Why software fails to match user behavior

Look at what they do, not just what they say. User behavior is how users interact with and use software. It includes things like: → how people navigate the interface → which features people use most often → the order in which people perform tasks → how much time people spend on activities → how people react to prompts or feedback Product managers and designers must understand these behaviors. Analyzing user behavior can enhance the user experience, simplify processes, spot issues, and make the software more effective. Discovering the "why" behind user actions is the key to creating great software. In many of my sales discussions with teams, I notice that most rely too heavily on interviews to understand user problems. While interviews are a good starting point, they only cover half of the picture. What’s the benefit of going beyond interviews? → See actual user behavior, not just reported actions → Gain insights into unspoken needs in natural settings → Minimize behavior changes by observing discreetly → Capture genuine interactions for better data → Document detailed behaviors and interactions → Understand the full user journey and hidden pain points → Discover issues and opportunities users miss → Identify outside impacts on user behavior Most people don't think in a hyper-rational way—they're just trying to fit in. That's why when we built Helio, we included task-based activities to learn from users' actions and then provided follow-up questions about their thoughts and feelings. User behaviors aren't always rational. Several factors contribute to this: Cognitive Biases ↳ Users rely on mental shortcuts, often sticking to familiar but inefficient methods. Emotional Influence ↳ Emotions like stress or frustration can lead to hasty or illogical decisions. Habits and Routine ↳ Established habits may cause users to overlook better options or new features. Lack of Understanding ↳ Users may make choices based on limited knowledge, leading to seemingly irrational actions. Contextual Factors ↳ External factors like time pressure or distractions can impact user behavior. Social Influence ↳ Peer pressure or the desire to conform can also drive irrational choices. Observing user behavior, especially in large sample sizes, helps designers see how people naturally use products. This method gives a clearer and more accurate view of user behavior, uncovering hidden needs and issues that might not surface in interviews. #productdesign #productdiscovery #userresearch #uxresearch

Why most software fails? It wasn’t built for the people using it. It was built for a spec. For a stakeholder. For a presentation slide. But not for the person who opens it every morning and just wants things to work. We’ve seen it happen: → Systems that look sleek, but no one understands → Dashboards that report everything, but say nothing → Tools packed with features, and full of frustration That’s not progress. That’s noise. Here’s what we do instead: We start with the user. → We watch how they actually work → We ask what they avoid, and why → We test early, not just at the end → We cut what’s confusing → We refine what’s unclear → We keep it honest, simple over clever If your team needs a tutorial to use it, we built it wrong. Good software feels obvious. Comfortable. Almost invisible. That’s the goal. If you’ve been burned by “great tools” that never landed with your team, Let’s build something they’ll actually want to use. Because that’s where the real ROI lives.

Most digital transformation projects don’t fail due to implementation issues. They fail in silence when users quietly return to Excel. This is the uncomfortable truth behind most digital transformation projects. The software goes live, the email goes out, and within 90 days, your most experienced people are performing a Silent Rebellion. They are not complaining, but they are using WhatsApp for approvals, copying data into spreadsheets, or relying on paper logs. Why? Because the tool failed to earn their trust. Here are four ways tools lose user trust: (1) The "Accuracy Cliff" of New Data You launch an AI document processing system like YellowChunks. The first hundred documents are perfect. The next one, an oddly formatted invoice, breaks the model. The lie is that 98 percent accuracy is good enough. The reality is that one mistake makes users double-check everything. Trust drops to zero. (2) The Black Box Diagnosis An AI tool like BODHI flags a major component failure but gives no reasoning. The lie is that the AI knows best. The reality is that engineers will not act without proof. If they cannot see vibration logs or temperature spikes, they will run their own manual checks. (3) The Workflow Detour You deploy a voice AI like VirtuAI to improve call queues. But agents must click through six screens to tag a call. The lie is that the process was mapped correctly. The reality is that agents find faster shortcuts, and your data quality suffers. (4) The Cannot Fix My Own Mistake Barrier An employee makes a small entry error. To fix it, they must raise a ticket that takes 48 hours. The lie is that layered security ensures control. The reality is that users need flexibility. If they cannot correct simple mistakes, they will build their own workarounds in Excel. AI adoption is 20 percent technology and 80 percent trust. If your system does not deliver accuracy, transparency, and respect for how people actually work, the rebellion has already begun. Leaders, what was the biggest reason your last software rollout failed to achieve full adoption? #DigitalTransformation #ChangeManagement #AIAdoption #YellowChunks #BODHI #VirtuAI #Approlabs

