How to Develop Deep Product Empathy

Here’s a startling fact: Empathy among American college students has dropped 40% since 1980. Yet in our increasingly digital world, understanding our users has never been more crucial. When surveying 1,000 product managers across companies like Google, Apple, and Microsoft, empathy ranked second-to-last in valued hiring skills. We’re facing an empathy crisis at precisely the moment we need it most.

But here’s the good news: empathy can be learned. While data drives decisions, it’s empathy that helps us ask the right questions and build products that truly matter. As Ryan Siemens, founder of Groove, puts it: “Without empathy, you almost guarantee you will miss out on insights about the best problem to solve.”

Why Product Empathy Matters

Product empathy isn’t just about feeling what users feel – it’s about translating that understanding into better products. Ken Norton, former Google product director, explains: “If you’re building something for someone else, you’ll be much more successful if you can identify with their needs first.”

But there’s a common trap: designing only for people like ourselves. As Ravi Mehta, former CPO at Tinder, warns: “It’s a failure of empathy to collapse all users into a single persona.” Great product managers can empathize with and design for people very different from themselves.

The Love Pyramid: A Framework for Deep Empathy

Product empathy builds on three foundational layers:

1. Understanding: The ability to truly comprehend others’ experiences
2. Intention: The conscious choice to act on that understanding
3. Action: Converting empathy into tangible product decisions

Remember: Love isn’t just a noun – it’s a verb. Each layer supports the ones above it, and missing any layer makes the others less effective.

The 7 Core Skills of Product Empathy

Understanding Layer

1. Emotional Literacy

• Learn to read emotions on faces (even through Zoom)
• Practice with tools like Berkeley’s Greater Good Center quiz
• Look for subtle cues in user interviews and team meetings

2. Perspective Taking

Follow this three-step process:

1. Look for emotional signs
2. Imagine yourself in their situation
3. Test your understanding by seeking feedback

3. Moral Imagination

• Practice empathy through fiction and entertainment
• Research shows reading fiction increases empathy
• Apply these insights to real-world product scenarios

Intention Layer

4. Moral Identity

• Develop a personal mantra (mine is “Cultivate awareness, love everyone”)
• Use it to guide product decisions
• Let it anchor your leadership style

5. Self Regulation

• Develop practices to prevent empathy burnout
• Manage stress to stay engaged during difficult decisions
• Remember: You can’t help others if you’re depleted

Action Layer

6. Practicing Kindness

• Build small acts of kindness into your daily routine
• Use the habit loop: cue → routine → reward
• Let the positive impact on others be your motivation

7. Moral Courage

• Speak up for users when they’re not in the room
• Prepare responses for common rationalizations
• Practice difficult conversations with peers

Bringing Together Love and Data

Here’s where many product managers get stuck: they see empathy and data as opposing forces. They’re not. Data validates empathy and empathy gives meaning to data.

When you’re practicing perspective taking, getting feedback isn’t just good practice – it’s data collection. When you’re reading user feedback, emotional literacy helps you see beyond the words to the underlying needs.

Moving Forward

The empathy deficit in product management is real, but it’s not insurmountable. Start small:

• Pick one skill to practice this week
• Set up regular user interviews
• Share these practices with your team

Remember: The goal isn’t perfection; it’s progress. As Thich Nhat Hanh says, “Loving without knowing how to love wounds the ones we love.” We owe it to our users to love skillfully.

By combining deep empathy with solid data, you can create products that don’t just work well – they change lives.

Want to get started? Check out these resources:

• Berkeley’s Emotional Intelligence Quiz
• UVA’s Ethical Leadership Course
• The Habit Loop by Charles Duhigg

 

Lessons from YouTube

Remember when creating professional videos required expensive equipment, technical expertise, and a massive studio budget? Today, teenagers with smartphones are building million-dollar content empires from their bedrooms. This transformation didn’t just change how we create media—it revolutionized entire industries. It wasn’t the first time either–this has been a common pattern for emerging technologies all the way back to the printing press.

Now, artificial intelligence is following the same path. Just as YouTube and affordable cameras democratized video production, AI is transforming from an exclusive tool of tech giants into technology that any business can leverage. The costs are dropping, the technology is becoming more accessible, and the barriers to entry are crumbling faster than anyone predicted.

For business leaders, this creates an urgent question: How do you position your company to benefit from this transformation?

What Does AI Democratization Really Mean?

AI democratization is the process of making artificial intelligence technology accessible to businesses and individuals of all sizes, not just tech giants with massive budgets. It’s happening through three main drivers:

• Dramatically lower hardware requirements than initially expected
• The rise of open-source models that anyone can use and adapt
• Reduction in the technical knowledge needed to make use of AI capabilities

This shift means that small businesses can now access AI capabilities that were previously reserved for companies with multi-million dollar budgets.

