Delightful Intelligence

How to Create AI Products Users Love

With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

Ramli John, C. Todd Lombardo, and Michael Connors

Delightful Intelligence

by Ramli John, C. Todd Lombardo, and Michael Connors

Copyright © 2025 Ramli John, C. Todd Lombardo and Michael Conners. All rights reserved.

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Brief Table of Contents (Not Yet Final)

Chapter 1: The Magic of Delight (available)

Chapter 2: Key Principles of Delight (available)

Chapter 3: What is AI? A Crash Course (unavailable)

Chapter 4: Human-Machine Interaction (unavailable)

Chapter 5: Empathy (available)

Chapter 6: Hyper-Personalization (unavailable)

Chapter 7: Ethical Implications (unavailable)

Chapter 8: Applying Design Principles to AI (unavailable)

Chapter 9: Delightful Intelligence (unavailable)

Chapter 1. The Magic of Delight

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the authors’ raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be the 1st chapter of the final book.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at mcronin@oreilly.com.

This chapter introduces the concept of delight and its significance in user experience, exploring different levels of delight and their impact on user satisfaction. It sets the foundation for understanding how AI can elevate user experiences beyond mere functionality. The chapter aims to inspire readers to think beyond basic usability and consider how to create truly delightful interactions.

People will forget what you do; they’ll forget what you said. But they’ll never forget how you made them feel.

Maya Angelou

On my (Ramli’s) 39th birthday, my wife and I had dinner at Eleven Madison Park (EMP), a 3-Michelin-Star restaurant in New York City. Of course, the food was tasty and beautiful. But what set it above other restaurants is how delightful and magical the dining experience was from start to finish.

“Good evening, Mr. and Mrs. John—and welcome to Eleven Madison Park. My name is Justin,” said the maître d’.

Unlike most other restaurants, he welcomed us at the entrance, not behind a table with an iPad screen to confirm our reservations.

“I hope you’re having an amazing birthday so far. We’re excited to have you with us tonight.”

Joanna and I paused and smiled at each other.

“How did you know who we were?”, we asked.

I would find out later that they’d take the list of reservations and Google the names on it, creating a cheat sheet with photos for each seating. If your photo had ever been put on the Internet (on LinkedIn, Facebook, Instagram, or elsewhere), the maître d’ would greet you by name.

Justin handed us off to our host.

As we settled in our seats, the host asked what kind of water we’d like to start with—iced, bottled still, or bottled sparkling. Moments later, even before the host left our table without any visible communication, a server poured our preferred beverage.

It felt like magic!

I couldn’t see any hidden lapel mic on him to signal to the servers.

“How’d you do that?”

“It’s a secret,” he winked.

I googled it. The EMP team devised hand signals to discreetly let our server know our preference. It’s the same technique baseball catchers use to communicate with pitchers about the best pitch to throw.

Near the end of the 11-course chef’s tasting menu, a lady approached our table.

“Happy birthday again, Mr. John! My name is Jennifer from Guest Relations. Would you both like a kitchen tour and enjoy a treat at the chef’s table?”

We couldn’t pass up the offer.

For most restaurants, only the biggest VIPs—celebrities, billionaires, politicians, and elites—got a kitchen tour and dined at the chef’s table. Joanna and I were just a couple of Canadians visiting New York City. Why us?

The EMP team devised a plan to level the playing field and offered that kitchen tour to everyone who showed interest in experiencing it. They created a standing-only chef’s table in their kitchen with an expansive view of their thirty precision-trained cooks working in near silence.

One of the chefs prepared a liquid nitrogen-cooled citrus dessert before us. Jennifer shared the history of EMP with us, and how Miles Davis (the iconic jazz trumpet player) inspired the restaurant’s menu and decor.

Back at our table, we wrapped up the meal. We were in for one more delightful surprise: getting the bill.

Presenting the check to guests at the end of the meal can often turn a magical dining experience sour. Placing it down before the guests ask for it can make them feel like you’re trying to rush them out of the door. But, when guests are ready to leave, they get impatient if it takes too long.

At EMP, at the end of the meal, our server brought the bill over on a custom-made plate with chocolate pretzels playfully hanging from the edges.

“Please help yourself as much as you like with our compliments. And when you’re ready, your check is right here.”

With the complimentary treats, we didn’t feel like we were being rushed out of the door. At the same time, the check was right there, ready and waiting.

That night, I learned that a restaurant could create a dining experience that felt like magic. I haven’t stopped gushing about it to anyone who would care and listen to me. It’s a birthday I won’t forget anytime soon.

This experience reminded me of another transformative moment in customer delight—but this time, powered by artificial intelligence. In early 2023, millions of people experienced a similar sense of magic when they first interacted with ChatGPT. The AI didn’t just answer questions; it delighted users with its ability to understand context, maintain engaging conversations, and even show personality. Like EMP’s hidden hand signals, ChatGPT’s seemingly magical abilities were built on careful attention to user experience and a deep understanding of what makes interactions feel special.

Unreasonable Hospitality

Will Guidara and Daniel Humm took over Eleven Madison Park in 2006. It was a middling brasserie, far from the 3-Michelin-starred spot it is today. Together, they set out to transform it into the best restaurant in the world.

They had two options to do that.

Focusing on food innovation and cutting-edge cooking techniques could help them become the best. This is how restaurants like El Bulli (which introduced molecular gastronomy to the world) and Noma (which championed foraging and wild-caught ingredients) reached the top. Chefs at the finest restaurants in the world have long been celebrated for serving food that delights the senses.

They found one such magician with Chef Daniel. He worked at other Michelin-rated restaurants, and they had a real shot at greatness by out-innovating other restaurants and serving never-before-seen dishes to patrons.

With years of experience in hospitality, Will (EMP’s general manager) knew the power of creating a delightful and unforgettable dining experience. He saw this at Wolfgang Puck’s Spago in Beverly Hills and Danny Meyer’s Tabla and Café 2 in New York City. An extraordinary dinner service can put the world on pause and create magic for guests.

Together, Will and Daniel opted to become the best at creating an unreasonable dining experience in how they made people feel. In his book, Unreasonable Hospitality, Will shares:

“When you create a hospitality-first culture, everything about your business improves—whether that means finding and retaining great talent, turning customers into raving fans, and increasing profitability.”

Almost twenty years later, EMP is at the top because it focuses on creating that hospitality-first culture. It’s a three-Michelin-star, World’s 50 Best, and James Beard Award-winning restaurant. Those awards are what the Oscars are to acting, Grammys are to music, and the Vince Lombardi Cup is to American football.

Today, as AI reshapes how businesses interact with customers, this lesson becomes even more crucial. Companies aren’t just competing on features or efficiency—they’re competing on their ability to create magical moments of connection, whether through human touch or intelligent automation.

The Delight Edge

C. Todd and I (the book’s authors) are not culinary chefs, but what Will wrote in his book resonated with us deeply. We’ve seen it in our work at dozens of startups and companies, including Appcues, GoDaddy, Openly, MachineMetrics, and PepsiCo. Creating a “hospitality-first culture” improves everything about a business.

But there’s a problem.

“Hospitality” doesn’t sound right. It’s what people who work at hotels and restaurants do, not what applies in the world of AI. So rather than that word, in Delightfully Awesome, we propose that when you create a delight-first culture, everything about your business improves.

To prove to ourselves that our experiences were not outliers, we talked to over 100 business leaders from companies like Zappos, HubSpot, Mailchimp, Chewy, Johnson & Johnson, Twillio, and more. They shared stories, case studies, and (for some) numbers that show the positive impact of delighting their customers, even when it didn’t have immediate ROI.

Time and time again, from these interviews, we heard three things emerge as the benefits of creating a delight-first culture.

1. Stand out from competitors.

Delighting your customers can be a powerful way to differentiate your business from the competition. It’s especially true in a highly commoditized and competitive market.

For example, it’s how Mailchimp, an AI-powered marketing communication provider, stood out from other email marketing tools. There are hundreds, if not thousands, of them. From their playful brand personality to their helpful educational content and user-friendly platform, Mailchimp has made delight a core part of their customer experience.

The product team has also built features that make the lives of their customers easier, such as a tool that automatically creates display ads for Mailchimp stores. This has helped them build a devoted user base and establish themselves as a leader in their industry.

2. Turn customers into evangelists.

When you go above and beyond to provide an exceptional experience, customers are more likely to share their positive experiences with friends and family. This word-of-mouth marketing is incredibly valuable, as people trust recommendations from people they know.

For example, Domino’s Pizza underwent a major brand transformation a few years ago, overhauling its recipes and customer experience. They listened to customer feedback and improved their pizza quality and delivery speed.

By delighting customers with a better product and service, Domino’s transformed its brand reputation and created a legion of loyal fans who actively promoted the brand on social media and to their peers. This helped Domino’s regain market share and become one of the top pizza chains in the world.

3. Building long-term customer loyalty.

Delightful experiences foster a deep emotional connection beyond just the product or service. When cheaper and better products hit the market, customers stay loyal to brands that consistently provide exceptional experiences. This loyalty translates into higher customer lifetime value and reduced churn rates.

Canyon Bike is not the cheapest bike you can buy. Its direct-to-consumer model means customers can’t go into a shop, try it out, and just buy a bike. So their team had to be more intentional and thoughtful about delighting customers. First, they designed an online bike-fitting process so meticulously that customers can confidently select the perfect size.

They also offer a 30-day return policy, allowing riders to test the bike on their local terrain. Canyon’s customer service team provides expert advice, ensuring customers feel supported throughout their purchase journey. These deliberate efforts to delight customers have resulted in a loyal following, with many cyclists choosing Canyon bikes despite the lack of in-person shopping experiences.

