AI in UX: a truly ultimate guide to better UX of digital products with AI

30 min
Yuriy Mykhasyak
Yuriy Mykhasyak
18 Oct 2023
AI in UX: a truly ultimate guide to better UX of digital products with AI Cieden
Table of content

Artificial intelligence (AI) is reshaping the very fabric of human interactions with digital products. With a skyrocketing AI software market, everyone — be it a CEO, a product owner, or a junior designer — needs to get on board with using AI in UX.

So how about we dive into the AI principles in UX design together? You will learn more about key AI advancements, their impact on UX, and the power they hold to push digital products further. All of it is served with real-world examples — both as implemented AI in a final product and as AI tools used to speed up the design process.

This guide is a summary of Cieden’s observations of AI technology in the past years. Not only have we observed; we’ve actively used our knowledge to implement AI in design.

Now grab your coffee, and let’s explore the future of UX with AI technology.

How far along are companies with AI?

Over the years, the history of tech advancements within the digital landscape has rapidly progressed. Top companies have embraced technology in diverse ways.

a timeline of the latest pioneering breakthroughs in the tech industry (1984 - 2025).
This journey through various technological epochs, from the early days of the internet to the rise of cloud computing, has paved the way for the next significant leap in business technology: the integration of artificial intelligence. Based on a May 2023 Forrester Research survey, businesses eager to integrate AI are spreading their investments across various tech niches. The survey, polling 1,981 global data and analytics leaders, reveals machine learning platforms (77%), machine vision (76%), and AutoML (76%) as the primary investment hotspots.
Forrester Research statistics on implementing AI among businesses (May 2023).
Despite going through multiple AI breakthroughs over the last four decades, the journey is not complete. Businesses continue investing in tech niches like machine learning (ML) changing the way humans interact with computers. Next up, learn how AI is used to redefine user interaction in real-life digital products.

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AI in UX: changes to human-computer interaction

AI and UX are converging, transforming how users interact with digital products. This shift isn't just about new features; it's about reimagining the user experience. AI's integration into UX design is making products smarter, more intuitive, and highly responsive to user needs. 

Consider Duolingo, which employs AI to customize language learning paths, or Grammarly, which uses advanced NLP to provide real-time writing assistance, going beyond basic spell check. These apps, alongside countless others, demonstrate a proactive approach, setting new standards in user engagement and satisfaction.

For product developers and managers, this means adapting to a landscape where AI-driven interfaces are becoming the norm. 

Computers understand and speak human language

The latest tech breakthrough has changed forever the way we interact with machines. Natural Language Processing (NLP) became a bridge between computers and humans enabling both to interact using natural language. It is the NLP that allows computers to understand, interpret, and generate human language.

Traditionally, NLP relied primarily on text-based inputs, processing them to generate text-based outputs. Now, NLP has evolved to encompass auditory and visual tasks like speech recognition, speech synthesis, text-to-speech, and image captioning. Researchers have also delved into multi-modal settings for tasks like sentiment (emotional) analysis.

visual representation of how programming progressed from machine code to AI-driven communication.

The evolution of NLP is evident in the increased adoption of Conversational User Interfaces (CUI). In the world of e-commerce and customer support, chatbots and virtual assistants have become the norm, using NLP to provide contextual, real-time assistance. 

In personalizing user experiences, NLP is revolutionizing how e-commerce and streaming services tailor content to user preferences. By analyzing inputs, Netflix and Spotify provide almost intuitive recommendations, boosting user engagement. Additionally, NLP's role in enhancing accessibility is significant, broadening access for users with visual or cognitive impairments and fostering a more inclusive digital environment.

This shift towards conversational interfaces is more than technical advancement; it reflects a deeper understanding of user needs and behaviors.

Multimodal interfaces redefine user experience

As of 2024, multimodal technologies are reshaping the look and feel of digital product interfaces, ensuring an immersive, cohesive experience across multiple devices. Combining various modes of input (text, voice, touch, or gestures) and output (like visual, auditory, or haptic responses) multimodal technologies facilitate more natural and intuitive communication with digital systems.

For instance, smart assistants have evolved to understand voice commands, recognize images, interpret text, and even perceive emotions, enabling users to engage in more human-like dialogue.

a list of companies leveraging multimodal interactions with dates of release and application.

Smart assistance and flow optimization by AI agents

AI agents are redefining user experiences by enhancing user flows, automating tasks, and offering smart assistance. These agents, powered by artificial intelligence, not only perform tasks on behalf of users but also learn from interactions to provide more personalized experiences over time.

A great example of this is the innovative startup HyperWrite. It revealed an AI agent capable of navigating and interacting with websites. CEO Matt Shumer demonstrated the AI's abilities, such as completing an online order on Domino's Pizza, via their Chrome extension. 

HyperWrite's AI learns from past interactions and promises to usher in a new era of web automation and personalized assistance.

Matt Shumer’s tweet about releasing HyperWriteAI personal assistant.

