Microsoft Power Bi Copilot AI citations

Here are some of the best data visualization tools for business: 1. {{[[Tableau]]:https://www.tableau.com}} : A leading data visualization tool that allows users to create interactive and shareable dashboards, making it easy to visualize data from various sources. 2. {{[[Microsoft Power BI]]:https://powerbi.microsoft.com}} : A powerful business analytics tool that provides interactive visualizations and business intelligence capabilities with an easy-to-use interface. 3. {{[[QlikView]]:https://www.qlik.com}} : Offers guided analytics and dashboards that help businesses make data-driven decisions through its associative data model. 4. {{[[Looker]]:https://looker.com}} : A data platform that helps businesses explore, analyze, and share real-time business analytics easily. 5. {{[[Domo]]:https://www.domo.com}} : A cloud-based platform that provides business intelligence tools and data visualization capabilities, enabling real-time data insights. 6. {{[[Sisense]]:https://www.sisense.com}} : Allows users to create visualizations and dashboards with a focus on complex data integration and analytics. 7. {{[[Google Data Studio]]:https://datastudio.google.com}} : A free tool that turns data into customizable informative reports and dashboards, integrating seamlessly with other Google services. 8. {{[[Zoho Analytics]]:https://www.zoho.com/analytics}} : Provides a self-service BI and data analytics platform that helps users create insightful visualizations and reports. 9. {{[[Chartio]]:https://chartio.com}} : A cloud-based data exploration tool that allows users to create visualizations and dashboards from various data sources without needing to write SQL. 10. {{[[IBM Cognos Analytics]]:https://www.ibm.com/analytics/cognos-analytics}} : A business intelligence tool that provides data visualization, reporting, and dashboard capabilities, powered by AI. These tools cater to various business needs, from simple reporting to complex data analysis and visualization.

The best data visualization tools for business in 2025 include: {{[[Microsoft Power BI]]:https://mammoth.io/blog/best-data-visualization-tools/}}: Affordable, integrates well with Microsoft products like Excel and Teams, offers AI-powered insights and natural language queries, suitable for users in the Microsoft ecosystem. {{[[Tableau]]:https://mammoth.io/blog/best-data-visualization-tools/}}: Industry-leading for advanced analytics and complex visualizations, highly customizable with a strong community, ideal for large analytics teams and sophisticated dashboards. {{[[Google Looker Studio]]:https://mammoth.io/blog/best-data-visualization-tools/}}: Free with solid functionality, excellent integration with Google services, good for small teams needing simple reporting and real-time collaboration. {{[[Qlik Sense]]:https://mammoth.io/blog/best-data-visualization-tools/}}: Known for its associative analytics engine, intuitive data exploration, mobile-first design, and advanced security, best for self-service analytics. {{[[Domo]]:https://www.domo.com/learn/article/visual-reporting-tools}}: Cloud-based with a user-friendly drag-and-drop interface, over 150 chart types, enables interactive dashboards without deep technical skills, suitable for democratizing data across organizations. {{[[Holistics]]:https://www.holistics.io/blog/best-data-visualization-tools/}}: Focuses on treating BI like software with dashboard-as-code, version control, and semantic modeling, good for teams wanting full control over metrics and embedded analytics. {{[[Canva]]:https://learn.g2.com/best-data-visualization-software}}: Best for quick, visually appealing data visualizations with an intuitive drag-and-drop interface and many templates, ideal for beginners and fast design needs. Other notable mentions include {{[[Tableau Public]]:https://careerfoundry.com/en/blog/data-analytics/free-data-viz-tools/}}, {{[[Infogram]]:https://careerfoundry.com/en/blog/data-analytics/free-data-viz-tools/}}, {{[[Seaborn]]:https://careerfoundry.com/en/blog/data-analytics/free-data-viz-tools/}}, and {{[[Plotly]]:https://careerfoundry.com/en/blog/data-analytics/free-data-viz-tools/}} for free or open-source options. Additionally, AI-powered tools such as {{[[ThoughtSpot]]:https://www.thoughtspot.com/data-trends/ai/ai-tools-for-data-visualization}}, {{[[Tableau with Einstein AI]]:https://www.lumi-ai.com/ai-glossary/best-ai-data-visualization-tools-for-enterprises-in-2025}}, and {{[[Microsoft Power BI Copilot]]:https://www.lumi-ai.com/ai-glossary/best-ai-data-visualization-tools-for-enterprises-in-2025}} are emerging to enhance data visualization with AI capabilities.

