Benchmark study · Wave 1 · September 2026
How design teams work: focus is the leading indicator of project health
Findings from the first wave of the Enterprise Design Telemetry Benchmark: 43 design projects in six organisations, measured passively through Figma activity logs.
- Published:
- 25 September 2026
- By:
- MoreThought, makers of FlowStatus
- Sample:
- 43 projects · 6 organisations
Summary
The findings
Across 43 design projects in six organisations, focus emerged as the strongest predictor of project success among the factors we examined. Focus, measured by whether designers sustained attention on individual files or fragmented their effort across many, explained more variation in outcomes than team size, seniority, tooling or process.
Projects with low file-switching averaged 3.1 out of 5 on business and customer outcomes. Projects with high file-switching averaged 2.1. That gap, a full point on a five-point scale, held regardless of team size.
And it is visible early. The same behavioural signature that predicts final outcomes is already present in the first three weeks of a project. Organisations do not need to wait for a project to finish to know whether it is in trouble. Week-three metrics for activity, consistency and file-switching correlate at 0.97–0.98 with the full project, so the early read is representative.
Why this matters
Design leaders currently have almost no leading indicators. Outcome data (whether a project shipped well, whether it moved the metrics it was supposed to) arrives too late to act on. Everything else available today, such as velocity, headcount and ticket counts, measures activity, not coherence.
Focus is different. It is a measurable proxy for the cognitive conditions under which good design work actually happens: sustained, undivided attention on a problem, rather than shallow, repeated context-switching across many. This is consistent with research on flow states and attention residue: when people switch tasks, part of their cognitive capacity stays attached to the task they left, degrading performance on the one they moved to. File-switching in Figma is an unusually clean behavioural trace of exactly this pattern, captured automatically and at scale.
This converts an abstract management concern about team focus into a real-time number, available three weeks into a twelve-week delivery cycle instead of at a retrospective.
The evidence
Two modes of working emerged clearly when projects were grouped by behaviour.
| Measure | High focus | Medium | Low focus |
|---|---|---|---|
| Projects | 15 | 11 | 17 |
| Average outcome score (1–5) | 3.1 | 2.7 | 2.1 |
| File-switching | Very low | Moderate | High |
| Files worked on by multiple people | 36% | 29% | 23% |
High-focus projects were not simply smaller or better resourced; team size did not explain the gap. They were distinguished by fewer people jumping between files and more files receiving sustained, shared attention.
How we tested alternative explanations
We ran four separate models to see what best predicts project outcomes. File-switching consistently outperformed the obvious alternatives.
| What we tested | Measures used | Pseudo-R² | Finding |
|---|---|---|---|
| File-switching + team size | Switching rate, number of designers | 0.21 | Switching dominates; adding people does not compensate |
| Switching alone | Switching rate only | 0.15 | Strong predictor even without controls |
| Duration + momentum | Project length, consistency of work | 0.11 | Both help, but jointly weaker than switching alone |
| Team composition | Editors versus viewers | 0.11 | Viewer-heavy teams score lower |
The effect persists after accounting for the obvious confounders. Team size, project duration and the proportion of active editors were all tested as alternative explanations. File-switching remained the dominant factor even when these were controlled for.
Separately, early collaboration on shared files accounted for a meaningful share of outcome variation, a consequential effect for a single early-stage signal.
The week-three signal
The signal appears early enough to act on. Early-window metrics (the first 21 days) for activity, consistency and file-switching correlated at 0.97–0.98 with full-project metrics. A three-week read is, in practical terms, the whole story.
| Week-three signal | Correlation with final project signal |
|---|---|
| Total activity | 0.98 |
| Work consistency (momentum) | 0.98 |
| File-switching | 0.97 |
How focus is measured
Focus (switch rate) is the share of a project's Figma activity where a designer's very next logged action is on a different file. Values range from 0 to 1: higher means more file-hopping.
High and low groups are relative to the 43-project sample, not industry norms. Organisations with heavy viewer traffic may show a lower number for reasons unrelated to focus. For how FlowStatus builds on this signal in the product, see the methodology page.
