Recently, everyone in the design industry has been talking about design sprints. So, naturally, the team at Optimal Workshop wanted to see what all the fuss was about. I picked up a copy of The Sprint Book and suggested to the team that we try out the technique.
In order to keep momentum, we identified a current problem and decided to run the sprint only two weeks later. The short notice was a bit of a challenge, but in the end we made it work. Here’s a run down of how things went, what worked, what didn’t, and lessons learned.
A sprint is an intensive focused period of time to get a product or feature designed and tested with the goal of knowing whether or not the team should keep investing in the development of the idea. The idea needs to be either validated or not validated by the end of the sprint. In turn, this saves time and resource further down the track by being able to pivot early if the idea doesn’t float.
If you’re following The Sprint Book you might have a structured 5 day plan that looks likes this:
Day 1 - Understand: Discover the business opportunity, the audience, the competition, the value proposition and define metrics of success.
Day 2 - Diverge: Explore, develop and iterate creative ways of solving the problem, regardless of feasibility.
Day 3 - Converge: Identify ideas that fit the next product cycle and explore them in further detail through storyboarding.
Day 4 - Prototype: Design and prepare prototype(s) that can be tested with people.
Day 5 - Test: User testing with the product's primary target audience.
With a Design Sprint, a product doesn't need to go full cycle to learn about the opportunities and gather feedback.
When you’re running a design sprint, it’s important that you have the right people in the room. It’s all about focus and working fast; you need the right people around in order to do this and not have any blocks down the path. Team, stakeholder and expert buy-in is key — this is not a task just for a design team!After getting buy in and picking out the people who should be involved (developers, designers, product owner, customer success rep, marketing rep, user researcher), these were my next steps:
Pre-sprint
Read the book
Panic
Send out invites
Write the agenda
Book a meeting room
Organize food and coffee
Get supplies (Post-its, paper, Sharpies, laptops, chargers, cameras)
Some fresh smoothies for the sprinters made by our juice technician
The sprint
Due to scheduling issues we had to split the sprint over the end of the week and weekend. Sprint guidelines suggest you hold it over Monday to Friday — this is a nice block of time but we had to do Thursday to Thursday, with the weekend off in between, which in turn worked really well. We are all self confessed introverts and, to be honest, the thought of spending five solid days workshopping was daunting. At about two days in, we were exhausted and went away for the weekend and came back on Monday feeling sociable and recharged again and ready to examine the work we’d done in the first two days with fresh eyes.
Design sprint activities
During our sprint we completed a range of different activities but here’s a list of some that worked well for us. You can find out more information about how to run most of these over at The Sprint Book website or checkout some great resources over at Design Sprint Kit.
Lightning talks
We kicked off our sprint by having each person give a quick 5-minute talk on one of these topics in the list below. This gave us all an overview of the whole project and since we each had to present, we in turn became the expert in that area and engaged with the topic (rather than just listening to one person deliver all the information).
Our lightning talk topics included:
Product history - where have we come from so the whole group has an understanding of who we are and why we’ve made the things we’ve made.
Vision and business goals - (from the product owner or CEO) a look ahead not just of the tools we provide but where we want the business to go in the future.
User feedback - what have users been saying so far about the idea we’ve chosen for our sprint. This information is collected by our User Research and Customer Success teams.
Technical review - an overview of our tech and anything we should be aware of (or a look at possible available tech). This is a good chance to get an engineering lead in to share technical opportunities.
Comparative research - what else is out there, how have other teams or products addressed this problem space?
Empathy exercise
I asked the sprinters to participate in an exercise so that we could gain empathy for those who are using our tools. The task was to pretend we were one of our customers who had to present a dendrogram to some of our team members who are not involved in product development or user research. In this frame of mind, we had to talk through how we might start to draw conclusions from the data presented to the stakeholders. We all gained more empathy for what it’s like to be a researcher trying to use the graphs in our tools to gain insights.
How Might We
In the beginning, it’s important to be open to all ideas. One way we did this was to phrase questions in the format: “How might we…” At this stage (day two) we weren’t trying to come up with solutions — we were trying to work out what problems there were to solve. ‘We’ is a reminder that this is a team effort, and ‘might’ reminds us that it’s just one suggestion that may or may not work (and that’s OK). These questions then get voted on and moved into a workshop for generating ideas (see Crazy 8s).Read a more detailed instructions on how to run a ‘How might we’ session on the Design Sprint Kit website.
Crazy 8s
This activity is a super quick-fire idea generation technique. The gist of it is that each person gets a piece of paper that has been folded 8 times and has 8 minutes to come up with eight ideas (really rough sketches). When time is up, it’s all pens down and the rest of the team gets to review each other's ideas.In our sprint, we gave each person Post-it notes, paper, and set the timer for 8 minutes. At the end of the activity, we put all the sketches on a wall (this is where the art gallery exercise comes in).
