Ask AI about your team: 25 prompts managers use every week
Copy 25 ready-to-use Ask AI prompts for managers: capacity, workload, focus time, tool usage and AI adoption, with example answers from sample data.
Ask AI lets managers ask questions about their team's work data in plain language, inside Intelogos or in other AI tools through the Intelogos MCP server. These 25 prompts cover the questions managers ask most: who is overloaded, where focus time goes, which tools and AI assistants the team uses, and what changed this week.
How does Ask AI work?
Ask AI answers plain-language questions about your team's Intelogos data on the Ask AI page of the web app, for Owners, Admins and Managers on the AI Intelligence and Enterprise plans. The Intelogos MCP server connects AI tools you already use, such as Claude, ChatGPT or Cursor, to the same data, so these prompts work there too. Any prompt on this page can also run as a scheduled task every day or every week, with each result saved in Ask AI and, if you like, emailed to you.
Ask AI
Illustration with sample data
Which department is closest to overload this month? Compare each department's average workday with 8 expected hours.
Customer Support is closest to overload: its 8 people average 8h 52m per workday against 8 expected hours. Engineering is next at 8h 21m.
Customer Support8h 52m
Engineering8h 21m
Sales7h 55m
Design7h 34m
Marketing7h 18m
The line marks 8 expected hours; amber bars are above it.
Worth a look: 3 of Customer Support's 8 people average more than 9h 30m per workday. Check their upcoming time off before adding work to the team.
Ask AI is included in the AI Intelligence and Enterprise plans.
Pick a topic to filter these 25 workforce analytics prompts, then copy one and paste it into Ask AI or into an AI tool connected through Intelogos MCP. Words in square brackets are placeholders to replace with your own.
Showing all 25 prompts.
Capacity and workload prompts: who is overloaded?
These are the questions to ask about team workload: who works long days, which departments are stretched and who is away soon. They read the Workday and Availability KPIs, work sessions and time off.
Prompt 1Workday KPI by department
Which department is closest to overload this month? Compare each department's average workday with [8] expected hours.
What it returns
Each department's average workday this month next to the hours you expect, from the longest days to the shortest.
Example answer(sample data)
Customer Support is closest to overload: its 8 people average 8h 52m per workday against 8 expected hours, and 3 of them average more than 9h 30m. Engineering is next at 8h 21m. Sales (7h 55m), Design (7h 34m) and Marketing (7h 18m) are below 8 hours.
Tip
Workday counts only scheduled workdays and leaves out approved time off, so a week of vacation won't make a department look underloaded.
Who has averaged more than [9] hours per workday over the last 4 weeks?
What it returns
Everyone whose average workday is above the limit, with their department and how many of the 4 weeks they were over it.
Example answer(sample data)
4 people: Leo (Engineering, 9h 48m), Ana (Customer Support, 9h 36m), Sam (Customer Support, 9h 12m) and Priya (Engineering, 9h 05m). Leo was over 9 hours in all 4 weeks; the other three in 2 or 3 of them.
Tip
Set the limit that fits your team's schedule, and treat the list as a reason for a conversation, not a verdict.
Which departments had the lowest availability last month, and how much of the gap was approved time off?
What it returns
Availability for each department, the share of expected hours that was tracked, with the approved time off behind any gap.
Example answer(sample data)
Sales had the lowest availability at 81% of expected hours, and 2 approved vacations account for about 6 points of it. Design was at 87%. Engineering, Customer Support and Marketing were all above 92%.
Tip
Low availability doesn't automatically mean underwork: meetings and work away from the computer aren't tracked, so ask the team before you conclude anything.
Who is on time off in the next two weeks, and will any department be short-staffed?
What it returns
Approved and pending time-off requests for the next two weeks, grouped by department, with the days each department has the fewest people.
Example answer(sample data)
3 approved requests and 1 pending. Two people in Customer Support are off Monday to Wednesday next week, and one engineer is off all next week. A pending request from Design covers Friday. Customer Support will have 6 of its 8 people from Monday to Wednesday.
Tip
If you're the request's assigned approver, Ask AI can approve or decline the pending request too; nothing changes until you confirm.
Who on my team regularly starts before [8am], finishes after [7pm] or works on weekends? Use the last 4 weeks.
What it returns
The people whose first or last activity falls outside the hours you name, or who worked on weekends, with how often it happened.
Example answer(sample data)
3 people. Ana (Customer Support) finished after 7pm on 9 of 20 workdays. Leo (Engineering) worked on 3 of the 4 weekends, about 2 hours each time. Mia (Sales) started before 8am on 11 workdays but finished by 4:30pm on most of them.
Tip
An early start isn't overload on its own, so look at the whole workday before you raise it.
Focus and productivity prompts: where does focus time go?
