Human + AI Performance Intelligence
AI is everywhere.But is it working?
Scallar reveals how humans and AI actually work together, what creates value, and what needs to change.
Everyone is adopting AI. Almost nobody can see what it's changing.
Companies buy AI licenses. Employees experiment with new tools. Agents become part of everyday workflows.
Usage grows. But activity isn't impact.
A thousand prompts don't necessarily mean better work. An automated workflow doesn't necessarily create value.
The question isn't whether AI is being used. It's whether AI is making a difference.
- 01AI licenses
- 02Active users
- 03Tool usage
- 04Prompt volume
Easy to count. Says little about value.
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.
From invisible work to intelligent action.
Layer 01
See how work actually happens.
Scallar is designed to connect to authorized workplace tools and reconstruct observable work as structured contributions — without imposing self-report questionnaires on employees.
- Who with whomSlack · Calendar · Code reviewsCollaboration patterns.
- Working on whatJira · Linear · HubSpot · DocsTasks, projects and priorities receiving attention.
- Working howTools and methods where data allowsHuman alone, human + AI, or autonomous agent.
- With what resultTasks · PRs · Opportunities · MilestonesDeliverables and observable outcomes.
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.
Great performance is more than productivity.
“Are we using the most effective tools and working methods?”
Observed signals
- AI usage by task type
- Method selection
- Under-used tools
- Excessive or ineffective AI use
Potential inefficiency
AI applied where it adds rework, absent where it would help.
Possible improvement
Refined workflows, shared practices, targeted automation.
Team-level patterns, interpreted in context. Not individual productivity scores.
Insight changes nothing.
Action does. Six domains where Scallar is designed to act.
A-04
Methods & AI
Better AI, not just more AI.
Designed to help organizations use AI more effectively — and to spread the working methods that actually work.
- Example · hypothetical
- AI is used widely to draft documents, but the associated workflow shows no clear improvement in cycle time.
- Possible action
- Suggest refining the workflow and adding a clearer human review stage.
Human involvementThe team decides whether to try it. Relevant outcomes are monitored afterwards.
See what intelligence looks like.
Overview
Contributions this month
4,812
With AI participation
41%
Teams observed
9
Aggregated across the organization. No individual figures in this view.
Understand work. Respect people.
Organizational intelligence must not become workplace surveillance. Individuals keep access to their private detail. Managers work with appropriately aggregated, team-level information.
Visible only to you · Sam (fictional)
- Focus time this week6h 20m
- Out-of-hours activity↑ 3 evenings
- AI-assisted contributions14
- Private suggestion: block two focus mornings next week.
- Privacy by design
- Appropriate aggregation
- Human oversight
- Explainable recommendations
- Contestability
- Responsible data use
- Regulatory considerations
Design principles, not certifications. No external audit or legal approval is claimed.
Work has changed. The way we understand it hasn't.
For decades, organizations have tried to understand performance by measuring people.
Their hours. Their activities. Their objectives. Their ratings. Their reviews.
Then AI arrived. And the nature of work began to change.
Today, a contribution might come from a person.
A person working with AI. Or an autonomous agent.
The boundaries are shifting. But most organizations still use yesterday's measurements to understand tomorrow's work.
We believe this has to change.
- Because using AI is not the same as creating value.
- Because activity is not the same as impact.
- Because performance is the result of people, technology, methods, relationships and decisions working together.
The future of performance won't be about counting more. It will be about understanding better.
Seeing what works. Discovering what doesn't. And having the intelligence to improve it.
Continuously. Responsibly. Humanly.
SCALLAR.
Understand how work works.
Finally, a clearer picture of AI's real impact.
- 01
CEO & executive team
Understand where AI adoption may be changing organizational performance, and where to improve operations.
- 02
CIO, CTO & AI transformation
Move beyond licenses and usage toward contextual insight into working methods.
- 03
HR & people leaders
Understand team-level collaboration, sustainable performance and skills opportunities.
- 04
Operations leaders
Identify friction, ineffective methods and opportunities for improvement.
No guaranteed ROI. Scallar is designed to make outcomes measurable so each organization can judge for itself.
Book a DemoReady to see beyond AI adoption?
See how Scallar is designed to connect AI usage, real work and organizational performance.
Or get early access