Enhancing User Experience In SaaS With AI

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

The SaaS Funnel is Dead. AI Broke It. Forget "optimizing" the funnel. AI isn't just tweaking the classic model—it's demolishing it. The linear path of Discover > Activate > Convert > Retain is being replaced by a single, persistent, intelligent conversation. And yet, I see so many B2B and healthcare product teams trying bolt AI onto their old funnel framework. They're missing the seismic shift. Today's buyers don't "enter" a funnel. They pose a question. Note these 4 disruptions and implications to your product: 1. Intent Isn't Captured, It's Revealed. Old way: Gate value behind a multi-field form, forcing users to provide their credentials, payments, or eligibility. New way: Start with a single prompt. An AI agent engages in a 2-3 turn conversation to diagnose the user's true job-to-be-done. Qualification happens through conversation, not interrogation. This isn’t just about reducing friction; it’s about starting the relationship with service instead of extraction. 🔑 Your first touchpoint should feel like a diagnosis, not a registration. The goal is to understand intent, not just collect data. 2. Deliver Value Before the "Aha!" Moment. Old way: Rush users to your core feature, hoping they find the "aha!" moment before they churn. New way: Deliver "Day 0 Value." While a user waits for a demo, a human, or a paid feature, an AI assistant provides a micro-solution. It could be a personalized resource, a quick analysis of a small data set, or a self-guided assessment. You solve a small part of their problem for free, right now. 🔑 The best way to build trust isn't a promise of future value; it's the delivery of immediate, autonomous value. 3. The Product Becomes a Proactive Partner. Old way: Nudge users with periodic emails and notifications, reminding them to come back. New way: The product is always-on, working for the user in the background. Think of an AI that surfaces risks, drafts reports, or suggests optimizations without being asked. Engagement becomes a byproduct of the product's persistent, agentive help. 🔑 Don't design for check-ins; design for a partnership. Your product shouldn't be a tool the user picks up, but a partner that never stops working for them. 4. Your Product Is Your Best Salesperson (Literally). Old way: Use product usage data to inform sales conversations. New way: The AI interactions generate undeniable, real-time proof of value. This outcome data isn't just a signal; it's the core of your commercial motion. The AI can surface an ROI calculation or a performance benchmark directly to the user or account owner at the perfect moment. 🔑 Stop selling features. Let the AI surface outcomes, and the outcomes will sell the product. Your GTM and product feedback loops should be one and the same. The bottom line for builders: Stop optimizing the steps in a dying funnel. Instead, build a single, AI-powered front door that serves, diagnoses, and solves from the very first touch. This is the new moat.

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

A Director of UX at a SaaS company recently shared a painful calculation with me: Their team of 3 researchers spent 75% of their time on manual analysis. At an average salary of $150K, that's nearly $300K annually spent on analyzing data. But the bigger cost? Critical product decisions made without insights because "we can't wait for research." Most UX and product teams are trapped in a costly cycle of inefficiency: Conduct user interviews → Spend 30+ hours manually analyzing → Create a report → Make decisions based on gut feeling before the report is ready. After watching UX teams struggle with this for years, I've identified the core problem: research insights are treated as artifacts, not conversations. This is why we built AI Wizard into Looppanel - a conversational research companion that transforms how teams extract value from user research. Instead of static reports and manual analysis, AI Wizard allows anyone to simply ask: "What pain points did users mention about the onboarding process?" "Summarize the key recommendations users suggested for improving the checkout flow." "What were the main differences in how novice users versus power users approached this task?" You start by selecting from templates like Pain Points, Recommendations, or Summary. AI Wizard instantly analyzes your project data and engages in a natural conversation - complete with follow-up questions to dig deeper into specific areas. The way I see it, AI Wizard helps solve 3 critical problems: 1. The speed-to-decision problem Waiting weeks for analysis means missing decision windows. AI Wizard delivers TLDR overviews in seconds, not days. 2. The iteration problem No more spending time on data again because of a follow-up question. Answer unexpected stakeholder questions on the spot instead of scheduling another week of analysis 3. The tailored communication problem Automatically format the same insights for different audiences: executives get metrics, designers get details, all without rebuilding presentations. With AI Wizard, your team can: → Start conversations with templates like Pain Points, Recommendations, or Summary → Ask follow-up questions to dig deeper → Get insights from across your entire research repository in seconds → Democratize access to insights throughout your organization Will your team be leading this transformation or catching up to it? If you want to make the shift, sign up for a personalized demo here: https://bit.ly/42PEOlX

