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AI awareness, fundamentals and guardrailsBuilds a shared baseline: what AI is, where it helps, and what guardrails matter.
Pathways
- Understanding AI and your role
- AI in everyday work
- Responsible and ethical AI use
4journey stages
4academy levels
7core AI skills
8success dimensions
5business outcomes
The problem and the promise
Access to AI is not the same as capability
People are already experimenting with AI on their own. Without shared guidance the result is uneven quality, exposed data, and output nobody has checked.
The promise is an outcome-based program that moves people from awareness to consistent, repeatable behavior change, so they know when and how to use AI, can judge whether the output is any good, and apply guardrails without being told to.
AudienceThe economic buyer who owns L&D strategy and investment, and the admins, curators, and learners who use it.
What it replacesManual, fragmented program design spread across many tools and content sources.
How it is judgedSkill progression and business outcomes, not completion counts.
The learner journey
From foundational awareness to enterprise-wide AI leadership
Four stages, each built from practice experiences, coaching, and real work.
1
Onboard
Safe start- Safe AI ethics orientation
- Basic prompt practice
- Security and compliance foundation
Delivered with a foundational pathway library
2
Enable
Everyday skill- Advanced prompting drill
- Tool integration coaching
- Workflow automation practice
Delivered with ready-made solution accelerators
3
Develop
Applied depth- Agent-building workshop
- Output validation coaching
- Model tuning simulation
Delivered with ready-made solution accelerators
4
Adapt
Stay current- Disruption response coaching
- Process reassessment
- Process improvement
Configured to the client's own tools and workflows
Academy structure
Four levels that close the gap to responsible AI fluency
The academies move an organization from inconsistent understanding and use to consistent, ethical, effective use. Every level has three pathways, each tagged to skills.
Build
AI foundations, safe independent useEveryday fluency: better questions, validated outputs, and consistent guardrails.
Pathways
- Asking better questions with AI
- Using AI safely in everyday work
- Applying AI to real tasks
Enhance
Applied AI, workflow optimizationIntentional workflow redesign, quality checks, and reduced rework.
Pathways
- Designing high-value AI use cases
- Advanced prompting and quality control
- AI-enabled workflow optimization
Lead
AI fluency, strategy and oversightEquips leaders to set guardrails, reinforce accountability, and govern AI use at scale.
Pathways
- Setting direction and guardrails
- Governance, trust, and oversight
- Enabling teams and culture
Skills spine
Every asset is tagged to a skill, so learning surfaces by level and role
Pathways, resources, and academy assets are tagged against a shared set of core skills. Content then surfaces by proficiency and role rather than by keyword. Each skill is rated on a three-point range, Novice to Expert.
| Skill | What it means | Range |
|---|---|---|
| AI use case identification | Determining where and how to apply AI tools to specific business problems. | |
| Prompt engineering | Strategic design and refinement of instructions to get specific, high-quality, actionable responses. | |
| Workflow optimization | Deconstructing a process, finding friction points for AI, and redesigning for speed, quality, and oversight. | |
| Critical thinking | Objective analysis and evaluation of AI-generated insights to form reasoned judgment. | |
| Data analytics | Interpreting, visualizing, and drawing logical conclusions from data sets. | |
| AI ethics | Using AI transparently, accountably, and in line with legal, social, and organizational values. | |
| Learning agility | Staying durable by unlearning old habits quickly and mastering new tools. |
Practice layer
Practice, not consumption
Practice experiences sit across the academies. This is where judgment gets built, and where it gets measured.
| Experience | Activity | Skills built | What it measures |
|---|---|---|---|
| Responsible AI gatekeeper | Pause and check an AI-assisted decision against guardrails before acting on it. | AI ethics, critical thinking | Safe decisions in realistic scenarios |
| Use case and value selector | Identify and prioritize the highest-value AI use cases for a role. | Use case identification, workflow optimization | Choosing the right use case over novelty |
| Output trust and quality lab | Walk through validating AI output before relying on it. | Critical thinking, data analytics | First-pass quality and validation habits |
| Responsible AI decision coach | Practice judgment on realistic AI scenarios, with voice mode supported. | AI ethics, critical thinking | Applied judgment, not content consumption |
How content becomes capability
Four learning mechanics on three layers of infrastructure
Structured journeysSkills, content, and activities come together in a purposeful path.
AI-powered throughoutAI-enhanced practice, simulation, and reinforcement sit where they add the most value.
Applied learningHands-on, collaborative experiences reinforce learning through projects and real work.
Assessment and validationBenchmarks, quizzes, and assessments set a starting point and validate growth over time.
The ecosystem behind it
Integrated tech stackConnects the solution to the client's wider technology environment.
Insights and analyticsTracks engagement, growth, and outcomes, feeding views of confidence, responsible use, work transformation, and skill gaps.
Automations and workflowsNudges and automated enrollment keep momentum without manual assignment.
The experience also includes a personalized home base, a front-door strategy hub that is kept current, and tool-specific pathways for the AI assistants people already use, covering what the tool is, clearer prompts, output refinement, and responsible use. A foundation of three prerequisites sits underneath: skills alignment to business priorities, content readiness, and one unified, branded point of access.
Success metrics
Eight dimensions, four checkpoints and four that never stop
The program reduces unmanaged AI risk while building measurable workforce capability, productivity, and responsible adoption.