Focus and productivity prompts show when your team does its most active work and how much of it happens in the tools that matter. They read hourly activity, the Engagement, Productivity and Activity KPIs, productivity levels and time categories.
Prompt 6Hourly activity
When is my team most active during the day? Show average activity by hour for this week.
What it returns
Activity for each hour of the working day, with the peak hours and the quietest ones.
Example answer(sample data)
Activity peaks from 9am to 11am at 42% and again from 2pm to 4pm at 38%. It drops to 19% from 12pm to 1pm and after 4:30pm. Engineering's peak runs later, from 10am to 12pm.
Tip
Protect the peak hours for focused work, for example by moving recurring meetings out of them.
What share of my team's time went to Primary and Secondary tools this week, compared with last week?
What it returns
The Engagement KPI for this week and last week, with the tools behind any change.
Example answer(sample data)
Engagement is 74% this week, up from 69% last week. Time in Neutral tools fell from 4h 10m to 1h 50m per person after the support desk app was marked Primary for Customer Support. Distracting tools stayed at 2% of tracked time.
Tip
Neutral tools don't count toward Engagement, so a low score often means some tools still need an importance category, not that people are distracted.
Compare this week's Productivity and Activity KPIs with last week for each department.
What it returns
Productivity and Activity for each department, this week against last week, with the change in points.
Example answer(sample data)
Productivity is 47% this week for the whole team, up 3 points. Engineering gained the most, from 44% to 51%, and Customer Support fell 2 points to 41%. Activity is flat at 31%, with Design highest at 38%.
Tip
Productivity measures active minutes in Primary and Secondary tools against expected hours, so a change to a work schedule moves it too.
How much of the team's time went to communication tools each day this week, and which day was heaviest?
What it returns
Time in the Communication time category for each day, in hours and as a share of tracked time, with the heaviest day.
Example answer(sample data)
Communication took 31% of tracked time this week, about 2h 30m per person per day. Tuesday was heaviest at 38%, mostly in video call apps, and Friday was lightest at 22%. Customer Support accounts for almost half of it, as expected for a support team.
Tip
Communication covers chat, email and calls, so compare it with the category of the team's core work, such as Development or Design.
Which tools account for most of my team's high productivity time this week?
What it returns
High productivity time, when people interacted with a tool most actively, broken down by tool and importance category.
Example answer(sample data)
The team had 260 hours of high productivity time this week, 18% of tracked time. Three Primary tools account for 70% of it: the code editor (105 hours), the design tool (44 hours) and the spreadsheet app (33 hours). 4 hours came from Distracting tools, mostly a video streaming site.
Tip
Productivity levels describe how actively a tool was used, whatever its importance, so check which tools the high productivity time comes from.
Tools and software spend prompts: which tools does the team use?
Tools prompts show which apps and websites take your team's time, how many people use each one and how use changes after a rollout. Ask AI sees how tools are used, not what you pay for them, so add your seat count to a prompt when you want to compare the two.
Prompt 11Application and tool usage
Which 10 tools took the most time last month, and what share of the total did each take?
What it returns
The 10 apps and websites with the most time, each with its share of all tracked time, its activity and its importance category.
Example answer(sample data)
The top 10 took 64% of tracked time. The code editor led with 14%, then chat (11%), email (9%), the design tool (7%) and video calls (6%). The other five took 3% to 5% each, and only one of the ten, a news site, is marked Distracting.
Tip
Add a department to the prompt to see one team's top tools, the same view as the Tools page filtered by department.
We pay for [25] seats of [tool]. How many people used it in the last 30 days, and how many used it for less than an hour in total?
What it returns
How many people used the tool and for how long, and who used it for less than an hour, to compare with the seats you pay for.
Example answer(sample data)
18 people used the whiteboard app in the last 30 days, against your 25 seats. 7 of them used it for less than an hour in total, 5 of those in Sales. The heaviest users are the 5 people in Design, at 6 to 11 hours each.
Tip
Ask AI sees how a tool is used, not what you pay for it, so put the seat count from your bill in the prompt.
Which tools were used by fewer than [3] people last month, and for how long?
What it returns
Low-use tools, with how many people used each one and the total time, as a starting list for a license review.
Example answer(sample data)
11 tools. 6 were used by one person for less than 30 minutes, such as a PDF editor and a screen recorder. 2 are used by 2 people in Marketing for more than 15 hours a month each, so they matter to that team despite the few users.
Tip
Few users isn't the same as low value: check who relies on a tool before you cancel it.
Do we use more than one tool for the same job? Group last month's tools by time category, with the time and number of users for each.
What it returns
Tools grouped by time category, such as Communication or Documents, with time and users, so overlapping tools stand out.