Deep Delight

When C. Todd and I talked to business leaders about customer delight, we got one of two reactions:

“Yes, I totally get it. Delighting customers is so important.”

“Ugh, that again. We just have to deliver a product that solves our customers’ problems. We don’t need to add flashy animations and cute illustrations. We sell to mature B2B buyers who don’t care about that.”

Those who didn’t get it often had an incorrect notion that delight comes from superficial cute illustrations, pleasing designs, and humorous copy. They’re the icing on the cake, the nice-to-have–not the must-haves. They think that when you have time, you add “delighters” to your product, rather than making “delight” an integral part of the design itself.

A classic example of this kind of “add-on” delight is when you schedule an email campaign with Mailchimp. Freddie (their chimp mascot) gives you a high-five (Figure 1-1).

Figure 1-1. High five from Mailchimp

Was this necessary? No.

Does it make you smile? For me, always.

However, true delight goes much deeper than that. If you look at the dictionary, delight means to “please someone greatly” and to “satisfy their needs beyond their expectations.” It’s understanding your customers’ needs, anticipating their pains, and going above and beyond.

In his book Designing for Emotions, Aaron Walter discusses the hierarchy of user needs. At the top of it is pleasurable. C. Todd and I call this “surface-level delight.” These include visual aesthetics, animations, and humorous copy. True delight goes deeper, addressing fundamental user needs like functionality, reliability, and usability.

For Mailchimp, one example of a functional delight was when their team launched a feature to help Mailchimp store owners bring in more sales. Their team heard through customer interviews that it was taking too much time and costing too much to create on-brand display ads. They’d tinker with other tools like Canva and Photoshop. Those with a budget would hire a freelance designer.

“How do we make it easier for our customers?” One of the Mailchimp designers asked the team.

So they launched a feature that automatically created social media images, display ads, and email campaigns based on their store brand guidelines.

“Wow, this felt like magic!” said one of their customers.

We’ll explore more about the difference between surface-level and deep delight in the next chapter. For now, let’s turn towards delight and core memories.

Delight and Core Memories

This “wow” or “magic” moment is why delight matters. They create core memories.

I borrowed that term from the Pixar movie Inside Out. I rewatched it recently with my wife and two-year-old toddler.

In the movie, core memories are pivotal moments that shape a person’s personality and values. These experiences leave a lasting mark on one’s psyche, influencing future thoughts, behaviors, and emotions. For example, for Riley Anderson (the main character in the movie), a core memory is when she scores her first hockey goal, shaping her love for the sport and her competitive spirit.

“Aha, this is why delight matters!” I screamed.

Both Joanna and Zane stared at me.

Delightful experiences create core memories for your customers.

They leave a lasting impression, influencing future behaviors, purchasing decisions, and brand perceptions. This is not just a feeling; it’s backed by psychology and science.

In 1996, Nobel laureate Daniel Kahneman and professor Donald Redelmeier from the University of Toronto published a study that found that colonoscopy patients consistently evaluated the discomfort of the experience based on the intensity of pain at the worst (peak) and final (end) moments.

They conclude that people mostly remember an experience based on the most intense part (the highest point or peak) and how it ended. They don’t usually think about every moment of the experience or try to average it all out. Instead, their brain focuses on these two key parts to decide how they feel about the whole thing.

For example, if you went to a concert, you might remember the excitement of your favorite song being played (the peak) and how you felt leaving the venue (the end). These two moments would shape your overall memory of the concert more than all the combined parts.

When businesses focus on creating peak moments of delight, they increase the likelihood of forming a positive core memory for the customer. These memories can then serve as anchors for future interactions with the brand, influencing customer loyalty and overall satisfaction.

In the age of AI, creating core memories takes on new dimensions. When OpenAI’s GPT-4 helped a blind user understand complex images for the first time, or when Google’s AI helped doctors detect breast cancer earlier than traditional methods, these weren’t just useful features—they were transformative moments that created lasting emotional connections with the technology.

The Bottom Line

Of course, the rationale C. Todd and I often heard for not investing to deliver a great experience is that the cost is high. Speaking to business leaders who said it was a game-changer, however, we often heard the opposite.

Delightful experiences can significantly impact a company’s bottom line. A study by Harvard Business Review found that customers who have the best experiences spend 140% more than those who have the poorest experiences (Figure 1-2).

Figure 1-2. Customers who have the best experiences spend more money.

They were also nearly twice as likely to remain customers after one year and three times more likely to continue as paying customers long-term (Figure 1-3).

Figure 1-3. Membership is driven by good customer experiences.

Moreover, delighted customers are more likely to become brand advocates, sharing their positive experiences with friends, family, and colleagues. This word-of-mouth marketing can be incredibly powerful, as people tend to trust recommendations from their personal networks more than traditional advertising.

Delivering great experiences reduces the cost of serving customers from what it was previously. Unhappy customers are expensive. They’re more likely to return products, require support, and churn. While those customers with positive core memories about your product are more likely to buy quickly, tell others about it, and stick around for life.

The Magic of Delight

In the climactic scene of the Pixar movie Ratatouille (can you tell I’m a big fan?), the stern food critic Anton Ego takes a bite of the Ratatouille and is instantly transported to his childhood. The flavors evoke a powerful core memory of his mother’s cooking, reminding him of the comfort and love he felt as a boy. This moment of pure delight breaks through Ego’s hardened exterior, softening his heart and changing his perspective.

Moved by the delightful experience, Ego writes a glowing review of the restaurant, praising not only the food but also the philosophy behind it. He eloquently states:

“In the past, I have made no secret of my disdain for Chef Gusteau’s famous motto: ‘Anyone can cook.’ But I realize, only now do I truly understand what he meant. Not everyone can become a great artist, but a great artist can come from anywhere.”

That’s the true power and magic of delightful experiences. They can break down barriers, challenge preconceptions, and create lasting connections. In business, this translates to loyal customers who become brand advocates, spreading their positive experiences far and wide. By focusing on creating moments of delight, companies can tap into this transformative power, fostering emotional connections that go beyond mere transactions.

As we venture further into the age of artificial intelligence, the magic of delight becomes more crucial than ever. Whether through human touch or intelligent automation, creating moments of wonder that become core memories isn’t just good business—it’s how we build technology that truly enriches people’s lives.

We’ve gotten this far without defining what delight is, but that’s for the next chapter.

Delight is a powerful tool for creating memorable experiences that resonate with customers on a deep, emotional level. Focusing on moments of true delight, you can forge stronger connections with customers, foster loyalty, and ultimately drive growth. In the next chapter, we’ll explore the science, psychology, and definition of delight.

Chapter 2. Key Principles of Delight

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the authors’ raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be the 2nd chapter of the final book.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at mcronin@oreilly.com.

We explore the core principles that underpin delightful experiences, including mindset, empathy, culture, and data-driven approaches. This chapter equips readers with the fundamental concepts necessary to create truly delightful products. By exploring these principles, readers will gain a holistic understanding of what it takes to design and implement delightful AI experiences, which we’ll explore in Chapters 4 to 7.

“I’ve never seen a business that delights the customer that doesn’t succeed. You never forget whether you’ve had a good experience or a poor experience.”

Warren Buffet

When we ask technology product people about delightful experiences, some reply that it involves small things like animations and surprise emojis along touchpoints of a user journey. When you think of the phrase “delightful experience,” we would bet you $20 that home insurance is not the first (or second, or tenth) thing that comes to mind. To many people, home insurance is not even on a list of something that’s delightful in any way, shape, or form. However, let’s look at this from a different perspective than the homeowner/end consumer. What if you’re an independent insurance agent looking to get your customer the best policy to insure their home?

We need to tell you a little about the homeowners insurance market in the United States to set the context. We’ll oversimplify a little for brevity and understanding sake. Apologies if you’re an insurance pro as some of this may be oversimplified.

In the United States, there are three channels a homeowner can purchase home insurance: direct, captive agent, and independent agent. First, there are direct-to-consumer insurance companies: these are companies where you can go straight to their website, enter information, and purchase insurance. Progressive and Lemonade are well-known examples of this. Next, are the two distinct agent channels, one is the captive agent and the other is an independent agent. Captive agents can only sell insurance policies from one company; StateFarm is a good example of this. If you’re a StateFarm agent, you can sell StateFarm insurance policies, but you cannot sell an Arbella policy or a Kin policy. These captive agents often operate like a franchise, and most have restrictions on how they are run and what policies they can offer. Independent agents are less restricted and can offer policies from any insurance company they are appointed to. An ‘appointment’ is the industry term for an agent being authorized to sell a company’s insurance products.

When C. Todd worked for Openly Insurance, an Insure-tech startup headquartered in Boston, the key objective of the company was: make insurance for independent agents simple yet amazing. This independent agent channel had been historically underserved from a technology perspective, possibly because since the dawn of the internet, there have been predictions that this channel was going to shrink. Recall that the travel agent industry declined when online travel booking capabilities increased. This self-service model democratized booking flights and hotels and the market need for travel agents slowly subsided. It was a valid assumption to think that this same impact would occur in the insurance market. However this did not happen and the independent agent channel remained quite strong. In fact, the channels that shrunk the most percentage-wise were the captive agent.

If you’ve never had to get a homeowner’s policy, here’s the basic process:, an insurance representative (such as an agent or a customer service representative) would collect information from the homeowner through a conversation. They ask questions such as, “ How much square footage is there in the house? How many bathrooms? How new is the roof? Is there a garage or a shed? How old is the roof? What type of HVAC exists?” and so on. These conversations could take anywhere from 10 to 20 minutes, depending on the size and complexity of the home.

