ETGEnabling Technical GTM
LinkedInJason Svir
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Customer Discovery Worksheet

Discovery, without the interrogation.

A conversational guide for technical GTM teams exploring an AI product opportunity. Follow the thread that feels most useful; capture high-leverage insights and generate an executive recap.

1

Listen for the change

Start with their world, the moment that prompted action, and what better looks like.

What is changing for the business or customer?
Start in their language, not your product language.
Why is this worth attention now?
Listen for urgency, a trigger, or a consequence.
If this goes well, what is different six to twelve months from now?
Invite a picture of success.
2

Launch an enterprise AI copilot

Adoption, trust, time-to-value. Keep this focused on technical paths and constraints.

Which employee or customer task should the copilot make meaningfully easier?
Anchor the AI experience in a job that matters.
Where could an incorrect or unhelpful answer create risk?
Surface the quality, governance, and trust bar.
What would prove the copilot is delivering value?
Define adoption and outcome signals before discussing features.
3

Qualify the design Optional depth

Use when the opportunity is mature enough to explore safety, tooling, and economics.

Technical qualification promptsSelect the prompts that advance architecture and risk alignment.
Does the AI experience answer, recommend, or take action? Which systems must it use, and where should a person approve?
Clarify autonomy, tools, and human control.
How will the team decide whether an output is good enough? Which errors are acceptable, and which need a citation, test, or other validation?
Establish the evaluation and quality bar.
What data, permissions, and retention constraints shape the design?
Surface the data boundary before proposing an architecture.
When the system is uncertain, blocked, or wrong, what should happen - and what needs to be logged?
Explore fallback, auditability, and operating risk.
What does a successful task cost today, including models, tools, retries, and human rework?
Move beyond token price to total task economics.
4

Make it real

Translate the technical story into business urgency and an owned next step.

What is the cost of staying where you are?
Connect the technical issue to a business consequence.
What needs to be true to choose a path forward?
Reveal decision criteria, people, and proof.
What is the smallest useful next step?
Leave with a specific action, owner, and date.
5

Simple Call Recap

Consolidate the discovery narrative into an executive summary ready for CRM or follow-up email.

Formatted Plaintext Output:
DISCOVERY RECAP

Account / team: —
Initiative: —
People: —
Thread: Launch an enterprise AI copilot

Current reality
—

Desired change
—

Capability to explore
—

Business impact
—

Technical qualification
—

Next step, owner, and timing
—
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Learn how to ingest call transcripts into structured MEDDPICC scorecards, extract technical architecture constraints, and formulate value proposals in the interactive workshop.

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