Strategic Implementation of AI in User Interfaces

While most organizations get tangled in committees and ROI requirements for AI implementation, one global firm with 65,000 employees took a radically different approach—and it worked brilliantly. Their CIO's philosophy? "If they use it, great. If they don't, fine—I don't care." Instead of months of planning and millions in costs, they: - Built a simple chat interface over OpenAI's API - Made security automatic rather than policy-dependent - Created a fun, badge-based training system - Scaled through real employee needs - Deployed new AI apps in just 20 minutes The results? 25 million API calls, 300+ custom applications, and only $300K spent in the first year. This case flips traditional AI implementation on its head. Sometimes you need to get the flywheel spinning first. When you remove barriers and enable exploration, innovation follows naturally. What's your organization's approach to AI implementation? Are you moving fast or getting stuck in red tape?

🔮 AI Interaction Design Patterns (https://www.shapeof.ai), a fantastic (!) living catalog of emerging design patterns, heuristics, anti-patterns and real-life examples that shape the experience of AI — from identifiers and wayfinding to prompts, tuners and trust indicators. Incredible project by incredible Emily Campbell. 👏🏼 👏🏽 👏🏾 AI experience can go way beyond a text box. One of the most underrated yet impactful patterns for AI interfaces is the ability to tune AI experiences. This could show itself as a style lenses or temperature knobs — little tools to help users generate a more personalized output easier. E.g. Risky ↔ Risk-averse, Sad ↔ Happy, Concrete ↔ Abstract, Creative ↔ Precise. Instead of expecting large and highly detailed text prompts, we could slow people down when they prompt — e.g. with prompt constructors, prompt strength meters, presets or templates. Perhaps by defining an expected format, structure, personas, roles as checkboxes or chips — both for user input and AI responses (priming). Another much-needed feature is scoping. Users should be able to quickly scope their inquiry to a particular domain, level of expertise, sources or even a set of videos or PDFs. We need pre-screening of sources, and proactive alignment with users. These are features that would make output much more specific without having to write a long prompt. And: the AI output shouldn’t be bulky nor static. Users should be able to granularly iterate or revise little bits of it — e.g. by asking for sources of specific statements, or diverging from one view to another, or manipulating small parts of an image or a video. These refinements should happen not via text prompts, but contextually — acting on the relevant parts of AI outcome. We can go way beyond a text prompt. Better results come from combining good old-fashioned design patterns such as search, filtering and sorting with AI — to first find relevant and trustworthy sources, and then generate insights from them. That’s a great way to boost accuracy and make AI more relevant to more people. 💎 Design Patterns For AI Interfaces Prompt UX Patterns, by Sharang Sharma https://lnkd.in/eCytfAe9 Where should AI sit in your UI?, by Sharang Sharma https://lnkd.in/dyyMKuU9 AI UX Patterns, by Luke Bennis https://lnkd.in/dF9AZeKZ Design Patterns For Building Trust, by If https://lnkd.in/eEJngtVv AI Design Patterns Catalogue, by Maggie Appleton https://lnkd.in/ebAp9Sb8 --- 🚀 Fantastic AI Examples: Elicit (research tables): https://elicit.com Consensus (confidence levels): https://consensus.app/ Scispace (search + AI): https://scispace.com v7 Labs (AI auto-fill): https://v7labs.com/ Exa (semantic grid): https://exa.ai DeepL (translation): https://deepl.com NotebookLM (scoping): https://notebooklm.google/ [continues in comments] #ux #ai

Want to create AI-powered products that users actually love? Master the essential UX principles for AI and build experiences that are intuitive, trustworthy, and effective principles include..... 1) Human-centered AI design Prioritizing user needs and aligning AI features with user expectations to augment human capabilities 2) Seamless human-AI interaction Designing intuitive interfaces and clear communication to ensure a smooth collaboration between humans and AI 3) Balancing AI capabilities and constraints Understanding the strengths and limitations of AI to optimize algorithms and data quality 4) Explainability and Transparency Explaining to the user why the AI behaves, recommends, or suggests a result by providing clear explanations for AI decisions 5) User control balancing AI automation with user control by offering settings and preferences to adapt AI behavior and override AI decisions 6) Feedback mechanisms Establishing channels for users to offer feedback on system performance, enabling continuous improvement based on real user experiences 7) Managing user expectations Providing a detailed description of what users can expect from the app to manage expectations successfully 8) Error Handling Providing clear feedback and guidance to help users understand and address errors effectively #ux #ui #uxui #ai #aiux #llm #generativeai #productdesign #deepseek #chstgpt