Once the representative obtains that information, they submit it to an insurance company (who officially underwrites the policy) to get a formal quote. Usually they get a range relatively quickly if not instantaneously, but only after all the information is sent to the insurance company and verified can a precise and bindable quote be generated. (Note: binding is the “sale” of a policy in simple terms). If you’re an agency looking to increase your productivity, you want your staff to complete as many of these conversations in a day as possible. If they take 10 to 20 minutes each, that is only 3 to 6 quotes per hour, and not every quote turns into a sale. What if there was a better way?

Ty Harris and Matt Wielbut, the founders of Openly, identified that gap, understood the opportunity, and became determined to fix it. Matt thought about the amount of data available on the data-broker market and created a way to enter only three pieces of information about the property–the rest would be filled in programmatically. They knew that there would be limitations, such as brand new constructions which might have no preexisting data, but the majority of homes have already been built and there’s sufficient information available to quote a homeowners policy. Using technology and an elegant user experience, those 10 to 20 minute conversations were reduced to literally seconds: Homeowners first and last names and the property address were all that were needed to generate a bindable quote in around 30 seconds (or less). This meant that the agent could sell the policy right then and there. There was no more initial quotes range, and no more waiting for verification until a formal, final dollar amount was generated for the quote. The policy was immediately sellable. Agents absolutely loved it. When C. Todd joined as a product leader in the early stages of the company, he and his team conducted discovery sessions with agents for future iterations of the product, there was an overwhelming adoration for this time-savings. Many of the agents said that Openly was “so delightful because it’s so fast and accurate.” The Openly team didn’t incorporate emojis or fancy animations. Instead, they simply solved a problem that allowed a user to do something they previously couldn’t. In this case, it was to quote and sell more insurance policies thanks to the data-driven technology and user experience they developed.

So if delight is not animations and emojis, what is it, then?

Defining Delight

Let’s get this out of the way up front: There’s no universal definition of delight. There are, however, many theories. What is delightful to you, may not be to me, or to your colleagues. By nature, delight is subjective, and that’s what makes it elusive to define. Before we get into showing how companies use artificial intelligence to drive delightful interactions, let’s examine the existing theories on delight that have surfaced over the last few decades.

Delighters and Satisfiers

We would be remiss if we didn’t discuss the Kano model proposed by Noriaki Kano of Japan in the 1980s. Kano and his colleagues proposed that there were three types of product investments: basic expectations, performance pay-offs and excitement generators.

The Kano model identifies five “quality elements'' that product features affect customer satisfaction. We’ll swap “quality element” with the term “attribute”. Here’s a simplified explanation:

Attractive: These are unexpected features that pleasantly surprise customers when they are present, leading to high satisfaction. However, if they are missing, customers don’t feel disappointed because they weren’t expecting them in the first place. Think of them as delightful surprises. One example could be Spotify’s “Wrapped” feature, which gives users a personalized summary of their listening habits at the end of the year. It’s a delightful surprise that users love, but they don’t expect it to be a core part of the service. These features have been commonly labeled “delighters.”

One-Dimensional: These features are the main aspects customers use to compare different products. The better these features are, the happier customers will be. On the flip side, if these features are lacking, customers will be dissatisfied. Essentially, the more of these features, the better. For example, when your Zoom call has higher quality audio and video quality, the more you appreciate the service.

Must-Be: These are basic features that customers expect in a product. When these features are present, customers feel neutral because they are taken for granted. However, if these features are missing, customers will be very dissatisfied. For instance, having reliable wifi in your hotel room is a basic expectation, even though some of us can remember days when there was no hotel wifi.

Indifferent: These features don’t impact customer satisfaction one way or the other. Customers don’t care if they are present or absent.

Reverse: These are features that customers dislike. The more these features are present, the less satisfied customers will be. Conversely, if these features are absent, customers will be happier. Automatic playing of video ads on a video platform such as YouTube is a common feature that some users find disruptive and prefer not to have them. Removing these auto-play ads would likely increase user satisfaction.

The following figure shows the typical depictions when the Kano model is cited. The left graph has been cited more commonly with only referencing three of the attributes, but the right references all five described in the original paper. Curiously there’s no explanation why the paper did not include all five for their visualization. To further complicate matters, these missing two are often ignored when considering product features and the impact on customer emotion¹.

There have been many interpretations of the Kano model for product evaluation and even product prioritization. You might have heard of the “must-have versus nice-to-have versus want-to-have priority matrix? Here’s where that likely originated. The Must-be attributes become the must-haves, the one-dimensional becomes the “nice-to-have” and the attractive becomes want-to-have product features.

It is in these terms we sometimes find ourselves with “delight” being used as a noun for a particular customer interaction: a delighter. We take issue with this, as it minimizes the definition of delight to something that’s not valuable… like the frosting on the cake rather than the cake itself.

Disconfirming Expectations

Disco-what? Let’s start with an example: When you go to a movie and expect it to be bad but find yourself thinking “That was actually a great film” as you leave the theatre, your expectations going in were disconfirmed, but in a positive manner. Conversely, if you go to a restaurant expecting amazing food but the kitchen has an off night–your food arrives cold and/or over cooked, your expectations are also disconfirmed but this time in a negative manner. The experience you had wasn’t the experience you anticipated.

Expectation disconfirmation theory (Oliver, 1980², Oliver, 1997³) states that you have expectations about an experience before even having the experience, and if those expectations are not met (for good or bad) then those expectations are disconfirmed. From these theories we could define delight as the surpassing of anticipated expectations in a positive manner.

Extreme Customer Satisfaction

Another theory claimed that the segment of extremely satisfied customers are delighted (Estelami 2000, Keiningham et al.1999), however when compared to satisfied customers, delighted customers report higher levels of loyalty and retention, and a strategy of delight in business is sees as a way to decrease customer churn (Barnes et al. 2010; Barnes et al. 2013; Barnes et al. 2016). But here’s the challenge: while satisfaction can and does play a role in delight, a satisfied customer is not necessarily a delighted customer. A clearer definition still eludes us.

A Mix of Emotions

Related to the expectation theory mentioned above, Arnold et al. 2005; Finn 2005 - Delight can be classified as a mix of two emotions: surprise and joy⁴. Recall the Openly example cited in the opening of this chapter: the independent agents were completely surprised by the speed of the Openly quoting system and experienced joy that they could immediately bind (aka sell) the policy to the customer.

The key here is that there is an emotional component of delight, an element of surprise, or joy and happiness. It’s not limited to just one emotion. This mixture of emotions leads to a definition of delight being the outcome of a positive feeling that exists after a transcendent customer experience. The particular theory focuses on delight being a moment-in-time experience. But what if delight could be more long-lasting and enduring?

Gratitude as Delight

Research done by Joan Ball and Donald Barnes in 2016 added the concept of gratitude to a definition of delight. Their theory was that delightful experiences can be transcendent individual experiences, yet there could be a shared experience as well. Consider the delight experienced by a group of runners together at a Nike Run Club event, or a group of Harley-Davidson motorcycle riders on a group ride together. These are examples of a psychological sense of brand community. Ball and Barnes proposed that these shared experiences can leave members not only with a feeling of joy or surprise, but also with gratitude and a sense of belonging. Since joy and surprise were already well-known, the addition of gratitude is interesting in that this can develop a commitment to a particular brand because of a feeling of belonging that evolves over time. Their theory is that gratitude arises from thanking the product or brand for bringing them together with others who have in-common shared values and a shared experience. In the case of Nike Run club: “I’m really grateful for that run” which leads to a sustained emotion.

A Blend of Elements

The most recent research (at the time of this writing) on delight published in 2021 articulates delight as a composition of at least three of six different elements. The authors made it clear that there was no single property associated with delight. I know, further proof that humans are complex, right? Just call us Captain Obvious, but Parasuraman, et. al⁵, described delight as a mix of following properties, of which there had to be at least three present. What is insightful about this research was that inclusion of more elements isn’t necessarily better: three really is the magic number here. This tells us that delight is nuanced and varied, but too much of any one element could turn out to be less delightful (Figure 2-1).

Figure 2-1. from More than a Feeling

The elements they identified were: Emotional, Interpersonal, Problem-Solving, Timing, Sense, Control. Let’s unpack each one of them.

Emotions

Positively valenced, similar to joy and surprise.

When you think of emotions there’s quite a spectrum to choose from. Of course, the positive emotions are associated with delight more than the negative ones. More specifically, emotions such as joy and/or surprise are frequently cited when it comes to the defining emotions of delight.

In our travels we have found the following emotions very applicable to delight: Awe, Amusement, Joy, Surprise, Satisfaction and Accomplishment.

Awe meaning the extraordinary. For example: Google Earth inspires awe as users explore its virtual landscapes.

Amusement comes from playfulness and humor, triggering the brain’s reward system. Consider TikTok’s video content that sparks delight.

Happiness, linked to dopamine and endorphin release, is key to delight. Think about how Spotify evokes happiness through their year-in-review, evoking positive memories of a listener’s year in music.

Surprise boosts delight by exceeding expectations. For example: Duolingo surprises users with unexpected bonuses .

Satisfaction and Accomplishment also add to delight. Trello helps users achieve their goals, bringing a sense of personal satisfaction. Learning platforms like Khan Academy, Udemy, or edX give users a sense of accomplishment as they complete lessons.

Interpersonal interactions

Delight is an outcome of a person-to-person interaction. How can personal interactions be a large driver of delight? Let’s go back to the restaurant example, let’s say the food was some of the most delicious you’ve ever tasted, but the waiter treated you like dirt. The interaction with others significantly influences delight. This also has implications when it comes to artificial intelligence. Are we just as delighted interacting with AI as we are with other humans?

