NOTE: To use this template, copy the content using the “Copy page” button above, open the Google Doc to make your own copy, or customize the framework below for your organization.

Functional Team AI Use Case Discovery

This template provides a structured approach to discovering AI opportunities through systematic interviews with employees across your organization.

Interview Framework

Introduction

Interviewer Context Setting:

“This is not an evaluation, but an opportunity to improve your working experience. We’re exploring how AI tools might help eliminate repetitive tasks, save time, and let you focus on more strategic, creative, or enjoyable work. There are no wrong answers - we want to understand your actual daily workflow and challenges.”

Key Message: You’re here to learn, not judge. Emphasize confidentiality and that their input will shape AI tools that make their work better.

A Day in the Life

Objective: Understand daily workflow and identify high-frequency tasks suitable for AI automation.

Discovery Questions

Walk me through a typical workday:

• What specific tasks do you perform on a daily basis?
• Do you do these tasks independently or collaboratively with others?
• What systems, tools, or software do you use for these daily tasks?

Time and Energy Analysis:

• What wastes most of your time during the day?
• What tasks do you tend to put off or procrastinate on?
• What would you eliminate if you could?

Capture: List specific daily tasks, tools used, collaboration patterns, and time-wasters.

Weekly Schedule

Objective: Identify recurring weekly tasks that consume significant time or mental energy.

Discovery Questions

Are there specific tasks you complete on a weekly basis?

• Examples: Timesheets, weekly summaries, 1:1 meetings, progress reports, team updates, data compilation, status reports
• How are these weekly tasks different from your daily work?
• What wastes most of your time on these weekly activities?

Capture: Weekly recurring tasks, their purpose, time investment, and friction points.

Monthly or Quarterly Schedule

Objective: Uncover periodic high-value tasks where AI could provide significant leverage.

Discovery Questions

Are there tasks you complete monthly or quarterly?

• Examples: Closing the books, payroll processing, expense reports, forecasting, management reports, board presentations, tax preparation, compliance reviews, quarterly business reviews
• How do these differ from your weekly and daily work?
• What takes the most time in these periodic activities?
• What causes the most stress or requires the most manual effort?

Capture: Periodic tasks, complexity levels, manual effort required, and high-stress components.

One-Off and Irregular Tasks

Objective: Identify ad-hoc work that creates interruptions and eats productive time.

Discovery Questions

What irregular tasks take up significant time?

• Examples: Answering repetitive questions from colleagues, writing new policies, onboarding new team members, updating other departments, finding the latest version of documents, special projects, crisis management
• What makes these tasks particularly time-consuming?
• How often do these irregular tasks interrupt your planned work?

Capture: Common ad-hoc requests, information-seeking patterns, knowledge transfer activities.

Pain Points and Improvement Suggestions

Objective: Understand what frustrates employees most and what they wish was easier.

Discovery Questions

What are the biggest challenges you face in completing your work?

• What tasks prevent you from doing higher-value work?
• If you’re doing [repetitive task X], what are you not doing instead?
• What would be most helpful in improving these tasks?
• If you had a magic wand to eliminate or automate one thing, what would it be?

Priority Assessment:

• Which pain points affect you daily vs. occasionally?
• Which have the biggest impact on your ability to do strategic work?
• Which create the most frustration or stress?

Capture: Top 3-5 pain points, their frequency, and the opportunity cost (what they’re not doing because of these tasks).

Wrap Up and Next Steps

Summarize What You Heard:

• Reflect back the key tasks, patterns, and pain points you identified
• Validate your understanding: “Did I capture that correctly?”
• Ask if there’s anything important they didn’t get to mention

Explain How This Will Be Used:

• “We’ll analyze patterns across all interviews to identify where AI can have the biggest impact”
• “Your input will directly shape which AI tools and use cases we prioritize”
• “We’ll share aggregated findings and next steps within [timeframe]”

Invite to AI Ambassadors Program:

• “Based on this conversation, I think you’d be a great fit for our AI Ambassadors program”
• “Ambassadors get early access to new AI tools, training, and the opportunity to shape how AI is implemented in [their function]”
• “Would you be interested in being part of this group?”

Thank Them: Express genuine appreciation for their time and insights. Make it clear their participation matters.

Implementation Notes

When using this template for AI use case discovery:

Before Interviews

1. Select a diverse interview pool: Include different functions, seniority levels, and work styles. Don’t only interview “tech-savvy” employees - often the best insights come from people doing highly repetitive manual work.

2. Schedule appropriately: 30-45 minutes is ideal. Book meetings as “AI opportunity discovery” or “workflow improvement discussion” - avoid calling it an “AI interview” which may intimidate some participants.

3. Prepare your mindset: You’re not looking for people who already know AI solutions. You’re looking for patterns of repetitive work, manual data handling, content creation, scheduling, analysis, or communication tasks that consume time.

During Interviews

1. Lead with curiosity, not solutions: Don’t suggest AI applications too early. First understand their actual workflow, then identify opportunities afterward.

2. Ask “why” and “how” questions: When they mention a task, dig deeper: “How long does that typically take?” “Why is that difficult?” “How often does this happen?”

3. Listen for specific language: Pay attention when people say:

   • “I spend hours on…”
   • “This is so repetitive…”
   • “I wish I could just…”
   • “I have to manually…”
   • “I’m always searching for…”

4. Don’t promise solutions: Say “That’s really helpful context” instead of “AI can definitely solve that.” Set expectations that you’re gathering insights, not committing to specific implementations.

After Interviews

1. Consolidate findings quickly: Within 24 hours, capture key insights while the conversation is fresh. Use a simple spreadsheet with columns: Function | Role | Daily Tasks | Weekly Tasks | Monthly Tasks | Top Pain Points | AI Opportunity Areas | Priority.

2. Look for patterns across roles: The same use case appearing across multiple departments (e.g., “summarizing meeting notes”) signals high-value, horizontal AI opportunities.

3. Categorize by AI capability type:

   • Content generation: Writing, emails, reports, documentation
   • Data analysis: Extracting insights, creating summaries, identifying patterns
   • Research & information retrieval: Finding documents, answering questions, gathering data
   • Administrative coordination: Scheduling, reminders, status updates
   • Repetitive processing: Data entry, formatting, quality checks

4. Prioritize with a simple framework:

   • Quick wins: High frequency + Low complexity (daily tasks that are straightforward to automate)
   • High impact: Significant time savings or quality improvement (even if more complex)
   • Strategic: Unlocks higher-value work or supports business priorities

5. Share back with participants: Close the loop by sending aggregated findings (anonymized) and next steps. This builds engagement and validates that their input mattered.

Adaptation Tips

Customize this template based on your:

1. Organization size: Smaller organizations (< 100 people) can interview more comprehensively. Larger organizations should focus on representative samples per function.

2. Industry context: Add industry-specific task examples to the framework. Manufacturing might emphasize quality control and equipment monitoring; professional services might focus on client deliverables and proposal development.

3. AI maturity level: For AI beginners, spend more time on the introduction explaining what AI can and can’t do. For more advanced organizations, ask about current AI usage and barriers to adoption.

4. Cultural factors: Some cultures are more direct about pain points; others require more rapport-building before people share frustrations. Adapt your interview style accordingly.

5. Remote vs. in-person: Video interviews work well but lose some spontaneous follow-up opportunities. In-person allows better reading of body language and energy when discussing pain points.

Remember: The goal isn’t to become an AI expert - it’s to become an expert in your employees’ workflows. AI solutions follow naturally from deep workflow understanding.