Tips for Keeping Automation User-Friendly

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

Most AI strategies sound like this: “Let’s automate. Let’s cut costs. Let’s do more with less.” But if that’s all you’re doing, you’re already losing ground. The real edge? Building empathy into your AI from Day 1. Here’s how to move beyond efficiency and build AI that actually connects: → 1. Map the Human Journey Don’t just map the workflow. Interview real people who use or are impacted by your system. Ask: “Where does the process create friction or frustration?” → 2. Program for Context, Not Just Output Your AI doesn’t operate in a vacuum. Feed in contextual data—environment, stress signals, user preferences. Let people override the system. → 3. Prioritize Feedback Loops Set up rapid feedback channels. Watch for emotional cues: confusion, hesitation, delight. Tweak your product every week, not every quarter. → 4. Measure What Matters Don’t just track uptime or throughput. Track user trust. Track adoption rates. Ask for emotional feedback—not just star ratings. → 5. Champion Empathy in Leadership If your leaders only ask about efficiency, you’ll never build a product people love. Make empathy a boardroom metric. Here’s the brutal truth: Anyone can build an efficient tool. Only the bold build something people want to use every day. If you want your AI to stick around, start by making people feel seen.

💡Design Principles for AI Chatbots Recently, I had an interesting (but somewhat frustrating) experience with an AI chatbot designed by one of the world’s largest eCommerce platforms. The issue was that the AI assistant wasn’t very helpful in resolving a simple problem I encountered with my order, and it kept giving me generic suggestions that didn’t work in my case. What made things worse was that the assistant pretended to be a real human, and it took me three attempts (three separate dialogues) to realize it was actually AI. In the end, I solved my problem by reaching out to a real human agent—although the AI was reluctant to connect me to one. I've decided to write this post not to mock a poorly designed AI product, but rather to share 3 foundational rules that will help product designers create more human-friendly AI chatbots: 1️⃣ Be transparent and communicate system status ✅ Be transparent about who users speak to. If user is speaking with AI chatbot, they should know it upfront, at the begging of the conversation. Never make AI pretend to be a real human. ✅ Display disclaimers where AI might generate uncertain or probabilistic answers. This is especially important in areas that can cause risks for users (such as financial operations) ✅ Add system messages (e.g., "AI is typing…") to clearly communicate the waiting time for the user. ✅ Allow users to get a transcript of a conversation with a chatbot in one click. 2️⃣ Clarity is a top priority ✅ Keep responses concise, structured, and scannable; avoid overwhelming users with long text blocks. ✅ Maintain context within the session and remind users when needed ("Earlier you mentioned…"). ✅ Use onboarding hints or contextual examples to set expectations what AI can or cannot do. ✅ Use different size of chat window for different tasks. For long, complex tasks, it’s better to use full page screen. For short, momentary tasks, contextual chat widget works great. 3️⃣ Offer a freedom of choice for users ✅ Users should have the freedom to interact with AI in the way they like—using a full spectrum of natural language or quick replies (i.e., contextual shortcuts). Offer quick replies or buttons to reduce typing effort and guide interactions. ✅ Provide undo, edit, or re-ask options. For example, if the user decides to go back to the previous step, AI should not restrict this. ✅ Always keep the exit routes clear (return to home, escalate to human). ✅ Allow users to "speak to a human" if AI is not helpful (usually, people write "operator" to speak to a human). 📖 Chat UI Patterns by Visa Design System: https://lnkd.in/de8-WG_S 🖼️ Chat interface by Dennis Snellenberg #UX #uxdesign #productdesign #design #chatbot

Most of the consulting I've been doing over the last few weeks has been related to setting up chatbots, generative AI for the front and back end of the service journey or knowledge management, and other automation tools for CX. When I come into these conversations, I first want to know what problem they're trying to solve and why they want to do it with automation. There are many problems automation can help you solve, but I’m finding too many people want to use it to replace a larger percentage of their workflow than is probably healthy for their customer experience. It CAN save time and money, but it still takes someone (or many people) to manage to make sure users are having the experience they deserve. Some Common Examples: Chatbots: A conversational chatbot requires constant management - your product and services CHANGE, so the chatbot needs training, new prompts, new decision trees, new conversation flows, etc. When you let them go stale they create infuriating, looping experiences for your customers. Auto-responders: Great when you want to let people know you received their request, trigger an update if wait times are longer than expected, or anytime you know your auto-response is 100% relevant to whatever action the customer took. When the path to contact support is a maze, users will take whichever path will get them to a text box - you can’t be certain they’re using the correct category, and then create an auto-response specifically for that category. Ticket Deflection: This usually comes in the form of serving knowledge base articles before a customer can reach out to support for a self-serviceable task. Again, this is great and can reduce your queue to mostly inbounds that require interaction from a person, but if you’re not keeping your KB content up to date, it’s useless and creates a headache fast. Be smart about the automation you're introducing to your service journey and make sure they're serving the customer and your team.

