Understanding Consumer Expectations in Technology

Consumers want to flourish with crypto and AI. If you think the biggest impact of AI is technical, you’re missing the real story unfolding in 2025: the mainstream adoption of GenAI. Contrary to the news headlines, the leading consumer use cases for generative AI today are no longer about productivity or automation. They center on therapy, finding purpose, and organizing life. These are emotional, deeply human needs that drives long term engagement. Therapy and companionship is now the most common AI use case among consumers, closely followed by personal development and life management. This shift is not an accident. When you solve big issues for consumer, they will adopt a technology. Over the past year, new AI models and tools have enabled more personalized, emotionally intelligent experiences. These are experiences that users used to pay a lot for: therapists, life coaches, exec coaches, life companions.... Now, they are available at accessible (subscription) pricing. People are actively seeking out and relying on GenAI. Thirty-eight of the top 100 use cases for Gen AI are new this year, with the largest growth in applications offering personal and professional support. Users are now turning to AI for judgment-free advice, confidence-building, and even self-discovery. What does this mean for founders building at the intersection of consumer crypto AI? 1. Consumers want AI products over technical "jarg-bage". Product experiences that flourish matter. 2. Consumers want AI that understands their everyday lives and solves real problems. AI trust, memory and feedback matters. 3. Consumers want AI that helps them think, create, and grow, beyond automation. AI with personalized context matters. In short, consumers want to flourish with AI. This is where Web3 has an edge, but only if we build for these needs from the outset. The next chapter of consumer crypto AI will be defined by founders who prioritize emotional value alongside technical utility. The bar is higher, and the opportunity is bigger than ever before.

We spend a lot of time looking at customer insights, and one thing the data shows us over and over again is this: AI adoption is not one-size-fits-all. Age, for example, plays a huge role in how people actually want to engage with technology.   Millennials may embrace AI assistants with ease, while older customers with more complex needs often prefer human interaction. First-class travelers expect a high-touch experience, whereas younger, budget-conscious customers are comfortable with fully digital solutions. In banking, for example, AI can help young individuals discuss financial goals confidently, while more established clientele with complex finances seek personal guidance. The lesson for leaders is clear: success isn’t about the technology itself. It’s about understanding the person in front of you and designing strategies that flex around their needs. The next era will belong to organizations that combine insight, hyper-personalization, and strategic agility. Those that understand their customers deeply, and act on it, will define the future.

While it can be easily believed that customers are the ultimate experts about their own needs, there are ways to gain insights and knowledge that customers may not be aware of or able to articulate directly. While customers are the ultimate source of truth about their needs, product managers can complement this knowledge by employing a combination of research, data analysis, and empathetic understanding to gain a more comprehensive understanding of customer needs and expectations. The goal is not to know more than customers but to use various tools and methods to gain insights that can lead to building better products and delivering exceptional user experiences. ➡️ User Research: Conducting thorough user research, such as interviews, surveys, and observational studies, can reveal underlying needs and pain points that customers may not have fully recognized or articulated. By learning from many users, we gain holistic insights and deeper insights into their motivations and behaviors. ➡️ Data Analysis: Analyzing user data, including behavioral data and usage patterns, can provide valuable insights into customer preferences and pain points. By identifying trends and patterns in the data, product managers can make informed decisions about what features or improvements are most likely to address customer needs effectively. ➡️ Contextual Inquiry: Observing customers in their real-life environment while using the product can uncover valuable insights into their needs and challenges. Contextual inquiry helps product managers understand the context in which customers use the product and how it fits into their daily lives. ➡️ Competitor Analysis: By studying competitors and their products, product managers can identify gaps in the market and potential unmet needs that customers may not even be aware of. Understanding what competitors offer can inspire product improvements and innovation. ➡️ Surfacing Implicit Needs: Sometimes, customers may not be able to express their needs explicitly, but through careful analysis and empathetic understanding, product managers can infer these implicit needs. This requires the ability to interpret feedback, observe behaviors, and understand the context in which customers use the product. ➡️ Iterative Prototyping and Testing: Continuously iterating and testing product prototypes with users allows product managers to gather feedback and refine the product based on real-world usage. Through this iterative process, product managers can uncover deeper customer needs and iteratively improve the product to meet those needs effectively. ➡️ Expertise in the Domain: Product managers, industry thought leaders, academic researchers, and others with deep domain knowledge and expertise can anticipate customer needs based on industry trends, best practices, and a comprehensive understanding of the market. #productinnovation #discovery #productmanagement #productleadership