Though promising in automating web tasks, concerns around security like phishing, hacking, and fraud were addressed, with Shumer ensuring they are working on these issues. 

The current generation of assistants primarily functions through text analysis rather than computer vision and is limited by small context windows. However, with enhanced training datasets and integration with GPT-4, which can process hundreds of thousands of tokens, their performance will significantly improve. Thus, the question isn't whether AI agents can be enhanced, but rather when this advancement will occur – whether it will be within a year or even in the next six months.

No more inaccurate support due to speech recognition technologies

– You: "Hey, Siri, lower the lights.

– Siri: "Sorry, I can't find the song 'Love is the light' in your music history."

Don’t we all hate when this happens? Hopefully, things are going to be much better in the near future. Speech recognition is the ultimate marriage of NLP and AI, bringing us closer to a world where computers can understand and transcribe human speech with ease. As for now, it is possible to accurately transcribe spoken words, even in noisy environments or with different accents.

Fortune Business Insights projects that the global automatic speech recognition market size is expected to grow from $12.62 billion in 2023 to $59.62 billion by 2030, at a CAGR of 23.7% during this period (2023-2030).

Trends in automatic speech recognition: friendly smart cards, consumer electronic devices and speed recognition for kids.

One of the multiple сases of speech recognition enhancements is Whisper AI, a cutting-edge AI model that cuts transcription errors by 50%, effectively dealing with accents, background noise, and complex vocabulary. It can transcribe in 99 languages and translate them to English. 

It was trained on 680,000 hours of multilingual internet data and includes extensive punctuation support. Whisper stands out from earlier OpenAI models like DALLE-2 and GPT-3 as it's open-source and freely available.

Historically, transcription methods were often inaccurate and lacked language support. Whisper overcomes these limitations, offering high accuracy and extensive language support.

This innovation can be used across various industries. In customer service, it's great for quickly resolving issues. Thanks to its broad language support, Whisper becomes invaluable in international business, education, and diplomacy. It effectively eliminates language barriers, facilitating smooth conversations with international partners and allowing for participation in university lectures abroad.

AI’s role in food ordering automation

Advances in artificial intelligence, particularly in language models such as LLM and ChatGPT, are transforming customer service and order processing in the food industry. 

As illustrated in our simulation, these models can mimic the nuance and adaptability of human conversation, ensuring customers feel understood and catered to. As these technologies become more prevalent, we can anticipate a future where the boundary between human and automated customer service becomes increasingly blurred.

Testing ChatGPT as a MacDonald’s cashier mimicking human conversation.

With the acquisition of Apprente, a start-up specializing in voice-based, McDonald's aims to automate its drive-thru ordering process. This AI-powered system can understand multiple languages and accents, decipher complex orders, and operate seamlessly in noisy environments. However, it is not devoid of initial teething problems. Customers have been quick to share humorous anecdotes on social media platforms like TikTok, recounting instances of misunderstood orders. 

Taco Bell recently showcased a concept restaurant featuring four drive-thru lanes, with orders delivered via a 'vertical lift' from the kitchen.

Starbucks utilizes its AI program, Deep Brew, to create personalized customer experiences, manage inventory, and assist in-store placement strategy.

This AI solution also automates tasks like preventive maintenance on IoT-connected espresso machines. As a result, Starbucks has transformed into a data-driven company, leading to significant growth (for example, a 6% increase in same-store sales growth in the U.S.).

According to a McKinsey study, automating customer interactions could reduce the time taken to serve a customer by 20%, drastically improving service efficiency.

Further, envision a future where browsing through menus becomes redundant. With AI advancements, customers could potentially communicate their preferences via voice or text.

The era of Generative AI breakthroughs

Generative AI (GenAI) has made significant leaps forward and its influence is far-reaching. Industries such as healthcare, retail, and manufacturing are employing generative AI to predict future scenarios, optimize processes, and customize products. 

For instance, in healthcare, generative AI can help generate digital models of human organs for better diagnoses and surgical planning. In retail, it is used to personalize product designs, catering to each consumer.

An IDC report predicts that by 2026, 50% of EMEA organizations are expected to adopt Generative AI to boost human creativity in co-designing products and services. This integration is projected to cut the time to market for new products and services in half.

AI’s impact on photo-related applications 

In the rapidly advancing realm of AI, breakthroughs in photo-related applications are becoming increasingly prominent. According to data from PitchBook, funding for generative AI startups soared to $21.4 billion this year through September 30, a significant increase from $5.1 billion in 2022, which indicates a flourishing interest.

Photo-related AI startups are drawing a significant share of this investment. Companies like Luminar AI and, which leverage AI for photo editing and artistic effects, have attracted considerable funding. Similarly, MyHeritage, which uses AI to animate historical photographs, secured a substantial investment in its latest funding round.

list of AI startups at the forefront of the photo industry with their key features and notable achievements.

The influx of VC funding in this field underscores the perceived potential and lucrative opportunities that AI-powered photo startups offer. The appeal lies not only in the direct consumer applications but also in the vast array of industries these startups could revolutionize – from advertising and real estate to fashion and entertainment.