AI and automation offer us an incredible opportunity: the chance to free up time, energy, and attention for the human connections that matter most in healthcare. When we're intentional about implementation, we can create systems that are both more efficient and more deeply human - where technology handles the transactional so people can focus on the relational. Here are ten principles for using AI and automation to strengthen human connection: 1. Start with Human Needs, Not Technical Capabilities Before asking what you can automate, ask what people actually need. Observe where friction exists. Listen to where patients and staff struggle. Let those insights guide your technology decisions. 2. Automate the Transactional to Protect the Relational Routine scheduling, wayfinding, and basic information transfer are ideal for automation. This frees up your team for moments that truly need human attention - difficult conversations, emotional support, and relationship building. 3. Test with Real People in Real Conditions What works in an outpatient setting might not work in an inpatient procedural space. Prototype different approaches and observe how people respond in the specific contexts where they'll use these tools. 4. Design for Everyone, Especially the Most Vulnerable When your automation works for people with varying comfort with technology, different language needs, and different digital access levels, you've created something that expands access rather than creating new barriers. 5. Make Human Interaction Always Available Give people easy, judgment-free ways to connect with a human whenever they need to. When automation is truly helpful, most people will use it. When they need a person, that option should be readily available. 6. Measure Whether You're Creating Capacity for Connection The best automation frees staff from routine tasks so they can spend more time on complex care conversations, emotional support, and personalized attention. If your team isn't gaining that capacity, refine your approach. 7. Be Clear About What's Automated and What's Human People appreciate knowing when they're interacting with AI versus a person. Transparency builds trust and sets appropriate expectations. 8. Design Seamless Handoffs Between Technology and Humans When someone moves from an automated system to human interaction, the transition should feel smooth. Information should carry forward, staff should have context, and patients shouldn't repeat themselves. 9. Learn and Adapt Continuously Pay attention to what's actually happening as people use your systems. Where does automation help? Where does it frustrate? Use these insights to keep improving. 10. Let Your Values Guide What Stays Human Your organizational values should illuminate where human presence is essential. If you value dignity and compassion, those values can guide which moments need human interaction and which can be effectively supported by technology.

Most ITSM Workflows Are Built Backward—Here’s How to Fix Yours Your ITSM workflow is a masterpiece. A bureaucratic masterpiece. Tickets go on a scenic tour through 12 unnecessary approvals, SLAs are broken before lunch, and users are more confused than ever. The problem? Most ITSM workflows are built backward—designed around IT’s comfort, not actual user needs. Let’s fix that before your users start calling shadow IT for help. 1. Stop Designing for IT—Start Designing for Humans Your workflow isn’t for IT, it’s for people who just want their tech to work. ↳ If a user has to Google how to submit a ticket, it’s already broken. ↳ Cut the 20-field forms—nobody wants to write a novel to get Wi-Fi access. ↳ Golden rule: If your grandma can’t follow it, it’s too complicated. 2. Approvals Shouldn’t Require a Seance If getting a simple IT request approved takes longer than getting a mortgage, rethink your life choices. ↳ Cut the pointless approvals—one green light should be enough. ↳ Automate routine requests so users aren’t waiting on a manager who’s OOO for two weeks. ↳ If nobody remembers why a step exists, delete it. 3. SLAs Are a Lie If Users Hate You Fast ticket resolution doesn’t mean good service—just faster frustration. ↳ Track first-contact resolution, not just speed. ↳ If users dread opening a ticket, your process is failing. ↳ Make the process so easy and clear that they don’t rage-email your boss. 4. Align ITSM With the Business, Not Just IT’s Ego ITSM isn’t about ticket closures—it’s about keeping the business running. ↳ If your reports look amazing, but everyone’s productivity is down, you’re measuring the wrong things. ↳ Workflows should help people do their jobs, not make them IT’s problem. ↳ Ask users what they actually need—shocking, I know. 5. Automate, But Don’t Create a Digital Hellscape Automation is great until users end up trapped in an AI chatbot loop from hell. ↳ Automate only when it makes sense—nobody wants a chatbot for major incidents. ↳ Give people an escape hatch—a human should always be one step away. ↳ If AI keeps escalating everything to “turn it off and on again”, rethink your approach. Final Takeaway: If your ITSM process works perfectly on paper but makes users miserable, you’ve built a bureaucracy, not a service. Flip the script—design for real people, remove nonsense steps, and make ITSM a business enabler, not a bottleneck. What’s the worst ITSM workflow you’ve ever seen? Let’s share horror stories. ♻️ Repost to help IT leaders escape process purgatory. 🔔 Follow Bob Roark & DayOneReadyLabs for ITSM that actually works.

