AI for Scheduling & Capacity Planning

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-operations-scheduling-capacity-planning-final6-adults

1. What is the primary difficulty in capacity planning, according to the core premise of this approach?

   1. Getting accurate inputs about real availability, cycle time, and interruptions
   2. Teams typically have too much free time to calculate
   3. AI systems cannot handle more than ten projects at once
   4. The mathematical calculations are too complex for most managers

2. Why does the 75% capacity rule recommend planning to only 75% of nominal capacity?

   1. Because most teams are only willing to work three-quarters of their hours
   2. To account for unplanned interruptions and prevent quarterly burnout
   3. To give AI more data points to work with for accurate predictions
   4. To ensure projects finish earlier than promised

3. Which of the following is AI well-suited to do in capacity planning?

   1. Replace the human conversation about priorities and tradeoffs
   2. Predict exactly when unexpected interruptions will occur
   3. Determine which project estimates are sandbagged versus honest
   4. Convert team availability and estimates into a feasible schedule

4. What information should you provide to AI when requesting a capacity plan?

   1. Just the project deadlines and team salaries
   2. A list of all interrupts from the past year
   3. Team headcount, average focus hours per week, and the project list with estimates
   4. Only the names of team members and their vacation dates

5. A manager receives an AI-generated schedule that uses 100% of every team member's available hours. What is the most appropriate response?

   1. Accept the schedule since AI optimized it
   2. Request a second opinion from another AI system
   3. Ask for three scenarios: optimistic, realistic, and one with someone out for a week
   4. Reject the schedule outright as unusable

6. Why will a schedule built using 100% of available hours likely fail?

   1. Because the math was done incorrectly
   2. It fails at the first cold, family emergency, or unplanned interrupt
   3. Teams refuse to follow schedules with no buffer
   4. AI schedules always contain calculation errors

7. A project manager notices their AI-generated capacity plan shows a critical week at 92% capacity with no buffer. What specific action does the lesson recommend?

   1. Flag this week as a risk area and build in additional buffer before promising dates
   2. Assign the work to contractors instead
   3. Reduce the number of projects in the pipeline
   4. Approve overtime for that week

8. A team lead asks AI to generate three scenarios: optimistic, realistic, and 'what if someone is out for a week.' What is the purpose of the third scenario?

   1. To calculate exactly how much overtime will be needed
   2. To determine who the weakest performer is
   3. To test schedule resilience against unexpected absences
   4. To identify which team member might leave soon

9. What limitation prevents AI from being a complete replacement for human capacity planning?

   1. AI cannot perform basic addition and subtraction
   2. AI lacks access to company email calendars
   3. AI cannot know which estimates are sandbagged versus honest, and cannot predict unplanned interrupts
   4. AI-generated schedules are always too short

10. When should buffer time be built into project schedules, according to this approach?

    1. During the project retrospective
    2. Only when the client explicitly requests it
    3. At the planning stage, before committing to delivery dates
    4. After promises have already been made to stakeholders

11. A manager wants AI to translate ranges into confidence-banded delivery dates. What does this output represent?

    1. A range of dates with associated probability levels showing likely completion windows
    2. The earliest possible date if everything goes perfectly
    3. The average of all previous project completion times
    4. Exact dates when each task will definitely be completed

12. Why is it important to include human judgment in AI-assisted capacity planning?

    1. AI systems require approval for every calculation
    2. Human judgment is needed for priorities, tradeoffs, and to verify AI assumptions are reasonable
    3. Humans must manually redo all AI calculations anyway
    4. AI cannot generate schedules without human intervention

13. An AI produces a perfect schedule that fits every project into available time. Why should this be viewed with skepticism?

    1. AI always makes mistakes in perfect-looking schedules
    2. Perfect schedules are against company policy
    3. AI schedules must be approved by legal
    4. The schedule likely doesn't account for realistic interruptions and assumes zero buffer

14. What distinguishes an honest project estimate from a sandbagged one, from AI's perspective?

    1. AI cannot determine this—only humans know the true intent behind estimates
    2. Sandbagged estimates always use round numbers
    3. Honest estimates are always shorter than sandbagged ones
    4. AI can analyze writing style to detect sandbagging

15. A team member mentions they typically lose about 20% of their week to unexpected meetings and urgent requests. How should this inform capacity planning?

    1. Plan for only 60% capacity to be safe
    2. The team member needs better time management training
    3. This is already accounted for in the 75% rule
    4. This information is irrelevant to capacity planning