The latest breakthroughs in image generation

Stable Diffusion XL offers a transformative experience for non-artists. The rapidly growing open-source AI platform empowers non-artists to create descriptive 3D images with shorter prompts. The platform boasts next-level photorealism capabilities and enhances image composition and face generation.

Example of Stable Diffusion XL enhancing image composition to 3D illustrations.

Source: Gigazine

Adobe Firefly is a state-of-the-art AI tool that transforms the way we create multimedia content. Using natural language processing, it allows users to describe their desired artwork in words, and the tool brings these visions to life. Its deep learning algorithm understands the text's intent, producing high-quality visuals that match your description. Firefly excels in enhancing images, applying styles, and adding textures to letters and objects. It also features a selection of inspirational.

Example of Adobe Firefly changing the image based on the inquiry described in words.

Source: Adobe Firefly

Deep Agency, an AI-powered virtual photo studio and modeling agency, is at the forefront of a rapidly developing and controversial industry. The platform provides “virtual models” for hire, which can be used in diverse settings, from social media content to e-commerce product photography.

Example of Deep Agency virtual models used in diverse online content.

Source: Deep Agency

Video enhancements developed by AI

The video industry has also witnessed ground-breaking achievements by AI-powered startups in recent years. For instance, Nvidia Maxine has redefined the video conferencing landscape with its audio effects (speaker focus, noise removal, room echo removal, acoustic echo cancellation) and video effects (virtual background, eye contact) and up to 4X resolution quality improving.

Example of NVIDIA Maxine reposing eye contact during a video conference.


D-ID, with its AI-based video personalization capabilities, has added a new dimension to digital advertising, fostering increased customization and engagement.

a timeline of the latest pioneering breakthrough in video industry.

According to a recent report by Grand View Research, Inc., the worldwide video streaming market is projected to grow to USD 416.84 billion by 2030. This represents an impressive annual growth rate of 21.5% from 2023 to 2030. Key technological developments, such as the integration of blockchain technology in video streaming and the use of Artificial Intelligence (AI) to enhance video quality, are anticipated to drive significant growth in the video streaming market during this period.

Considering the rise of advanced AI tools like ChatGPT, which could potentially replace most of informational Google search queries, there's a concurrent surge in the significance of video format content in companies’ marketing strategy. This trend is underscored by the viral success and explosive growth of platforms like TikTok and Instagram Reels, reflecting the enormous, proven potential of AI to shape and cater to the dynamic demands of the digital landscape.

Advancements in AI are also enhancing the functionality and quality of meeting management software, making virtual meetings more efficient and user-friendly. At Cieden, we've launched a series of videos exploring AI concepts and how AI can significantly improve software. One of our videos focuses specifically on how AI can enhance video meeting software, demonstrating the potential for smarter, more interactive, and more engaging virtual meetings.

Watch our video for insightful perspectives on leveraging AI to elevate your digital product.

AI in UX: a truly ultimate guide to better UX of digital products with AI Cieden

AI’s transformative power in app development

AI democratizes app development. By blending its advanced capabilities with the ease of low-code systems, it's opening up app creation to a broader audience. Now, even those without deep coding knowledge can build AI-enhanced apps, bringing sophisticated functionalities like machine learning and natural language processing to the table. 

This means quicker development cycles, cost-effective solutions, and the ability to rapidly adapt to market changes. With these tools, businesses can scale their software to meet growing demands without a surge in resources, ensuring apps perform efficiently and intelligently. This shift is crucial for businesses aiming to stay ahead in a digitally evolving market, where AI-driven solutions are becoming the standard.

AI and UX: revolution by AI-powered dynamic interfaces

Generative AI's ability to customize designs at an individual level can lead to a paradigm shift in UX/UI design. It allows for design personalization, where user interfaces dynamically adapt to each user's needs and preferences, offering a significantly enhanced user experience. This for sure leads to more immersive, interactive, and personalized digital experiences.

This level of customization is a significant step beyond traditional personalization features such as user profiles or personalized content. It allows each interaction with the app to be a unique, tailored experience that caters precisely to the user's needs at that moment. 

In addition, generative AI in UX can facilitate A/B testing of different interface designs in real-time. Based on user responses, the most effective design elements can be identified and incorporated dynamically, leading to an iterative design process that continually improves the user experience.

Co-founder and CEO of CTRL-Labs, Thomas Reardon, quoting how AI introduces us to the new age of design.

Personalization becomes individual-driven

The latest breakthrough in personalization, enabled by machine learning, centers around hyper-personalization, a step beyond traditional personalization. Instead of segmenting customers into broad groups, hyper-personalization focuses on the individual. 

Leveraging real-time data and advanced machine learning algorithms, businesses can create highly individualized interactions that improve user experience and customer loyalty. For instance, companies like GlaxoSmithKline, MasterCard, Walmart, Uber, and Sony have developed advanced AI-powered services that can provide personalized experiences based on real-time user interactions and historical data

a list of AI-personalization applications across industries.