When he was at Openly, C. Todd and the team had a real-time chat for these agents to interact and make requests. While today many of these are being augmented by large-language model AI programs, there was something delightful about an agent making a request to a person over a chat, and then within minutes to hours later learning that request was fulfilled. This speaks to expectation theories as those agents had an pre-conceived expectation that was set by other insurance companies: that the issue might take days to resolve.

Problem solving

You know that feeling, when you solve a problem that has been haunting you, you feel a degree of joy, elation and satisfaction. Most likely, you feel accomplished. If you’ve ever heard of the “jobs-to-be-done” framework for products and services, you know that when you solve a customer’s problem, or help them to solve the problem themselves, a significant windfall can be had. A few examples of problem solving delight:

Grammarly: Solves writing problems by offering real-time grammar and style suggestions, making writing clear and professional.

Nest Thermostat: Solves home temperature control and energy efficiency by learning user schedules and preferences, automatically adjusting temperature while offering remote control and energy reports.

Headspace: Solves stress and mental health through guided meditation sessions and mindfulness exercises via an easy-to-use app, promoting better mental well-being.

Senses

Sights, sounds, flavors, and smells can greatly increase or decrease a customer’s delight. If that great meal smells good, it’s awesome. If the fish smells… fishy, then it’s not-so-good! In many malls in the USA there’s a cosmetic store called Sephora. Anytime we walk by one, they are filled with people opening product samples and putting them up to their noses. The scents might remind customers of specific memories or emotions. The customers may struggle to articulate why a particular scent delights them but might compare it to walking through a blooming garden or evoke nostalgic memories of a loved one’s scent.

Timing

The speed, or slowness of something can greatly impact the level of delight. Recall the opening story of Openly. The speed that the quoting system produced a bindable result was significantly faster than the same process took with other insurance carriers. Empowering a customer to accomplish more in less time is a great example of functional delight

Control

Delight is triggered by the locus of control over a situation. If you, the user, feel like you have things in control you are more likely to experience delight. What’s interesting is when all of these are stack-ranked with the other, control came out to be the lowest factor in their research.

Combinations Matter

Of all of these, the three that are more of a delight driver than the others are emotions, interpersonal interactions and problem solving. The researchers suggested that at least three need to be present to drive delight, not just one, and that more is not always better..

While these definitions are academic research and we’ve attempted to give you some proper context one aspect is still left to define: These don’t describe how delightful something is. Was it a little delightful or off-the-charts delightful? In fact, is there a delight scale?

How deep is delight?

There are several emotional components that contribute to the overall experience of delight, and this answers the questions of what delight is. But what about measuring how delightful something is? The Nielsen Norman group describes three levels of delight: visceral, behavioral and reflective (see Table 2-1). These are experienced at different time points. The first is related to the surprise noted by many studies: it’s an immediate response to an interaction. The second is a behavioral level, which occurs during the use of the product, whether the usage time is seconds, minutes, hours, or days. And the final aspect, the reflective level, is an enduring one that can occur long after a touchpoint with a product.

Level
Visceral Level (VL)
Behavioral Level (BL)
Reflective Level (RL)

Related to

Appearance

Usability

Semantic and meaning

User response

First emotional connection between the user and the product

User experience during the use of the product

Message and image the product communicates to the user

Response time

Immediately

During the product use

Less over time

The Depth of Delight

One aspect the Kano model (introduced early in this chapter) raised is that it categorizes a specific product features as a “delighter” or a”satisfier”. The model implied that delighters were impactful in emotional response but not very deep when it came to anything else. More recent research alludes to the nuances of delight and we propose the surface versus deep delight spectrum. This matches a few models from the research and hopefully simplifies this for you, the product practitioner!

Surface level delight

We classify surface level delight as things like animations, or the small interactions that may not make a huge difference in your product experience but do provide small moments of joy and / or surprise. Examples are Slack when you have no unread items, and you get a positive message and an animation that attempts to offer the user a virtual high-five. These types of features won’t drive a user to become an evangelist, but it’s the micro-intersactions that can add-up to a better experience. If you’ve heard of the expression” death by 1000 paper cuts” this is the opposite of “Life by 1000 high fives.” They may not move specific product or company metrics, but they do offer a fleeting burst of positive emotion.

Deep delight

Deep delight are the experiences that go beyond a short initial reaction but go deeper to elements of interaction and problem solving as we mention above.

In 2XXX, Aaron Walter shared a pyramid with a hierarchy of user needs, similar to Maslov’s hierarchy of human needs.

Walter breaks his pyramid up into four segments: functional, reliable, usable, and pleasurable. Each level cannot be achieved until the lower one is established. The challenge is that this may not always apply. Let’s say I go to a restaurant, but the chef is often hit-or-miss. If I go on a “hit” night, the reliability may not be there, but I had a delightful experience anyway. Yes, there’s a risk over time of this not being consistent or reliable. Depending on how frequently your product is used this may not have the intended effect.

Another way to think about this is that there are two levels of delight: There’s surface-level delight and deep delight. They are made of multiple components.

A Delightful Mindset

When Raphael Schaad was working with colleagues across time-zones, he had a problem scheduling his meetings. At the time, Google’s Calendar allowed one additional time-zone to be displayed, but he had more teammates in more than two time zones. Raphael did what many problem-solvers do– he created a solution. In many of his conversations with colleagues, he found that they were very driven, and even religious about their calendars, but no one seemed to be truly satisfied (er.. delighted) with the calendar tooling they used. He saw that people were adding calendar hacks and 3rd party apps. The calendar app Sunrise was loved by many, and not long after Microsoft acquired it, they sunset the app (yes, pun intended).

Like any good entrepreneur, Raphael saw an opportunity. At the time, Google Calendar displayed time in the same blocks, but he realized that the time outside of work hours (between midnight and 8am, or 8pm to midnight), took the same amount of space on his calendar… but why? He was sleeping and certainly not about to schedule a meeting in that time. Those extra blocks also cluttered the view. So he created a Chrome extension that hid the late evening and morning hours, and he put it in the Chrome store. Thousands of downloads later, his insight validated that the current Calendar tooling was deficient.

With additional research and feedback from his Chrome store app, Raphael felt more motivated to create an even better solution.

Earlier in his career, Raphael worked at Flipboard. If you recall, Flipboard was a news reading app that took news stories and presented them in a visually engaging manner, and it also worked to incorporate many social network features as well. This combined well with his experience at MIT’s Media Lab, and Raphael developed an approach to drive positive customer interactions.

“Delight doesn’t mean success, and no delight doesn’t mean a lack of success. There are plenty of successful products that may not be very delightful, but are successful,” Raphael said.

In a world full of calendar apps and calendar tooling, why would someone believe they could create a new calendar tooling app, especially when industry titans like Google Calendar and Outlook Calendar take the lion’s share of the market and are already integrated with just about everything else?

Raphael has a belief that delight is not just a simple animation, that it’s not just some surface-level sugar high. It goes much deeper. The Kano model categorizes that delight is the “cherry on top” , not the cake itself. and holds this belief that deep delight is where the magic lies.

“It’s [just] a little sparkle, and that to me is not wrong, but it’s just a very superficial answer. It’s veneer. You can have a bad product and then sprinkle these things on them and they’re almost annoying. They get in the way. They’re anti-delightful, if they’re done wrong. So it’s much more inherent qualities.”

–Raphael Schaad

Let’s step back, before we get into a delightful mindset: what is a mindset?

You may have heard of many different types of mindsets: Fixed versus growth, open versus closed, scarcity versus abundance. In each of these the actions of someone are heavily influenced by their mindset. In the case of creating delightful experiences for a customer there’s no difference. Your mindset matters, as does the collective shared belief of your team (We’ll touch more on that in Chapter XYZ). Table 2-2 shows this kind of mindset at work.

Fixed Mindset
Growth Mindset

Either I’m good at it or I’m not.

I can learn to do anything I want.

That’s just who I am. I can’t change it.

I’m a constantly evolving work in progress.

If you have to work hard, you don’t have the ability.

The more you challenge yourself, the smarter you become.

A mindset is a set of attitudes and beliefs that shape how we interpret the world. Think of it as the glasses you wear that determine whether you see chances or problems, growth or stagnation, success or failure. Mindsets orient us to the way we handle situations. They can help you identify opportunities, and they can also trap us in a vicious circle.

The idea of mindset was introduced by psychologist Carol Dweck. Her research in the late 20th century highlighted how important mindset is in influencing our behavior and achievements. Since then, it’s become a key topic of study, showing its deep impact on many areas of life.

As you saw with Raphael, his mindset about delight and how the last mile of design allowed him to create an amazingly delightful product (Author confession: we both are avid users of what is now Notion Calendar).

Why are mindsets important?

Since your mindset shapes how you see things, it will obviously shape how you take actions. It determines whether you face challenges with resilience or avoid them out of fear. It also dictates whether you strive for self-improvement or stick to the status quo. Mindset is simply the way we see and interact with the world. It’s like a mental filter that shapes our thoughts, beliefs, and reactions to different experiences.

Two (Macro) mindsets of delight

As we kept talking with product people, we observed two emerging patterns in those who shared their thoughts about delight. The first appeared heavily influenced by the Kano model, and they seemed to define delight and something superficial and surface level. From a customer interaction perspective, they referred to aspects such as fun brand-relevant animations on a loading screen.

We call this first mindset the “Veneer” mindset. Here, you believe that delighting a customer has little impact on your business and product metrics. This mindset often came into view in the responses to our questions of “how do you define delight?” The answers all fit under this belief that delight is an add-on.

