Usefulness of AI assistants for day-to-day work
This survey assesses the usefulness of AI assistants (chatbots, copilots, and similar tools) in day-to-day work as a tool: where and how they are used, how much time they save, satisfaction with output quality and usability, how often verification is needed, common issues, and which improvements would have the biggest impact. The results help prioritize integrations, training, and quality requirements.
The "Usefulness of AI assistants for day-to-day work" template measures how much value chatbots, copilots and similar tools actually deliver in everyday tasks. It looks at how often they are used, where they save time, how satisfied people are with the output, and how much verification the results still need. Use it to decide where to invest in AI integrations, training and quality requirements.
What the “Usefulness of AI assistants for day-to-day work” survey measures
The survey captures usage frequency (from daily to non-users), the concrete scenarios where the assistant helps most (quick answers, drafting and editing, summarizing, planning, brainstorming, preparing materials, code, translation) and where it is accessed — web interface, work chat bot, email/calendar, documents, IDE copilot or an internal corporate assistant. It quantifies weekly time saved and rates the assistant across accuracy, usefulness, context handling, output format, speed and ease of refining answers. Crucially, it records verification behavior (use as-is, minor edits, check key facts, almost always verify) and the most common problems — hallucinations, overly generic answers, hard-to-get results, missing internal context, security concerns, poor formatting, slowness — plus what blocks non-users from adopting it.
Who the “Usefulness of AI assistants for day-to-day work” template is for
It suits team leads and operations managers assessing productivity gains, IT and enablement teams planning rollouts and integrations, HR and L&D building training programs, and knowledge-work teams (marketing, support, engineering, analytics) evaluating tools before scaling. Companies comparing several assistants or building a business case for AI adoption will get directly usable evidence.
How to adapt the template to your needs
Tailor the scenario and channel lists to your stack — name the actual tools your team uses instead of generic categories, and adjust the IDE/copilot options for non-technical audiences. Add branching so non-users skip the rating questions and go straight to the barriers block, or expand the time-saved scale to match your reporting. You can turn the rating matrix into your own quality criteria, add an open field for the single most valuable use case, or insert a role/department question to segment results.
Questions and answer options
Question type: single choice.
Answer options:
— Daily
— Several times a week
— About once a week
— Less often
— I do not use an AI assistant
Question type: multiple choice.
Answer options:
— Quick answers and help with work-related questions
— Drafting and editing texts (emails, messages, documents)
— Summarizing (texts, discussions, notes)
— Planning (task structure, plan, checklist)
— Ideas and solution options (brainstorming)
— Preparing materials (presentations, descriptions, instructions)
— Code, scripts, automation
— Translation and improving wording
Question type: multiple choice.
Answer options:
— In a web interface (browser)
— In a work chat/messenger (bot/integration)
— In email or calendar (built-in assistant)
— In documents/spreadsheets (built-in assistant)
— In an IDE/development environment (copilot/plugin)
— In a corporate portal/internal assistant
Question type: single choice.
Answer options:
— No time saved
— Up to 1 hour
— 1–3 hours
— 3–5 hours
— 5–10 hours
— More than 10 hours
— Hard to estimate
Question type: single-answer matrix.
Answer options:
— Accuracy of answers
— Usefulness and applicability to the task
— Consideration of context and constraints
— Quality of the output format (structure, clarity)
— Speed and stability
— Ease of clarifying and refining the answer
Question type: single choice.
Answer options:
— Use as is without checking
— Make minor edits and use it
— Check only key facts/numbers
— Almost always verify
— More often don’t use the output than use it
Question type: multiple choice.
Answer options:
— Errors or made-up facts
— Answers are too general and not very applicable
— Hard to get the right result on the first try
— The needed context is unavailable (no access to internal data/documents)
— Security and confidentiality concerns
— Unhelpful output format (too long/unstructured)
— Slow performance or instability
— Frequent limitations/feature restrictions
Question type: multiple choice.
Answer options:
— I don’t see tasks where it would be useful
— No access to suitable tools
— Unclear what I can and cannot do/share with AI
— I don’t trust the output quality enough
— Security and confidentiality concerns
— Not enough time to learn
— Unclear how to fit it into my workflow
Similar survey templates
Frequently asked questions
The template does both: a dedicated question asks for estimated weekly hours saved, while the rating matrix and scenario questions capture perceived value. Cross-tabbing high satisfaction against low reported hours (or vice versa) shows where the perception and the actual payoff diverge.
Scenarios cover the type of task (drafting, summarizing, code, planning), while the 'where' question covers the access point — browser, work chat, email, documents, IDE, internal portal. Together they show not just what people do with AI but which integration surface actually gets used.
It reveals how much people trust the output in practice. A high share of 'almost always verify' or 'more often don't use the output' signals quality or trust problems that raw satisfaction scores can hide, and it flags where accuracy improvements would remove the most rework.
Yes — the final block asks non-adopters what holds them back: no useful tasks, no access to tools, unclear rules about what they can share, low trust in quality, security concerns, no time to learn, or not knowing how to fit it into their workflow.