Who Owns Customer Interviews? Product, Marketing, or CX?

When you ask who owns customer interviews, the ideal answer is deceptively simple: everyone.

Every single person in the organization, from the engineer writing code to the account executive closing a deal, should feel responsible for understanding the customer.

But this is not a success formula, and maybe you have done it before. Bringing a PM, a marketer, and a CX lead into a call with a customer, and letting them “co-own” the interview. Probably the conversations are great, plenty of enthusiasm, meaningful stories, maybe even new insights. But when it’s over, the data will look like a scattered puzzle.

Each person will interpret it differently, store it separately, and return to their own team with a slightly modified version of the truth.

The result?

Inconsistencies, biases, and disconnected follow-ups make the whole effort feel like it never happened.

So, how can companies actually stay on top of their customers’ voice without overloading already-stretched teams?

The Core Problems With Running Customer Interviews

Before we delve into solutions, let’s outline the pain points that most teams silently face when conducting customer interviews.

• No Clear Ownership
  No one really knows who’s supposed to run interviews or analyse them 
• Inconsistent Quality
  Untrained specialist leading interviews, producing shallow or biased findings
• Slow, Manual Processes
  Scheduling calls, chasing no-shows, writing notes, and waiting weeks for summaries affecting sprints
  

Trying to fix those issues by simply mandating more meetings or stricter guidelines is like trying to patch a leaky dam with duct tape. 

You definitely can do better than this by breaking problems down into a system that works.

Shift the Focus From Who Runs Customer Interviews to How They’re Run

Building a working system doesn’t necessarily mean hiring more researchers who know their work. This is not scalable and often puts you in a fragile situation.

As read in this Reddit thread, the user mentions, “The hybrid approach is how we’ve been doing it. Dedicated researchers follow directions given by other teams. Product sometimes also conduct their own interviews, but the quality rarely matches.” 

This observation highlights the need for standardization. If specialized research teams are a bottleneck, every team member must be enabled to contribute high-quality data. 

The solution requires three foundational components that have always worked for me:

1. A Shared Repository: A single source of truth (SSoT) where all customer conversations, interviews, support tickets, sales calls, and survey data live and are accessible. Such as Snowflake AI data cloud or Amazon Web Services (AWS) Redshift
2. Standardized Methodology: A set of shared guidelines for conversational flow, note-taking, and tagging to ensure the quality of input is consistent, regardless of the person asking the questions. Use something like Zoom’s note-taking features
3. Intelligent Analysis Layer: This is the most crucial component. It’s a mechanism that automatically processes, synthesizes, and cross-references data, eliminating manual effort, bias, and slow turnaround times. And we are lucky enough to be living in times where AI gives us indispensable power.

At the end of the day, no one should “own” interviews alone. To be more efficient in the process, the core idea shifts the focus from who conducts the interviews to how the interview is conducted, analyzed, and shared across the organization.

This is where an AI tool can save you time and money.

How AI Removes the Limitations of Manual Research

With the current development of AI tools, they are uniquely positioned to solve issues related to time scarcity, human bias, and slow latency.

Instead of relying on memory, personal note-taking styles, or subjective interpretation, AI can pull out themes, spot patterns, and turn conversations into usable insights in a way that manual work simply can’t keep up with. And with AI interviewers, the whole customer interview process, from running the call to organizing the findings, gets fully automated.

The Real Difference between AI Interviewers vs Individual Teams

The core of the problem, as we have established, is not the capability or quality of the people but the limitations of the manual process, where AI masters them. 

To make the differences easier to see, I pulled everything into a simple side-by-side view. It shows exactly where human teams fall short, no matter how dedicated they are, and where AI interviewers take over.

“Hiring an AI interviewer” will enable your human researchers, PMs, and marketers to focus their efforts on strategic actions and personal growth, rather than spending 80% of their time on logistics. The AI handles the logistics, allowing human expertise to focus 100% on high-value, strategic applications.

Shifting from Ownership to Strategic Contribution

At the end of the day, customer interviews shouldn’t be a tug-of-war between departments.

The question “Who owns customer interviews?” is ultimately the wrong question. It implies a zero-sum game of resources and control. The correct mindset for the modern, high-growth company is: How can we create a system where every team can strategically contribute their unique expertise to a unified, scalable, and unbiased pool of customer insight?

Today, AI interviewers can do the “search engine” work on a larger scale. They democratize access and instantly provide personalized answers to everyone, from the engineer to the marketer.

Ownership doesn’t matter when every department is consistently nourished by the same stream of objective customer truth. The entire company owns the outcome, while the intelligent system owns the process.

If you read this far, you are definitely here to grow! Join the early access list for the Prelaunch AI Interviewer before its launch to stay ahead of the product curve.