The Five Seat Archetypes: A Classification System for Enterprise AI Waste

Most enterprise AI cost conversations start in the wrong place. They start with the power users — the developers running Claude Code all day, the analysts burning through Gemini Pro, the engineers maxing out GitHub Copilot. Those users are visible. They show up in usage reports. Finance can see them.

The real problem is invisible. It sits in the seats that aren't doing anything. The seats that are licenced, billed monthly, provisioned with full access — and generating near-zero consumption. The seats nobody reviews because there is nothing to see. The seats that quietly renew at $100 or $200 a month because no one has built a system that notices they exist.

That is where the waste lives. And finding it systematically — not by accident, not at renewal, not after the invoice arrives — is what Seat Behavioral Intelligence is built to do.

The Classification System

Every AI licence in a managed enterprise portfolio falls into one of five behavioural archetypes at any given measurement period. Not by what the seat holder was told they would do. Not by what their job title suggests. By what the consumption data shows.

These five archetypes are the core IP of the PromptKing platform — patent pending with the USPTO — and the foundation of every rightsizing recommendation the platform produces.

Ghost Seat — The Primary Finding

A Ghost Seat is a licenced AI seat generating near-zero consumption over a rolling 30-day measurement period. The seat holder has been provisioned, the invoice is being paid, and nothing is happening.

Ghost Seats are not edge cases. Industry data for 2026 puts the average enterprise ghost seat rate at 18–22% of total provisioned AI licences. That means roughly one in five seats your organisation pays for every month is a Ghost Seat right now.

At $100–$200 per seat per month for premium AI plans, the math is immediate. A 100-seat enterprise with an 18% ghost seat rate is paying $1,800–$3,600 per month for seats that generate zero value. Annually: $21,600–$43,200. Recoverable in full. No capability impact. No workflow disruption.

Ghost Seats are the highest-confidence rightsizing target in the platform — confidence scores typically above 90% — because there is no trade-off to analyse. You are not downgrading a productive user. You are stopping payment on a seat that is not being used.

FinOps action: Deprovision or downgrade immediately. Confidence threshold for action: two consecutive 30-day periods below 5% of plan capacity.

Underutilised Seat — The Mismatched Plan

An Underutilised Seat is active — the seat holder is using the platform — but consuming a fraction of what their plan tier provides. They are paying for a Max plan and behaving like a Pro user. The plan and the behaviour do not match.

This is the most common waste pattern in enterprise AI portfolios. It occurs because AI seat provisioning typically happens at the department level: IT buys Max 5× for the entire engineering team because the top three engineers need it. The other seven engineers on Pro-level usage are paying $100/month for a $20 workflow.

The cost implication is direct. A Max 5× seat ($100/month) with 12% plan utilisation could be served by a Pro plan ($20/month) with identical capability for that user. The $80/month difference is recoverable per seat, every month.

FinOps action: Downgrade recommendation with projected monthly saving. Confidence threshold: two consecutive 30-day periods between 5% and 20% of plan capacity.

Normal Seat — The Baseline

A Normal Seat is consuming a healthy proportion of its plan capacity — within the expected range for the plan tier, sustained over time. No rightsizing action is warranted. No upgrade risk is imminent.

Normal Seats are the reference state. They tell you what good looks like in your organisation — the utilisation pattern of a seat that is provisioned at the right tier for its actual workload.

The Normal archetype also serves as the baseline for anomaly detection. When a Normal Seat suddenly drops to Underutilised or spikes toward Power User territory, the behaviour change is the signal — not the utilisation level itself, but the deviation from the established pattern.

FinOps action: No action. Monitor monthly. Document as organisational baseline.

Power User Seat — The Ceiling Signal

A Power User Seat is consuming a high proportion of its plan capacity — approaching the plan ceiling. The seat holder's workload is well-matched to the current tier. There is no waste here.

But there is a different risk: ceiling breach. A Power User seat that hits 100% of plan capacity faces two outcomes, neither desirable — hard limits that interrupt workflow, or overage billing that arrives without warning.

The goal of an AI FinOps program is not to turn every seat into a Power User. It is to match every seat to the plan tier that reflects its actual behaviour — which for most seats in most enterprises is Normal, not Power User.

FinOps action: Monitor. Upgrade recommendation if ceiling is hit in two consecutive periods. Do not downgrade under any circumstances.

At-Risk Seat — The Anomaly

An At-Risk Seat has changed. Its consumption pattern has deviated materially from its established baseline — either declining sharply toward Ghost Seat territory or spiking unexpectedly toward Power User or beyond.

The At-Risk classification is defined not by an absolute utilisation level but by a change in behaviour. A seat consuming 65% of plan capacity for four months and then dropping to 4% in the most recent period is At-Risk — not because 4% is automatically a problem, but because the change is unexplained and unexplained changes carry both cost and compliance implications.

Two At-Risk patterns, two different causes:

Sharp decline: personnel change, workflow shift, or shadow AI migration

Sharp spike: agentic cost event or misconfigured automation

Both require investigation before any action is taken. The At-Risk label is an alert, not a recommendation.

FinOps action: Investigate the cause of the pattern change. Do not act until the cause is understood. Document the finding.

Why the Classification Order Matters

Ghost Seat → Underutilised → Normal → Power User → At-Risk.

That is the order of FinOps priority — from the highest-confidence, highest-dollar-impact finding to the most complex investigation.

A FinOps practitioner working through a 100-seat portfolio starts with the Ghost Seats. Fastest wins — highest confidence, zero capability trade-off, immediate dollar recovery. Then Underutilised. Then Normal (document, no action). Then Power User (monitor, upgrade plan ready). Then At-Risk (investigate, hold action until cause is known).

This sequence is the 90-day FinOps playbook compressed into a classification system. It tells you where to look, in which order, and what to do when you find each archetype.

The Patent and What It Covers

The Seat Behavioral Classification System — the method by which PromptKing assigns these five archetypes to each seat in a managed AI portfolio, calculates confidence scores for rightsizing recommendations, and tracks behavioural drift over time — is the subject of a pending US patent application filed with the USPTO.

Title: System and Method for Seat-Level Behavioral Classification of Enterprise AI Subscription Seats and Automated Rightsizing.

The five archetype names, their definitions, and their FinOps implications are published here as working vocabulary offered to the field. The underlying classification method and scoring system are protected separately.

See your portfolio's archetypes →

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