Output: A change stewardship brief with participation, dependencies, evaluation and renewal.
Before this: Experience of a change project; complete the beginner route if AI basics are unfamiliar.
Work through the fictional examples and feedback. Short notes are enough.
LecXie suggested study allocations, not provider runtimes or verified learning time. Optional deeper work is outside the total.
0 / 4
Stops marked explored
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Stop 1 of 4
Map participation before the change
Identify whose knowledge and experience can change the design.
Journey fit: Start with the prerequisite concepts above. The selected resource supplies context; the local case is where you use it.
Selected resource · Engaging stakeholders in AI
The Alan Turing Institute
Engaging stakeholders in AI
What you will learn: Map affected groups and plan how they can influence design.
Why this is worth the time: Adds a structured participation method to a workflow or change plan; paper can replace optional collaborative-board tools.
Before this
A candidate AI use and affected groups
Study time
Provider range 1–5 hours; suggested 45–60 minute initial mapping, not completion
Access
Free account and enrolment required; optional Miro or paper; no iframe permission assumed
Self-reported and shared across routes. Source date: not established · Last update: not established · Checked 2026-10-15. Part of this stop’s selected reading.
What you will learn: Organise questions about job effects and what affected workers can tell you.
Why this is worth the time: Useful alongside a workflow redesign. PAI is an industry-participating nonprofit; consult people rather than guessing their experience.
Before this
A workflow and access to affected workers or clearly recorded gaps
Study time
Suggested 30–45 minutes for a bounded assessment
Access
Free web/PDF resource; no account needed
Currency / limits
7 Jun 2023; assessment guidance, not evidence that a particular implementation works
Self-reported and shared across routes. Source date: 2023-06-07 · Last update: not established · Checked 2026-10-15. Part of this stop’s selected reading.
Original local activity · Fictional case
Identify whose knowledge and experience can change the design.
A programme consults sponsors and vendors but not the people doing the work. What evidence is missing?
Apply it to a working example
Use a hypothetical or approved non-sensitive example. Map affected workers, service recipients, representatives, decision owners and how they can influence the design.
A self-report of exploration, not verified proficiency. Your work is not sent to LecXie.
Stop 2 of 4
Trace capability dependencies
Plan the supporting conditions across the workforce system.
Journey fit: Build on Map participation before the change before this activity. The selected resource supplies context; the local case is where you use it.
Selected resource · How might generative AI impact different occupations?
International Labour Organization
How might generative AI impact different occupations?
What you will learn: Separate task exposure from a prediction that an entire job will disappear.
Why this is worth the time: Supports a task-by-task redesign conversation rather than a whole-job assumption.
Before this
Basic model limits and a safeguarded task idea
Study time
15 min reading and chart inspection
Access
Free public article; no account needed
Currency / limits
20 May 2025; potential exposure estimates, not realised job losses
Self-reported and shared across routes. Source date: 2025-05-20 · Last update: not established · Checked 2026-10-15. Part of this stop’s selected reading.
Selected resource · Bridging the AI skills gap
OECD
Bridging the AI skills gap
What you will learn: Distinguish broad AI literacy from specialised skills when planning learning.
Why this is worth the time: Makes the capability plan broader than training people to use a single tool.
Before this
A redesigned task and the people expected to do it
Study time
6-page brief; 10 min suggested reading
Access
Public brief and PDF; no account needed to read
Currency / limits
24 Apr 2025; policy context, not an organisation’s skills assessment
Self-reported and shared across routes. Source date: 2025-04-24 · Last update: not established · Checked 2026-10-15. Part of this stop’s selected reading.
Original local activity · Fictional case
Plan the supporting conditions across the workforce system.
A new matching tool is ready, but skills records are incomplete and managers cannot release people. What constrains its value?
Apply it to a working example
Use a hypothetical or approved non-sensitive example. Map dependencies across planning, allocation, learning, services and data; record unknowns.
A self-report of exploration, not verified proficiency. Your work is not sent to LecXie.
Stop 3 of 4
Design an evaluation with affected groups
Learn whether outcomes differ across teams or groups.
Journey fit: Build on Trace capability dependencies before this activity. The selected resource supplies context; the local case is where you use it.