AI advancements in configuration management 

In an age where technology is the cornerstone of business, IT configuration management has become a mission-critical task for companies. However, traditional methods often fall short. They are time-consuming and susceptible to error — a ticking time bomb in a digital landscape that demands efficiency and accuracy. Here's where AI swoops in to save the day. 

Let’s take Netflix, for instance. They manage a massive global IT infrastructure that streams shows to millions of viewers simultaneously. With thousands of servers, managing configurations manually is impractical. Instead, they've employed AI to automatically track and manage these configurations. The AI system can instantly detect when a server is underperforming or when there's a change in configuration, and automatically adjust settings to ensure optimal streaming quality.

On the other end of the spectrum, let’s consider a small startup like FooBar Inc. They might not have the budget to employ a dedicated IT team to manage their systems. AI can fill this gap. Instead of spending time manually configuring IT components, FooBar's team can utilize an AI solution like Puppet, which automates the deployment and management of their software across various platforms. This allows the team to focus on their core business, while AI handles the technical details.

Wave of low-code tools

Low-code platforms, which rely on pre-constructed templates and drag-and-drop interfaces, have significantly expedited the incorporation of AI into existing business processes. In comparison to traditional development methods, these tools reduce costs and speed up development timeframes significantly.

low-code platforms possibilities and limitation (basic, intermediate, and advanced).

Forrester reported that 66% of developers are either currently using these tools (39%) or planning to do so in the next 12 months (27%).

FlutterFlow drives innovation in visual app building

AI in UX: a truly ultimate guide to better UX of digital products with AI Cieden

Source: Flutter

is built by two former Google engineers who wanted to simplify the journey of creating an app and make it more accessible to designers, developers, and entrepreneurs. They aimed for a simple drag-and-drop interface that lets them build an app within an hour. With just a simple prompt, the tool generates both visually captivating app designs and the necessary code, which can be imported into FlutterFlow with ease.

This all runs on the ChatGPT API, offering a user-friendly experience across both mobile and Mac OS platforms. The tool also instantly turns textual prompts into project structures, making it easier to customize designs or export generated code.

Additionally, it quickly creates backend schema, transforming your ideas into scalable and interconnected databases. One of the standout features is an AI-driven color scheme generator that crafts unique and aesthetically pleasing color palettes based on text descriptions. Within the custom code editor, users can benefit from real-time AI-powered code suggestions, further enhancing the app development process.

Buzzy transforms text prompts into fully functional applications

AI in UX: a truly ultimate guide to better UX of digital products with AI Cieden

Source: buzzybuzz

announced the launch of the Figma plugin that enables users to turn their app or website designs into a functional prototype or even a live application.

Here's how it may work:

  1. Design. You begin by creating your user interface design in Figma. This can include anything from the layout of your app to the placement of buttons, forms, text fields, and so on. You can also import pre-existing Figma designs.
  2. Annotate. Once your design is ready, you add annotations using Buzzy. These annotations are instructions that help Buzzy understand what each element of your design is supposed to do. For example, you might annotate a button to perform a certain action when clicked, or a text field to display data from a certain source.
  3. Transform. After you've annotated your design, Buzzy's AI engine gets to work. It interprets your annotations and transforms your design into a working app. This isn't just a static prototype - it's a real, live app that you can interact with, test, and refine.
  4. Deploy. Once you're happy with your app, you can deploy it directly from Buzzy. There's no need to worry about servers, hosting, or scaling — Buzzy takes care of all that for you. enables building a no-code AI app in minutes

AI in UX: a truly ultimate guide to better UX of digital products with AI Cieden

Source: BrainAI

Since its inception in 2016, Brain AI has been a force in the realm of natural language processing. In 2021, Brain AI furthered its legacy with Natural AI, a digital commerce solution flaunting the world's first generative interface, refining how we engage with AI.

 On March 29th, Brain AI introduced its newest gem: Imagica

This cutting-edge, no-code AI toolkit offers users an intuitive platform to breathe life into their concepts, regardless of their tech know-how. It boasts real-time data accessibility, versatile functions, and rapid performance, positioning Imagica as a go-to hub for swift app development.

With Imagica AI, users can build functional apps without writing a single line of code. This revolutionary approach to app development has democratized the field, making it accessible to individuals who may not have traditional coding knowledge. The platform employs a chat interface, allowing users to interact with a data source and add the interface to their app.

Changes in data processing and automation 

Software development has shifted from traditional code-centric approaches to more dynamic and conversational methodologies. AI-based agents are now capable of independent actions and collaboration, redefining user interaction in software design.

What should we know about synergy between agent-orienting programming (AOP) and AI in UX/UI design? How can automation in data processing enhance user experiences and create more efficient and adaptable software interfaces?