The second group shared stories that demonstrated that delight is not about small effects and animations. Rather it was about exceeding expectations, or solving a challenging problem that leaves a user with a positive feeling or sentiment. If that sounds familiar, that’s because it’s all about reaching that level of deep delight discussed earlier.

We call the second mindset the “Core” Mindset. Those with this mindset believe that delight is absolutely necessary in solving a problem for the user.

Delight as Veneer

Believes delight is an additional, aspirational element

Believes they must have both functional and delightful to win

Focused on delivering product that meets customers’ needs

Delight as Core

Focused on finding a solution that exceeds customers expectations

Considers delighters to be small in value, and in scope

Doesn’t consider “delighters” as a separate items

If we oversimplify for a minute, the Veneer mindset takes the view that I can either delight my customer or solve a problem for them but I cannot do both. Conversely, the “Core” mindset is a both/and framing, meaning that I can solve a problem for my customer and delight them at the same time.

Separating the Veneer from the Core

What underlies the difference between these two mindsets? The more powerful underlying force of any mindset is belief. Beliefs are different from knowledge. Let’s turn to an exercise example: are you becoming fitter and stronger because of the time spent at the gym? Or was it because you believe you will get fitter and stronger? Dr. Alia Crum of the Stanford Mind Body Institute studied the effect of belief.

In one study of 84 hotel workers, she shared a 15-minute presentation to half of them that explained how only 15 minutes of moderate physical activity per day could increase your health. Further, because they were hotel housekeepers, their work was exercise! A few weeks later, she took measures of weight, bodyfat, blood pressure, and job satisfaction, and then compared them to the first measures before showing the presentation. The effects were significant. The group who viewed the presentation had lower weight, blood pressure and body-fat, while job satisfaction rose compared to the group that did not view the presentation.

Dr. Crum took this further with a cleverly-designed study that took measures of the hunger hormone, Ghrelin, which is present when we are hungry and absent when we are not. She invited participants to come in twice and drink a shake while hooked up to an IV. The first time they would get a “Sense-i-shake” a healthy 120 calorie shake with only 20g of sugar and no fat. The second time the participants came in they were given an “Indulgence” shake that had 640 calories, 30g of fat and 56g of sugar. At least, that’s what the participants were told for each of the shakes. They were actually given a shake with the same nutritional components: a 380 calorie shake with 13g of fat and 44g of sugar both times. However, when comparing the measure of the hunger hormone, Ghrelin, it dropped three-times more than the first, suggesting that our bodies will react to what we believe is true.

A third study by Dr. Crum showed two different groups of workers a three minute video about stress that emphasized two different mindsets: “Stress is debilitating” or “Stress is enhancing”. The result? You guessed it, the group who watched the video with “Stress is enhancing” reported a drop in health symptoms and observed an increase in performance at work.

So if we continue with this logic, just by reading this book about delight, you may soon be delivering delightful experiences to your customers!

The takeaway is that what you believe and what you expect matters. Our minds have an incredible amount of power when it comes to anticipation and expectations, so when it comes to delighting customers, if you believe delight is important, your actions will follow.

A Shared Mindset

It cannot just be your own beliefs about delight, they must be shared across people in order to take the most effect. A now well-known study by Google called Project Aristotle, which researched team performance. At first they looked at past research on teams and they didn’t fund much by way of patterns. The researchers looked through the data they had collected on their own and looked for social norms among the teams. They looked for instances where team members described a behavior as an ‘‘unwritten rule’’ or as part of the ‘‘team culture.” We’ll talk more about how teams and environments can impact delight but for now, that belief needs to not just be yours, but also shared among the team. A shared set of beliefs is important in building trust, employee-to-employee bonds as well as delighting customers. To extend beyond the individual and onto the team we need to consider what the company culture is like. Remember: Culture eats strategy for breakfast.

Delight-driven Culture

When it comes to a culture that puts delight first there are a few companies that have done this for years. Company culture can be shaped regardless of what technology is used. So let’s start with a story about a company that’s well-known for customer happiness: Zappos.

At Zappos, a strong commitment to both customer service and internal company culture was foundational. This wasn’t just a stated ideal, but a principle that was actively managed, consistently measured, and continuously nurtured, deeply influencing every business decision

Brian Kalma was a product manager at Zappos in the 2000s while Zappos was in its early growth mode phase. He had a very enlightening meeting with the CFO that shifted their entire customer engagement strategy.

“Our CFO, Alfred, was going through some interesting data. And he threw out this one tidbit that basically changed our business. If a first time customer, on their first purchase ever, buys a product that they have a problem with - that doesn’t fit for example - and they have to return or exchange it, that customer is four times more likely to make a repeat purchase than someone who had a perfect experience. Meaning, they went to the site, they clicked buy, they received the product in 24 hours, and it fit fine. That interaction created a less valuable customer than the one who came to us and had a problem. –Brian Kalma”

A defining feature of Zappos’ culture was the requirement that 100% of employees, regardless of their position, including any c-suite hires, spend their first weeks as an employee answering customer service calls. Furthermore, all staff would have to answer phones for some time during the Christmas holiday. This ensured everyone in the company gained a firsthand understanding of customer issues, needs, and expectations. This created empathy and a shared sense of responsibility towards the customer experience.

When an employee is first on-boarded at Zappos, they are given a choice: $5,000 to quit after their first two weeks if they felt the company culture or customer-centric approach wasn’t a good fit for their career, or stay on as an employee. While we don’t have exact numbers, we’ve been told that a low single-digit percentage of people who actually take the $5,000. This unusual policy reinforces the company’s dedication to maintaining a team of individuals who genuinely embrace and are motivated by the company’s core values, ie, have a shared set of values and a shared experience.

While these core values and cultural guidelines were established, they weren’t rigidly enforced, allowing teams and individuals the freedom to develop their own unique approaches to customer service and problem-solving. Brian and his team even had cultural impact as a prioritization metric. “On the product team, we would use impact to culture as one of the prioritization metrics on features we were building,” he said.

Prioritizing Culture in Product Development Zappos weighed the cultural impact when deciding on product features. The company considered how new features would affect employees and customers alike. For example: Brian shared a story of how product comparison features were created. A member of his team watched a dozen customers open 10 or more windows next to each other to compare products before they added anything to a shopping cart. So they thought, “There’s gotta be an easier way. Right? In a design session we came up with a few ideas to try to solve that problem, do some testing, then prioritize it, and the feature goes live a few weeks later.”

The way Zappos treated its employees allowed them to treat their customers a certain way. “I just can’t stress that enough.” Brian remarked. The team at Zappos extended this same behavior to its vendors. When sales reps from shoe companies like Clarks or Nike came to visit, the sales reps expected to foot the bill for dinner like they did at their other brick-and-mortar retailers, but when they arrived at Zappos, it was Zappos’ treat. They flipped the script on this traditional dynamic in the channel partner relationship.

Further Zappos embraced transparency. The team posted the top 10 positive customer quotes from the previous day in every break room along with the sales numbers from that day also. It didn’t matter if the numbers were good or bad, they posted them.

Brian has a unique perspective on culture driving delight because after he left Zappos in 2011, he joined Gilt group. His experience at Gilt Group stood in stark contrast to the culture he experienced at Zappos. At Gilt Group, the primary focus was on immediate financial returns, often at the expense of the customer experience. Gilt group was a fashion retailer, just like Zappos, but they approached the market very differently. They rose to prominence by holding their flash-sales with steep discounts in short windows. They became famous for holding sales that had to occur between 12:00pm and 12:10pm. It was early days of the web and driving a delightful experience of “hey I just got a $1,000 dress for $200” can be a delightful experience to some. The business grew quickly.

However, when you have 2000 customers trying to buy the same pair of shoes in the same size you might leave some wanting. The business model created winners and losers across its customer base. This approach led to a transactional view of customers, where they were seen as numbers rather than individuals with needs and expectations. Further, Gilt Group’s operating model, which relied heavily on flash sales and drop-shipping, resulted in inconsistent shipping times and disjointed customer experiences. Drop shipping meant that Gilt did not control the shipping time, so after some orders, it might take months for a customer to get that dress they bought for 80% off.

Table 2-3 shows a comparison between the experience at Zappos vs Gilt Group.

Zappos
Gilt Group

Company
Data Shares

Yesterday’s Sales

Top 10 Customer Quotes

Recent Flash Sale Data

Employee Onboarding

First two weeks every employee

works in customer call center

Standard employee onboarding

Call
Center

No time limits per call

Time limits per call

End-to-End
Experience

Owned it

40% Direct, 60% Drop-Shipped

Team
Rotations

Every week one member of the product team rotated into call center

N/A

Acquisition
Price

$807M (Amazon)

$250M (Hudson Bay)

$100M (Rue La La)

Lessons Learned

Culture matters. The contrasting experiences of Zappos and Gilt Group offer valuable insights into the critical role of company culture in shaping delightful experiences and driving business success. This phenomenon is not just limited to these two anecdotes. A study by McKinsey & Company published in 2018 found that companies with a strong organizational culture experience a 65% improvement in customer satisfaction and a 25% increase in profitability⁶ More recently in 2023, Columbia Business School researcher Wei Cai found that a company with strong culture and good relationships among internal employees, leads to higher levels of trust with external stakeholders (i.e. customers).⁷

Zappos’ success can be attributed to its genuine commitment to its employees, which translated into exceptional customer service and enduring brand loyalty. Gilt Group’s struggles highlight the limitations of a purely ROI-driven approach, where a lack of investment in customer service and employee empowerment can ultimately undermine the company’s ability to create meaningful and delightful experiences.

