Insights Unlocked

Episode web page: https://bit.ly/4ywaHhh

Episode summary

In this episode of Insights Unlocked, Mike Mace, Director of Solution Marketing at UserTesting, talks with veteran product leader, author, and executive coach Rich Mironov about what AI-powered development really means for product teams—and why dramatically faster coding doesn’t automatically translate into better products, happier customers, or more revenue.

Rich argues that as AI removes engineering constraints, the bigger challenge becomes deciding what is actually worth building. He explores the risks of “cognitive surrender,” where teams equate faster output with better outcomes, and explains why product management, UX research, customer discovery, business judgment, and taste become more important—not less—as organizations gain the ability to build at unprecedented speed. He also challenges the growing enthusiasm for synthetic users, warning that plausible AI-generated feedback can reinforce what teams already believe rather than uncover the unexpected insights that emerge from conversations with real customers.

The conversation also examines why “10x coding speed” won’t produce 10x revenue, how AI shifts bottlenecks from engineering toward customer adoption and go-to-market execution, and why product managers may need to become more “barbell shaped”—spending more time understanding meaningful customer problems at the front end and turning products into business results at the other. Rich also shares ideas from his book Money Stories, including why product teams need to communicate the financial value of their work in language executives understand.

You’ll learn

Why faster AI-assisted coding doesn’t necessarily create better products or more revenue

How “cognitive surrender” can cause teams to prioritize output over customer and business outcomes

Why human discovery, judgment, empathy, and taste become more valuable as building gets easier

The risks of replacing conversations with real customers with synthetic users

Why product waste is fundamentally different from engineering waste

How AI shifts product bottlenecks toward discovery, customer adoption, sales, and go-to-market execution

Why product managers may need to become more “barbell shaped” in an AI-driven environment

How “money stories” can help product teams connect their work to revenue and business impact

Resources and links

Rich on LinkedIn (https://www.linkedin.com/in/richmironov/)

Rich’s Product Bytes (https://www.mironov.com/) and Substack (https://richmironov.substack.com/)

The Art of Product Management (https://www.amazon.com/Art-Product-Management-Lessons-Innovator/dp/1439216061)

Money Stories: Communicating the Value of Product Work (https://www.amazon.com/Money-Stories-Communicating-Value-Product-ebook/dp/B0GJTS2CW6)

Our past interview with Rich on product waste and how to prevent it (https://www.usertesting.com/blog/how-prevent-product-waste)

Mike Mace on LinkedIn (https://www.linkedin.com/in/mikemace/)

Nathan Isaacs on LinkedIn (https://www.linkedin.com/in/nathanisaacs/)

Learn more about Insights Unlocked (https://www.usertesting.com/resources/podcast)

Episode web page: https://bit.ly/4cqygzK

Episode summary:

In this episode of Insights Unlocked, host Jason Giles talks with Roger Wong, former Head of Design at BuildOps and an independent design consultant whose career spans Apple, Microsoft, Samsung, and startups, about what AI is (and isn’t) changing about product design.

Roger says that while AI can dramatically accelerate how teams research, prototype, and build, the fundamentals remain the same: understand the customer, solve the right problem, and use strong judgment to turn insights into better outcomes.

Drawing on his work at BuildOps, Roger shares how his team stays close to commercial contractors and field technicians to uncover problems AI alone can’t see. He describes how observing technicians in their real working environments led to an AI-powered visit summaries feature that lets technicians dictate notes after a job, reducing the burden of typing after a long day in the field. The lesson: customers may not be asking for AI—they want reliable products that solve real problems, and trust has to be earned before teams introduce more ambitious AI experiences.

Roger and Jason also explore how AI is reshaping the design process itself. Faster prototyping can tighten customer feedback loops and improve collaboration with engineers, but generating more output doesn’t necessarily create a better product. Roger discusses why teams need to measure outcomes rather than velocity, why people must take ownership of AI-generated work, and why craft, empathy, systems thinking, and judgment are becoming even more important as AI tools become widely available.

The conversation closes with a challenge for design and product leaders: treat AI as a new design material and experiment with it firsthand without abandoning the foundational skills that make great designers great. Roger also raises an emerging concern for the profession—how junior designers will develop those skills and judgment if AI increasingly takes over the “grunt work” that traditionally helped early-career designers learn their craft.

You’ll learn

Why faster product development doesn’t necessarily lead to better customer outcomes

How firsthand customer research can uncover AI opportunities teams might otherwise miss

Why trust starts with reliability—not flashy AI features

How AI is accelerating prototyping and tightening customer feedback loops

Why teams need human ownership and judgment over AI-generated work

What skills Roger looks for in designers as AI tools become ubiquitous

Why design leaders should treat AI as a new material and experiment with it themselves

How AI could reshape the way the next generation of designers develops its craft

Resources and links

Roger Wong on LinkedIn (https://www.linkedin.com/in/rogerwong/ )

RogerWong.me (http://RogerWong.me )

Roger Wong’s Substack newsletter (https://newsletter.rogerwong.me/ )

Jason Giles on LinkedIn (https://www.linkedin.com/in/jaygiles/ )

Learn more about Insights Unlocked at UserTesting.com/podcast

Episode web page: https://bit.ly/4gt1TTy

Episode summary:

In this episode of Insights Unlocked, Nathan Isaacs sits down with UserTesting Chief Scientist Ranjitha Kumar and Vice President of Customer Intelligence Jason Giles to explore how AI is reshaping the product development lifecycle—and what happens when the traditional boundaries between designers, engineers, and product teams begin to blur.