The rise of agent-oriented programming

The rise of agent-oriented programming (AOP) has been meteoric, especially between 2022 and 2023. For those new to this concept, AOP introduces a software design approach focusing on creating independent agents. These agents, which are autonomous entities, can perceive, react to, and interact with their surroundings to accomplish specific tasks. They can function independently but also have the ability to collaborate, communicating seamlessly with one another to tackle more intricate goals.

Pinecone succinctly described the role of agents by comparing them to auxiliary tools for Large Language Models (LLMs): “just as a human might use a calculator for calculations or initiate a Google search for information, agents act as instruments for LLMs to achieve analogous outcomes”.

Pinecone visually describing the role of AOP by comparing them to LLM tools.

Source: Pinecone

One recent breakthrough in AOP involves autonomous vehicle technology. In 2022, RoboCar Inc., an emerging autonomous vehicle startup, leveraged AOP to develop autonomous agents that simulate the behaviors and decision-making processes of human drivers. These agents constantly interact with their environment, making decisions on vehicle speed, direction, and braking based on road conditions, traffic signals, and the presence of other vehicles and pedestrians. 

It's worth noting the advent of the LongChain framework, constructed with LLMs at its core. This framework can be employed across various applications such as chatbots, Generative Question-Answering (GQA), text summarization, and beyond. LongChain's brilliance lies in its ability to "chain" diverse components, facilitating the creation of more intricate use cases anchored around LLMs.

Furthermore, the LongChain agents possess a remarkable capability. Input a task in textual form, and these agents will craft a comprehensive plan for its execution. Not stopping there, they can initiate the plan, dynamically altering it based on real-time feedback until the desired outcome is achieved. This adaptive planning and execution mechanism is emblematic of the flexibility and intelligence that AOP brings to software development.

AI-powered prompts 

In the 2023 software development landscape, the integration of AI-driven prompts has optimized the way developers interact with underlying algorithms. On platforms like Coda, what previously required extensive coding can now be communicated through succinct prompts. 

For instance, a prompt like "Group action items by individual and prioritize by deadline" acts as an interface to trigger complex underlying algorithms. The AI model, equipped with its vast training and algorithms, interprets and executes tasks based on these prompts. This evolution has profound implications for the business world, especially startups, as the timeline to develop MVPs has seen a significant reduction.

It is worth mentioning that recently Coda launched the AI at Work Challenge, attracting over 1,000 participants. The outcome? Over 150 templates addressing how AI can aid daily work in areas like research, product development, marketing, and more. 

GitHub Copilot, for example, greatly streamlines the coding process with the use of inline commands. For instance, if a developer is working on a Python script and requires a specific function, rather than searching online for the correct syntax and parameters, they can simply type a command in the form of a comment, with no need to switch.

the use of GitHub Copilot in a Python script.

Source: pixegami

One click instead of text input

AI tools like Tappy have revolutionized data processing by enabling dynamic interaction with digital content. Its key feature, the ability to choose the sentiment of a comment (for example whether arguing, supporting, or questioning) — paves the way for more personalized and context-appropriate interactions, especially for sales prospecting or personal brand development.

Example of personalizing content with Tappy with a particular sentiment.

Source: Tappy

Let's take another example of Microsoft 365 Copilot. Imagine that you need to create a proposal. You don’t need to manually dig through files and manually type out the proposal. With Microsoft 365 Copilot, all you need to do is instruct it to create a proposal based on the meeting and include product offers from the product roadmap document. This AI-based automation, coupled with the powerful suite of tools offered by Microsoft 365, represents the future of work.

Example of creating a proposal with Microsoft 365 Copilot.

Source: DeccanHerald

Text requests instead of using filters: Atlassian

Emphasizing intuitive interactions, Atlassian introduced a feature where users can make text requests to fetch information, as opposed to applying filters manually. To ensure a user the applied filters were correct, the system shows the code section.

Atlassian showing the code section to apply correct filters.

With natural language processing at its core, this feature understands users' queries, digs into the relevant data, and presents precise results.

Example of Atlassian using NLP to understand user queries.

Source: Atlassian

It also employs machine learning algorithms to understand team behaviors and project requirements, predicting and recommending the best workflow patterns. Furthermore, the AI can take over routine tasks, such as setting reminders or following up on tasks, freeing up human resources for more complex tasks.

Revolutionizing CRM practices with AI autocompletion

AI-powered autocomplete of fields is a transformative technology that is a must-have in modern CRM systems. It harnesses Natural Language Processing (NLP) to extract essential data from text inputs for efficient CRM querying.

It indicates an imminent shift to more automated, precise, and user-friendly CRM systems. Automation means more than convenience for users. It can reduce data entry errors and cut down data entry times, dramatically increasing business productivity.

example of AI autocompletion in modern CRM systems.

Our experiment showcases AI's transformative potential in CRM practices. Utilizing a sales rep's email, our AI model, ChatGPT, discerned critical deal specifics and automatically populated corresponding CRM fields, demonstrating a significant leap in data entry efficiency and precision.