The YouTube Revolution: A Preview of AI’s Future

Before YouTube, video production was a gated community. Only established media companies could afford to create and distribute professional content. Sound familiar? It’s exactly where AI was just a few years ago.

YouTube changed everything by:

• Making distribution free and global
• Allowing creators to monetize directly
• Creating an ecosystem where anyone could compete with traditional media

6 Striking Parallels Between Video and AI Democratization

1. Democratization of Tools

Then: Professional video went from requiring expensive cameras and editing suites to being possible with a smartphone. 

Now: AI is moving from requiring massive data centers to running on standard computers and even phones.

2. Rise of Platforms as Ecosystems

Then: YouTube created a complete ecosystem for creators, viewers, and advertisers. 

Now: Platforms like Hugging Face and GitHub are becoming the “YouTube of AI,” where developers share models and businesses find solutions.

3. Explosion of Niche Applications

Then: YouTubers created content for every conceivable interest, from knitting tutorials to urban exploration. 

Now: Businesses are developing AI solutions for hyper-specific needs, from local agriculture to specialized education.

4. Shift from Hardware to Software

Then: Success became about creativity and editing skills, not camera quality. 

Now: AI success is increasingly about how you apply models, not how much computing power you have.

5. Empowerment of Small Players

Then: Individual creators competed successfully with major media companies. 

Now: Small businesses are using AI to compete with larger corporations in customer service, content creation, and analytics.

6. Rapid Innovation Cycles

Then: Video technology evolved rapidly from HD to 4K to live streaming. 

Now: AI capabilities are advancing at an even faster pace, with new breakthroughs monthly.

What This Means for Your Business

Key Actions to Take Now:

1. Start Small but Start Now
   • Begin with readily available AI tools for specific tasks
   • Focus on solving real business problems, not chasing technology
2. Build on Platforms
   • Use established AI platforms rather than building from scratch
   • Look for solutions that integrate with your existing systems
3. Focus on Application, Not Technology
   • Success will come from how you use AI, not just having it
   • Invest in understanding your specific use cases
4. Prepare for Rapid Change
   • Do not tie your AI strategy to one model or company
   • Build systems that can evaluate and adopt new capabilities (See also:
     performance-driven development)
5. Watch for Oversaturation
   • AI will be added to everything until AI alone isn’t differentiator
   • Look for unique applications in your industry and differentiate through expertise, not just technology

The Path Forward

The democratization of AI isn’t just making technology more accessible, it’s reshaping how businesses compete. Just as YouTube created opportunities for new types of media businesses, AI democratization will create new business models and opportunities.

The winners won’t necessarily be the companies with the biggest AI budgets, but those who best understand how to apply AI to solve real problems for their customers. The time to start preparing for this future is now.

Remember: YouTube didn’t kill Hollywood, it created a whole new entertainment ecosystem alongside it. Similarly, AI democratization won’t eliminate the need for expertise, but it will change how we think about, access, and apply artificial intelligence in business.

The question isn’t whether to participate in this transformation, but how to position your business to benefit from it.

Remember that viral TED Talk about how standing like Superman could boost your confidence and change your life? Over 60 million people watched it. Millions tried it. And it turned out to be wrong.

It’s not alone. Some of psychology’s most famous and influential findings haven’t held up under scientific scrutiny. The marshmallow test that supposedly predicted life success? Not quite. The idea that willpower works like a muscle that gets tired? That’s looking shaky too.

These aren’t just academic footnotes. These findings shaped self-help books, corporate training programs, and parenting advice. Some still circulate on social media today, years after being debunked.

Let’s examine five of psychology’s most notable reversals and what they teach us about both human behavior and the scientific process.

1. Power Posing: The Confidence Trick That Wasn’t

In 2010, Harvard researcher Amy Cuddy told us that standing in “power poses” for just two minutes could boost confidence and even change hormone levels. Her TED Talk became the second most viewed of all time.

But when other scientists tried to replicate these results? Nothing. No hormonal changes. No meaningful behavioral effects. While some people might feel more confident after power posing, the biological impact claimed in the original study simply wasn’t there.

Lesson learned: Just because something feels true doesn’t mean it is.

2. Ego Depletion: The Willpower Myth

The theory was compelling: willpower works like a muscle that gets tired with use. Need to resist that cookie? Better not make any big decisions afterward – you’ve depleted your willpower reserves.

But in 2016, a massive replication effort involving 2,000 participants found no evidence for this effect. While mental effort is real, the idea that willpower is a limited resource that runs out like a battery appears to be wrong.

Lesson learned: Simple metaphors don’t always capture complex psychological processes.