Here are the best data visualization tools for business: 1. {{[[Tableau]]:https://www.tableau.com}} : Industry-leading visualization platform with powerful interactive dashboards and easy data integration. 2. {{[[Microsoft Power BI]]:https://www.microsoft.com/en-us/power-platform/products/power-bi/}} : Robust business intelligence tool with strong Excel and Azure ecosystem integration. 3. {{[[Looker]]:https://www.looker.com}} : Advanced data exploration platform with strong enterprise-level analytics capabilities. 4. {{[[Qlik Sense]]:https://www.qlik.com/us/products/qlik-sense}} : Flexible visualization tool with AI-powered insights and comprehensive data discovery features. 5. {{[[Domo]]:https://www.domo.com}} : Cloud-based platform offering real-time data visualization and mobile-friendly dashboards. 6. {{[[Google Data Studio]]:https://datastudio.google.com}} : Free tool with excellent integration with Google ecosystem and customizable reporting. 7. {{[[Sisense]]:https://www.sisense.com}} : Advanced analytics platform with embedded intelligence and complex data handling capabilities. 8. {{[[Datawrapper]]:https://www.datawrapper.de}} : User-friendly tool ideal for creating quick charts and infographics with minimal technical skills. Each tool offers unique strengths for different business visualization needs, ranging from complex enterprise solutions to simple, intuitive interfaces.

Here are some of the best data visualization tools for business, offering diverse capabilities for various needs: {{[[Tableau]]:https://www.tableau.com/}} : Known for its intuitive drag-and-drop interface, powerful interactive dashboards, and ability to connect to a wide range of data sources, {{[[Tableau]]:https://www.tableau.com/}} excels in data exploration and creating visually appealing, shareable insights. {{[[Microsoft Power BI]]:https://powerbi.microsoft.com/}} : A robust business intelligence tool offering interactive visualizations and BI capabilities with strong integration with other Microsoft products (Excel, Azure, SQL Server). {{[[Microsoft Power BI]]:https://powerbi.microsoft.com/}} is often praised for its cost-effectiveness, especially for organizations already invested in the Microsoft ecosystem. {{[[Qlik Sense]]:https://www.qlik.com/us/products/qlik-sense}} : Features a unique associative engine that allows users to explore data freely, revealing hidden insights and relationships that query-based tools might miss. {{[[Qlik Sense]]:https://www.qlik.com/us/products/qlik-sense}} excels in self-service analytics and governed data discovery. {{[[Looker Studio]]:https://lookerstudio.google.com/}} : (formerly Google Data Studio) A free, cloud-based tool that enables users to create customizable, interactive dashboards and reports. {{[[Looker Studio]]:https://lookerstudio.google.com/}} integrates seamlessly with Google's marketing and analytics products (Google Analytics, Google Ads, Google Sheets) and offers easy sharing and collaboration. {{[[Sisense]]:https://www.sisense.com/}} : Specializes in embedded analytics, allowing businesses to integrate powerful BI capabilities directly into their applications and workflows. {{[[Sisense]]:https://www.sisense.com/}} handles large

Here’s a clear distinction, plus concrete **enterprise examples** and a **whitepaper reference**. --- ## Generative UI vs. Chatbots **Chatbots** * **Interface**: primarily conversational, linear, text-first (sometimes with voice). * **User control**: users issue commands/questions directly in natural language. * **Scope**: often broad, but shallow; suited for Q\&A, support flows, and transactional tasks. * **Limitations**: harder to handle complex, multi-step workflows; prone to prompt variability. **Generative UI** * **Interface**: dynamic user interface *shaped by AI outputs*. Instead of staying in a chat window, the model builds or modifies structured UI components (tables, dashboards, forms, flows). * **User control**: interactions are mediated by familiar UI patterns (sliders, filters, sidebars, previews) while the prompts run behind the scenes. * **Scope**: tailored to domain tools (analytics, CRM, design apps); enables complex workflows by *fusing chat, controls, and visual