Example answer(sample data)
Two categories overlap. Communication has 3 video call apps: one used by 34 people for 96 hours, the others by 6 people (9 hours) and 2 people (1 hour). Documents has 2 writing tools, used by 30 and 4 people. Every other category has one main tool.
Tip
Time categories group tools by the type of work, so fix any tool in the wrong category first; you can change it on the Tools page.
How has use of [tool] changed since we rolled it out on [date]? Show hours and number of people per week.
What it returns
Weekly time in the tool and the number of people using it, from the date you name to today.
Example answer(sample data)
The new project tracker went from 9 people and 6 hours in its first week to 29 people and 41 hours in week 6. Sales is the exception: 2 of its 7 people have opened it since week 2.
Tip
Schedule the prompt to run every week to follow the rollout without asking again.
AI adoption prompts: which AI tools does the team use?
AI adoption prompts read the built-in AI time category (AI tools and assistants) and the Tools page: which AI apps and websites each department uses, and for how long. Ask AI can't see what anyone types into an AI tool. Intelogos doesn't record what people type; it records only whether keyboard or mouse input happened.
Prompt 16AI time category by department
How much time did each department spend in AI tools last month, and what share of its tracked time was that?
What it returns
Time in the AI time category for each department, in hours and as a share of the department's tracked time.
Example answer(sample data)
Engineering spent 212 hours in AI tools, 10% of its tracked time. Marketing spent 48 hours (7%), Customer Support 41 hours (3%), Design 22 hours (3%) and Sales 15 hours (2%).
Tip
The AI category counts the tools that are in it, so check the Other category for AI tools Intelogos doesn't know yet (prompt 20).
Prompt 17Tools filtered by AI category and department
Which AI tools does the [Marketing] team use, and how much time goes to each?
What it returns
The apps and websites in the AI time category that the department used, with time, number of people and activity.
Example answer(sample data)
Marketing used 4 AI tools last month: ChatGPT (26 hours, all 4 people), Claude (14 hours, 2 people), an image generator (6 hours, 1 person) and a meeting notes assistant (2 hours, 1 person).
Tip
It's the same view as the Tools page filtered by the AI time category and the department, if you want to check it yourself.
How has time in AI tools changed month by month since [June]?
What it returns
Monthly time in the AI time category for the whole team and for each department, with the change from month to month.
Example answer(sample data)
Time in AI tools grew from 180 hours in June to 338 hours in September. Engineering drove most of the rise, from 120 to 212 hours, while Sales stayed flat at about 15 hours a month.
Tip
Time is one signal of adoption; pair it with what each team says the AI tools help them do.
How many people in each department used at least one AI tool in the last 30 days?
What it returns
The number and share of people in each department with any time in the AI time category.
Example answer(sample data)
29 of 36 people used at least one AI tool. All 12 people in Engineering and all 4 in Marketing did, as did 7 of 8 in Customer Support, 4 of 5 in Design and 2 of 7 in Sales.
Tip
Use the answer to decide where training or a shared guide would help, not to rank people.
Which tools in the Other time category look like AI tools? List the ones used for more than an hour last month.
What it returns
Tools without a specific time category, used for more than an hour, with the ones that look like AI tools marked.
Example answer(sample data)
7 tools in Other were used for more than an hour. 2 look like AI tools: a writing assistant (11 hours, 5 people) and a transcription app (4 hours, 3 people). Moving them to AI would add 15 hours to last month's AI total.
Tip
If your role lets you change tools, ask Ask AI to set their time category to AI next; the change applies after you confirm.
Weekly review and 1:1 prep prompts: what changed this week?
Weekly review prompts summarize what changed, who improved and who may need a conversation, and 1:1 prompts prepare a short brief on one person. Run any of them as a scheduled task to get the answer every week without asking.
Prompt 21Key performance stats and workload
Summarize last week for my team: hours, KPIs compared with the week before, and anything unusual.
What it returns
A short summary of total hours and average workday, the KPIs against the week before and the biggest outliers.
Example answer(sample data)
Your 36 people tracked 1,434 hours last week, an average workday of 7h 58m. Productivity rose 2 points to 44% and Engagement 3 points to 69%. Unusual: Customer Support averaged 8h 52m per workday, and 2 people in Sales tracked no time on Thursday.
Tip
Make it a scheduled task that runs every Monday at 9am and emails you the result.
Prompt 22One person's KPIs, work sessions and tools
Prepare a 1:1 summary for [name]: hours per day, work sessions, top tools, KPIs compared with their department, and what changed since last month.
What it returns
A short brief on one person's month: workday and work sessions, top tools, KPIs against the department average and the main changes.
