Capability is about whether your people have the skills and mindset to use AI effectively. It covers AI literacy, training programs, champions, and organizational culture around AI adoption.
This dimension covers:
- • AI awareness and literacy
- • Role-based training programs
- • Power users and champions
- • Citizen developers and learning culture
Cost of getting it wrong:
- • Training without adoption
- • Skills atrophy from lack of use
- • Resistance and skepticism persist
- • Innovation concentrated in few individuals
Maturity Levels
Find your current level, then see what it takes to progress.
1 Unaware Few experiments, no programs
"A few individuals experiment with AI on their own initiative. There are no training programs, standards, or shared awareness."
You recognize this when:
- Isolated experimentation: A handful of curious individuals trying things on their own
- No training: No formal AI training programs or learning resources
- No guidelines: No standards or guidelines for AI use
- Low awareness: Most employees don't know AI tools exist or are available
- Knowledge silos: When someone figures something out, knowledge stays with them
To reach Level 2
- Raise awareness of available AI tools
- Create basic literacy programs
- Identify potential early adopters
2 Aware Tools known, basic training
"Employees know AI tools exist. We have basic literacy programs and tool access, but usage varies widely."
You recognize this when:
- Basic awareness: People know AI is available—there's an e-learning or lunch-and-learn
- Tool access: AI tools are accessible to employees who want them
- Varied adoption: Some enthusiasts dive deep; most barely touch it
- No expectations: No clear expectations about AI skills for any role
- Risk awareness: Understanding of AI risks starting to emerge
Common trap: Training without adoption — Invested in training but skills don't translate to changed behavior. Need to create pull, not just push.
To reach Level 3
- Develop role-based training programs
- Identify and support power users
- Create champion networks
3 Developing Champions and role-based training
"We have role-based training programs. Power users are emerging and champions drive peer adoption."
You recognize this when:
- Role-based training: Training tailored to roles—marketing differs from operations
- Power users: People who've gone deep and can help others
- Active champions: Champions actively promoting adoption in their teams
- Success stories: Stories of AI wins spreading organically across the org
- Narrowing gap: The gap between heavy users and non-users is shrinking
Watch out: Champion burnout — Early adopters driving all adoption work without recognition or support. Need to formalize the champion role.
To reach Level 4
- Enable citizen developers
- Build peer learning networks
- Make AI usage normal, not exceptional
4 Capable AI embedded, citizen developers
"AI is embedded in daily work across the organization. Non-technical staff build their own solutions, and peer learning networks thrive."
You recognize this when:
- Embedded in work: AI is just part of how work gets done, not a special project
- Citizen developers: Non-technical staff building their own automations and solutions
- Peer networks: People share techniques and solve problems together
- New normal: New employees expect to use AI—it's baseline
- Mature change: Change management is sophisticated and effective
To reach Level 5
- Build external recognition
- Attract talent for AI culture
- Develop internal thought leaders
5 Leading Org-wide fluency, external recognition
"Organization-wide AI fluency is the norm. We're recognized externally and attract talent for our AI culture."
You recognize this when:
- Universal fluency: Everyone is fluent in AI across the organization
- External recognition: Written up in case studies, invited to speak at conferences
- Talent magnet: Job candidates cite AI culture as why they want to join
- Thought leadership: Internal leaders shape industry conversation
- Continuous learning: Learning is embedded in how the organization operates
People-constrained organization — If Capability lags despite good technology, tools are available but people don't use them effectively.
Maintaining excellence: Maintain learning culture as AI evolves. Continue developing thought leaders. Share knowledge externally to attract talent.