I work upstream — structure, scope, and design direction before the team executes.
Technical systems I could read structurally — patterns in complex data, structural failures in workflows, the hidden load beneath a clean interface. The harder problems were never in the system. They were in the gap between what the system did and what humans needed it to do.
I/O Psychology gave me a second diagnostic lens. It taught me to read human and organisational systems the way engineering taught me to read technical ones — structurally, not symptomatically.
Twelve years later: Google gTech, Experion Technologies, Folka, McKinsey. Environments where the cost of misalignment between human intent and system behaviour is high, and where intuition-based design breaks fast.
I get called in when the usual approach has already failed.
Most organisations track design capacity by hours. This model fails because cognitive work is not uniform in weight, a large portion of design work never appears in any project tool, and coordination overhead in distributed teams is invisible to every burndown chart.
Leadership made resourcing decisions on visible deliverables. Designers experienced workload as cognitive, emotional, and coordination weight. The two never matched.
I identified this as a translation problem, not a productivity problem. I built WDCF: a five-dimension weighted scoring system that gives every design task a Weighted Capacity Score — a language leadership can read and designers can use without it sounding like complaint.
The same problem exists in AI product design. When a system's behaviour is invisible to the people depending on it, trust breaks. Making invisible behaviour legible is the design problem in both cases.
The instinct when capacity feels broken is to ask for more headcount or a better process. I went one step back and asked why every existing measurement method was failing.
Story points are engineering-native — they don't transfer to design work. Hours logged capture input time but nothing about quality of attention or context-switching cost. Deliverable count rewards volume without accounting for revision cycles or upstream ambiguity. Headcount ratios ignore skill specialisation and cross-team reach. Self-reported load suffers from anchoring bias and systematic under-reporting in low-trust environments.
The reframe: this was not a resourcing problem. It was a translation problem. The cognitive cost of design work existed — generating burnout, missed commitments, and failed headcount cases. It had no number attached to it. And without a number, it had no voice in an enterprise decision room.
| Method | What it measures | What it misses |
|---|---|---|
| Story Points | Relative effort (engineering-native) | Cognitive load, invisible work, coordination cost |
| Hours Logged | Time spent on tracked tasks | Quality of attention, context-switching, async work |
| Deliverable Count | Quantity of outputs | Revision cycles, upstream ambiguity, strategic weight |
| Headcount Ratio | Designer-to-engineer ratio | Skill specialisation, project complexity, cross-team reach |
| Self-Reported Load | Subjective sense of busyness | Anchoring bias, imposter syndrome under-reporting |
Cognitive Load (30%) — Sweller's theory distinguishes intrinsic load from extraneous load added by coordination and process. Miller and Cowan's working memory research establishes a ~4-chunk capacity limit. Tasks exceeding this threshold degrade output quality regardless of reported effort.
Collaboration Complexity (25%) — Wegner's Transactive Memory Systems research shows coordination cost scales non-linearly with team size in distributed environments. Never appears in any task tracker.
Iteration Depth (20%) — Each revision cycle involves re-engagement with prior decisions, stakeholder re-explanation, and sunk-effort re-anchoring. The psychological cost compounds across cycles.
Invisible Work (15%) — Hochschild's emotional labour theory: stakeholder management, conflict absorption, and ambiguity facilitation are psychological work with a measurable cost. They do not appear in Jira.
Strategic Weight (10%) — Carries lowest share because its psychological pressure is partially captured in Cognitive Load through Prospect Theory's loss-aversion effect.
The formula: WCS = (CL × 0.30) + (CC × 0.25) + (ID × 0.20) + (IW × 0.15) + (SW × 0.10). Scale 1.0–5.0. Each dimension scored 1–5 against explicit behavioural anchors to prevent inter-rater drift and allow longitudinal comparison.
Four operational states. The Optimal Zone (2.5–3.4) is grounded in the Yerkes-Dodson performance curve: too low is disengagement, too high is degraded output quality.
The deliberate constraint: the entire system runs in Google Sheets. Zero procurement. Zero onboarding friction. Any manager opens the live dashboard in a browser today. The constraint is a feature. A tool that requires an approval process to adopt would die in that process.
| Designer | Mon | Tue | Wed | Thu | Fri | Avg | State |
|---|---|---|---|---|---|---|---|
| Designer A | 3.2 | 3.8 | 4.1 | 4.3 | 3.9 | 3.86 | Caution |
| Designer B | 2.1 | 2.4 | 2.8 | 3.1 | 2.9 | 2.66 | Optimal |
| Designer C | 4.2 | 4.5 | 4.4 | 3.9 | 4.1 | 4.22 | Overload |
| Designer D | 1.8 | 2.1 | 1.9 | 2.3 | 2.0 | 2.02 | Underutil. |
WDCF is a trust calibration system. It exists because there was a structural mismatch between the true state of a system — actual designer capacity — and the signals available to the people making decisions about it.
That mismatch is the central design problem in human-AI products. When an AI system takes an action without making its reasoning visible, when its confidence is uncalibrated, when its failure modes are undisclosed — the person depending on it is making decisions based on incomplete signals.
The design question is identical in both cases: what needs to be made legible, to whom, in what form, so that trust is earned through transparency rather than assumed through absence of failure.
"WDCF is not an AI product. It is a proof of concept that I think in this structure."
I work best when the brief is incomplete, the scope is contested, or the previous approach has already failed. That's not a constraint — that's the job.