UX AI Transformation: Automation: Redefining User Experiences
This transformation examines how AI automation in SaaS is revolutionizing end-user experiences across various domains such as e-commerce, customer service, marketing, sales, HR, IT operations, and project management, while addressing user perceptions, concerns, and the need for explainable AI.
This post covers one of four transformations and is part of a larger blog series titled 'From Hype to Reality: A Practical Guide to AI-driven UX in SaaS'. You can find the first post of the series here.
AI Automation in SaaS: Redefining End-User Experiences
From elevators to ATMs, technology has long automated our experiences. But in recent years, SaaS (Software as a Service) has taken automation to a whole new level, thanks to AI.
Everywhere you look, there's AI streamlining tasks:
E-commerce: Shopify's Generative AI redesigns websites based on sales data.
Customer Service: Salesforce uses AI to classify cases and predict customer needs.
Marketing: HubSpot's AI optimizes content creation and customer segmentation.
Sales: LinkedIn Sales Navigator automates lead scoring and provides insights.
HR: Workday automates talent acquisition and payroll processing.
IT Operations: ServiceNow uses AI to categorize and respond to incidents.
Project Management: Asana's AI prioritizes tasks and schedules meetings.
The Impact of AI Automation: Shifting User Perceptions and Concerns
AI automation is changing the game, making experiences not only more efficient and personalized but also user are more satisfied. This research report states ‘there is a direct impact between the automation of tasks carried out by organizations and the satisfaction perceived by the user’
But with the perks come worries: Will automation cost jobs? Lead to loss of control? Degrade skills? And what about privacy and bias? These concerns aren't baseless—there are plenty of examples of bias in AI: from Workdays recruitment tools (in which there is an ongoing courtcase) and remember when amazon had to close down their biased AI recruitment tool), Google had to apologize for what it describes as “inaccuracies in some historical image generation depictions” with its Gemini AI tool. And we all have experiences the skill degradation, whether its doing math sums without a calculator or navigating around the city without google maps
Trust in the black box of automation is shaky. WIth little or no visibility into how AI decisions and results have been made, people see it as less fair that decision made by humans (as highlighted in the research In AI we trust? Perceptions about automated decision-making by artificial intelligence)
Designing with Automation: Addressing User Trust and Control
Here's the challenge: AI needs to explain itself. One of the critical and pervasive design issues of AI systems is their explainability… into AI’s functions and decisions. ’ as stated in Question-Driven Design Process for Explainable AI User Experiences.
We need Explainable AI (XAI) that users can understand and trust. Designing XAI means treating it as an interaction problem, not just a technical one.
We have to keep users in the driver's seat, empowering them with AI instead of letting it take over. Just like a navigator app guides you but doesn't decide where you go, AI should empower users without taking away their control.
UX AI Transformation: Diversification of Interaction Methods
This shift delves into the expanding array of interaction methods in SaaS, driven by AI advancements, including text and voice-based interfaces, biometrics, AR, and VR, and its impact on user engagement and privacy concerns.
This post covers one of four transformations and is part of a larger blog series titled 'From Hype to Reality: A Practical Guide to AI-driven UX in SaaS'. You can find the first post of the series here.
Expanding Interaction Methods in SaaS: AI's Impact on Interface Diversity
The way we interact with software is getting a major upgrade, thanks to AI. We're talking about everything from chatting with text and voice to using biometrics, augmented reality (AR), and virtual reality (VR). AI's driving this change, making it all possible.
NLP (Natural Language Processing) is turbocharging text-based conversations, while Automatic Speech Recognition (ASR) and computer vision are making voice and image interaction a breeze and there is an increasing amt of multi model LLMS (with open AIs chat gpt, googles Gemini, Anthropics claude3, leading the pack)
Biometric authentication is booming too, tripling in adoption between 2019 - 2022, thanks to AI making it faster and more accurate, reported by Grand View Research, thanks to AI improving its accuracy, speed, and the overall capability. There growing about of examples of biometrics being used outside of security to other usecases such as BioNimbus a marketing analytics company, which collects eye-tracking, GPS, and EEG data to track emotional excitement and improve the targeting of marketing campaigns.
