Skip to main content

Transparent AI use

📖 Lesson content

What you'll learn

Estimated time: 30 minutes

By the end of this lesson you'll be able to:

  • Articulate privacy concerns and evaluate AI tools based on their data handling policies
  • Practice data hygiene strategies for safely working with sensitive business information

The Delegation-Diligence Loop

(7 minutes)

Real example of the Delegation-Diligence loop in practice — deciding what to hand off to AI and how to be transparent with customers about AI use.

The Delegation ↔ Diligence loop

The outer loop frames every AI interaction: you decide upfront what's right to hand off—the right task, the right tool, the right data—and afterward you take responsibility for what comes back. Description and Discernment do the work in between.

DescriptionCommunicate the vision so the model can act on it.

DiscernmentJudge what came back—and feed that judgment forward.

DelegationDecide what's worth handing to AI in the first place.

DiligenceVerify, attribute, and own the final product.

Decide what's
right to hand off.

Be thoughtful upfront: what's the right task, the right tool, and the right data to bring to AI—and what should stay with you?

Hand off →

Outer loop

The Loop

← Validate

Own what
comes back.

Did AI get this right, and am I owning the result? Verify against what you know, be transparent about AI's role, and stand behind the output.

Key takeaways

  • AI introduces new privacy considerations — some tools use your inputs to train future models.
  • Match the tool to the task — higher sensitivity data needs stricter privacy settings.
  • Strip what isn't needed — remove identifying details before sharing data with AI.
  • If something goes wrong, act fast — delete the conversation and request data deletion.
  • Build validated approaches, not blind trust: document what works so you can replicate it next time.
  • AI can help even if you're not data-savvy — brainstorming, spreadsheet formulas, and plain-language explanations of your numbers.

Exercise

These three exercises run as a sequence: you'll choose a piece of real business data, prepare it safely for AI, and then evaluate and own what comes back. Together they walk you through one complete Delegation–Diligence loop with your own data.

Exercise 1: Decide what to share

Your Delegation move — deciding what data is appropriate to bring to AI and what stays protected.

  1. Pick one real document from your business that you've already looked at and have some familiarity with — a sales report, customer feedback summary, inventory list, or similar.
  2. Read through it and mark anything you wouldn't want outside your business — names, contact details, payment information, proprietary pricing.
  3. Make a copy and strip those details: replace names with "Customer A / Vendor X," remove exact figures if they're not needed, delete contact info entirely.
  4. Before opening AI, take a few minutes to write down:
    • What are you trying to accomplish? Be specific — "understand why repeat bookings dropped last quarter" beats "analyze my sales data."
    • What do you already know or suspect from looking at this data yourself? Note 2–3 observations or patterns you've already noticed.
    • What would a useful AI output look like? A summary? Specific trends? A list of anomalies?This is your brief. The clearer it is, the easier it will be to evaluate what comes back.

Exercise 2: Validate with AI

You've already looked at this data. Now see if AI catches the same things you did — and what it misses.

Share your sanitized document and the goal you defined in Exercise 1 with AI. Ask it to surface the key patterns or findings.

Then compare AI's response against your own notes:

  • Did it catch the patterns you already identified?
  • Did it surface anything you hadn't noticed?
  • What did it miss or get wrong that you already knew from your own read?

The gap between what AI sees and what you see is where your judgment as a business owner is irreplaceable.

Exercise 3: Own the result

Apply Diligence — evaluate what came back and decide what you'll actually use.

Read AI's response and answer three questions:

  1. Accuracy: Does anything go beyond what your data actually shows? Flag numbers, conclusions, or recommendations that need checking against your real records.
  2. Usefulness: What would you keep as-is, what needs your editing, and what would you cut?
  3. Accountability: Is this something you'd put your name on — share with a customer, use in a report, act on? If not, what would need to change?

Stretch goal: What's your plan if AI got something meaningfully wrong? Write one sentence on how you'd catch it and correct it.

Lesson reflection

  • How does AI privacy compare to how you already think about your other business software?
  • What's one change you'll make to how you share business data with AI?
  • Which category of sensitive data feels most important to protect, and what's your plan for it?

What's next

In the next lesson, we'll put all four dimensions of the 4D Framework together to automate a full business workflow from start to finish.

Feedback

As you progress through the course, we'd love to hear from you about how you are using concepts from the course in your work, plus any feedback you may have. Share your feedback here.

🎬 Video transcript

Source video: E6lE7IHP4Y0

No transcript available — see lesson body for narrative content.

🔁 Related lessons

📚 Source & attribution

Feedback / ReportSpotted an issue or have an improvement idea?