Workforce Productivity Intelligence: The Productivity of Cognition
- Maturity
- L4
- Domain
- Grow & Keep
- Analytics
- predictive
- Income per FTE
- ≈$425k
- Sponsor
- Group Chief Human Resources Officer
- Confidence
- High
The situation
Where is cognitive-work productivity created and lost across Aurora, and which levers — AI augmentation, work redesign, spans, demand management — move it, measured honestly without crude output proxies?
The recommendation on the table
Stand up honest productivity intelligence before scaling AI
Books AI value against real movement in income-per-FTE and value-adding task share, not against tool roll-out.
Trade-offRequires measuring cognitive-work productivity with stated limitations — harder than headcount ratios, but defensible.
The evidence
In a knowledge business where ~half the controllable cost is people, a few points of productivity is a cost-income story worth nine figures — yet Aurora had never had a defensible measure of cognitive-work productivity, the OEE-equivalent that manufacturing takes for granted. BNK-02 built honest productivity intelligence (income per FTE, value-adding task share, cost-income by function) and modelled the uplift available from AI augmentation, finding a large reworkable-task reserve in operations and an adoption gap that means uplift is being assumed before it is earned.
Aurora — Workforce Productivity Intelligence
Cognitive-work productivity made measurable: income per FTE, value-adding task share, and the augmentation uplift available — gated on adoption.
Key takeawayRetail operations lags peers while carrying the highest AI exposure — the first redesign target.
- Value-adding55%55%
- Reworkable/low-value45%45%
Key takeaway~45% of operations task-time is the productivity reserve augmentation is meant to release.
Key findings
About 45% of operations task-time goes to rework, coordination and low-value processing — the productivity reserve AI augmentation is meant to release. A modelled ~18% uplift (±5pp) is available, but only if work is redesigned around the freed capacity rather than the capacity simply being cut.
What we can’t claim
Augmentation tool licences are well ahead of actual adoption — only ~45% of eligible users are active. The productivity case is being counted on roll-out, not on use, which is the banking echo of manufacturing's 'trained is not the same as capable.' Until adoption rises, the uplift is an assumption, not a result.
Recommendations
Stand up honest productivity intelligence before scaling AI
high priorityBooks AI value against real movement in income-per-FTE and value-adding task share, not against tool roll-out.
Trade-off
Requires measuring cognitive-work productivity with stated limitations — harder than headcount ratios, but defensible.
Tie productivity-value booking to measured adoption, not licences
medium priorityCloses the gap between assumed and earned value by shifting focus from licence roll-out to adoption enablement.
Trade-off
Slows the booking of headline savings until adoption is real.
Analytical framework
How we reached this
Predictive — measure cognitive-work productivity honestly and model the uplift available from augmentation and work redesign, with confidence intervals.
ConfidenceMedium
Analytical framework
How we reached this
Predictive — measure cognitive-work productivity honestly and model the uplift available from augmentation and work redesign, with confidence intervals.
Methods applied
Statistical techniques
Algorithms
Data sources
Outputs generated
Why this confidence
Knowledge-work productivity is partly unmeasurable; outputs are ranges with a stated measurement caveat as the binding limit.
The reasoning
Business context
Builds directly on the BNK-01 baseline. Sponsored by the Group CHRO because the cost-income target cannot be met by technology alone — only by re-shaping what 96,500 people do, and measuring whether the re-shaping actually lifts value.
Expected value
Replaces crude headcount ratios with defensible productivity intelligence, sizes the augmentation uplift with confidence intervals, and ties value booking to measured adoption rather than tool roll-out. The largest closable part of the cost-income gap is productivity, not structural change.
Workforce landscape
Operations spends ~45% of task-time on reworkable/low-value work — the productivity reserve. Modelled augmentation uplift is ~18% (±5pp) in operations, contingent on adoption. Augmentation adoption (~45%) lags licences badly — the banking echo of 'trained ≠ capable.'
The analytics journey
Level 4, predictive. Models the productivity uplift from specific interventions with explicit confidence intervals, and is honest that knowledge-work productivity is partly unmeasurable — treating that as a stated limitation, not a footnote.
Under the hood
Activity and income data are joined to the task taxonomy to compute income per FTE and value-adding task share; an interpretable regression-based model projects uplift from augmentation/redesign with confidence bands; adoption telemetry gates whether modelled uplift is being captured.
Confidence & evidence
Why you can rely on this
The inconvenient truth
Augmentation tool licences are well ahead of actual adoption — only ~45% of eligible users are active. The productivity case is being counted on roll-out, not on use, which is the banking echo of manufacturing's 'trained is not the same as capable.' Until adoption rises, the uplift is an assumption, not a result.
Method
Confidence is a deterministic read of KPI strength, target and benchmark coverage across this project — shown on an illustrative reference dataset, computed the same way it would be on live data.
Take this further