Study design
| Element | Wave 1 approach |
|---|---|
| Sample | 43 design projects across six organisations |
| Data source | Passive Figma activity-log telemetry: actor, timestamp, action type and file, with no surveys, plugins or access to design contents |
| Outcome measure | Business and customer outcome score on a 1–5 scale, self-reported by participating teams |
| Focus measure | Switch rate: share of actions where the next action by the same designer is on a different file |
| Other variables | Team size, project duration, momentum (consistency of work), editor versus viewer composition, shared-file collaboration |
| Grouping | High, medium and low focus, relative to the sample distribution |
| Model evaluation | McFadden's pseudo-R² across four competing models |
| Early-window test | Correlation between first-21-day metrics and full-project metrics |
Organisations operated different design models, including centralised pools, embedded squads and design system teams. For how to get the same activity data out of your own Figma organisation, see the Figma activity audit guide.
What design leaders should do
1. Run a focus check at week three
If designers are bouncing across many files with little overlap, intervene on scope and stakeholder alignment before adding headcount. Adding people to an unfocused project compounds the problem.
2. Stop equating visibility with contribution
A large roster of Figma viewers is not a sign of health. Track who is actually building, not who is watching.
3. Don't apply one collaboration template everywhere
Different operating models (centralised pools, embedded squads, design system teams) reach good outcomes through different patterns. The focus signal travels across all of them; specific collaboration norms do not.
What we're confident about, and what we're not
This is the first wave of an ongoing benchmark. The headline finding, that focus predicts outcomes and predicts them early, is consistent, holds after controlling for team size, and is corroborated by a separate early-window analysis. We're confident in it.
What's next
The methodology can also interpret the impact of momentum (how consistently a project is worked on), team size, collaboration and participation, iteration rounds and speed. Early signals showed some effects on outcomes, but the sample was too small in this round to report them with confidence, so a second wave is planned.
Wave 2 opens in October 2026. It will expand the sample and test these signals against independent performance data. If you lead design teams and want to participate, email hello@morethought.co or use the button at the foot of this page.
Frequently asked questions
What is the strongest predictor of design project success?
In the 2026 Design Telemetry Benchmark (43 projects, six organisations), focus was the strongest predictor of project outcomes among the factors examined. Focus was measured as file-switching in Figma: projects where designers sustained attention on individual files outperformed projects where effort was fragmented across many files. It explained more variation in outcomes than team size, seniority, tooling or process.
How much better do high-focus design projects perform?
High-focus projects (low file-switching) averaged 3.1 out of 5 on business and customer outcomes. Low-focus projects (high file-switching) averaged 2.1 out of 5. Medium-focus projects averaged 2.7. The one-point gap held regardless of team size.
How early can you tell whether a design project is in trouble?
By week three. Activity, work consistency (momentum) and file-switching measured in the first 21 days of a project correlated at 0.97 to 0.98 with the same metrics measured over the full project. The behavioural signature that predicts final outcomes is already present in the first three weeks.
Does adding designers fix an unfocused design project?
The benchmark suggests not. In the model combining file-switching and team size (McFadden pseudo-R² 0.21), switching dominated and adding people did not compensate for low focus. The recommendation is to intervene on scope and stakeholder alignment before adding headcount, because adding people to an unfocused project compounds the problem.
What is file-switching or switch rate in Figma?
Switch rate is the share of a project's Figma activity where a designer's very next logged action is on a different file. It ranges from 0 to 1; higher values mean more file-hopping and lower focus. It is derived passively from Figma activity logs, with no surveys or plugins.
Does the benchmark prove that focus causes better outcomes?
No. The benchmark shows a consistent, early and robust association, not strict causation. Chaotic projects could generate switching rather than the reverse, although the early-prediction result makes pure reverse causation less likely. Outcomes in Wave 1 were self-reported by participating teams.
Can my organisation take part in the next benchmark wave?
Yes. Wave 2 opens in October 2026 and will expand the sample and test the signals against independent performance data. Design leaders can register interest by emailing hello@morethought.co or using the Talk to us form on flowstatus.io.
Cite this study
Suggested citation
MoreThought (2026). How Design Teams Work: Focus is the leading indicator of project health. Enterprise Design Telemetry Benchmark, Wave 1. FlowStatus. https://flowstatus.io/telemetry-benchmark
About MoreThought
MoreThought provides operational telemetry and diagnostic intelligence for enterprise product and design organisations, and builds FlowStatus. We help executive leaders replace subjective advocacy, manual spreadsheets and disruptive workshops with objective activity data. By analysing passive workflow telemetry from the tools teams use every day, we expose where delivery stalls, quantify true team capacity and isolate cross-functional friction without interrupting ongoing work.