Mila our data scientist sketching intensely during Crazy 8s
A close up of some sketches from the team
Art gallery/Silent critique
The art gallery is the place where all the sketches go. We give everyone dot stickers so they can vote and pull out key ideas from each sketch. This is done silently, as the ideas should be understood without needing explanation from the person who made them. At the end of it you’ve got a kind of heat map, and you can see the ideas that stand out the most. After this first round of voting, the authors of the sketches get to talk through their ideas, then another round of voting begins.
Mila putting some sticky dots on some sketches
Bowie, our head of security, even took part in the sprint...kind of
Usability testing and validation
The key part of a design sprint is validation. For one of our sprints we had two parts of our concept that needed validating. To test one part we conducted simple user tests with other members of Optimal Workshop (the feature was an internal tool). For the second part we needed to validate whether we had the data to continue with this project, so we had our data scientist run some numbers and predictions for us.
Our remote worker Rebecca dialed in to watch one of our user tests live
Actual feedback
Challenges and outcomes
One of our key team members, Rebecca, was working remotely during the sprint. To make things easier for her, we set up 2 cameras: one pointed to the whiteboard, the other was focused on the rest of the sprint team sitting at the table. Next to that, we set up a monitor so we could see Rebecca.
Engaging in workshop activities is a lot harder when working remotely. Rebecca would get around this by completing the activities and take photos to send to us.
Lightning talks are a great way to have each person contribute up front and feel invested in the process.
Sprints are energy intensive. Make sure you’re in a good place with plenty of fresh air with comfortable chairs and a break out space. We like to split the five days up so that we get a weekend break.
Give people plenty of notice to clear their schedules. Asking busy people to take five days from their schedule might not go down too well. Make sure they know why you’d like them there and what they should expect from the week. Send them an outline of the agenda. Ideally, have a chat in person and get them excited to be part of it.
Invite the right people. It’s important that you get the right kind of people from different parts of the company involved in your sprint. The role they play in day-to-day work doesn’t matter too much for this. We’re all mainly using pens and paper and the more types of brains in the room the better. Looking back, what we really needed on our team was a customer support team member. They have the experience and knowledge about our customers that we don’t have.
Choose the right sprint problem. The project we chose for our first sprint wasn’t really suited for a design sprint. We went in with a well defined problem and a suggested solution from the team instead of having a project that needed fresh ideas. This made the activities like ‘How Might We’ seem very redundant. The challenge we decided to tackle ended up being more of a data prototype (spreadsheets!). We used the week to validate assumptions around how we can better use data and how we can write a script to automate some internal processes. We got the prototype working and tested but due to the nature of the project we will have to run this experiment in the background for a few months before any building happens.
Overall, this design sprint was a great team bonding experience and we felt pleased with what we achieved in such a short amount of time. Naturally, here at Optimal Workshop, we're experimenters at heart and we will keep exploring new ways to work across teams and find a good middle ground.
AI is transforming how businesses approach product development. From AI-powered chatbots and recommendation engines to predictive analytics and generative models, AI-first products are reshaping user interactions with technology, which in turn impacts every phase of the product development lifecycle.
Whether you're skeptical about AI or enthusiastic about its potential, the fundamental truth about product development in an AI-driven future remains unchanged: a product is only as good as its user experience.
No matter how powerful the underlying AI, if users don't trust it, can't understand it, or struggle to use it, the product will fail. Good UX isn't simply an add-on for AI-first products, it's a fundamental requirement.
Why UX Is More Critical Than Ever
Unlike traditional software, where users typically follow structured, planned workflows, AI-first products introduce dynamic, unpredictable experiences. This creates several unique UX challenges:
Users struggle to understand AI's decisions – Why did the AI generate this particular response? Can they trust it?
AI doesn't always get it right – How does the product handle mistakes, errors, or bias?
Users expect AI to "just work" like magic – If interactions feel confusing, people will abandon the product.
AI only succeeds when it's intuitive, accessible, and easy-to-use: the fundamental components of good user experience. That's why product teams need to embed strong UX research and design into AI development, right from the start.
Key UX Focus Areas for AI-First Products
To Trust Your AI, Users Need to Understand It
AI can feel like a black box, users often don't know how it works or why it's making certain decisions or recommendations. If people don't understand or trust your AI, they simply won't use it. The user experiences you need to build for an AI-first product must be grounded in transparency.
What does a transparent experience look like?
Show users why AI makes certain decisions (e.g., "Recommended for you because…")
Allow users to adjust AI settings to customize their experience
Enable users to provide feedback when AI gets something wrong—and offer ways to correct it
A strong example: Spotify's AI recommendations explain why a song was suggested, helping users understand the reasoning behind specific song recommendations.
AI Should Augment Human Expertise Not Replace It
AI often goes hand-in-hand with automation, but this approach ignores one of AI's biggest limitations: incorporating human nuance and intuition into recommendations or answers. While AI products strive to feel seamless and automated, users need clarity on what's happening when AI makes mistakes.