Real-world advice for making tech actually work where the work gets done. Tech can speed things up —or get in the way. It all comes down to how you use it. Here's what works out in the field, where the signal drops, fingers are muddy, and time is tight. 1. Use Tools Built for the Field Don’t force office tools into the dirt. Use mobile-first apps that work offline, with big buttons, fast inputs, and simple screens. If it needs training, you’ve already lost most of the crew. 2. Train for the Job, Not the Features Skip the 40-minute software tour. Show people how it saves time, helps them get home earlier, or keeps things off their plate. That’s what sticks. 3. Sync Every Day If it lives only on someone’s phone, it didn’t happen. Make daily syncing part of the routine—end of day, every day. No excuses. 4. Shoot Photos Like Proof Photos aren't just nice to have—they're insurance. Make them sharp, timestamped, geotagged, and useful. Don't rely on memory when the questions come later. 5. Protect Your Devices Tablets break. Batteries die. Plan for it. Use rugged cases. Keep battery packs in the truck. Set up charging stations on site. No one’s taking notes on a dead screen. 6. Set Up Before You Walk Get your maps, layers, and forms loaded before you step on site. Saves time, saves frustration, and keeps you moving. 7. Standardize Everything Everyone doing it their own way = a mess. Use templates. Use defaults. Make it fast and foolproof. Clean inputs mean clean reports. 8. Add Context “Complete” doesn’t cut it. Add notes. Snap a photo. Drop a pin. You need to tell the story—not just check a box. 9. Make Tech Part of the Kit It’s not extra. It’s standard gear. Just like a hard hat or tape measure. If it’s optional, it’ll get skipped. 10. Look at the Data Don’t just collect it—use it. Review reports, spot trends, catch issues early. A week of delay caught now is better than a month of cleanup later. Tech doesn’t solve problems by itself. It amplifies what you put in. Sloppy inputs? You’ll get confusion faster. But used right—tech can help be the bridge between jobsite reality and project success.

The most frustrating thing about IT, Software Engineering (SWE), and Software Development (SD) professionals entering the industrial automation space is that a significant portion genuinely believes: More code equates to better functionality. Increased complexity in logic equals impressive results. Clever use cases are inherently valuable, even without practical relevance. Modern engineering schools typically emphasize languages such as C, C++, Python, and Java, often overlooking ladder logic unless it's part of a hands-on practical engineering curriculum. Even then, the teachings frequently come from Ph.D. professors with minimal real-world automation experience, leading students to adopt impractical methods like overusing alias tags or unnecessarily complex sequencers. While C is valuable for networking and embedded systems programming, higher-level languages like Python or Java rarely suit real-time automation (RTA) scenarios effectively. Despite this, many recent graduates, particularly those holding Engineering Management degrees, end up supervising PLC programming teams within industrial automation integrators. Consequently, they lean heavily on practices learned in academia: FPGA programming in Verilog, microcontroller programming in C, or applying Python-based machine vision AI solutions—rather than the practical, robust approaches needed in automation environments. So, if this message feels like it "hits", remember: As you enter or progress into our realm of industrial automation, always adhere to the fundamental principle: Keep It Simple, Stupid (KISS). Complexity and excessive cleverness rarely impress anyone who genuinely understands industrial automation. No one is saying don't add good practice or software management methods— but remember the customer is the end user: NOT your shops need for modularity and massive code libraries. You're code needs to be safe, easily read, execute and error handle well, and be easily expandable before anything. Practical, straightforward, and maintainable solutions always hold greater value.