In our second video on AI concepts, we explore the potential of AI in refining data management tools, particularly CRM systems. We delve into features like autofill, automated description generation, data unification, and more. Covering five key concepts, this video offers insights into how AI can elevate data management systems. Watch to gather ideas for enhancing your own data management solutions.

AI in UX: a truly ultimate guide to better UX of digital products with AI Cieden

Autonomous generation of commit summaries 

Imagine that you will never need to write reports on what was done anymore. 

This vision is now closer to reality, thanks to GitHub's innovative AI-powered feature, which autonomously generates commit summaries. The mechanism harnesses NLP algorithms to analyze code changes, understand the context and essence of each alteration, and then succinctly summarize it. The auto-generated commit summary feature can revolutionize development workflows. Thus, eliminating manual inputs could significantly streamline workflows, allowing developers to focus more on coding and less on administrative tasks.

example of GitHub’s auto-generated commit summary.

Source: Mumas

Similarly, content creation tools like have employed AI to automatically draft summaries for long-form content, thereby increasing productivity.

This transformative functionality echoes the strides being made across various industries. 

Let’s take a healthcare domain. Diagnostic error, being the most complex of the eight harm domains, offers significant potential for improvement through new data sources and AI. Machine learning (ML) can aid in reducing diagnostic errors by harnessing strengths in pattern recognition and minimizing bias, as well as its vast processing capacity – areas where human diagnosticians may encounter challenges. 

Automation: HubSpot's integration with OpenAI

HubSpot's offers a new realm of interaction within CRM, whether it's processing leads or handling data. For instance, when a new lead submits a contact form, GPT-3 can instantaneously understand the context, generate an appropriate response, and send it to the lead, reducing the time taken by traditional manual responses.

Example of HubSpot’s integration with OpenAI allowing to generate automatic responses.

Source: ProductHunt

Moreover, AI can adapt previous user-generated templates for diverse scenarios, increasing efficiency while maintaining personalization.

Example of HubSpot’s user-generated templates with OpenAI.

Source: ProductHunt

HubSpot isn't alone in this endeavor. We're witnessing an AI-infused CRM trend across the industry. 

For example, Salesforce's Einstein AI provides predictive lead scoring and intelligent insights, while Zoho's Zia uses AI to help businesses analyze customer sentiments in real time, enabling timely and effective responses.

AI-powered prioritization: streamlining customer support

HubSpot's AI now prioritizes and summarizes customer requests, interpreting sentiment and urgency from each interaction. Representatives no longer have to manually sift through a deluge of emails each day. Instead, the AI flags messages with detected urgency or negative sentiment, ensuring that pressing issues are swiftly addressed, enhancing the customer experience.

Example of how HubSpot flags messages with detected urgency with OpenAI.

Source: ProductHunt

On top of this, HubSpot’s AI can synthesize summaries of requests, providing sales reps with succinct overviews of each interaction, thereby expediting comprehension and response times. This AI application is akin to ServiceNow's technology which summarizes and routes requests.

Simplifying online search with AI-powered tools

Advanced AI tools like AutoGPT are redefining how we approach complex online research. They delve deep into topics, uncovering layers of information and presenting it in an easy-to-digest format. This capability is not just about data collection; it's about providing a comprehensive, contextual understanding that enables better decision-making.

AutoGPT quickly analyzes and summarizes large volumes of data and market reports, generating insights on opportunities and risks, which significantly enhances the efficiency and depth of the research.

example of AutoGPT analyzing and recognizing large volumes of financial data.


Brain Technologies, a software company that builds “computers that think”, released an innovative supperapp - Natural. It’s the world's first generative interface. To use Natural, users either speak or type their queries or commands. The software then processes these inputs and the interface transforms into the relevant app form.

For instance, if a user states "I'd like sushi tonight", the app might respond with options for ordering sushi from a variety of restaurants, recipes for making sushi at home, or places to eat sushi. This works across various domains, including travel, food, and more.

AI in UX: a truly ultimate guide to better UX of digital products with AI Cieden

Source: BrainAI

NaturalAI has secured over $50 million in funding, attracting a diverse pool of investors, and keeps on developing.

Automation of routine tasks 

The general consensus is that tasks traditionally completed by individuals without specific qualifications can now be undertaken by AI systems. Companies are increasingly delegating tasks typically performed by junior-level employees to AI.

For instance, JPMorgan Chase & Co., a leading global financial services firm, employs an AI system named COIN (Contract Intelligence) to analyze complex legal contracts – a job that would normally take thousands of human hours. This allows junior employees, who previously spent hours reviewing these documents, to focus on tasks that require critical thinking and strategic input.

Breakthrough in productivity management

Microsoft reinventing search with AI-powered Copilot. Copilot is more than just a chatbot. It’s an AI assistant that gives different ways to command Office apps as well as a web browser.  It will change forever how we create documents and interact with software. 

In the Office apps, Microsoft Copilot can generate the entire document for you as well as give precise recommendations to improve your text by introducing UI prompts that highlight your spelling mistakes. Copilot even can help you write emails in Outlook and so much more. 