Selected resource · Impact Evaluation of AI Interventions
HM Treasury · Evaluation Task Force
Impact Evaluation of AI Interventions
What you will learn: Specify a baseline, comparison and outcome measures before starting a pilot.
Why this is worth the time: Connects the earlier notes to a testable pilot rather than a demonstration.
Before this
Task, safeguards, workflow and capability notes from stops 1–5
Study time
20 min selected sections; full guidance takes longer
Access
Free public guidance; no account needed
Currency / limits
Updated 15 May 2026; UK public-sector guidance, adapt to context
Self-reported and shared across routes. Source date: not established · Last update: 2026-05-15 · Checked 2026-10-15. Part of this stop’s selected reading.
Selected resource · Toward Good Automation: what the law says
Institute for the Future of Work
Toward Good Automation: what the law says
What you will learn: Explore Involve, Assess, Act and Monitor through a selected workplace archetype.
Why this is worth the time: A structured prompt for governance discussions. Provider calls it an interactive toolkit download; it is not presented here as a verified browser game or legal compliance test.
Before this
Workplace context and relevant jurisdiction
Study time
Suggested 25–40 minutes for one archetype
Access
Public toolkit download; jurisdiction-sensitive legal application
Currency / limits
22 Sep 2026; recheck applicability with the responsible specialist
Self-reported and shared across routes. Source date: 2026-09-22 · Last update: not established · Checked 2026-10-15. Part of this stop’s selected reading.
Original local activity · Fictional case
Learn whether outcomes differ across teams or groups.
A programme-wide average improves, while a small team’s workload worsens. What should the evaluation preserve?
Apply it to a working example
Use a hypothetical or approved non-sensitive example. Define an appropriate comparison, subgroup questions, feedback method and safeguards for reporting.
A self-report of exploration, not verified proficiency. Your work is not sent to LecXie.
Stop 4 of 4
Keep change reversible and knowledge current
Define ongoing ownership, renewal and retirement.
Journey fit: Build on Design an evaluation with affected groups before this activity. The selected resource supplies context; the local case is where you use it.
Selected resource · AI Risk Management Framework
NIST
AI Risk Management Framework
What you will learn: Turn a vague risk concern into ownership, context, measurement and controls.
Why this is worth the time: Provides a general risk lens. It does not replace local controls or legal advice.
Before this
A candidate task and people affected by it
Study time
Suggested 25-minute exploration of Govern, Map, Measure and Manage; not a provider runtime
Access
Free public framework; no account needed
Currency / limits
AI RMF 1.0 (2023) is being revised; check current guidance before use
Self-reported and shared across routes. Source date: 2023-01-26 · Last update: not established · Checked 2026-10-15. Part of this stop’s selected reading.
Selected resource · Toward Good Automation: what the law says
Institute for the Future of Work
Toward Good Automation: what the law says
What you will learn: Explore Involve, Assess, Act and Monitor through a selected workplace archetype.
Why this is worth the time: A structured prompt for governance discussions. Provider calls it an interactive toolkit download; it is not presented here as a verified browser game or legal compliance test.
Before this
Workplace context and relevant jurisdiction
Study time
Suggested 25–40 minutes for one archetype
Access
Public toolkit download; jurisdiction-sensitive legal application
Currency / limits
22 Sep 2026; recheck applicability with the responsible specialist
Self-reported and shared across routes. Source date: 2026-09-22 · Last update: not established · Checked 2026-10-15. Part of this stop’s selected reading.
Original local activity · Fictional case
Define ongoing ownership, renewal and retirement.
After rollout, the task changes and the original training examples become obsolete. What should the programme retain?
Apply it to a working example
Use a hypothetical or approved non-sensitive example. Write a change stewardship brief: owner, worker input, review triggers, capability renewal and exit/fallback.
A self-report of exploration, not verified proficiency. Your work is not sent to LecXie.
A change stewardship brief with participation, dependencies, evaluation and renewal.
Assembled from your working notes. Empty sections remain prompts. This is a draft for discussion with the responsible people.
Optional deeper branches
Separate from the core route. Alternatives cover a similar outcome; deeper work extends it. Choose deliberately rather than repeating everything.
Optional fairness workshop before applying the governance brief to recruitment or other consequential contexts. Before this: Design an evaluation with affected groups.