Four checkpoint dimensions are measured at a single stage, so each stage has a clear finish line. Four continuous dimensions run across all four stages, so the program proves it is working and keeps working.
Checkpoint dimensions · measured at a stage
1
Baseline fluency
Checkpoint · Onboard stageEmployees understand responsible AI use and can apply foundational skills.
Recommended measures
- Participation and completion rates
- Assessment scores
- Share reaching Levels 1 to 2
- Confidence using AI safely
Evidence
Learning analytics, skills assessments, learner confidence survey
CadenceMonthlyTarget80%+ reach Level 1 to 2
2
Practitioner advancement
Checkpoint · Enable stageEmployees move from foundational knowledge to independent application.
Recommended measures
- Number and share reaching Level 3
- Applied assessments completed
- Validated prompts, use cases, or work products
Evidence
Skills assessments, portfolio submissions, manager validation
CadenceMonthly or quarterlyTargetA Level 3 group is formed
3
Team and workflow impact
Checkpoint · Develop stageTargeted teams apply AI to improve real workflows and outcomes.
Recommended measures
- Number of Level 4 use cases
- Time saved
- Cycle-time reduction
- Quality improvement
- Adoption within targeted teams
Evidence
Workflow data, team reports, before-and-after analysis
CadenceQuarterlyTargetLevel 4 impact is shown
4
Leadership enablement
Checkpoint · Adapt stageLeaders define guardrails, reinforce standards, and support responsible adoption.
Recommended measures
- Leader participation
- Guardrails approved
- Governance decisions documented
- Frequency of reinforcement and communication
Evidence
Leadership artifacts, governance records, communications review
CadenceQuarterlyTargetGuardrails are set and held
Continuous dimensions · measured across all stages
5
Risk reduction
Continuous · all four stagesEmployees use AI within clear standards, with fewer policy or data-protection issues.
Recommended measures
- AI-related violations
- Misuse incidents
- Data-handling issues
- Policy awareness
- Escalation rates
Evidence
Compliance records, risk reporting, policy assessments
CadenceQuarterlyTargetPolicy violations decline from baseline
6
Performance impact
Continuous · all four stagesAI improves productivity, quality, and decision support.
Recommended measures
- Rework reduction
- Drafting or analysis time
- Output quality
- Decision cycle time
- Consistency across teams
Evidence
Operational metrics, quality reviews, manager feedback
CadenceQuarterlyTargetLess rework, faster execution
7
Organizational adoption
Continuous · all four stagesResponsible AI use becomes consistent across functions and teams.
Recommended measures
- Active AI users
- Repeat usage
- Cross-team participation
- Shared practices
- Employee confidence and accountability
Evidence
Usage analytics, employee surveys, team assessments
CadenceQuarterly or biannualTargetConfident use with human judgment
8
Sustained capability
Continuous · all four stagesThe workforce keeps adapting as tools, risks, and business needs evolve.
Recommended measures
- Refresher participation
- Skill progression over time
- New use cases
- Collaboration across teams
- Ethical-review adoption
Evidence
Quarterly skill reviews, use-case portfolio, governance reporting
CadenceQuarterly or biannualTargetGovernance becomes normal operations
Targets are starting points for discussion. Each is set with the customer against its own baseline. "Level" refers to the four academy levels above.
Business outcomes
Five outcomes a buyer can take to the business
Each outcome is tied to one measure, so the conversation moves from completions to value.
Reduce operating cost
Common knowledge-work tasks get done faster with the same headcount.
Measured byHours saved per employee per monthIncrease speed of execution
Work moves from idea to deliverable faster across teams.
Measured byCycle-time reductionImprove quality, reduce rework
Better first drafts and fewer iterations to reach done.
Measured byFirst-pass acceptance rateReduce risk exposure
Fewer unsafe AI behaviors that create legal, security, or compliance risk.
Measured byAI-related incidentsAccelerate time to productivity
New hires and new AI users ramp faster and need less support.
Measured byTime to independent executionThe shift
From a library people browse to a system that builds capability
Library people browse
System that builds capability
Compliance-led, limited L&D ownership
Value creation with targeted, high-value upskilling
Reactive support
Proactive support that shapes the learning agenda
Manual content creation
AI-assisted content curation
Learning as a separate event
Learning in the workflow
Completion-based ROI
Value connected to business strategy
Design thinking
How the pieces were designed to work together
The page above is the result. This is the reasoning behind it, and what to adapt for a new customer.
Start from skills, not contentAnchoring everything to a small set of skills keeps pathways, practice, and measurement speaking the same language.
One finish line per stageCheckpoint measures give each stage a clear target and give sponsors something to celebrate.
Continuous measures prove it sticksRisk, performance, adoption, and sustained capability are reviewed on a steady cadence, so early gains are not mistaken for lasting change.
Judgment is practicedScenarios and coaching build the habit of checking output and applying guardrails, which completion counts cannot show.
Responsible by defaultEthics and security sit in the first stage, and governance is owned by leaders in the last, so guardrails are built in at both ends.
Tailor to the customerSkills, use cases, tools, targets, and review cadence are configured to each organization's context and baseline.
Skills shown
Learning strategy, program and curriculum architecture, skills framework design, measurement design, executive storytelling, and translating a complex offering into a clear buyer story.
Described in general terms. Product names, customer details, and company-specific results are intentionally left out.