There’s a quiet shift happening all around us. Today’s consumer isn’t just buying a product or a service, they’re stepping into a relationship. One built on transparency and a sense of shared control. Think about it: whether it’s a streaming subscription, a health app, or the mobile network that keeps us connected every day, expectation is the same. “Treat me fairly. Keep me in the loop. Let me decide how technology fits into my life.” Simple, but powerful. As AI becomes more of a guide by suggesting plans or recommending new digital experiences, people are starting to draw their own boundaries. They want to set the rules, understand the “why” behind every suggestion, and know their data is safe. For telecom, this means giving people real control: over how their data is used, over spending and usage limits, and making every AI-driven recommendation easy to understand. The industries that will thrive are those that make consent and clarity feel as seamless as the technology itself. And then there’s the emotional connection. In a world where algorithms and automation are everywhere, those human moments matter more than ever. Sometimes it’s a reassuring voice during a complex decision, sometimes it’s just a simple, honest message about a change in service. Authenticity and empathy are what people truly crave. The future belongs to organizations that get the balance right, blending automation with empathy. It’s about finding the right chemistry between humans and AI. Every day, consumers are quietly raising the bar for all of us. For telecom and beyond, the challenge is clear: earn trust not just through what we build, but through the human experiences we make real.

Health tech is about to struggle, more… In U.S. #healthcare, we spend a lot of time focusing on clinical outcomes, safety, and adherence. Appropriately so. But there’s an underlying layer that often determines whether someone stays or goes with a product or service: expectation. Expectation is an unwritten contract between a person and a system, shaped before a product or service is even used. It’s built through messaging, word of mouth, past and analogous experiences, and hope. When it’s met, you build trust. When it’s not, you lose people. Mis-matched expectations show up all the time: - A “personalized” onboarding that feels generic - A “seamless” tool that’s glitchy - A promise of simplicity that hides complexity Now add #AI to the mix. Smarter, faster digital tools are accelerating the shift. People are expecting more hyper-personalization, real-time responses, and near-perfect predictive accuracy. Strong products can lose relevance overnight when expectations outpace the experience. In fact, Reforge recently published a story on Product-Market Fit collapse, and how it’s happening in real time (link in comments).  For health tech companies, this is a call to action. Innovation takes time. But alignment can start now. Here’s my advice to leaders: + Understand what customers expect + Design experiences to meet or reset expectation early and often + Be honest about what your product can and can’t deliver Because once expectation breaks, so does trust.

🤖 Who’s Really Driving AI Adoption? Not Who You Think. A fascinating study from the American Marketing Association Journal of Marketing challenges one of the most widely held assumptions in tech adoption: 👉 The most AI-savvy consumers are not the fastest adopters 👉 Those with lower AI literacy are actually adopting AI faster Why? Because they experience AI as magical ✨ And that changes everything. 🧠 The Real Driver: Emotion > Understanding Consumers with less technical knowledge tend to: • Feel awe and curiosity • Focus on possibility over limitation • Engage with AI as something transformational More experienced users, on the other hand: • Focus on constraints and risks • Question ethics, accuracy, and bias • Move forward with greater caution 💡 The takeaway is powerful: Adoption isn’t driven by capability—it’s driven by perception. 🎯 What This Means for Marketers This is where strategy needs to evolve: 1️⃣ “Show the magic” to drive discovery ✨ → Focus on what AI enables, not how it works → Creativity, personalization, empathy, speed 2️⃣ “Show the truth” to sustain trust 🔍 → Balance inspiration with transparency → Set clear expectations, explain limitations 3️⃣ Segment by mindset, not just demographics 🧩 → Curious explorers vs. critical evaluators → Wonder-led vs. proof-led narratives ⚖️ The Strategic Tension Here’s the paradox: The more we educate consumers about AI… the less “magical” it feels. But without education… we risk misuse, mistrust, and overpromising. 💡 The opportunity is not to choose between the two, it’s to design experiences that preserve curiosity while building understanding. 💭 The real question: Are you positioning AI as a tool to understand… or an experience to feel? #AI #ArtificialIntelligence #MarketingStrategy #CustomerExperience #Innovation #DigitalTransformation #Trust #Growth #ProductStrategy #AIAdoption #ConsumerBehavior https://lnkd.in/g8gHScir

