Product Advisory Council research
Five linked research activities at a Product Advisory Council (PAC) gathering with more than 100 customer practitioners examined adoption, demonstrable product value, and AI readiness.
I designed and led the five activities, from prompts and participation materials through analysis and synthesis, with product and research partners.
Part of a continuing inquiry into how customers demonstrate product value to leadership.
Customers needed evidence for their next investment
Customers needed to explain product value to leadership when seeking resources, including further investment in AI. An earlier depth inquiry with 12 customer organizations and an internal study examined how that proof was assembled. A separate virtual PAC engagement broadened the inquiry before the in-person gathering.
The council returned emerging questions to customers for comparison, discussion, and challenge. Practitioners could see where experiences differed across organizations and explain the conditions behind an answer. That setting helped examine adoption and measurement alongside expectations for AI.
The related depth inquiry: The Proof Gap ↗Five activities connected adoption, value, and readiness
Prompts, worksheets, stickers, and table discussion made the questions tangible. A common organization marker connected contributions across activities. Facilitators helped customers explain their responses and interpret the differences appearing around them.
- 01 · Outcomes and measurement
Which outcomes matter, and can you measure them?
Separate importance to the business from confidence in the evidence.
- 02 · Adoption barriers
What gets in the way of use?
Compare obstacles to core adoption with barriers to AI readiness.
- 03 · Leadership alignment
What is agreed before scaling?
Examine shared outcomes, responsibility, and measurement expectations.
- 04 · AI readiness
What needs to be in place next?
Understand the support needed to turn interest into readiness.
- 05 · Capability and value
What is available, used, and producing recognizable value?
Distinguish having a capability from putting it to work and demonstrating its value.
Activity descriptions are paraphrased. More than 100 practitioners attended the gathering; participation varied by activity.
Does an important outcome have a credible measurement path?
Consider the same organization’s answers about value and AI readiness. The linked responses let the researcher ask whether difficulty demonstrating an outcome also shaped what the customer needed before investing further.
How measurement confidence connects with AI readiness
Method reconstruction · Prompts paraphrased. All marked responses below are illustrative.
What matters, and can you measure it?
Separate the two judgments. An outcome can matter before there is a credible way to measure it.
What needs to be in place next?
Read the answers together. The marker connects a proof gap with a readiness need.
The marker links these illustrative answers. Each activity keeps its own response base; gathering attendance is not a response count.
Available capability did not guarantee use or demonstrable value
Customers’ value stories depended on implementation and measurement conditions as well as the product’s capabilities. The comparisons separated an outcome’s importance from the ability to measure it and readiness to invest. A valued capability could remain unused when implementation conditions were missing; an important outcome could remain difficult to demonstrate.
The findings distinguished product gaps from needs for implementation support, clearer ownership, and stronger measurement. Linked responses exposed those relationships; discussion explained the conditions behind them.
Optional depth Explore the interactive Signals dashboard
Explore the comparison model
Switch between four views of the questions behind the analysis: measurement, barriers, capability value, and readiness. This later portfolio model illustrates how related responses can be examined.
Later public reconstruction · Synthetic values illustrate the comparison, not measured research results.
Priority versus measurability
Customer engagements and product follow-through
The combined research shaped two customer engagements that helped customers strengthen their value stories for leadership. It also informed a cross-functional value effort and product work on identified gaps.
The council connected customer comparisons with the earlier depth inquiry and internal study, giving research, product, and engineering partners a shared basis for action.