Forget what you know about UI. (here comes outcome-oriented UI) A new paradigm is emerging in UI design. Now where user goals trump traditional UI elements. Thanks to AI and generative UI principles. Outcome-oriented design will revolutionize how we create digital experiences. 5 ways to implement Outcome-oriented UI design: 1. GOAL-BASED NAVIGATION: Ditch traditional menus for AI-powered, goal-oriented navigation. Example: A banking app that presents options based on the user's financial goals (e.g., "Save for a house," "Reduce debt") rather than generic account categories. 2. ADAPTIVE WORKFLOWS: Create interfaces that morph to match the user's current objective. Example: A video editing tool that simplifies or expands its interface based on whether the user is making a quick social media clip or a professional-grade film. 3. PREDICTIVE TASK COMPLETION: Leverage AI to anticipate and streamline user tasks. Example: A project management platform that automatically generates and populates task lists based on team goals, past projects, and current deadlines. 4. CONTEXTUAL INFORMATION HIERARCHY: Dynamically adjust info prominence based on user context and goals. Example: An e-commerce site that prioritizes different product descriptions (e.g., sustainability, price, delivery time) based on each user's shopping priorities and behavior. 5. INTELLIGENT FORM OPTIMIZATION: Design forms that adapt to user goals and known information. Example: A travel booking system that only asks for relevant information based on the type of trip (business vs. leisure) and automatically fills in known preferences. ................................................................................. Outcome-oriented UI design focuses on what users want to achieve, not how they navigate an interface. Designers embracing this approach will create more intuitive, efficient, and personalized digital experiences. The future of UI isn't about buttons and menus – it's about understanding and facilitating user goals.

Gartner: Generative AI and LLMs are fueling Multiexperience User Interfaces in Augmented Analytics and Data Platforms WHAT IS IT? A multiexperience user interface (UI) for analytics and business intelligence (ABI) aligns modes of interaction and analytics capabilities, which optimize a user’s experience of analytics development and consumption of content for a given decision-making process. The increase in possible combinations of approaches is due to advancements in technologies such as augmented analytics, generative AI, data storytelling, natural language query, virtual reality (VR) and augmented reality (AR). WHY IS IT IMPORTANT? Much like the customized user experiences we are used to in our day-to-day interactions with technology, consumer-oriented analytics experiences are needed to drive adoption of data-driven decisions. Organizations must be able to deliver the most relevant, contextualized and consumable analytics outputs possible. This requires tapping into the unique intersection of various devices, interaction modalities and analytics capabilities that can augment users’ ability to consume insights. WHAT IS THE BUSINESS IMPACT? Transitioning from static analytics outputs to dynamic contextualized insights, embedded or automated, means analytics are delivered with increased relevance closer to the point of decision. Aligning analytics with an optimal interface and consumption modality will impact the approach to measuring ABI adoption. Quantifying adoption must shift from counting how many users leverage a tool to how many people consult data in making a decision and what pathway of capabilities they use. WHAT I RECOMMEND? 🧿 Account for multiexperience approaches to consuming data by aligning the right analytic capability to the right user interface and experience. 🧿 Evaluate where new consumption mechanisms could add value to decision-making processes, rather than simply lifting and shifting the same traditional analytics outputs to a modern cloud platform. 🧿 Evaluate, on a regular basis, your existing ABI tools and innovative startups to offer new augmented user experiences beyond the predefined dashboard, such as AI-powered coding assistants. 🧿 Place analytics capabilities as close to relevant business decision makers as possible, by evaluating when ABI platform capabilities are best embedded in line with other business applications or workflows. 🧿 Take a data-driven approach to analytics adoption by leveraging the usage data available within today’s ABI platforms. If not prebuilt, discuss with vendors the options available to tap into such data. You can read more of my research on this in our Hype Cycle for the Future of Enterprise Applications, 2023: https://lnkd.in/ebkWTE33 #Analytics #BI #BusinessIntelligence #Multiexperience #AR #VR #GenerativeAI #GenAI #Immersive #Data #AI

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

𝐃𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞𝐬 𝐟𝐨𝐫 𝐀𝐈 𝐚𝐧𝐝 𝐇𝐮𝐦𝐚𝐧𝐬: 𝐀𝐫𝐞 𝐖𝐞 𝐑𝐞𝐚𝐝𝐲 𝐟𝐨𝐫 𝐇𝐲𝐛𝐫𝐢𝐝 𝐔𝐬𝐞𝐫𝐬? The rise of AI as an active user of digital interfaces is reshaping how we think about design. These systems aren’t just tools anymore - they’re navigating interfaces, completing tasks, and collaborating with humans.  This shift introduces complex challenges:   - Balancing visual clarity for humans with data structure needs for AI.   - Designing interfaces that accommodate both human comprehension and AI’s near-instant processing.   - Enabling seamless collaboration between human and AI users.  A new approach - 𝐃𝐮𝐚𝐥-𝐂𝐡𝐚𝐧𝐧𝐞𝐥 𝐃𝐞𝐬𝐢𝐠𝐧 - offers a way forward. It emphasizes creating interfaces that work simultaneously for both user types, leveraging principles like parallel information architecture, state transparency, and adaptive complexity.  This isn’t just a technical problem; it’s an opportunity to rethink metrics, workflows, and the role of UX design in a hybrid future.  👉 Explore the principles, examples, and risks in this detailed breakdown: #uxdesign