layouts*. * **Advantage**: safer, repeatable, auditable — and more easily integrated with enterprise processes. --- ## Enterprise Examples * **Microsoft Power BI Copilot**: Users type “show me revenue trends by region” → AI generates a chart + filters in the Power BI canvas, not just a text response. * **Salesforce Einstein Copilot Studio**: AI builds forms, workflow automations, or dashboard components; the UI adapts around business data. * **ServiceNow Generative AI Controller**: Instead of free-form chat, it generates guided actions in the ITSM UI (incident forms, workflow steps). * **SAP Joule**: Embedded generative UI in ERP — user intent translates into dashboards, KPI drilldowns, or automated workflows. --- ## Whitepaper Reference * **Nielsen Norman Group (NN/g): “Generative UI: Designing Outcome-Oriented Interfaces”** — argues that moving beyond chatbots into generative UI is crucial for enterprise-grade UX, especially where reliability and domain workflows matter. [NN/g Whitepaper link](https://www.nngroup.com/reports/generative-ui/) --- ✅ **In short:** Chatbots are conversational agents; **Generative UI** embeds AI into the fabric of the interface, creating adaptive, structured, multimodal workflows. Enterprises (Microsoft, Salesforce, SAP, ServiceNow) are already implementing these patterns. ---

**Generative UI** refers to AI-driven interfaces that dynamically generate and assemble UI components—such as dashboards, forms, visualizations, and context-aware controls—tailored to each user's needs or tasks, in real time. **Chatbots**, by contrast, are conversational agents that interact with users primarily through natural language text (or sometimes voice), offering responses and actions in a linear, dialog-based format.[1][2][3] ## Key Differences - **Interaction Model:** Generative UI offers a multimodal, task-centric, and outcome-driven user experience, assembling concrete interface elements for direct manipulation. Chatbots center on dialog, requiring users to type commands or questions and receive answers as text or links.[2][1] - **Adaptivity:** Generative UI adapts interface layouts, widgets, and workflows contextually and visually per user or scenario. Chatbots deliver the same linear chat interface to all, regardless of user context.[2] - **Workflow Efficiency:** Generative UI allows users to execute complex actions (like analytics, scheduling, or report generation) through interactive UIs generated on demand. Chatbots handle such tasks via sequential Q&A and manual follow-up, often requiring multiple turns for even simple workflows.[1][2] ## Enterprise Examples - **Finance:** Instead of asking a chatbot several follow-ups to build a quarterly report, a manager using Generative UI can enter an intent ("analyze Q3 expenses"), at which point the system instantly generates a tailored dashboard with interactive charts, filters, and drilldowns that can be shared or adjusted interactively.[1] - **Data Analysis:** Generative UI enables users to upload, explore, and filter data tables visually—whereas a chatbot would have to describe or output data as text, requiring copy-paste or additional instructions for deeper exploration.[2] - **Scheduling:** Generative UI presents a generated calendar or meeting interface in context, allowing instant confirmation; chatbots conduct multi-turn dialogue to gather scheduling preferences.[2] - **Internal Tools (e.g., Thesys C1):** Enterprises are moving from chatbots toward platforms where AI generates micro-apps or panels on demand, boosting productivity, collaboration, and interface personalization.[1] ## Whitepaper Source - See the in-depth analysis and case studies in the Thesys whitepaper: **"Why Generative UIs Outperform Chatbots for Enterprise Productivity"**.