🔥 The tech industry's brutal truth about customer satisfaction... In the restaurant business, a single burnt meal affects one table. In tech or software? One product decision impacts THOUSANDS. I was thinking about this stark difference after reading Paul Graham's insight on product development. He nailed something most founders miss: The gap between what customers want and what you deliver isn't just a problem... It's a MULTIPLIER. Every decision you make either delights or disappoints at scale. There's no "table 7 had a bad experience but everyone else was fine." It's all or nothing. And here's the thing: The companies that generate the most wealth aren't necessarily the ones with the fanciest tech or biggest marketing budgets. They're the ones who obsessively close the gap between customer expectations and product reality. Think about it: → Every feature that misses the mark → Every UX frustration → Every performance issue These aren't just minor inconveniences. They're wealth-destruction machines operating at scale. But the reverse is also true! When you nail exactly what users want, that value creation compounds exponentially. So what's the takeaway? ✅ Invest disproportionately in understanding user needs ✅ Create tight feedback loops with actual customers ✅ Be willing to kill your darlings when the data speaks ✅ Remember that in tech, you're cooking for everyone at once The most successful founders I know aren't just building products. They're closing gaps. Between what exists and what should exist. Between what frustrates and what delights. Between what is and what could be. How close is YOUR product to what customers actually want? Because in tech, that gap isn't just a metric. It's your future. I’ve felt this firsthand building Mailberry. When we first launched, we thought ease-of-use was our killer feature. But we quickly learned that what businesses really wanted wasn’t just simplicity... They wanted done-for-you email marketing that actually drove results, without lifting a finger. That insight forced us to kill features we loved, rethink the UX, and rebuild around a single goal: removing all the friction between “idea” and “impact.” That shift didn’t just improve our product but it transformed our business. Because when you shrink that gap between what people wish existed and what actually does? You win. At scale.

Many organizations are implementing #AI too carelessly. I see a lot of effort to push AI at consumers, but consumers are nowhere near as positive about AI as you are. Gartner's research reveals consumers reservations about AI. A few months ago, Gartner found that 53% of consumers distrust or have a lack of confidence in the reliability and impartiality of AI search (https://lnkd.in/gUM_grgM). And in December 2023, 64% of customers said they'd prefer that companies didn’t use AI in their customer service, and 53% would consider switching to a competitor if they found out a company was going to use AI for customer service (https://lnkd.in/gJ2MBF9w). Pew Research finds much the same. While business leaders only appear to grow more excited about AI, consumer attitudes have shifted in the opposite direction: The number concerned about AI grew from 37% to 50% since 2021, and the number of people excited about AI fell by almost half, from 18% to 10% (https://lnkd.in/g8sTp6Tn). This concern is not due to a lack of knowledge and experience, as Pew found the number of people who've heard “a lot” about AI has skyrocketed, and 95% have heard a lot or a little. A majority report they interact with AI at least several times a week (https://lnkd.in/gnq4VGK9). While business leaders are full steam ahead, your customers are growing more concerned about the impact of AI on their jobs, their privacy, and their #CustomerExperience. This means you cannot expect customers to universally reward you for your new AI chatbot, AI ad or AI tool. It is important that brands: - Move more cautiously. - Research consumer attitudes and conduct thorough consumer testing of AI concepts. - Explain AI benefits to customers. - Be transparent about AI usage and offer alternatives and control. - Avoid pushing AI to everyone and, instead, personalize your approach to fit AI to the right audience. As experience with AI grows, consumers are not warming to the technology as most thought they would. You don't have to tap the AI brakes, but you should realize your AI features, communications, and content could do as much to frustrate and alienate customers as to meet their expectations.

What will force the use of AI is not 'thought leaders' parroting about AI, or tech companies cramming it into literally every product. What will force the use of AI is customer expectations. This has been the case for every technology ever. When the first combines replaced manual labor, the expectation became 20 bushels of corn harvested a day instead of 2. When email replaced postal mail, delivery expectations were measured in minutes, not days. When smartphones and apps took over, consumers expected to swipe right on everything. Want a box of cereal, a bottle of wine, a car, a date? Swipe right and it's on your doorstep. Expectations expand to fill the standard set by the technology. What expectations does AI set for your industry? If your content marketing team takes 2 weeks to produce a piece and a competitor using AI takes 2 minutes to produce a piece, the expectation is set at 2 minutes. That's the standard, the bar, right or wrong. "But quality!" some will say, reasonably so. Yet we gladly sacrifice quality, privacy, sustainability, equity, and pretty much everything else in favor of faster and cheaper. Faster and cheaper is what the consumer wants because we've abandoned patience, as Ann Handley pointed out recently: patience is a liability. And faster and cheaper is what our businesses want because it means increased profit margins. Take a hard, honest look at your value chain. Where will AI change expectations and set standards? If you're not adopting it, where will you be expected to meet those standards, with or without AI? You can absolutely say, as a differentiator, that you deliver 100% authentic human content - but you had better be able to meet the faster and cheaper standards set by AI regardless. #AI #GenerativeAI #GenAI #ChatGPT #ArtificialIntelligence #LargeLanguageModels #MachineLearning #IntelligenceRevolution