AR is finally hitting its stride, especially in real estate and e-commerce, where it's changing how we shop. Shopify has had a significant role in that.
VR's still in the "Trough of Disillusionment,”. Whilst benefiting from the developments of AI, its high costs are holding it back.
Gesture-based interactions are getting some love for their natural feel, but we're reminded that tech should solve user problems first, not the other way around, thanks to the Humane Pin and the barrage of bad reviews it is getting. See Marques Brownlee’s infamous review
Navigating Diverse Interaction Methods: Evolving User Experiences
Typing and clicking aren't cutting it anymore. People want interactions to feel intuitive and natural. If they don't get it within seconds, they're moving on. Plus, folks are getting savvy about data privacy—biometrics raise some red flags, with people worried about misuse and consent. Getapp reported a decline in consumer trust in biometric technology in 2024
Designing for Diverse Interactions: Guidelines for UX Designers
With all these new ways to interact, designers need to step up their game. Forget about static screens and point-and-click interfaces—it's time to get creative. Learn from the leaders like Perplexity, who nailed a simple GUI for complex answers, or Google Maps, which seamlessly blends voice, touch, and visuals.
But with great power comes great responsibility. Biometrics, in particular, need extra care to ensure user trust and data security. And never forget to talk to your users—they're the ones who'll show you the way. So, be like Perplexity—not like humane
UX AI Transformation: The Rise of Conversational Interfaces
This transformation explores the growing integration of conversational interfaces in SaaS, powered by AI and NLP, and its implications for user interaction and experience
This post covers one of four transformations and is part of a larger blog series titled 'From Hype to Reality: A Practical Guide to AI-driven UX in SaaS'. You can find the first post of the series here.
Conversational Interfaces in SaaS: AI's Influence on User Interaction
Technology's become such a big part of our lives that we want to chat with our devices like we do with people. That's where conversational interfaces come in. Instead of following rigid steps or tapping on buttons, we can now just talk to our software like we would with a friend. This shift, powered by advancements in Natural Language Processing (NLP) and AI, has led to a surge in conversational interactions.
Chatbots, the helpful ones, are popping up everywhere— salesforce, microsoft, IBM, intercom, zendesk, hubspot, freshworks,, slack, zoho, zapier, ada, tars, manychat, overloop and many others are integrating them into their platforms. The rate of worldwide retail spending on chatbots is set to soar from $12 billion in 2023 to $72 billion by 2028 according to emarketer
They're not just for answering simple questions anymore; now we have Co-Pilots, more advanced chatbots that can handle complex tasks. First seen only in the market leaders: Salesforce, IBM, microsoft, amazon, now increasingly being developed across a diverse spectrum of SaaS, from customer service to project management, including clare, dixa, chatfield, bitrix24
Shaping User Experiences: The Influence of Conversational Interfaces
Users love the natural feel of conversational interfaces. It's like having a real conversation, but with your software. Research shows that people find these interactions more engaging and personal. They can get exactly what they need without navigating through menus and buttons. Plus, it's a game-changer for accessibility, making digital experiences easier for everyone. This study found that 70% of users chose ChatGPT-powered conversational interfaces over traditional techniques, citing convenience, efficiency, and personalization.
But it's not all sunshine and rainbows. Sometimes, these interactions can leave users feeling frustrated or confused, or feelings such as distrust, intrusion, inconvenience, as indicated by this literature review. Open-ended dialogue boxes can be vague, making it hard for users to know what to say. And not everyone finds it easy to express their needs in words. Chat GPT, the tool that propelled the conversational experience, was never meant to be a consumer product (It was a demo to internally test out the LLM versions) and is a good example of a tool, that whilst is very useful, has many usability issues
Crafting Conversational Experiences: Insights for UX Designers
As cool as conversational interfaces are, they're not always the best solution for every situation. Designers need to be careful not to jump on the chatbot bandwagon just because it's trendy.