Here’s the secret to AI-first products: If your AI isn’t where your users already work, it’s just a cool tool they’ll never adopt. Too many teams build standalone apps for developer convenience, only to see low adoption because they disrupt user workflows. Want to create AI that feels like a co-pilot, not a detour? Too many teams treat AI like an add-on instead of designing around how people actually work. If you want your tool to stick, start by testing where and how users will reach for it—not just which feature they like. 1. Watch before you wireframe Shadow your users for days. Note which apps they open first, what data they reference, where they pause. When you map their natural workflow, you can slot your AI into it—rather than forcing them onto a new path. 2. Make the channel your core hypothesis Is the right interface a sidebar in your CRM, a chatbot in Teams, a Slack app, or a push notification on mobile? Instead of asking “is lead-scoring useful?”, test “will sales reps use this inside their CRM?” Show partners quick sketches in each context and see which one they instinctively click. 3. Decouple logic from presentation Build one robust AI engine that powers a chat widget, a browser extension or a simple web view. When someone asks for a new capability, ask “What decision are you making?” and “Where do you need to make it?” You avoid duplicate work and can adapt fast to new platforms. 4. Capture data as part of the flow The best way to train your model is to let users work as usual. If your AI suggests optimal campaign parameters, log every tweak automatically. Don’t make marketers export logs or fill out extra forms—that creates gaps and biases your training set. 5. Earn trust through real-time dialogue In a conversational UI, let the AI ask clarifying questions (“I see you’re about to launch the summer campaign—should we include last quarter’s top keywords?”) and explain its suggestions inline (“These three segments drove 18% more conversions last month”). Then package the output in a ready-to-send summary or email draft. 6. Shift from one-off tasks to continuous value If your tool only fires during project kick-off, users will forget it. Surface a lightweight insight each week—like an alert when support ticket volume spikes or when a key metric drifts. Those small, correct nudges build confidence and prime users for the big recommendations they’ll need later. Validate your assumptions about channel, data capture, trust and engagement before you write a line of production code. When your AI lives inside the tools people already use, it becomes part of their daily routine—and that’s when it becomes indispensable. The Big Takeaway: AI-first products must be invisible, conversational, and proactive, living inside users’ existing tools. Don’t build a standalone app for control—tackle the engineering to embed your AI where it belongs. That’s how you build a platform, not a feature.

Ever wondered why some systems make users feel like they need a PhD to operate them? Let's fix that. Many tech leaders think they know what users want, but often, they're missing the mark. It's time to stop designing for awards and start designing for your users. Here's how to create a truly user-friendly system: 1. Easy-to-Navigate Streets 🏙️ Imagine your software as a bustling city. A good city has clear street signs, and your software should too. Users shouldn't need GPS to find what they need. Make navigation intuitive. A well-planned grid beats a confusing tangle of alleys any day. Engagement ROI: Investing $1 in UX design can yield a $100 return. That's a 9,900% ROI! (Source: Forrester) 2. Efficient Public Transit 🚇 In cities and software, quick travel matters. Your system should be as fast as an express train. Make it run faster and simpler. Let users complete tasks before their coffee goes cold. 3. Helpful City Services 🏥 Every city faces issues. In software, they're bugs and errors. Handle them like a responsive city hall. Don't just say "Road Closed." Explain the detour and when it'll be fixed. Be the helpful mayor, not the grumpy bureaucrat. Cost Savings: Fixing design issues in development is 100x more expensive than addressing them during design. (Source: IBM) 4. Customizable Neighborhoods 🏘️ Some folks prefer downtown, others the suburbs. Let users customize their experience. It's like letting them choose their ideal neighborhood in your digital city. They'll feel more at home and stay longer. 5. Listen to the Locals 👥 Residents know their city best. Your users are the locals of your software city. Watch how they navigate. Listen to their feedback. Use their input to build a better user experience. Conversion Boost: A well-designed UI can boost website conversion rates by up to 200%, with UX improvements driving increases up to 400%. (Source: Forrester) Continuous Urban Planning 🏗️ Great cities evolve. So should your software. Keep refining based on user feedback. It's like urban renewal – consistent improvements lead to a thriving cityscape. Your goal isn't to build the tallest skyscraper. It's to create a place where users feel at home. When they can navigate your system as easily as their favorite city block, you've succeeded. Next time you're designing a system, think like an urban planner. Would YOU enjoy living in this digital city? If not, it might be time to revise those blueprints. What's your take on creating user-friendly systems? Share your best 'user-friendly' experience in the comments below. Think about a system you use regularly. What one change would make it significantly more user-friendly for you?