Ranjitha and Jason discuss how AI is making it easier for people to move beyond their functional lanes, from designers building working prototypes to engineers taking a more active role in design. But greater access to the tools doesn’t eliminate the need for expertise. As building becomes faster and cheaper, they argue that craft, judgment, decision-making, and understanding what customers actually need may become even more valuable.

The conversation also explores how teams can use AI to accelerate experimentation and learning without simply creating more things, why human feedback remains critical as organizations test new AI experiences, and where AI still falls short. Ranjitha and Jason also weigh in on AI slop, agentic AI and guardrails, and why teams should stay mindful about where AI genuinely improves their work—and where it may actually slow them down.

You’ll learn:

How AI is blurring the traditional boundaries between design and engineering

Why expertise and craft still matter when anyone can create prototypes and code

How AI could enable smaller, more collaborative product teams

Why faster execution makes decision-making and customer understanding more important

How teams can turn faster prototyping into faster learning and experimentation

Why AI won’t necessarily accelerate every part of the product development process

How human judgment, guardrails, and customer feedback can help teams use AI responsibly

Resources and links

Ranjitha Kumar on LinkedIn (https://www.linkedin.com/in/ranjithaskumar/)

Jason Giles on LinkedIn (https://www.linkedin.com/in/jaygiles/)

Nathan Isaacs on LinkedIn (https://www.linkedin.com/in/nathanisaacs/)

Learn more about Insights Unlocked: https://www.usertesting.com/podcast

Episode web page: https://bit.ly/4qpHthC

Episode summary:

In this episode of Insights Unlocked, Lija Hogan chats with Nick Cawthon, founder of Gauge and a veteran UX researcher, designer, and strategist, about what happens to UX research when AI makes it dramatically faster and easier to build digital experiences.

Nick explains why speed alone isn’t a competitive advantage. As AI accelerates prototyping and product development, teams need the humility to test, iterate, and make sure they’re solving the right problems—not simply moving faster in the wrong direction. That makes human insight and strategic research more important, not less.

The conversation explores where AI can create meaningful efficiencies in research, from transcription and tagging to indexing qualitative data, and where Nick believes humans should remain firmly in the loop. He also shares how researchers can make insights more accessible across an organization, demonstrate their connection to business outcomes, and evolve from gatekeepers of research into educators and strategic partners.

Finally, Nick offers advice for researchers thinking about the future of their profession: learn the new tools, invest in human connection, and make yourself indispensable by becoming the connective tissue between customers, product teams, designers, engineers, and business leaders.

You’ll learn:

Why faster product development makes human insight even more important

How AI can help teams prototype, test, and iterate at greater speed

Where AI can eliminate tedious research work—and where human connection still matters

Why researchers need to look beyond tactical usability questions to the broader customer experience

How research repositories can make customer insights more accessible to leaders and teams

Ways UX researchers can connect their work to business outcomes and product metrics

Why human connection and cross-functional influence can help researchers future-proof their careers

Resources & links

Nick Cawthon on LinkedIn: https://www.linkedin.com/in/nickcawthon-ux-digital-agency-product-design-leadership/

Gauge: http://Gauge.io

Nick Cawthon’s website: http://nickcawthon.com/

Lija Hogan on LinkedIn: https://www.linkedin.com/in/lija-hogan-894769/

Learn more about Insights Unlocked: https://www.usertesting.com/podcast

Episode web page: https://bit.ly/4wqOtMn

Episode summary

In this episode of Insights Unlocked, UserTesting’s Mike Mace sits down with AI product strategist, UX thought leader, and UX for AI author Greg Nudelman to explore how AI is reshaping the role of UX and product professionals—and what teams need to do differently to build AI products that deliver real customer and business value.

Greg argues that simply using AI to work faster isn’t enough. As traditional UX tasks become easier to automate, the bigger opportunity is for UX researchers, designers, and product leaders to move upstream: uncover customer needs, frame the right problems, understand how AI systems work, and rapidly validate working concepts with customers. He shares his “snowball sprint” approach, where teams start small and continuously add capabilities only after validating that they’re solving the right problem.

The conversation also explores why speed can become a liability when teams rush into AI development without considering real-world consequences. Greg discusses the importance of evaluating ROI and risk before building, including his use of a value matrix to examine what happens when an AI system gets something right—or wrong. For AI products with increasingly consequential actions, customer-centered design and responsible product leadership become more important, not less.

Ultimately, Greg believes the companies that succeed with AI will be those that stay focused on customers and use the technology to empower people. For UX and product professionals, that means rediscovering their core strengths in empathy, problem-solving, discovery, and strategic thinking—and applying them to a new generation of AI-powered experiences.

You’ll learn:

Why UX professionals need to move beyond simply using AI to make existing workflows faster

How customer discovery and strategic problem framing become even more valuable as AI accelerates product development

Why Greg believes UX professionals should become AI product and relationship designers, not just interface designers

How the “snowball sprint” approach keeps customers involved throughout AI product development

Why teams should evaluate AI ROI, risk, and the consequences of incorrect outputs before they build

How rapid prototyping and real-world customer feedback can help teams avoid expensive AI product failures

What will separate the companies that succeed with AI from those that fall behind

Resources & links

Greg Nudelman on LinkedIn

UX for AI book

UX for AI website

Greg’s “Iceberg UX” post

Mike Mace on LinkedIn

Learn more about Insights Unlocked: usertesting.com/podcast