[1] **References:** - Why Generative UIs Outperform Chatbots for Enterprise Productivity (Thesys)[1] - Agentic Interfaces in Action: How Generative UI Turns AI from Chatbot to Co-Pilot (Thesys)[2] - Conversational AI vs. Generative AI: Understanding the Key Differences (K2View)[3] [1](https://www.thesys.dev/blogs/why-generative-uis-outperform-chatbots-for-enterprise-productivity) [2](https://www.thesys.dev/blogs/agentic-interfaces-in-action-how-generative-ui-turns-ai-from-chatbot-to-co-pilot) [3](https://www.k2view.com/blog/conversational-ai-vs-generative-ai/) [4](https://quokkalabs.com/blog/conversational-ai-vs-generative-ai/) [5](https://www.nimbleappgenie.com/blogs/generative-ai-vs-conversational-ai-vs-chatbot/) [6](https://www.webio.com/faq/difference-between-generative-ai-and-conversational-ai) [7](https://workativ.com/ai-agent/blog/generative-ai-enterprise-pros-cons) [8](https://www.csgi.com/insights/what-is-the-difference-between-chatbot-and-generative-ai/) [9](https://www.sciencedirect.com/science/article/pii/S0007681324000582)

**Generative UI** refers to AI-driven interfaces that dynamically generate and assemble UI components—such as dashboards, forms, visualizations, and context-aware controls—tailored to each user's needs or tasks, in real time. **Chatbots**, by contrast, are conversational agents that interact with users primarily through natural language text (or sometimes voice), offering responses and actions in a linear, dialog-based format.[1][2][3] ## Key Differences - **Interaction Model:** Generative UI offers a multimodal, task-centric, and outcome-driven user experience, assembling concrete interface elements for direct manipulation. Chatbots center on dialog, requiring users to type commands or questions and receive answers as text or links.[2][1] - **Adaptivity:** Generative UI adapts interface layouts, widgets, and workflows contextually and visually per user or scenario. Chatbots deliver the same linear chat interface to all, regardless of user context.[2] - **Workflow Efficiency:** Generative UI allows users to execute complex actions (like analytics, scheduling, or report generation) through interactive UIs generated on demand. Chatbots handle such tasks via sequential Q&A and manual follow-up, often requiring multiple turns for even simple workflows.[1][2] ## Enterprise Examples - **Finance:** Instead of asking a chatbot several follow-ups to build a quarterly report, a manager using Generative UI can enter an intent ("analyze Q3 expenses"), at which point the system instantly generates a tailored dashboard with interactive charts, filters, and drilldowns that can be shared or adjusted interactively.[1] - **Data Analysis:** Generative UI enables users to upload, explore, and filter data tables visually—whereas a chatbot would have to describe or output data as text, requiring copy-paste or additional instructions for deeper exploration.[2] - **Scheduling:** Generative UI presents a generated calendar or meeting interface in context, allowing instant confirmation; chatbots conduct multi-turn dialogue to gather scheduling preferences.[2] - **Internal Tools (e.g., Thesys C1):** Enterprises are moving from chatbots toward platforms where AI generates micro-apps or panels on demand, boosting productivity, collaboration, and interface personalization.[1] ## Whitepaper Source - See the in-depth analysis and case studies in the Thesys whitepaper: **"Why Generative UIs Outperform Chatbots for Enterprise Productivity"**.[1] **References:** - Why Generative UIs Outperform Chatbots for Enterprise Productivity (Thesys)[1] - Agentic Interfaces in Action: How Generative UI Turns AI from Chatbot to Co-Pilot (Thesys)[2] - Conversational AI vs. Generative AI: Understanding the Key Differences (K2View)[3] [1](https://www.thesys.dev/blogs/why-generative-uis-outperform-chatbots-for-enterprise-productivity) [2](https://www.thesys.dev/blogs/agentic-interfaces-in-action-how-generative-ui-turns-ai-from-chatbot-to-co-pilot) [3](https://www.k2view.com/blog/conversational-ai-vs-generative-ai/) [4](https://quokkalabs.com/blog/conversational-ai-vs-generative-ai/) [5](https://www.nimbleappgenie.com/blogs/generative-ai-vs-conversational-ai-vs-chatbot/) [6](https://www.webio.com/faq/difference-between-generative-ai-and-conversational-ai) [7](https://workativ.com/ai-agent/blog/generative-ai-enterprise-pros-cons) [8](https://www.csgi.com/insights/what-is-the-difference-between-chatbot-and-generative-ai/) [9](https://www.sciencedirect.com/science/article/pii/S0007681324000582)

Here’s a compact set of **Generative-UI design patterns** that work well in domain/enterprise tools, with solid