AI Intelligence
Usage is not impact.
Stop counting prompts. Start understanding performance. Scallar is designed to connect AI adoption to the work it changes.
- 01
AI adoption
Who uses which AI tools, for which tasks?
The entry point — usage by task category and team, not a personal leaderboard.
- 02
AI contribution
Where does AI actually participate in work?
Contributions classified as human, human + AI, or autonomous agent, linked to deliverables.
- 03
AI effectiveness
Where does AI improve outcomes — and where doesn't it?
Methods compared in context, with improvement opportunities and tracked interventions.
Work is no longer just human. Measurement shouldn't be either.
The contribution is the core unit of Scallar's data model. It connects who → what → how → outcome — to understand where AI participates and what actually results.
Actor
Product designer + AI assistant
Task
Checkout redesign spec
Tools
Figma · Notion · Claude
Outcome
Spec delivered · 4 days · 2 review cycles
- Brief
- AI-assisted research
- Design
- Review
- Shipped spec
● Human + AI
The same contribution, with AI assistance at the research step. Faster here, but with an extra review loop — AI involvement alone doesn't prove better work.
AI involvement is recorded, not rewarded. It never equals performance by itself.
Adoption is easy to count. Impact is harder to understand.
Three fictional teams · selected workflow outcome index
Illustrative experience · fictional dataTeam B
Moderate AI usage concentrated on one workflow, where outcomes improved markedly.
More AI is not always better.
Better AI is better.
Fictional demo data. Observed differences are associations, not proof of causal impact — Scallar is designed to make that distinction explicit.