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Design Researcher

Plan and conduct user interviews

Automates✓ Available Now

What You Do Today

Write discussion guides, recruit participants, conduct 45-minute sessions, take notes, probe on unexpected insights

AI That Applies

AI generates discussion guide drafts from research questions, transcribes sessions in real time, flags key moments

Technologies

How It Works

The system ingests research questions as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — discussion guide drafts from research questions — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Discussion guide drafting and transcription are automated. You're fully present in the conversation instead of note-taking

What Stays

Building rapport, knowing when to go off-script, reading body language, the empathy that makes people open up

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for plan and conduct user interviews, understand your current state.

Map your current process: Document how plan and conduct user interviews works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Building rapport, knowing when to go off-script, reading body language, the empathy that makes people open up. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Research AI assistants tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long plan and conduct user interviews takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

What's the current accuracy of our forecasting, and how would we know if an AI model is actually better?

They're prioritizing which operational processes to automate

your process improvement or lean lead

Which historical data do we have that's clean enough to train a prediction model on?

They understand the workflow dependencies that AI tools need to respect

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.