How to Develop a ChatGPT User Experience Strategy

How proactive AI will change UX - 📆 schedule ChatGPT requests! OpenAI has introduced a new task scheduling feature for ChatGPT. This means you can now ask ChatGPT to handle tasks at a future time — like sending you a weekly global news update, recommending a daily personalized workout, or setting reminders for important events. 💡 Why is this interesting from a UX perspective? This shift is a step toward proactive AI — moving from reactive systems (waiting for user input) to anticipatory, context-aware experiences that help users save mental energy and stay on top of their routines. Let’s break it down from a real-life use case - creating daily recipes: I currently eat sugar-free, gluten-free (because I am celiac), and generally low-carb and like to let ChatGPT create recipes for me. I don’t want a fixed meal plan, but I do need flexible, personalized recipe suggestions that fit my nutrition goals. Ideally, I’d want ChatGPT to  → suggest automatically 3-4 recipes daily around 3 PM → send them to me  → and based on my choice adjust future suggestions for the next days based on what I’ve already eaten that week (for balanced nutrients). With the new task feature, this kind of personalized experience could become much much more seamless. I wouldn't need to ask repeatedly — the assistant would learn my preferences over time and adapt its suggestions accordingly. 🎯 What can we learn from this in AI-UX design? 1️⃣ From static interactions to dynamic experiences: We often design AI tools that rely on users asking for something. But this update shows the value of continuous, evolving interactions. Users shouldn’t need to start from scratch every time — systems can proactively adjust to their needs and context. 2️⃣ Mental models of AI assistants: For users to trust AI routines, they need to understand what the assistant will do and when. It’s about designing predictability and transparency in a way that still allows for flexibility and spontaneity. 3️⃣ Proactive ≠ intrusive: There’s a fine balance between helpful and annoying. The best AI interactions feel like a supportive partner — offering assistance at the right time, based on context and past behavior, without overwhelming users with irrelevant notifications. In AI-UX, we’re increasingly designing for systems that adapt and evolve with the user.  This new feature is a great example of how AI can shift might be able rom a passive tool to an active assistant — can’t wait to try it. How do you see proactive AI changing the way we design user experiences? Would love to hear your thoughts! 👀

𝗧𝗵𝗶𝗻𝗸 𝗹𝗶𝗸𝗲 𝗮 𝘁𝗲𝗮𝗺—𝗲𝘃𝗲𝗻 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂'𝗿𝗲 𝘀𝗼𝗹𝗼. Turn ChatGPT into your 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝘀𝘄𝗮𝗿𝗺 𝘁𝗲𝗮𝗺: Tackle tough problems by simulating a room full of experts—CEO, CFO, Innovator, Customer, and more. Think like a team. Decide like a strategist. Solve like a pro. 𝗧𝗵𝗲 𝗥𝗼𝗹𝗲 𝗟𝗲𝗻𝘀 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Role Lens Insights is a powerful way to swarm problems, expose blind spots, stress-test ideas, and generate better solutions. 𝗪𝗵𝘆 𝗜𝘁’𝘀 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 • Turns solo thinking into 𝗺𝘂𝗹𝘁𝗶-𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 • Builds 𝗲𝗺𝗽𝗮𝘁𝗵𝘆 for different stakeholders • Surfaces 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝘁𝗲𝗻𝘀𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗯𝗹𝗶𝗻𝗱 𝘀𝗽𝗼𝘁𝘀 • Helps you 𝘀𝘁𝗿𝗲𝘀𝘀-𝘁𝗲𝘀𝘁 and 𝗿𝗲𝗳𝗶𝗻𝗲 decisions fast • Amplifies your 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗛𝗼𝘄 𝘁𝗼 𝗨𝘀𝗲 𝗜𝘁 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Clearly state the problem, decision, or idea you want to explore. 2. 𝗖𝗵𝗼𝗼𝘀𝗲 𝗥𝗼𝗹𝗲𝘀 Select 3–5 expert lenses relevant to your challenge (e.g., CEO, CFO, Innovation Expert, Customer, etc.). 3. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗮𝗰𝗵 𝗥𝗼𝗹𝗲 Ask ChatGPT to respond from each role's perspective (e.g., “As the CFO, what risks do you see?”). 4. 𝗙𝗮𝗰𝗶𝗹𝗶𝘁𝗮𝘁𝗲 𝗗𝗶𝗮𝗹𝗼𝗴𝘂𝗲 Have the roles "discuss" the idea as if in a team meeting. This dialogue reveals tensions, assumptions, and synergies. 5. 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Identify key themes, trade-offs, blind spots, and opportunities across perspectives. 6. 𝗦𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘇𝗲 & 𝗔𝗰𝘁 Integrate the learnings into a better, more rounded solution. You can also apply thinking tools like 𝗦𝗶𝘅 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗛𝗮𝘁𝘀 or the 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗠𝗼𝗱𝗲𝗹 𝗖𝗮𝗻𝘃𝗮𝘀 to guide deeper analysis. 7. 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 𝗮𝘀 𝗡𝗲𝗲𝗱𝗲𝗱 Adjust roles, reframe the problem, or simulate new strategies to explore further. 𝗤𝘂𝗶𝗰𝗸 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜 𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝗜𝗱𝗲𝗮 You prompt ChatGPT to form a virtual team with 5 roles: • 𝗖𝗘𝗢: Focuses on vision and market opportunity. • 𝗖𝗙𝗢: Analyzes financial risk, ROI, and funding needs. • 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗘𝘅𝗽𝗲𝗿𝘁: Evaluates uniqueness and feasibility. • 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗟𝗲𝗮𝗱: Assesses customer fit and positioning. • 𝗔𝗜 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁: Explains the technical approach and scalability. Together, they discuss an AI-driven platform that predicts customer needs in real-time. Through their dialogue, you surface: • 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀: Personalized, proactive CX is a differentiator. • 𝗥𝗶𝘀𝗸𝘀: Cost of real-time data processing, competitive landscape. • 𝗡𝗲𝘅𝘁 𝘀𝘁𝗲𝗽𝘀: Build a lean MVP, target e-commerce, and validate with early adopters. You then 𝗮𝗽𝗽𝗹𝘆 𝗦𝗶𝘅 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗛𝗮𝘁𝘀 to explore the idea emotionally, logically, creatively, and cautiously—sharpening the strategy even further. What challenge will you swarm today?

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