Current picture
See what needs attention. Use AI Baseline to explore reported concerns where useful.
How WAIA turns workplace AI use into evidence for decisions.
WAIA brings AI Baseline, guidance, learning, and examples from real work into one view. Managers can see what changed in a workflow, how strong the evidence is, and what to review next.
Operating model
See what is happening, then decide what to do.
WAIA connects these parts of the work. Learning participation shows who has engaged; examples from real work show what may be changing. Management reviews both before deciding what to do.
Starts, completions and certificates describe learning participation. They don’t prove workplace application, behavior change or organizational improvement. WAIA keeps those signals separate from evidence about the work.
Inside WAIA
See what needs attention in WAIA.
AI Baseline
Use reported concerns to guide action
AI Baseline now leads with what the answers suggest, what to do next and what to check later.
Learning
Apply judgment in real work
A learner session asks people to inspect an AI recommendation, check its support and keep the final decision human.
Guidance
Make standards visible
Organization guidance gives people a local reference point for responsible use.
Evidence mechanics
WAIA separates evidence strength from value direction.
Strong evidence can show that AI is helping or adding burden. There may also be too little evidence to decide yet.
Reported → Observed → Repeated → Validated
A single structured observation can become observed. Repeated evidence needs observations across time. Validated requires accountable human management review.
Positive, neutral, negative, mixed or unclear
WAIA doesn’t assume AI is creating value. Negative and mixed signals help management see where AI may be increasing checking, rework or risk.
Capacity discipline
Indicative capacity is evidence dependent.
Where evidence is strong enough, consistent and current, WAIA can show conservative indicative capacity ranges. Weak evidence, stale evidence, unclear outcomes or unsuitable workflow frequency can prevent calculation.
Indicative capacity released.
No material capacity change evidenced.
Indicative additional capacity required.
Capacity isn’t automatically a financial saving.
Illustrative example
How evidence builds around a workflow.
This is an illustrative scenario, not a customer case study. It shows the kind of operating question WAIA is designed to make visible.
AI is already in use
An organization has introduced Copilot, while some teams also use ChatGPT for drafting, meeting notes and first-pass research. Usage spreads unevenly.
People record what happened
People record recurring workflows, frequency, net time change, checking and rework burden, outcome signal and plain workplace context.
Strength and direction separate
WAIA distinguishes Reported, Observed, Repeated and Validated evidence from whether the result is positive, neutral, negative, mixed or unclear.
Managers review what holds up
Stronger evidence can be grouped into organization workflows, reviewed by accountable managers and used to decide what needs investment, intervention or more evidence.
Capacity stays conservative
Where the evidence supports it, WAIA can show indicative capacity. Where it’s weak, mixed or stale, the product keeps that uncertainty visible.
Getting started
Start with one practical management question.
WAIA begins with the organization's current questions, then makes Guidance, learning and evidence from real work useful for the people who need them.
Fit conversation
Discuss current AI use, the operating concern and who needs to be involved.
Confirm scope
Agree the eligible population, administrators, sponsor and first evidence priority.
Configure the organization
Set the organization view, Guidance, AI Baseline and initial learning pathway.
Use AI Baseline where useful
Review reported concerns and stronger areas as a starting point for follow-up, not proof of performance.
Publish guidance
Make practical expectations visible to the people who need to apply them.
Invite participants
Give the agreed people access to WAIA and appropriate learning.
Review signals
Review learning participation and Guidance acknowledgment as context. Use examples from real work and evidence strength to decide the next action.
Experience
What people and managers see.
For participants
Practical workplace AI learning connected to local guidance.
- Scenario-based judgment practice.
- Visible organization guidance.
- Guidance acknowledgment.
- Lightweight examples from real work.
- Learning reminders and participation progress.
For managers and admins
A current view of workplace evidence and the follow-up that matters.
- Home: Needs attention, Current picture and Recommended next action.
- People: access, learning participation and human follow-up.
- AI Baseline findings and Guidance acknowledgment.
- Evidence from real work, strength and review signals.
- Practical resources for the next action.
Product boundaries
What WAIA doesn’t replace.
WAIA helps managers review evidence from work. The organization still owns its decisions.
Advice and assurance
WAIA doesn’t replace legal advice, regulatory advice, HR advice or compliance certification.
Governance ownership
WAIA doesn’t replace internal governance ownership, risk decisions, management conclusions or human judgment.
Technical monitoring
WAIA isn’t enterprise GRC, technical AI model monitoring or employee surveillance. It’s also not an autonomous ROI engine.
Next step
Ask to see WAIA in the context of your organization.
The first conversation is used to understand the current evidence gap, the eligible population and a practical first evidence priority.