It's crucial to test these solutions rigorously and learn from existing research. NNG research often writes about the usability issues with conversational experiences and Why Chatbots Are Not the Future covers some of the main issues.
Companies like Perplexity are paving the way with user-first AI products that prioritize simplicity and speed. Designers should take notes and focus on providing answers rather than creating complex conversational experiences. Henry Modisett,Head of Design at Perplexity AI, talks through his approach to Perplexities design here. By staying grounded in user needs and usability principles, designers can navigate the world of conversational interfaces more effectively.
UX AI Transformation: The Era of Personalization
This evolution explores the role of AI in enabling highly personalized experiences in SaaS, fueled by machine learning techniques like recommendation systems and NLP, and its implications for user engagement, privacy, and the need for transparency and user control.
This post covers one of four transformations and is part of a larger blog series titled 'From Hype to Reality: A Practical Guide to AI-driven UX in SaaS'. You can find the first post of the series here.
Personalization in SaaS: AI's Role in Customized Experiences
While personalization has long been a goal in SaaS, relying on static settings and generalized personas had its limitations. Now, with the advancement of machine learning techniques such as recommendation systems, Natural Language Processing (NLP), and deep learning, the era of personalization has dawned. From Deloitte reporting on the Segment of One, to Jacob Nielsen envisioning a future where everyone will have the perfectly auto generated accessible website the possibilities for personalized experiences are expanding rapidly, ushering in a new era where every interaction feels tailor-made for the individual user.
The pioneers in this realm have been leveraging their vast data sets and emerging technologies to deliver highly personalized experiences for some time. Amazon, for example, utilizes AI-powered recommendation engines to precisely match customers with products they desire. Netflix and Spotify employ similar algorithms to tailor content recommendations and playlists to individual user preferences.
While hyper-personalization, or "Segment of One", isn't yet ubiquitous across SaaS, notable examples of personalisation are emerging:
Canva: Utilizes user behavior data to suggest design templates and layouts that align with the user's previous projects or trending designs among similar users.
Notion: Offers a personalized onboarding experience by segmenting users during signup to showcase the most relevant features based on their needs.
Adobe's Sensei AI: Enables dynamic workflows in creative software that adapt to the user's style and preferences.
Grammarly: Provides personalized feedback tailored to the context of the user's writing and their specific goals, whether professional or casual.
Perplexity is in very early dates of exploring generative UI for its prompt input interface.
Personalized Experiences in Focus: Shaping User Engagement
There's a growing expectation for highly personalized experiences, fueled by the standards set by platforms like YouTube, Netflix, Amazon, and Spotify. Studies have consistently shown that personalization leads to higher engagement rates. However, the landscape of user experience regarding personalization is nuanced:
Privacy Concerns: While users demand personalized experiences, there's a simultaneous concern about data collection, usage, and storage. Overly personalized experiences can sometimes trigger a "creepy factor" when users become uncomfortably aware of extensive data monitoring.
Desire for Transparency and Control: Research indicates that users crave transparency and control over their data. They want to understand what data is being collected and have the ability to influence or opt out of data collection. Regulations like GDPR in Europe are driving companies to provide greater transparency and control to users.
Designing for Personalization: Balancing Expectations and Privacy
To navigate the complexities of personalization, designers should:
Get more familiar with the data and assessing it for biases.
Explore how to shape your static personas to be useful in this new dynamic world
Emphasize user control in personalization efforts such as adjusting what data the system uses to personalize the experience or opting out of certain types of personalization.
Provide transparency into where the experience is being personalised and how AI is being used
Clearly communicate the benefits of personalization and how AI enhances the user experience to build trust and acceptance.
In this era of personalization, balancing user expectations with privacy concerns is paramount to fostering trust and delivering exceptional experiences.