Surveys & Feedback — Aiske.ai AI

Confidential — internal Aiske.ai. All surveys are anonymous and aggregated; no individual is identified. Results need a handful of responses before the averages mean anything, and small groups are reported at arm level (Commercial / P&T) to protect anonymity.

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AI Adoption Pulse

What would actually help you get more value from AI in your role — the right examples, a bit of time, someone to show you, clearer data rules, or just getting it set up. Open to everyone, every role.

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Results below · re-run periodically

Monthly · 2 min · anonymous

Developer Experience Pulse

For the engineering & QA team — how it actually feels to build here: flow, feedback loops, code clarity, review speed, tools and AI value.

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Results on the Engineering Metrics page

AI Adoption Pulse — results

⚠ SAMPLE DATA — illustrative only, not real responses. Clear sample

Why we measured it this way — the science

This isn't a generic engagement survey. Every question maps to a validated construct from decades of research on how people adopt new technology — so the gaps it surfaces point to known, well-studied levers, not hunches.

“AI saves me meaningful time” and the reverse-coded “getting good results is more effort than it's worth” are performance expectancy and effort expectancy — two of the four core constructs of UTAUT (Venkatesh et al., 2003), the most widely-validated model of technology acceptance. They descend directly from Davis's TAM (1989): perceived usefulness and perceived ease of use.
“My manager encourages & models using AI” is UTAUT's social influence construct — adoption is strongly shaped by whether respected colleagues, especially leaders, are visibly using the tool.
“Can I point to a task in my role AI does well” is observability — one of the five perceived attributes of an innovation in Rogers' Diffusion of Innovations (relative advantage, compatibility, complexity, trialability, observability). Seeing it work on your kind of task is among the strongest predictors of whether you'll adopt it. The behaviour-anchored 1–5 journey ladder mirrors Rogers' progression from non-adoption to embedded use.
The “stuck before the payoff” reading is the productivity J-curve (Brynjolfsson, Rock & Syverson, 2021): output dips during the learning-and-investment phase of a general-purpose technology before it climbs — so a high effort score in the lower-adoption group flags people mid-dip who need time and support, not persuasion.

Reading the lower-vs-higher gap on each construct tells us which lever to pull — circulate role-specific worked examples (observability), protect time and pair people (the J-curve), or get leaders visibly modelling it (social influence) — rather than guessing. That's the point: it's a diagnostic, not a scoreboard.

Sources: Davis (1989), MIS Quarterly — TAM · Venkatesh, Morris, Davis & Davis (2003), MIS Quarterly — UTAUT · Rogers, Diffusion of Innovations (5th ed., 2003) · Brynjolfsson, Rock & Syverson (2021), American Economic Journal: Macroeconomics — the productivity J-curve.