These examples emphasize that a delight-driven culture requires a holistic approach that values employees, empowers them to prioritize customer needs, and fosters a shared sense of responsibility for delivering exceptional experiences

Summary

This chapter explored the essential principles that drive user delight, from mindset to cultural implementation, and from theoretical models to practical application. While “delight” can have different meanings depending on the context, the key takeaway is that products which solve a problem, connect on a personal level, and touch users emotionally are the ones that foster delight and achieve long-term success.

Defining Delight: There is no universal definition of delight because it is subjective. You should understand the various theories of delight to choose the best methods for measuring delight

Theories of Delight: Since there is no universal definition a few theories give us direction.

Kano model, which categorizes product features based on their impact on customer satisfaction

Expectation Disconfirmation Theory: Delight can be defined as surpassing anticipated expectations in a positive manner. If an experience exceeds expectations, it leads to delight

Extreme Customer Satisfaction: Delight can be achieved through a strategy of delight in business as a way to decrease customer churn

Surprise and Joy: Delight can be classified as a mix of surprise and joy and is the outcome of a positive feeling after a transcendent customer experience

Gratitude: Delightful experiences can be transcendent individual experiences, yet there could be a shared experience as well, leaving members not only with a feeling of joy or surprise but also with gratitude and a sense of belonging

Blended Elements: Delight can be a mix of emotional, interpersonal, problem-solving, timing, sense, and control. Of all these elements, emotions, interpersonal interactions, and problem-solving are more of a delight driver than the others.

Hierarchy of Delight: Keep in mind the levels of delight. Surface-level delight consists of small interactions, whereas deep delight involves problem-solving

Delightful Mindset: Adopt a “Core” mindset, believing that delight is not superficial but essential in exceeding expectations and solving user problems. Recognize that belief is the underlying force of any mindset, influencing actions and perceptions

Delight-Driven Culture: Cultivate a company culture that prioritizes delight, empowering employees to make customer-centric decisions. Culture needs to be managed, measured, and nurtured to be effective.

Ultimately, true delight goes beyond surface-level features; it requires a deep understanding of user needs and a commitment to creating experiences that are not only functional but also emotionally resonant

¹ Horton, Graham, Goers, Jana - 2019/04/09 - A Revised Kano Model and its Application in Product Feature Discovery

² Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.2307/3150499

³ Oliver, R.L., Rust, R.T. and Varki, S. (1997), “Customer delight: foundations, findings, and managerial insight”, Journal of Retailing, Vol. 73 No. 3, pp. 311-336.

⁴ (Crotts and Magnini, 2011; Arnold et al., 2005; Finn, 2005, 2012; Oliver et al., 1997;Crotts et al., 2008; Magnini et al., 2011

⁵ Parasuraman, A., Ball, J., Aksoy, L., Keiningham, T.L. and Zaki, M. (2021), “More than a feeling? Toward a theory of customer delight”, Journal of Service Management, Vol. 32 No. 1, pp. 1-26.

⁶ https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-business-value-of-design

⁷ Cai, Wei, Dennis Campbell, and Jiehang Yu. “Culture as a Signal: Evidence from a Natural Field Experiment.” SSRN Working Paper Series, No. 4447603, May 2023.

Chapter 3. The Empathy Spectrum

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the authors’ raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be the 5th chapter of the final book.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at mcronin@oreilly.com.

This chapter tackles the fundamental tension between making AI feel human and keeping it honest. Through the story of a product leader who refuses to name her AI chatbots, it explores how to design AI that feels appropriately empathetic without crossing into deception. Rather than asking whether machines can truly empathize, the chapter focuses on the practical challenge: how to create AI that understands user context and responds appropriately across an “empathy spectrum”—from users who want purely utilitarian efficiency to those seeking supportive, conversational guidance. You’ll learn why near-miss empathy often feels worse than obviously mechanical responses, how to avoid common empathy failures that damage user trust, and practical frameworks for building AI that amplifies human care rather than faking it. The result is a roadmap for creating authentic AI experiences that make users feel genuinely understood without pretending the machine is something it’s not.

Emotions are essential parts of human intelligence. Without emotional intelligence, artificial intelligence will remain incomplete.

Dr. Amit Ray, “Compassionate Artificial Intelligence”

“Ramli, I hate it when products ask me to name their chatbot,” Betsy Shaak, VP of Product at VoxPopMe, told me over coffee.

“Oh really? That’s surprising. You work on an AI customer research tool.”

VoxPopMe runs AI-moderated interviews at scale. Imagine an AI facilitating a live video conversation with your target customers.

“Yeah. I refuse to give our AI moderators names,” Betsy said.

“Why?”

“I don’t want to trick people. I don’t want anyone thinking it’s a person.”

Her stance caught me off guard. And then it clicked.

There’s a growing push to humanize machines. We’ve given large language models names like Claude, Jarvis, and Revo, as if a proper noun could carry warmth and judgment. Underneath, many teams are trying to engineer empathy by wrapping software in a human costume.

Betsy’s take is the countercurrent: don’t pretend. Be clear about what’s behind the glass.

Here’s the tension we have to navigate in this chapter: the industry impulse to humanize AI versus the user’s need for transparency. The empathy question isn’t “Can AI be human?” Instead, it’s “How human should AI feel—for this user, in this moment?”

Empathy, traditionally, is the ability to understand and share someone else’s feelings—to recognize their emotional state and respond in a way that acknowledges their experience. For AI, true empathy is impossible; machines don’t feel. But AI can demonstrate something equally valuable: contextual awareness that leads to appropriate responses. It’s empathy driven by intelligence rather than emotion—recognizing when someone is frustrated, celebrating, or in crisis, and adjusting behavior accordingly.

The difference matters. AI that tries to express feelings with you often comes across as fake. AI that understands your context and responds appropriately feels genuinely helpful. This is the empathy spectrum we need to design for—creating AI that feels right without pretending to be something it’s not.

The AI Empathy Spectrum

The empathy spectrum is how human users want an AI to feel in a moment. At one end, AI is a tool. Think hammer and saw. It drives nails, cuts wood, and gets the job done. You want clarity and speed. A crisp boundary between human and machine. Straight answers over small talk. Honesty about limits. No pretending. This is Betsy’s philosophy in practice.

On the other end, AI is a companion. It’s a coach, therapist, reflection partner, or junior teammate. It may have a name and a point of view. It makes small mistakes sometimes, and you forgive them because it shows warmth. Use cases such as therapy, reflection, and companionship have experienced significant growth since 2022, as noted by Harvard Business Review in 2025. These users want acknowledgment, warmth, and a bit of mirroring, even when they know it is software.

Figure  shows the range of the AI empathy spectrum.

Most of us live between those poles and move along the line. With the same product, I shift. If I ask Claude to analyze data, I want crisp logic and plain language. If I use it to reflect at the end of the day, I welcome a softer tone and one more question.

What nudges users along the spectrum? We’ll dig into five of the biggest factors that influence it.

1. Context and Use Case

Using a tax app at midnight calls for just the facts. A health app at 2 a.m. calls for slower pacing and clear reassurance. But some jobs need both at once. After a canceled flight, people want fast options and a clear recommendation; they also want a quick acknowledgment of the stress they’re experiencing.

This can be the same person. The context of what they’re doing and when necessitate the need for different approaches. The task and the stakes set the tone. When the outcome is procedural, users want brevity and certainty. When the outcome carries emotion or risk, users want an empathetic response: a steady tone, simple reassurance, and one clear next step.

2. User Demographics

Another factor is user demographics, like age and location. In general, younger users tend to tolerate more personality and light-hearted emojis. Others prefer directness and fewer flourishes, and may read emoji as inauthentic. Culture matters too. In Japan, a bank might use titles and concise language to convey respect. In the United States, a wellness app might use first names and softer phrasing to signal care. None of this is fixed in stone, of course. However, you can use these as starting points and then adjust them to the moment.

3. Company Philosophy

Leadership philosophy is another factor. Founders and product leaders set the default tone. At VoxPopMe, Betsy Shaak refuses to name the AI or dress it up as human because it can feel inauthentic. Anthropic takes a different tack with Claude. During onboarding at Anthropic, Claude introduces itself clearly as an AI assistant with defined limits. These choices signal where the experience starts on the spectrum and how far it should move toward warmth before handing off to a person.

4. Modality and Moment

Another factor is the modality, the channel the product uses to communicate, whether through voice, chat, an inline hint, or a notification. Considering the moment in the journey–what’s happening, what is being asked, and how urgent the question is are crucial. Both the modality and the moment shape how the AI should feel. For example, if I am on my phone on the way to the airport and my flight is delayed, I do not want to be prompted with the question, “How do you feel about the flightdelay?” I want a concise, factual alert with the new time and gate, along with quick assistance to adjust any meetings at the destination. That is different from being on a laptop at the end of the day, reflecting on how it went. I have time to think and type, so the assistant can slow down, acknowledge the mood, and offer one prompt to help me reflect. In short, modality and moment set the dial on warmth, showing where to be spare and factual and where to slow down and acknowledge.

5. Relationship History and Trust

History sets the ceiling for tone. As users interact with an AI product, it should learn the users’ empathy preferences: how direct it can be, how much to mirror, when to press, and when to pause. Early on, it should ask and confirm, not infer. With time, it can calibrate from reflective to challenging, and it should check whether you want that level of push before it goes there.

As I’ve mentioned, I use an AI product to reflect on my day. So I tried out a few products. One of them, after a few days, pulled from past entries and pushed too hard. It said, “I see you have been trying to include Joanna, your wife, as a partner in business decisions. Yet you chose a consultant to help grow your business without talking to her. Are you serious about making Joanna a partner? Because if you’re making the decisions, it doesn’t seem like it.”