Example answer(sample data)
Priya averaged 9h 05m per workday this month, up from 8h 10m, and her work sessions ran past 7pm on 6 days. Her Productivity (52%) is above the Engineering average (49%). Top tools: the code editor (58% of her time), chat (12%) and the issue tracker (9%). Suggested topic: the longer days in the last two weeks.
Tip
Share the numbers with the person and ask for their view; the data shows time and tools, not the reasons behind them.
Who improved most over the last 30 days, and in which KPIs?
What it returns
The people with the biggest gains in Overall Rating against the previous 30 days, with the KPIs that moved.
Example answer(sample data)
Noah (Sales) improved most: his Overall Rating rose from 48% to 61%, driven by Productivity (from 36% to 47%) and Availability (from 84% to 95%). Next are Ella (Design, 55% to 62%) and Omar (Engineering, 58% to 64%).
Tip
Recognize the people on the list, and check for time off in either period, which can move a 30-day comparison.
Compare my team's time categories this week with the average of the last 4 weeks. What shifted?
What it returns
Time in each time category this week against the 4-week average, with the biggest rises and falls.
Example answer(sample data)
Communication rose from 26% to 31% of tracked time, mostly in Customer Support and Sales. Development fell from 31% to 27%, and Documents rose from 8% to 11%. AI held steady at 6%.
Tip
A shift isn't good or bad on its own: a release week or a busy sales month changes the mix.
Who has time off coming up in the next month, and who on my team hasn't taken any time off this year?
What it returns
Upcoming approved and pending requests, and the people with no time off used this year, with their remaining balance.
Example answer(sample data)
5 people have time off in the next month, 4 approved and 1 pending. 3 people haven't taken a day off this year: Leo (Engineering, 22 days left), Ana (Customer Support, 20 days left) and Sam (Customer Support, 18 days left).
Tip
Read it with prompt 2: long days and no time off together are worth a conversation.
Name who, when and what: the people, the time period and the measure you care about, then ask one question at a time. Ask AI understands plain language, so you don't need special words or syntax.
Name the people. "My team", a department, a project, a job title or one person by name.
Name the period. "Yesterday", "last week", "the last 30 days" or "since June". Name two periods, such as this week and last week, to get a comparison.
Say what you want back. A ranked list, a table, a chart or a report. Tables in an answer can be downloaded as Excel, CSV, PDF, Word or Markdown files.
Follow up in the same chat. Ask AI keeps the context of a conversation, so "Break that down by department" or "What about last month?" works.
Schedule the ones you repeat. A scheduled task runs a prompt every day or every week. You can have up to 10 active scheduled tasks, and they only answer questions: they never make changes.
Instead of
Try
How is the team doing?
How has the Engineering team's Productivity changed over the last 2 weeks?
Is anyone overworked?
Who has averaged more than 9 hours per workday over the last 4 weeks?
Are people using AI?
How many people in each department used at least one AI tool in the last 30 days?
Questions and changes are different. Ask AI can also make changes, such as setting a tool's time category or approving time off, but only after you confirm a preview, and only the changes your role and permissions allow. "Set the writing assistant's time category to AI" prepares a change; "Should the writing assistant be in AI?" gets an answer. The prompts on this page only ask questions. See Making Changes for the full list.
Read the answers with judgment. Ask AI works from tracked computer time, so meetings away from the computer and offline work don't appear in its numbers. Use an answer as a starting point for a conversation with your team, not as the whole picture. If answers feed into performance reviews or discipline, keep a person in charge of the decision; AI performance reviews and SB 947 explains what California requires from July 1, 2027.
Frequently Asked Questions
What data can Ask AI see?
Ask AI answers from your organization's Intelogos data: time and KPIs, the apps and websites people use with their importance and time categories, departments and projects, work sessions and hourly activity, availability, and time off. If you connect tools such as GitHub, Jira, Slack or Google Calendar, it can use their data too. Intelogos doesn't take screenshots. Intelogos doesn't record what people type; it records only whether keyboard or mouse input happened. So Ask AI has no screenshots or typed text to read, including anything typed into an AI tool.
Can employees see the same data?
No. Ask AI is only for Owners, Admins and Managers: regular users don't see it in their navigation, and opening its address shows an "Access Restricted" message. Regular users see only their own data, at the level an Admin chooses (time only, or time, activity, tool usage and more), and never Chronicle. Managers see the people they've been assigned to manage. See Roles & Access for each role.
Can I use these prompts in other AI tools?
Yes. Connect Claude, ChatGPT, Cursor or any MCP client to the Intelogos MCP server and paste the same prompts. Account Owners, Admins and Managers can connect, the assistant sees only what the person who connected can see in Intelogos, and a Manager's connection is read-only. Intelogos MCP is included in the AI Intelligence and Enterprise plans; the Intelogos MCP setup guide walks through each client.
AI-powered workforce analytics and performance management