WorkRecode

For individuals

Build AI capability around the work you actually do.

AI-powered, work-based AI capability development for professionals and knowledge workers who want to become substantially better at using AI in their existing work.

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Learn AI. Apply AI. Redesign your work.

WorkRecode journey Adapts to your evidence
  1. 01Assess
  2. 02Learn
  3. 03Apply
  4. 04Redesign
  5. 05Demonstrate
  6. 06Measure

The next capability and experience are determined by your capability, evidence and work context—not a fixed curriculum.

What you get

Your work. Six concrete outputs.

See illustrative excerpts grounded in incoming-request work. They show the outputs, not a real learner’s progress or a before/after result. Your own profile, learning and workflow will be shaped by your work and evidence.

Assess

Starting Capability Profile

Understand where you stand, with evidence behind the picture.

Starting Capability ProfileIllustrative WorkRecode example

Work context: reviewing and routing incoming requests. Illustrative levels are not actual user results.

AI Interaction
Applied
Describes giving AI the request context, a clear output format and review criteria.
Critical Evaluation
Developing
Recognises that a suggested category may be wrong; checking against source information needs more detail.

Current evidence suggests…

You can frame a useful AI request. Next, practise checking suggestions and separating routine work from exceptions. Confidence and tool usage do not establish these levels.

An authored example, not a real learner record or customer result.

See Starting Capability Profile

Learn

Personalised Learning Path

Practise what you need next through the tasks in your role.

Your Learning PathIllustrative WorkRecode example

Based on the example profile: develop critical evaluation and task decomposition through incoming-request work.

  1. 01

    Understand how AI can work with your existing tasks

    Separate reading requests, suggesting a category and deciding what happens next.

  2. 02

    Improve AI interaction and context

    Give AI the categories, examples and information it needs to make a useful suggestion.

  3. 03

    Break complex work into AI-ready tasks

    Keep classification separate from priority and routing decisions.

The learning follows the work, not a one-size-fits-all course.

An authored example, not a real learner record or customer result.

See Personalised Learning Path

Learn & Apply

Joice learning experience

Explain, practise and check understanding with your WorkRecode AI Tutor.

Joice · WorkRecode AI TutorIllustrative WorkRecode example

Practice focus: giving AI context while retaining human judgement.

  1. Learner

    Just sort it for me.

  2. Joice

    Let’s make that more concrete. Think of AI as suggesting a tray for each request, not deciding what happens to it. Which details help you choose a tray?

  3. Learner

    The subject, what they need and whether it’s urgent.

Joice adapts the learning experience to the learner. This exchange checks understanding; it does not establish an overall capability level.

An authored example, not a real learner record or customer result.

See Joice learning experience

Redesign

Workflow Recode

A structured design proposal for your real workflow, with human decisions explicit.

Workflow Recode · Request routingIllustrative WorkRecode example

Blueprint proposal: routine assistance with explicit human review. Designed only — not implemented or measured.

Before

Current workflow

  1. 1Receive request
  2. 2Read request
  3. 3Categorise manually
  4. 4Decide priority

After

Redesigned workflow

  1. 1Request received
  2. 2AI classifies and extracts information
  3. 3AI proposes priority
  4. 4Human reviews exceptions

Designed

Blueprint proposed for review

Implemented

Not demonstrated

Measured

No outcome evidence

A redesigned workflow is not automatically an implemented workflow.

An authored example, not a real learner record or customer result.

See Workflow Recode

Demonstrate

Practical Assessment

Show your reasoning about the work you redesigned—not just that you finished learning.

Practical AssessmentIllustrative WorkRecode example

Illustrative task · 3 of 7

Identify where human oversight is required in the workflow you designed.

Learner response
“A person should review uncertain categories and sensitive requests. AI may suggest priority, but a person confirms decisions that affect others. Missing information should be escalated rather than guessed.”
Evidence captured
Reasoned oversight choices tied to the proposed request-routing workflow.

Task completion is not proof that the workflow was implemented.

An authored example, not a real learner record or customer result.

See Practical Assessment

Measure

Capability Report

A record of current capability, evidence and areas still unverified.

Capability ReportIllustrative WorkRecode example

A separate illustrative capability record, not a certificate or a before/after result. These levels come from this report’s example Assessment—not an upgrade from the Starting Capability Profile shown elsewhere.

Current capability

AI Fundamentals
Developing
Understands assistance; limitations need more consistent explanation.
AI Interaction
Applied
Frames tasks with context and review criteria.
Task Decomposition
Applied
Separates classification, prioritisation and human decisions in the example Assessment.

Capability level ≠ evidence status ≠ experience ≠ implementation ≠ outcome.

An authored example, not a real learner record or customer result.

See Capability Report

Apply it in your work

You bring the knowledge of your work.

WorkRecode provides the AI and workflow expertise. You keep responsibility for what is accurate, useful and safe. A design is not automatically implementation, and implementation is not a measured outcome.

See how real work becomes practice

Don’t just learn about AI. Learn AI—and learn how to work differently because of it.

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