Automation Examples and Evidence Standards

Representative scenarios, plus the standard we use before presenting a result as a real client outcome.

Baseline

Measure current effort first

Pilot

Test one bounded workflow

Review

Keep people accountable

Evidence

Scale only proven changes

Raeburn quantified case-study standard

A case study should make it easy to distinguish proof from modelling. We will not convert an illustrative scenario into a claimed client result without evidence.

Baseline

State the pre-change metric, measurement period and source.

Intervention

Describe exactly what changed rather than attributing all movement to 'AI'.

Outcome

State the post-change metric and the comparable measurement period.

Evidence type

Label each number as observed, client-reported, modelled or illustrative.

Verification

State whether the client has reviewed and approved the published result.

Limitations

Disclose material assumptions, confounding factors and scope limits.

Tech Recruitment12-person specialist teamIllustrative — not client-verified

Illustrative scenario: tech recruitment screening workflow

The Challenge

A growing tech recruitment agency is struggling to keep up with CV screening. Manual review is displacing candidate and client work, leading to slow response times and patchy prioritisation.

Our Solution

An AI-assisted triage workflow ranks candidates against role requirements, surfaces strong matches for faster human review, and keeps the wider pool organised by priority.

What to measure

BaselineCurrent screening effort
PilotHuman-reviewed triage workflow
MeasureShortlist turnaround and quality

These examples are illustrative scenarios, not named client case studies. Final outcomes depend on workflow quality, data quality and implementation scope.

Healthcare Recruitment8-person delivery teamIllustrative — not client-verified

Illustrative scenario: healthcare staffing follow-up workflow

The Challenge

Qualified candidates are falling through the cracks because follow-up steps are inconsistent and nobody has a clear view of response timing across the pipeline.

Our Solution

A structured outreach sequence, linked back to the CRM, keeps communication visible and ensures candidates receive timely follow-up without relying on memory alone.

What to measure

BaselineCurrent response time
MeasureCandidate engagement consistency
AssignPipeline ownership across the team

These examples are illustrative scenarios, not named client case studies. Final outcomes depend on workflow quality, data quality and implementation scope.

Executive Search5-person partner-led firmIllustrative — not client-verified

Illustrative scenario: executive search CRM cleanup

The Challenge

Duplicate records, inconsistent data entry, and weak reporting make it difficult to understand what is happening across live searches.

Our Solution

CRM cleanup, tighter data standards, clearer pipeline stages, and lightweight dashboards create a more reliable operational view for delivery and leadership.

What to measure

AuditPipeline visibility for partners
ScoreData quality and reporting confidence
MeasureWeekly reporting effort

These examples are illustrative scenarios, not named client case studies. Final outcomes depend on workflow quality, data quality and implementation scope.

Build evidence from your own baseline

Start with a measured assessment rather than an unsupported transformation promise.

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