Building AI products that customers love may be the most important skill for Product Managers in 2025. But most PMs are just sprinkling AI on top… instead of baking it into the experience. Here’s how to build AI that’s actually used and actually drives business value (with a real example): — I've written a whole blueprint here (no paywall): https://lnkd.in/eDGmsvZ5 Here's the short version. — AI SPRINKLES: FLASHY BUT FORGOTTEN You know these features when you see them: → Attention-grabbing but superficial → Sit on top of existing features → Used occasionally, if at all But it’s only a matter of time before users realise the truth. So here are some signs you can look for early on: - Features hidden in sidebars - Require new user behaviors to start - High initial curiosity, but lower retention - Marketed with fancy badges and animations - Can be removed without affecting the core product These are the "look, we have AI too!" features. Users try them once but then forget. — AI CAKE: INVISIBLE BUT INDISPENSABLE The best AI products don’t demand the spotlight. Their goal is to give the user the best experience. Here’s what makes them different: → Deeply integrated into product DNA → Work invisibly in the background → Re-imagines core experience → Solves real problems And here’s what it looks like for the user in the real world: - Self-naming workflows that adapt to what you're doing - Dynamically generated content without prompting - Insights that appear exactly when you need it - Problems solved before users notice them So the magic lies not in showcasing intelligence. But embedding it where it matters most. — THE KEY SHIFTS TO MAKE To move from AI sprinkling to baking: 1. Start with user needs first, not technology (Don't just ask where AI fits, identify real friction points) 2. Make intelligence invisible rather than showcasing it (Great AI doesn't rely on neon signs, it solves problems) 3. Enhance existing workflows instead of creating new (Take the existing user experience from 'good' to 'great') — AN EXAMPLE Take Attio for example. They didn't obsess over the fanciest AI transcription. They cared about turning call data into valuable insights. That's the difference. Because users don't say "Wow, cool AI." They think "Great, this makes my job way easier." — What AI products do you like most? Repost to share with others. P.S. If you liked this, you'll love my newsletter: https://lnkd.in/gp2cCv8K

For SaaS companies, customer churn is closely tied to growth. From an industry standpoint, the average churn rate for mid-market companies is between 12% and 13%. With renewal-based revenue models, churn directly affects both topline and bottom line. At Egnyte, AI and Machine Learning have been pivotal in our journey to improving customer retention and reducing churn. We have noted a 2.5 to 3 points reduction in churn rate by deploying AI programs that are actionable for both our customers and CSM teams. AI can offer powerful capabilities to help SaaS companies significantly reduce churn by enabling proactive and data-driven customer retention strategies. Some of these strategies are: 1. Predictive Churn Analytics Machine Learning models analyze vast amounts of customer data (usage patterns, support interactions, billing history, feature adoption, login frequency, etc.) to identify subtle patterns that precede churn. They can flag customers as "at-risk" before they can explicitly signal dissatisfaction, allowing for proactive intervention. It can further assign a "churn risk score" to each customer/ user, enabling customer success teams to prioritize their efforts on the most vulnerable and valuable accounts. The actionable operational data that we received by employing ML is the essence of churn analytics. 2. Hyper-Personalized Customer Experiences AI allows SaaS companies to move beyond generic communication to highly tailored interactions based on user behavior and feature adoption. AI can suggest relevant features, integrations, or workflows that the user might find valuable but hasn't yet discovered. AI can also determine the optimal timing and channel of customer-focused content, such as help desk articles, feature awareness videos, and case studies. 3. Automated Customer Support and Engagement AI can enhance customer support, making it more efficient and impactful. AI-powered chatbots can handle common customer queries 24/7, reducing wait times and providing instant solutions. Advanced chatbots use Natural Language Processing (NLP) to understand complex queries and provide personalized responses. It also helps in online enablement, reducing onboarding costs. While these strategies are already redefining the way CSM and enablement teams service customers, their significance in the cadence of customer retention strategies is going to increase hereon. Enterprises need to use AI intelligently and efficiently and focus on gleaning actionable insights from their AI strategies. #B2BSaaS #Churn #CustomerRetention