3. Social Priming: When Subtle Cues Weren’t So Subtle

Remember hearing that showing people words related to old age made them walk more slowly? Or that holding a warm cup of coffee made people feel “warmer” toward others? These were examples of social priming – the idea that subtle cues dramatically influence our behavior.

Most of these effects failed to replicate. The field faced a crisis when one prominent researcher was caught fabricating data. Even Nobel laureate Daniel Kahneman called social priming a “train wreck.”

Lesson learned: If something sounds too good (or neat) to be true, it probably is.

4. The Marshmallow Test: It’s Not Just About Willpower

The setup was simple: give a child a marshmallow and tell them if they wait 15 minutes without eating it, they’ll get two. The original study suggested this test of delayed gratification predicted success in life.

But recent research with larger, more diverse samples showed something different: a child’s ability to wait had more to do with their socioeconomic background than their willpower. For a child from an unstable environment, grabbing the marshmallow immediately might be the rational choice.

Lesson learned: Context matters more than we think.

5. Facial Feedback: Smile, But Don’t Expect Magic

The idea was beautifully intuitive: smile and you’ll feel happier. Frown and you’ll feel sad. Your facial expressions influence your emotions.

A massive replication effort across 17 labs failed to find evidence for this effect. While there might be a tiny influence under specific conditions, the strong version of facial feedback theory appears to be wrong.

Lesson learned: Even “obvious” psychological effects need rigorous testing.

What This Means for Psychology (and You)

These reversals don’t mean psychology isn’t scientific – they show science working as it should. When evidence challenges our beliefs, science changes its mind. That’s not a bug; it’s a feature.

For the rest of us, these cases offer valuable lessons:

• Be skeptical of dramatic claims about simple psychological tricks.
• Consider context and complexity in human behavior.
• Remember that correlation doesn’t equal causation.
• Look for replicated findings rather than single studies.

The next time you hear about a revolutionary psychological discovery, remember these cases. Good science takes time, replication, and a willingness to admit when we’re wrong. That’s how we get closer to the truth about human behavior.

That’s something worth striking a power pose about – or maybe not.

In the tech industry, team dynamics are critical to strategic success. Drawing from Marty Cagan’s insights on the shift from project to product teams, many companies recognize the importance of this transformation—a transition the best Project Management Offices (PMOs) have embraced and are actively improving.

The Challenge with Project Teams

Cagan highlights the significant drawbacks of project teams: they form to deliver specific projects and then disperse. This constant cycle of forming and re-forming introduces significant overhead, and often, teams don’t remain together long enough to reach their full performance potential. It also leads to a lack of deep domain knowledge and ownership, as teams don’t stay with the product long enough to see its evolution. Great PMOs, in partnership with product teams, work to overcome these challenges, but there may be a better model.

The Strength of Product Teams

Product teams are committed for the long run, deeply involved from inception through to lifecycle management. This continuous involvement fosters a robust understanding of the product and its market, cultivating a sense of ownership and dedication. PMOs support this by ensuring that teams have what they need to succeed over the long term and by facilitating coordination between product teams.

Agile as a Tool for Empowerment

In the best organizations, agile is adopted not merely as a methodology but as a mindset of continuous improvement that promotes empowerment. Unlike rigid applications of Agile seen in some frameworks like SAFe, this use of Agile encourages flexibility and rapid iteration. Great PMOs champion these principles, facilitating rather than dictating the Agile process. The balance between guidance and autonomy is constantly being refined, ensuring each team can customize their approach to best fit their product portfolio.

PMOs’ Role in Supporting Product Teams

The mission of PMOs extends beyond overseeing projects—it’s about genuinely empowering teams. Here’s how they can actively support product teams’ efforts:

• Strategic Alignment: Committed to ensuring all product and project initiatives align with long-term business goals.
• Resource Orchestration: Managing resources to ensure that product teams have what they need to execute their visions effectively
• Cross-functional Coordination: Facilitating collaboration across departments, supporting the smooth execution of complex projects. The aim is to reduce silos between departments while acting as a bridge when silos are unavoidable.
• Insight and Analytics: Providing actionable insights and data, which are essential for informed decision-making. Enhancing the timeliness and accuracy of these insights is an ongoing goal.
• Continuous Improvement: Fostering a culture of feedback and iterative improvement, constantly aiding teams in refining their products and processes. Combining facilitation with the development of mechanisms that capture and implement feedback more effectively.

A Real and Evolving Partnership

As PMOs continue to grow and evolve, their role as partners to product teams becomes ever more crucial. This partnership, based on a mutual understanding of goals and methodologies, fosters a productive environment where strategic alignment and empowerment are prioritized. Continuous refinement is the key to making sure product teams are supported in innovating and delivering outstanding products that meet customers’ needs.