delegation. AI handles complex tasks with minimal human oversight."
You recognize this when:
- Multi-agent systems: AI agents work together, delegating tasks dynamically
- Proactive AI: Systems identify problems and opportunities before humans notice
- Minimal oversight: Complex processes run with minimal human involvement
- Self-improving: Systems adapt and improve based on outcomes
- Organizational transformation: The organization has fundamentally changed how it operates
Maintaining excellence: Monitor for edge cases requiring human judgment. Maintain human expertise for oversight. Balance autonomy with accountability as AI capabilities evolve.