Speaking of the web experience, you can chat with the copilot side-by-side as you browse.  It can help you turn your ideas into images in chat, refine your search results by asking questions, get summarized answers to search results to save your time, as well as generate content for you from scratch.

Microsoft Word is evolving from a basic word processor to a comprehensive writing assistant, offering style suggestions, text predictions, rewrite options, and a 'read aloud' function. Combined with Microsoft Copilot, Microsoft Word users can now manipulate and analyze data within their documents, leveraging AI capabilities to extract insights, generate reports, and automate data-related tasks.

Microsoft PowerPoint now automatically designs professional presentations, generates relevant slide content, and provides dynamic, personalized suggestions for slide layout and design based on the context. Additionally, the software offers real-time closed captioning and translation during presentations, accommodating diverse audiences.

Microsoft Excel's AI capabilities now include the Ideas feature, which automatically recommends charts and helps identify trends in data, along with advanced data analysis tools such as automated forecasting. Excel also provides rich data types, converting simple text into something much more meaningful, and the XLOOKUP function to find and retrieve data.

Combined with Microsoft Copilot, Excel's potential expands further. Users can command Copilot to prepare complex data visualizations, perform intricate calculations, or analyze data in new ways. Copilot's capabilities effectively turn Excel into a robust data science tool, accessible to users without advanced technical expertise.

Microsoft Outlook, coupled with Copilot, is revolutionizing email management. Copilot's AI assists with organizing the inbox, highlighting important emails, and even drafting responses based on previous communication styles. It can also extract actionable tasks from emails, such as scheduling meetings or setting reminders, optimizing productivity, and reducing the chance of missing critical information. Furthermore, Copilot can provide brief summaries of long email threads, making it easier to catch up on ongoing conversations.

Microsoft 365 Copilot X enhances productivity in Microsoft Workspace by intelligently managing tasks across apps. It can delegate work in Teams, suggest OneDrive documents, and convert Teams meeting points into Planner tasks. It even provides automatic responses to messages, improving communication efficiency and fostering a collaborative work environment.

Watch this video to discover how AI has taken Microsoft Office apps to new heights, significantly improving the user experience.

AI in UX: a truly ultimate guide to better UX of digital products with AI Cieden

Source: AndiAI

Jupiter Agent's
integration with the Zapier Toolkit offers a transformative experience for programmers. Jupiter Agent, with its AI capabilities, assists in identifying code patterns, debugging, and even predicting developmental issues, while Zapier ensures a seamless workflow among the tools commonly used by developers. For instance, programmers can set up automated tasks like code backups, synchronize project management tools with code repositories, or even auto-generate bug reports. 

Revolutionizing collaboration with OpenAI-powered Slack assistant

Slack isn't just a platform for team communication anymore; it's now a tool that assists with your daily tasks. The integration of OpenAI into Slack transforms the way we work and collaborate. It gives you an AI assistant that understands your queries, automates responses, and even schedules your meetings.

For instance, in Slack, OpenAI can help manage your tasks, provide quick information without leaving your workspace, and even suggest responses to messages, saving you typing time. This AI assistant doesn't just react; it understands and learns from your interactions, providing increasingly accurate and relevant responses.

Example of Open AI in Slack helping users to manage tasks.

Source: Slack

The major shift in data visualization 

AI makes data visualization more user-friendly and efficient. Integrating AI for UX to visualize data adds a conversational aspect meaning users can interact with visuals using everyday language. Augmented analytics is part of this change, as it makes complex tasks easier and allows more people to work with advanced analytics. 

But AI's impact on data visualization goes beyond pure technology. It reimagines how we interact with data, transforming it from a static representation into a dynamic, conversational, and accessible tool for better decision-making.

Generating, editing, and improving data

Large language models are now capable of creating all sorts of visualization: tables, charts, and other formats.

For instance, before AI-powered tools, you had to add custom widgets to a dashboard and each time you add something, there was a need to fill in a lot of fields. Now, you can simply feed the data to such a tool as WolframAlpha and choose the way you want it to visualize it.

When combined with a Code interpreter or the Noteable plugin, ChatGPT demonstrates how effortlessly LLMs can handle data analysis. It can determine what needs visualization, select the optimal visualization tool, and render the visualization effectively.

LLMs like GPT can write code across numerous languages and frameworks, vastly expanding their utility. 

Here is an example of the creation of visualized data from a text prompt. GPT-4 is able to create HTML, CSS and JS code to visualize required information including information from user input and information it has in it’s memory or can get from online sources:

Example of creating visualized data from a text prompt using GPT-4.

User-friendly visualization of intricate data

Technological advancements in data visualization are redefining the way we represent and interpret data. Groundbreaking tools like Mermaid, Sketchviz, PlantUML, and have emerged as frontrunners in this revolution, each introducing unique, user-friendly methods for depicting intricate data.

example of script languages transforming text into detailed graphs.