Why Business Software Often Misses the Mark — And What We’re Doing About It In the world of business software, there’s a growing disconnect between the people who build the systems and the people who use them. Many developers spend their time focused on abstract requirements, product roadmaps, or internal tickets—without ever stepping into the shoes of the end user. They rarely see the messy realities of daily operations, the pressure of tight timelines, or the creative workarounds that users invent just to get through their day. The result? Software that feels out of touch. We’ve all seen it: Platforms that technically “check the boxes” but fall short in practice. They’re too complex, too rigid, or just don’t reflect how things actually happen on the ground. Users respond by: Exporting data to spreadsheets Building parallel systems in Google Sheets Stitching together third-party tools to fill in the gaps It’s not because users want to bypass the software—it’s because the software didn’t meet them where they are. This isn’t just a UX issue. It’s a structural problem in how business software is built: • Too many assumptions • Too little empathy • Not enough real-world exposure What We Do Differently at AIMS360 At AIMS360, we’ve taken a different approach. Our developers don’t just build features—they engage directly with the people who use them. That means: • Visiting customer sites • Walking through real workflows • Asking, “What’s actually happening here?” and “Why do you do it this way?” This approach leads to better understanding, faster problem-solving, and ultimately, software that fits into the customer’s world instead of forcing them to adapt to ours. When developers see the full picture, the product gets better. And when the product gets better, users stop building workarounds and start getting real value.

Tools create a path of least resistance through the way they are designed - some things become easier (encouraging users to do them) and some things become comparatively harder (discouraging that behavior). When it comes to AI chatbots, the design encourages users to trust the AI's outputs. Unfortunately, all LLMs hallucinate - it's only a matter of when. And as users get used to relying on the machine, their ability and willingness to spot these errors deteriorates. In the exact situation where the human in the loop is necessary, that human is unequipped to step in. Blaming the user for this is irresponsible. The problem is caused by the way these tools are designed - so it's up to us, as designers, to fix it. https://lnkd.in/eSmv_8yv

You ever watch someone freeze at a coffee menu? Maybe you have—but this happens to me all the time. I walk into a café, ready to grab a coffee, but the second I see 15+ options… my brain shuts down. I could try something new, but instead, I default to my usual order. It’s easier. And guess what? Your users behave the same way. When faced with too many choices, they hesitate. When something feels unfamiliar, they resist. And when a product doesn’t match their instincts, they abandon it. Here are 10 hidden behaviors shaping your users’ decisions (without them even realizing it): 👀 Users anchor decisions to the first thing they see 🔍 They only seek info that confirms their beliefs 🤯 Too many choices slow them down ⚠️ They fear losing more than they value gaining 🛑 They resist change—even when it’s better 💡 How you frame information changes their decisions 🔚 They remember the last thing they see 👥 They follow what others do (social proof is king!) 🎨 They value what they help create 🏗 They overvalue things they put effort into Your product isn’t just about features—it’s about how people actually behave. Master these behaviors, and you’ll skyrocket engagement, conversions, and user loyalty. Ignore them, and you risk confusing or losing users. Which of these behaviors have you noticed in your users? Let’s discuss in the comments. 👇 #UX #ProductDesign #UserPsychology #UIUX #CRO