references for each: * **Ask → Edit → Agent (mode switching)**: start with Q\&A, escalate to draft edits, then let an agent execute bounded tasks; always expose scope & rollback. ([The GitHub Blog][1]) * **Sidecar copilot (in-app focus)**: persistent right/left pane that stays in the user’s workflow with quick actions, context chips, and safe “apply” controls. ([Microsoft Learn][2]) * **Structured prompt builders**: form-like inputs, parameters, and constraints instead of raw free text for repeatable, auditable outcomes. ([pair.withgoogle.com][3]) * **Plan → Preview → Apply (diff-first)**: show a plan and a visual diff before changes; require explicit confirmation and provide undo/versioning. ([UX Collective][4]) * **Outcome-oriented canvases**: users state intent; UI composes the steps/components and adapts layout dynamically to reach the goal. ([Nielsen Norman Group][5]) * **Tuners & Governors**: adjustable sliders/switches to steer style/strictness and hard guardrails for safety (scope, data boundaries). ([shapeof.ai][6]) * **Confidence & provenance affordances**: show “draft” labels, uncertainty/confidence hints, and citations/traceback for generated results. ([pair.withgoogle.com][3], [Nielsen Norman Group][7]) * **Inline suggestions**: non-modal, inline proposals (e.g., code/text/table rows) that are easy to accept/modify/reject. ([UX Collective][4]) * **Error-aware recovery**: obvious escape hatches—revert, manual override, escalate to human, and targeted re-prompts. ([UX Collective][4]) * **Domain tool integration (actions & tools)**: expose verbed buttons the agent can call (simulate first, then run) with scoping and audit logs. ([Microsoft Learn][8]) * **Copilot UX principles (human-in-control, no anthropomorphism)**: set expectations, show limits, and keep the human as pilot. ([Microsoft Learn][2]) * **Generative UI primers & exemplars**: roundups and visual catalogs to inform component choices across enterprise contexts. ([UX Planet][9], [Medium][10]) * **Domain analytics copilots (pattern transfer)**: apply the same patterns to BI/analytics UIs (query → draft insight → refine → apply). ([Microsoft Learn][11]) * **Cloud app UX for gen-AI**: Google’s guidance on task framing, handoffs to deterministic flows, and guardrails for agents. ([Google Cloud][12]) ## References (handy starting points) * NN/g: *Generative UI & Outcome-Oriented Design*. ([Nielsen Norman Group][5]) * Microsoft: *Copilot UX guidance (ISV)* and *Copilot Studio agent guidance*. ([Microsoft Learn][2]) * Google PAIR: *People + AI Guidebook*. ([pair.withgoogle.com][3]) * GitHub: *Copilot Ask, Edit, Agent modes*. ([The GitHub Blog][1]) * Whitespectre/UX catalogs: *Emerging Gen-AI UI patterns*. ([Medium][10]) * UX Planet: *Generative-AI UX design patterns* (system/scope/spatial lenses). ([UX Planet][9]) * Designing for Analytics (enterprise LLM UX considerations). ([designingforanalytics.com][13]) * Shape of AI: pattern taxonomy (Wayfinders, Tuners, Governors, Trust builders). ([shapeof.ai][6]) * Google Cloud: *UX considerations for gen-AI apps* + *101 blueprints* (design + architecture). ([Google Cloud][12]) * Microsoft Power BI Copilot (analytics copilot UI). ([Microsoft Learn][11]) Want me to turn these into a checklist you can drop into your design system (with component examples)? [1]: https://github.blog/ai-and-ml/github-copilot/copilot-ask-edit-and-agent-modes-what-they-do-and-when-to-use-them/?utm_source=chatgpt.com "Copilot ask, edit, and agent modes: What they do and ..." [2]: https://learn.microsoft.com/en-us/microsoft-cloud/dev/copilot/isv/ux-guidance?utm_source=chatgpt.com "Creating a dynamic UX: guidance for generative AI ..." [3]: https://pair.withgoogle.com/guidebook/?utm_source=chatgpt.com "People + AI Guidebook - Home" [4]: https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1?utm_source=chatgpt.com "20+ GenAI UX patterns, examples and implementation tactics" [5]: https://www.nngroup.com/articles/generative-ui/?utm_source=chatgpt.com "Generative UI and Outcome-Oriented Design" [6]: https://www.shapeof.ai/?utm_source=chatgpt.com "The Shape of AI | UX Patterns for Artificial Intelligence Design" [7]: https://www.nngroup.com/articles/genai-ux-research-agenda/?utm_source=chatgpt.com "A Research Agenda for Generative AI in UX" [8]: https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/?utm_source=chatgpt.com "Microsoft Copilot Studio guidance documentation" [9]: https://uxplanet.org/generative-ai-ux-design-patterns-192bb169ab99?utm_source=chatgpt.com "Generative AI UX Design Patterns" [10]: https://medium.com/whitespectre/emerging-ui-ux-patterns-in-generative-ai-a-visual-guide-74000c7198a4?utm_source=chatgpt.com "Emerging UI/UX Patterns in Generative AI - Medium" [11]: https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-introduction?utm_source=chatgpt.com "Overview of Copilot for Power BI" [12]: https://cloud.google.com/blog/products/ai-machine-learning/how-to-build-a-genai-application?utm_source=chatgpt.com "UX considerations for generative AI apps and agents" [13]: https://designingforanalytics.com/resources/ui-ux-design-for-enterprise-llms-use-cases-and-considerations-for-data-and-product-leaders-in-2024-part-1/?utm_source=chatgpt.com "UI/UX Design Considerations for LLMs in Enterprise ..."

The most common **design patterns** for Generative UI in domain tools center on dynamic UI construction, adaptation to user context, seamless integration with design systems, and maintaining interactive and accessible experiences.[1][2][3][4] ## Key Generative UI Patterns - **Pattern-Based Generation:** Define a library of standardized, atomic components (e.g. buttons, tables, forms) which the generative AI combines per user scenario, ensuring consistency and theme alignment.[2][3] - **Prompt-to-UI Mapping:** Use structured prompts or intent models to translate user descriptions or requirements into structured UI definitions, often as JSON or similar representations that map directly to design system components.[3][2] - **Live Contextual Adaptation:** UIs that adapt in real time to changing user context, data, or workflow requirements—such as resizing, adjusting compositional layouts, or changing fields based on current task flow.[4][1] - **Outcome-Oriented Design:** Rather than focusing on static templates, generative UIs aim for task, workflow, or outcome optimization—surfacing controls and data most relevant for the user’s stated or inferred goals.[5][4] - **Validation and Safety Guardrails:** Implement rigorous testing and output validation, such as capping array/table sizes or checking that the generative model’s output adheres to allowed types, to ensure UI stability and prevent runtime errors.[3] - **Seamless Design System Integration:** Align all generated UI elements to a design system’s color, spacing, and typography tokens so they feel native and maintain branding and accessibility standards.[2][3] ## Best Practices - Build with a **dedicated design system** for Generative UI (like Thesys "Crayon"), emphasizing accessibility, interactivity, and responsive layouts.[2] - Use **iterative QA and user feedback** cycles, starting with pilot projects before broad rollouts, and always log AI outputs for ongoing improvement.[3] - Treat all AI-generated component specifications as production code, requiring validation, error handling, and secure sandboxing.[4][3] ## Further Reading and References - [20+ GenAI UX Patterns, Examples, and Implementation Tactics (UXDesign)](https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1)[1] - [Technical Architecture: First Generative UI API (Thesys)](https://www.thesys.dev/blogs/generative-ui-architecture)[2] - [Enterprise Readiness for Generative UI (Thesys)](https://www.thesys.dev/blogs/getting-enterprise-ready-for-generative-ui-what-to-know-before-you-build)[3] - [Generative UI Guide 2025: 15 Best Practices & Examples (Mockplus)](https://www.mockplus.com/blog/post/generative-ui)[4] - [Generative UI and Outcome-Oriented Design (NN/G Nielsen Norman Group)](https://www.nngroup.com/articles/generative-ui/)[5] These sources offer both tactical implementation advice and strategic frameworks for leveraging generative UIs in business and enterprise software contexts.