Source: Sketchviz

Mermaid stands out with its markdown-like script language, simplifying the process of generating charts and making modifications almost effortless. Sketchviz, drawing upon the capabilities of Graphviz, transforms simple text files into detailed graphs, a testament to its emphasis on user-centric design.

example of script languages transforming text into detailed graphs.

Source: David Mohr

In parallel with these tools, generative models like GPTs are pushing boundaries further. These advanced models can generate code that integrates seamlessly with visualization platforms, including the aforementioned offers a comprehensive suite of diagramming features, supporting a multitude of export formats and integrations, which broadens its application scope.

Example of diagrams with integrated GPTs.


While current adaptations function via plugins, facilitating a "plug-and-play" experience, the future holds even more promise. We can foresee a world where visualization tools come equipped with built-in chatbots, powered by models like GPTs. Users will be able to simply convey their visualization needs, and these intelligent chatbots will craft the requisite graph, chart, or diagram instantaneously. This evolution is not merely about enhancing efficiency but about democratizing data visualization, ensuring everyone, regardless of their technical skill level, can harness the power of visual data representation.

list of 10 AI supported frameworks.

Advancements in visual analytics

Interactive storytelling is more than just presenting data in a static chart. It involves creating a captivating narrative around datasets, with users actively engaging in the exploration process.

For the AI community, Chat GPT's plugins, Code Interpreter or Noteable, are trailblazers in this sense, allowing third-party incorporation. This technology enables data uploading and analysis while also facilitating the output of the results in both narrative form and visual illustration. It mixes the capabilities of LLMs and Python code interpreters with a rich toolset to analyze and visualize data. You can learn more about it here: 

Let's walk through an example of such interactive data visualization in an application's user interface. Let's think of some app that helps users understand crime hotspots in a locale. A traditional approach would involve statically displaying crime data on a 2D map. 

Oakland's crimes potting

visual display of Oakland’s crime spots on a 2D map.

However, with a Code Interpreter, this translates into an engaging exploration.

visual display of Oakland’s crime spots with Code Interpreter.

First, users are guided about how such analysis of potential crime trends should be conducted, then the system may generate charts and textual interpretation over a past period, sifted from a variety of data points. As users delve deeper, they are greeted with detailed interactive visuals, like crime intensity mapped over the locale or timelines representing spike hours.

The story does not end there. The system could also provide crime resolution rates and types that dominate a particular area. It could go as far as clustering areas based on crime type and identifying patterns that could shape future prevention strategies. 

Dynamic widgets and reports

AI has begun to revolutionize dynamic widget and report creation in recent years, offering a shift from static, one-size-fits-all solutions to highly personalized, interactive, and real-time experiences. In 2023, we see this transformation gaining traction in several advanced products and applications.

An innovative product to consider is Databox, which uses AI to build highly customizable dashboards. It uses predictive analytics and machine learning algorithms to provide real-time insight, allowing businesses to make data-driven decisions instantly

Screen of Databox customizable dashboards featuring AI-based insights.

Source: Databox

Then there are Power BI's AI-powered features like Q&A, Quick Insights, and the Key Influencer visual, which enable users to interrogate their data naturally, identify trends and outliers quickly, and identify the main factors that drive outcomes, respectively. These AI functionalities not only improve efficiency but also provide in-depth insights that would otherwise require expert data analysis.

screen of Microsoft’s Power BI featuring Key Influencers visual.

Source: Microsoft

Strategic adaptation to AI in UX for product leaders

Technology has come a long way, evolving from the command-based interfaces of the 1960s to the prevalence of graphical interfaces. Today, AI tech advancements bring a new era of "intent-based outcome specification," allowing users to express goals rather than specific commands. This shift adds a fresh twist towards a more dynamic user-AI relationship.

Product owners and managers must strategically adapt to remain competitive and must prioritize AI and UX design. Emphasizing intuitive and personalized user experiences is key. Products should be designed to understand and anticipate user needs, making interactions with AI as natural and efficient as possible. Additionally, ensuring seamless integration with existing systems and workflows will be crucial. By focusing on these core aspects - intuitive design, personalization, and seamless integration - product leaders can not only enhance user satisfaction but also drive innovation, positioning their products at the forefront of this exciting technological evolution.

In this journey, expert guidance can be invaluable. This is where Cieden’s expertise in AI design can be invaluable. Our expertise extends across a wide array of applications, including advanced areas such as lie detection, sentiment analysis, enhancing the efficiency of online exams, and refining sales strategies. Our approach to AI product design is rooted in a genuine enthusiasm for innovation and a dedication to staying ahead of the curve. 

We offer a guiding hand for product leaders facing the complex task of weaving AI seamlessly into their products. Our digital product designers understand the nuances of the AI landscape and are ready to assist in tackling any challenges you might face. 

Partnering with us can offer practical insights and solutions that are focused on enhancing your products, meeting and exceeding user expectations, and delivering truly standout user experiences.

To explore how AI can benefit your business, feel free to book a free consultation with us. We'll discuss your specific challenges and how AI might enhance your products.

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