Skip to content

Director of Product Management

Engage with customers on product direction

Enhances✓ Available Now

What You Do Today

Meet with customers regularly — advisory boards, feedback sessions, beta programs. Ensure the product reflects real needs.

AI That Applies

AI-synthesized customer feedback from support tickets, reviews, and usage data that quantifies demand for requested features.

Technologies

How It Works

The system ingests support tickets 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 results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Customer intelligence becomes comprehensive and quantified.

What Stays

Building customer relationships and the judgment on which feedback to act on.

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 engage with customers on product direction, understand your current state.

Map your current process: Document how engage with customers on product direction 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 customer relationships and the judgment on which feedback to act on. 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 Productboard 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 engage with customers on product direction 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 Product or CPO

What are the top 5 reasons customers contact us, and which of those could be resolved without a human?

They're deciding how AI capabilities show up in the product roadmap

your lead engineer or tech lead

How do we currently measure service quality, and would AI-assisted responses change that measurement?

They can tell you what's technically feasible vs. what sounds good in a demo

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.