AI Techniques For Building User-Centric Applications

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

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

7 ways to seamlessly integrate AI into your users journey 1. The core purpose of AI directly shapes the user’s journey. 
 Conduct user research to identify key pain points or tasks users want AI to solve. ↳ if the startup’s AI helps automate content creation, what’s the user’s biggest friction in the current workflow? 2. Where will the AI interact with users within the product flow? Map out where AI should intervene in the user journey. For instance, ↳ does it act as an assistant (suggesting actions)
 ↳ a decision-maker (making recommendations)
 ↳ a tool (executing commands) 3. Simplify feedback loops help build trust and comprehension
 Focus on how users will receive AI feedback. ↳ What kind of feedback does the user need to understand why the AI made a recommendation? 4. Build a modular, responsive interface that scales with AI’s complexity. Visual elements should adapt easily to different screen sizes, user behaviors, and data volume. ↳ if the AI recommends personalized content, how will it handle hundreds or thousands of users while maintaining accuracy?
 
 5. Use layers of transparency At first glance, provide a simple explanation, and offer deeper insights for users who want more detailed information. Visual cues like "Why?" buttons can help. For more on how layered feedback can improve UX, check out my post here 
https://lnkd.in/eABK5XiT 6. Leverage Emotion Detection patterns that shift the tone of feedback or assistance. ↳ when the system detects confusion, the interface could shift to a more supportive tone, offering simpler explanations or encouraging the user to ask for help. For tips on emotion detection, check this https://lnkd.in/ekVC6-HN 7. Prototype different AI patterns ⤷ such as proactive learning prompts ⤷ goal-based suggestions ⤷ confidence estimation based on the business goals and user needs Run usability tests focusing on how users interact with AI features. ↳ Track metrics like user engagement, completion rates, and satisfaction with AI recommendations. Check out the visual breakdown below 👇 How are you integrating AI into your product flows? #aiux #scalability #designsystems #uxdesign #startups