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VP of Design

Lead user research and insights

Enhances✓ Available Now

What You Do Today

Direct the research function — user interviews, usability testing, surveys, analytics. Ensure the team builds products based on real user needs, not assumptions.

AI That Applies

AI-assisted research synthesis that transcribes interviews, identifies themes across sessions, and generates insight summaries that would take researchers days to compile manually.

Technologies

How It Works

For lead user research and insights, the system identifies themes across sessions. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — insight summaries that would take researchers days to compile manually — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Research synthesis accelerates dramatically. AI transcribes, codes, and identifies patterns across dozens of interviews in hours instead of days.

What Stays

Asking the right questions, building rapport with participants, and the creative leap from observation to insight — those require human empathy and analytical thinking.

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 lead user research and insights, understand your current state.

Map your current process: Document how lead user research and insights works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Asking the right questions, building rapport with participants, and the creative leap from observation to insight — those require human empathy and analytical thinking. 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 Dovetail 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 lead user research and insights 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 board chair or lead independent director

What data do we already have that could improve how we handle lead user research and insights?

They shape expectations for how AI appears in governance

your CTO or CIO

Who on our team has the deepest experience with lead user research and insights, and what tools are they already using?

They own the technology infrastructure that enables AI adoption

a peer executive at a company further along on AI adoption

If we brought in AI tools for lead user research and insights, what would we measure before and after to know it actually helped?

Their lessons learned are worth more than any consultant's framework

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.