I pushed back. Why not ask if I had talked to Joanna? I had that day. The line felt accusatory. A good coach could ask a firm question, but only after trust has formed. The AI backtracked, but the trust was cracked.

The lesson is simple. Start gently, then earn the right to challenge. Ask before interpreting. Show the source of the claim. Offer a softer version first. Let the user set how far to go and adjust over time. Trust accrues. One sharp push too early can set it back. If you miss, own it and reset the tone.

Why Empathy Matters in AI Products

It requires time, effort, and experimentation to find the right balance between AI as a tool and AI as a companion. So, why aim for more empathy in AI products at all beyond the desire to anthropomorphize machines? Is it worth the investment in making AI products more empathetic?

The reality is that emotion and empathy are not decoration. They build trust and, done well, they fuel the business. Research summarized in Built to Love¹ (Boatwright and Cagan) found “high emotion” companies delivered returns of over 1,000% over ten years, compared to just 152% for their “low emotion” peers. Even during the 2008-2009 recession—the worst economic downturn since the Great Depression—high-emotion companies lost only 25.5% of their value compared to 39.5% for the S&P 500. When analyzing random investment scenarios over 15 years, 86% of investors who invested in high-emotion companies outperformed those who invested in standard market indices, such as the Dow (84% win rate), NASDAQ (85% win rate), and S&P 500 (86% win rate). The pattern holds across categories. When customers feel understood, they stay, spend, and advocate.

For software, empathy is not pretending to feel. It is perceiving context and responding appropriately. It is an AI that can navigate the empathy spectrum and adapt to the moment. That shift creates a better user experience in three ways.

1. Contextual Intelligence Reduces Friction

The best empathetic AI doesn’t just respond to what you say; it understands what you need. This contextual intelligence enables systems to prioritize information based on your emotional state and immediate needs, anticipate next steps during complex processes, and proactively surface relevant information rather than waiting to be asked.

Consider how Google Maps handles a sudden road closure. Instead of simply notifying you about the closure, it quietly reroutes around the problem and states, “Faster route found. Arrive 7 minutes earlier.” The system recognizes the context: you’re trying to get somewhere on time. It anticipates your need to avoid delay and communicates the benefit clearly without unnecessary chatter. You never have to think about alternative routes or calculate time differences; the AI handles that cognitive load for you.

This contextual awareness enhances the experience by allowing users to focus on their goals instead of managing a tool that helps them achieve that goal. Whether the AI is providing detailed guidance for learning moments or brief answers for quick tasks, it adjusts interaction depth based on what you actually need in that moment. The result is faster task completion, lower cognitive load, and significantly less stress, as users can trust that the system is working with them, not against them.

2. Honest Transparency That Builds Trust

Empathetic AI builds long-term relationships not by pretending to be more capable than it is, but by being honest about its limitations. This means clear communication about what it can and can’t do, explaining decisions in plain language rather than technical jargon, and acknowledging uncertainty rather than making confident guesses.

GitHub Copilot exemplifies this approach when it flags a security-risk pattern in your code. Rather than simply rejecting your approach or making changes without explanation, it offers a safer code snippet with a one-sentence rationale: “This prevents SQL injection attacks by using parameterized queries.” It’s completely transparent about being a tool, explains why it suggests changes, and provides the information needed to make an informed decision rather than blindly following its advice.

This transparency enhances the experience by eliminating the mental energy typically spent second-guessing AI recommendations. When the system is upfront about processing time (“This analysis will take about 30 seconds”) or scope limitations (“I can help you format this data, but you’ll need a human analyst for strategic recommendations”), users can allocate their time and effort appropriately. Clear communication prevents those frustrating cycles where users wonder if something is broken or whether they should trust the output, allowing them to focus entirely on their actual goals.

3. Adaptive Communication That Matches User Needs

The most empathetic AI systems recognize that different users and situations require fundamentally different approaches. This means matching personality to user preferences, calibrating verbosity based on expertise and time constraints, adjusting tone for high-stress versus routine interactions, and learning from user behavior to refine communication style over time.

Watch how a well-designed customer service chatbot handles emotional escalation. When someone types “I’m really frustrated,” an empathetic system immediately shifts from its standard cheerful tone to a more professional, solution-focused approach: “I understand this is frustrating. Let me connect you directly with a specialist who can resolve this issue quickly.” The AI recognizes the emotional context, adapts its communication style appropriately, and escalates to human support when needed, all without making the user navigate through standard troubleshooting steps that would only increase their frustration.

This adaptive communication creates a superior user experience because the interface feels genuinely tailored to the individual. Users don’t have to parse through irrelevant information, endure jarring tone mismatches, or repeatedly explain their preferences. Over time, the system learns their communication style, leading to increasingly personalized efficiency. The cognitive load drops significantly because users can trust that the AI will meet them where they are, rather than forcing them to adapt to a one-size-fits-all interaction model.

When Empathy with AI Goes Wrong

Empathy in AI products fails in predictable ways. It misreads tone or sarcasm and replies cheerfully to a serious concern, over-apologizes without offering a concrete next step, mirrors strong emotion when the user needs calm guidance, and uses human-like phrases that imply feelings the system does not have.

For example, I recently told an AI calendar assistant I was under the weather and needed to reschedule a demo. It replied, “Great. I am excited to help. How is your day going?” Then it launched a confetti animation and asked me to rate the chat before giving me the reschedule link. The tone was cheerful when I needed calm, it apologized without a clear next step, and it used human-like phrases that suggest feelings it does not have.

In another, more extreme example I found on Reddit, a user told a support assistant, “My dog passed away. Please cancel my subscription.” The assistant replied, “Awesome. I can help with that,” then showed a celebratory thumbs up while walking through cancellation. The mismatch made the user feel worse.

What makes these replies feel wrong is not just tone. It is the absence of emotional awareness. The AI recognizes the words but not the weight. In human terms, it resembles someone with no emotional intelligence: fluent language, intact logic, but completely missing the emotional context. It performs the motions of care while missing the meaning entirely. That gap jolts us, because we expect emotion to shape response when the stakes are high.

These failures cluster around a few patterns:

Timing failures occur when AI responds too quickly to serious topics that deserve a moment’s pause, or too slowly to urgent requests that need immediate attention.

Emotional mirroring gone wrong happens when systems reflect back the user’s distress instead of providing the calm, stabilizing response they actually need.

The “toxic positivity” problem emerges when AI maintains relentless cheerfulness regardless of context, like a perpetually upbeat colleague who can’t read the room.

Perhaps most damaging is the fake feelings trap, where AI uses phrases like “I’m excited to help” or “I feel confident this will work” without any actual emotional experience behind the words. Users can sense this performative empathy, and it creates a more negative reaction than straightforward, honest communication would.

The Empathy Uncanny Valley

This creates what we might call the empathy uncanny valley (see Figure ): AI responses that are almost human but not quite right, making them more unsettling than obviously mechanical interactions. Just as slightly off-looking robots trigger more discomfort than obviously artificial ones, near-miss empathy feels worse than no empathy at all.

The psychology behind this reaction is profound. When we encounter what appears to be empathetic communication, our brains expect certain patterns of response. We expect pauses for serious topics, appropriate emotional matching, and authentic care behind the words. When those expectations are violated by an AI that uses empathetic language without empathetic understanding, it creates a cognitive dissonance that many users find deeply uncomfortable.

This is why a simple, honest “I can help you cancel your subscription” feels better than “I’m so sorry for your loss, but I’m excited to assist with your cancellation!” The first response doesn’t pretend to feel what it cannot feel. The second creates false intimacy that immediately reveals itself as hollow, and the word choice (ie, excited) misaligns with the situation.

Authenticity Above All

Betsy’s line from the opening of the chapter holds up: Do not trick people into thinking AI is real or human. Transparency becomes a form of care in and of itself. One practical way to avoid the empathy valley is to be explicit about being a machine and start from a utilitarian default, then let users opt into more warmth if they choose.

The most successful empathetic AI systems are those that are honest about their nature while still being genuinely helpful. They acknowledge their limitations upfront, use straightforward language about their capabilities, and focus on being useful rather than appearing human. This approach builds trust precisely because it doesn’t try to manipulate users’ emotions through false intimacy.

Consider the difference between an AI that says “I’m sorry you’re having trouble, but I’m confident I can help!” versus one that says “I can see this issue is blocking your work. Let me walk you through three options that typically resolve this problem.” The second approach acknowledges the user’s situation without claiming to share their frustration, and focuses on concrete help rather than emotional performance.

Concrete Solutions for Authentic AI

Making a practical system requires specific design choices that signal honesty and competence rather than artificial humanity. Name the assistant plainly, not like a person, and avoid avatars that suggest human identity. A “Support Assistant” or “Help System” sets appropriate expectations, while “Sarah” or “Alex” implies a relationship that cannot be fulfilled.

State capabilities and limits upfront

Use language like “I’m an AI assistant” and “Here’s what I can and cannot do.” This transparency allows users to calibrate their expectations appropriately and reduces the disappointment that comes from discovering limitations mid-conversation.

Use neutral visuals and calm pacing throughout interactions

No confetti animations during serious conversations, no celebratory icons in sensitive flows, and no rushed responses to topics that deserve thoughtful consideration. The visual and temporal design should match the emotional weight of the conversation.

Offer tone controls that put users in charge of their experience

Default to a professional, helpful tone, but provide clear options for “Friendly” or “Just the facts” that users can select based on their preferences and the context of their task. This acknowledges that different people want different things from AI interactions, and the same person might want different approaches at different times.

When to Hand Off to Humans

Some moments simply call for human intervention, and recognizing this boundary is crucial for maintaining authenticity. Chewy empowers representatives to act with discretion during high-stakes emotional events, such as pet loss or illness, and to send personalized gestures that create genuine core memories. Zappos builds time for real conversation and gives permission to fix problems in human ways that go beyond standard procedures.