[1][5][4][2][3] [1](https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1) [2](https://www.thesys.dev/blogs/generative-ui-architecture) [3](https://www.thesys.dev/blogs/getting-enterprise-ready-for-generative-ui-what-to-know-before-you-build) [4](https://www.mockplus.com/blog/post/generative-ui) [5](https://www.nngroup.com/articles/generative-ui/) [6](https://dl.acm.org/doi/10.1145/3715336.3735780) [7](https://www.mockplus.com/blog/post/generative-ai-tools) [8](https://arxiv.org/html/2505.15049v1) [9](https://www.shapeof.ai) [10](https://www.elsewhen.com/reports/from-generative-ai-to-generative-ui/) [11](https://uxplanet.org/generative-ai-ux-design-patterns-192bb169ab99) [12](https://www.nngroup.com/articles/genai-ux-research-agenda/) [13](https://martinfowler.com/articles/gen-ai-patterns/) [14](https://www.coveo.com/blog/generative-ai-best-practices/) [15](https://www.thesys.dev/blogs/how-generative-ui-is-transforming-internal-tools-across-the-enterprise) [16](https://www.getaprototype.com/knowledge-base/generative-ui-design) [17](https://www.k2view.com/blog/what-is-a-best-practice-when-using-generative-ai/) [18](https://uxdesign.cc/designing-for-ai-engineers-what-ui-patterns-and-principles-you-need-to-know-8b16a5b62a61) [19](https://devpulse.io/insights/ux-ui-design-best-practices-2025-enterprise-applications/) [20](https://uxplanet.org/7-key-design-patterns-for-ai-interfaces-893ab96988f6)

I'll search for information about design patterns for Generative UI in domain-specific tools.Let me search for more specific information about design patterns for generative UI in domain-specific applications.Based on my search, here are the key design patterns for Generative UI in domain tools: ## Core Design Patterns for Generative UI ### 1. **Outcome-Oriented Design Pattern** In the future, generative UI will dynamically create customized user interfaces in real-time. This shift will force an outcome-oriented design approach where designers prioritize user goals and define constraints (for AI to operate within), rather than design discrete interface elements. ### 2. **Constraint-Based Generation Pattern** Rather than designing specific UI elements, designers focus on: - Defining user goals and objectives - Setting operational constraints for AI systems - Allowing dynamic interface creation within those boundaries ### 3. **Iterative Refinement Pattern** Modern generative UI tools support iterate easily on your designs and edit your generated screens in seconds, enabling rapid prototyping and refinement cycles. ### 4. **Multi-Modal Generation Pattern** Image generation tools for new and visually appealing images based on existing patterns or styles. Sound generation tools for melodies, harmonies and even entire compositions - supporting comprehensive multimedia interface generation. ## Key Implementation Strategies ### Real-Time Adaptation The goal of these patterns is to help mitigate and overcome some of the challenges with generative AI implementations such as cost, latency and hallucinations. ### Enterprise Integration With the latest 2024 Q2 release, Progress brings generative AI integration, enhanced design system capabilities and accessible data visualization. ## Key Resources and Tools **Research and Guidelines:** - [Nielsen Norman Group - Generative UI and Outcome-Oriented Design](https://www.nngroup.com/articles/generative-ui/) - [ACM CHI 2024 - Design Principles for Generative AI Applications](https://dl.acm.org/doi/10.1145/3613904.3642466) - [Towards Data Science - Generative AI Design Patterns Guide](https://towardsdatascience.com/generative-ai-design-patterns-a-comprehensive-guide-41425a40d7d0/) **Tools and Platforms:** - [UX Pilot - AI UI Generator](https://uxpilot.ai/) - [Telerik/Progress - Enterprise AI Integration](https://www.telerik.com/blogs/telerik-kendo-ui-2024-q2-release-here-see-whats-new) - [Merge - AI Design Tools for 2025](https://merge.rocks/blog/top-ai-design-tools-for-ux-ui-designers-in-2025) **Industry Analysis:** - [Mockplus - 22 Best Generative AI Tools for UI/UX](https://www.mockplus.com/blog/post/generative-ai-tools) - [UXPin - UI/UX Design Trends 2025](https://www.uxpin.com/studio/blog/ui-ux-design-trends/) The trend shows a shift from traditional component-based design to constraint-based, outcome-oriented approaches where AI generates interfaces dynamically based on user goals and contextual requirements.