The lesson for AI teams is straightforward: let machines perform anticipatory, context-aware tasks that reduce friction and surface helpful information. Let humans handle the rare but pivotal emotional spikes that require genuine empathy, creative problem-solving, or complex judgment calls. The key is designing the handoff so it feels seamless and supportive rather than like a failure of the AI system.

This means having clear criteria for escalation, training human teams to take over gracefully from AI interactions, and ensuring that the transition preserves any context or progress that was made during the AI portion of the conversation. When done well, users experience this as the system caring enough about their situation to connect them with the right level of support, rather than as an admission that the technology isn’t good enough.

Designing for the Empathy Spectrum

Building empathetic AI requires having empathy for your users first. This means understanding where in their journey they might feel frustrated, what style and tone of AI they prefer in different contexts, and how their needs shift based on their emotional state, expertise level, and the stakes of their task.

Understanding Your Users’ Empathy Needs

Start by researching how your users actually want to interact with AI across different scenarios. A software engineer debugging a critical production issue at 2 AM has very different empathy needs than someone casually exploring a new feature on a weekend. That same person might want efficiency-focused interactions during crunch time and more supportive, educational responses when they’re learning something new.

Map the emotional journey of your core user workflows. Where do people typically get stuck, confused, or frustrated? What moments require reassurance versus quick answers? When do users need encouragement, and when do they just want to get the job done? Understanding these patterns allows you to design AI that responds appropriately to emotional context rather than applying a one-size-fits-all approach.

Use specific research methods to uncover these preferences: conduct empathy interviews that focus on emotional reactions to different AI interaction styles, create journey maps that highlight emotional peaks and valleys, and run A/B tests comparing different tones in the same functional scenario to see what actually resonates with your users.

User Control and Adaptive Design

Give users control over interaction style from the start. Begin with a neutral, helpful voice, then adapt only after the user explicitly chooses a tone or your system detects clear, repeated behavioral cues. A user who consistently gives brief replies and skips explanatory content probably wants efficiency over chattiness. Someone who asks follow-up questions and engages with examples likely appreciates more detailed, conversational responses.

Make these adjustments visible and easy to reverse. When your AI shifts tone based on user behavior, acknowledge the change with simple, clear language that reinforces user control rather than making them feel monitored.

Scope preferences thoughtfully rather than using a single global switch. A user might want professional, efficient responses for work tasks but friendly, patient guidance when learning a new hobby. Context-aware empathy settings work better than forcing people to choose one interaction style for all situations.

The Co-Pilot Approach

Treat AI as a coach that supports human judgment rather than a driver that takes it over. The most successful empathetic AI systems enhance human decision-making rather than replacing it. They sense-check ideas against stated strategies, draft first passes of complex work, and offer practice-informed prompts that help users think through problems more effectively.

Consider how ProdPad’s AI co-pilot works in practice. When a product manager is struggling with quarterly planning, the system doesn’t generate a complete roadmap automatically. Instead, it asks clarifying questions about company strategy, surfaces relevant user feedback patterns, and suggests framework approaches while keeping all final decisions with the human. The user feels supported and informed, not replaced or undermined.

Keep the final decisions with humans while providing intelligent assistance. Show sources for recommendations, explain reasoning behind suggestions, and make it simple for users to accept, edit, or completely ignore AI input. This approach respects user agency while providing genuine value through enhanced information processing and pattern recognition.

Helpful Without Human Pretense

Practice anticipatory care by being useful without pretending to feel emotions you don’t have. Link related customer feedback automatically to relevant product ideas, identify and deduplicate similar support tickets, and assemble rough competitive analyses in the background. These actions demonstrate care through usefulness rather than through artificial emotional expression.

Narrate what’s happening and why, and always offer users a way to pause or opt out. Clear communication about process and timeline shows respect for user time and preferences while providing genuine value. When you explain what your AI can and cannot do, when you surface relevant information proactively, and when you make complex processes more understandable, users feel supported because their mental effort is reduced and their goals are more achievable.

Micro-Interactions That Show Care

Sweat the micro-interactions because they’re where empathy becomes tangible. Slow the response cadence on serious topics so your reply doesn’t feel inappropriately chipper. When someone reports a bug that’s blocking their work, don’t fire back an instant “Great! I can help with that!” Take a moment. Acknowledge the impact. Provide a thoughtful response that matches the gravity of their situation.

When processes take time, acknowledge the delay and explain what’s happening. Users appreciate knowing what to expect and understanding that the wait serves a purpose rather than wondering if something is broken.

Write error messages that own the problem and provide clear next steps rather than blaming users. This approach turns frustrating moments into helpful guidance and maintains user confidence in the system.

Over time, let your system learn from user corrections and preferences to match tone and formality without making a big deal about it. If someone consistently edits your responses to be more formal, gradually adjust to that style. If they regularly add more context to your suggestions, start providing richer detail. This quiet adaptation shows that the system is paying attention and evolving to serve users better.

Building an Empathetic AI Culture

These design principles only work when supported by organizational culture that genuinely values user emotions alongside functional outcomes.

At a roadmap review, a PM slid a board across the table. Scattered among the big feature releases were small pink tiles. “Those are our delight plays,” she said. “We want a little pink in every release.” It wasn’t decoration. It was a signal that feeling matters alongside speed and accuracy, and that emotional outcomes deserve dedicated resources and attention.

In another session, the team watched short film clips and listened to audio recordings of the same sentence spoken in different tones. We debated whether a particular response felt validating or empty, supportive or patronizing. Then we pulled up real user conversation transcripts to see how our actual AI responses landed. The contrast between our intentions and user reality taught more than any style guide ever could. Engineers began asking for “the calm version” of error flows when users were dealing with high-stakes problems. That shift didn’t come from a policy mandate. It came from hearing what users actually heard.

Measurement followed the same spirit. Instead of relying on giant quarterly surveys that felt disconnected from actual user moments, we added small, timely questions embedded in the experience. “Did this feel helpful right now?” “Did you feel understood?” “Did you get what you needed without extra effort?” On Fridays, we’d scan the week’s responses and review a few conversation transcripts. When something felt cold or tone-deaf, we logged it as “empathy debt”—the accumulated cost of interactions that made users feel misunderstood or frustrated rather than supported. Like technical debt, empathy debt slows down user progress and erodes trust over time, but unlike technical debt, it’s often invisible to teams focused purely on functional metrics.

The most delicate part was designing the handoff from AI to human support. When a conversation crossed an emotional threshold or complexity boundary, a human agent joined within 30 seconds with a simple introduction: “Hi, I’m here to help. I have the context from your conversation.” The AI stayed in the background to take notes and draft follow-up suggestions, but the human took over the relationship. Users didn’t have to repeat their story. Agents didn’t have to guess about previous context. It felt like a relay race with a smooth baton pass, not a dropped conversation that had to restart from zero.

This cultural foundation creates the environment where empathetic AI can actually work. Without teams that understand emotional impact, measurement systems that track how interactions feel, and organizational commitment to caring about user feelings alongside user tasks, even the most sophisticated empathy features will miss the mark. The technology enables the empathy, but the culture ensures it’s authentic and sustained.

The Empathy Choice

The future of AI isn’t about creating machines that feel—it’s about creating systems that help humans feel more understood. As we’ve seen throughout this chapter, the most successful empathetic AI doesn’t try to be human. Instead, it gives users agency to choose how they want to be supported, whether that’s through efficient, no-nonsense assistance or warmer, more conversational guidance.

The companies that master this balance will have a significant advantage. They’ll create AI that users trust because it’s honest about what it is, AI that users appreciate because it adapts to their needs, and AI that users recommend because it makes their lives genuinely better. These systems won’t just complete tasks—they’ll create positive memories and lasting relationships.

But empathetic AI is ultimately a means, not an end. The goal isn’t to make our tools more emotional; it’s to preserve and enhance the human connections that matter most. When AI handles the cognitive overhead of understanding context, matching communication styles, and providing appropriate responses at scale, it frees humans to focus on the creative, strategic, and deeply relational work that only we can do.

The empathy spectrum isn’t going away. Different users will always want different things from their interactions with AI, and the same user will want different approaches in different contexts. The organizations that embrace this complexity—that build systems flexible enough to serve both the utilitarian and the connection-seeking ends of the spectrum—will create experiences that feel genuinely caring rather than performatively empathetic.

This foundation of appropriate, user-controlled empathy becomes the launchpad for everything else we want from intelligent systems. When users feel understood and respected by AI, when they trust its transparency and appreciate its adaptability, they become open to deeper forms of assistance and personalization.

In our next chapter, we’ll explore how this empathetic foundation enables hyper-personalization that enhances rather than invades, creating AI experiences so tailored to individual needs that they feel like having a thoughtful assistant who truly knows how to help.

¹ Boatwright, Peter, and Jonathan Cagan. 2010. Built to Love : Creating Products That Captivate Customers. San Francisco, Ca: Berrett-Koehler Publishers.

About the Authors

Ramli John is the founder of Delight Path and the author of the bestselling book Product-Led Onboarding. As a product-led growth advisor and educator, he’s worked with companies such as Mixpanel, Zapier, Ubisoft, and more to accelerate their growth and level up their user onboarding experience to turn more users into lifelong customers.

C. Todd Lombardo is product leader and design strategist with over 15 years of experience in the tech industry. He specializes in helping teams build, launch and scale products. He co-authored three O’Reilly best-selling books on product and design: Design Sprint, Product Roadmaps Relaunched, and Product Research Rules.