How can you address this? Design for AI-Human Collaboration:
Guide users on the best ways to interact with and extract value from your AI
Provide the ability to refine results so users feel in control of the end output
Offer a hybrid approach: allow users to combine AI-driven automation with manual/human inputs
Consider Google's Gemini AI, which lets users edit generated responses rather than forcing them to accept AI's output as final, a thoughtful approach to human-AI collaboration.
Validate and Test AI UX Early and Often
Because AI-first products offer dynamic experiences that can behave unpredictably, traditional usability testing isn't sufficient. Product teams need to test AI interactions across multiple real-world scenarios before launch to ensure their product functions properly.
Run UX Research to Validate AI Models Throughout Development:
Implement First Click Testing to verify users understand where to interact with AI
Use Tree Testing to refine chatbot flows and decision trees
Conduct longitudinal studies to observe how users interact with AI over time
One notable example: A leading tech company used Optimal to test their new AI product with 2,400 global participants, helping them refine navigation and conversion points, ultimately leading to improved engagement and retention.
The Future of AI Products Relies on UX
The bottom line is that AI isn't replacing UX, it's making good UX even more essential. The more sophisticated the product, the more product teams need to invest in regular research, transparency, and usability testing to ensure they're building products people genuinely value and enjoy using.
Want to improve your AI product's UX? Start testing with Optimal today.
The Growing Challenge of AI Bias in Digital Products
AI is rapidly reshaping our digital landscape, powering everything from recommendation engines to automated customer service and content creation tools. But as these technologies become more widespread, we're facing a significant challenge: AI bias. When AI systems are trained on biased data, they end up reinforcing stereotypes, excluding marginalized groups, and creating inequitable digital experiences that harm both users and businesses.
This isn't just theoretical, we're seeing real-world consequences. Biased AI has led to resume screening tools that favor male candidates, facial recognition systems that perform poorly on darker skin tones, and language models that perpetuate harmful stereotypes. As AI becomes more deeply integrated into our digital experiences, addressing these biases isn't just an ethical imperative t's essential for creating products that truly work for everyone.
Why Does AI Bias Matter for UX?
For those of us in UX and product teams, AI bias isn't just an ethical issue it directly impacts usability, adoption, and trust. Research has shown that biased AI can result in discriminatory hiring algorithms, skewed facial recognition software, and search engines that reinforce societal prejudices (Buolamwini & Gebru, 2018).
When AI is applied to UX, these biases show up in several ways:
Navigation structures that favor certain user behaviors
Chatbots that struggle to recognize diverse dialects or cultural expressions
Recommendation engines that create "filter bubbles"
Personalization algorithms that make incorrect assumptions
These biases create real barriers that exclude users, diminish trust, and ultimately limit how effective our products can be. A 2022 study by the Pew Research Center found that 63% of Americans are concerned about algorithmic decision-making, with those concerns highest among groups that have historically faced discrimination.
The Root Causes of AI Bias
To tackle AI bias effectively, we need to understand where it comes from:
1. Biased Training Data
AI models learn from the data we feed them. If that data reflects historical inequities or lacks diversity, the AI will inevitably perpetuate these patterns. Think about a language model trained primarily on text written by and about men, it's going to struggle to represent women's experiences accurately.
2. Lack of Diversity in Development Teams
When our AI and product teams lack diversity, blind spots naturally emerge. Teams that are homogeneous in background, experience, and perspective are simply less likely to spot potential biases or consider the needs of users unlike themselves.
3. Insufficient Testing Across Diverse User Groups
Without thorough testing across diverse populations, biases often go undetected until after launch when the damage to trust and user experience has already occurred.
How UX Research Can Mitigate AI Bias
At Optimal, we believe that continuous, human-centered research is key to designing fair and inclusive AI-driven experiences. Good UX research helps ensure AI-driven products remain unbiased and effective by:
Ensuring Diverse Representation
Conducting usability tests with participants from varied backgrounds helps prevent exclusionary patterns. This means:
Recruiting research participants who truly reflect the full diversity of your user base
Paying special attention to traditionally underrepresented groups
Creating safe spaces where participants feel comfortable sharing their authentic experiences
Analyzing results with an intersectional lens, looking at how different aspects of identity affect user experiences
Establishing Bias Monitoring Systems
Product owners can create ongoing monitoring systems to detect bias:
Develop dashboards that track key metrics broken down by user demographics
Schedule regular bias audits of AI-powered features
Set clear thresholds for when disparities require intervention
Make it easy for users to report perceived bias through simple feedback mechanisms
Advocating for Ethical AI Practices
Product owners are in a unique position to advocate for ethical AI development:
Push for transparency in how AI makes decisions that affect users
Champion features that help users understand AI recommendations
Work with data scientists to develop success metrics that consider equity, not just efficiency
Promote inclusive design principles throughout the entire product development lifecycle
The Future of AI and Inclusive UX
As AI becomes more sophisticated and pervasive, the role of customer insight and UX in ensuring fairness will only grow in importance. By combining AI's efficiency with human insight, we can ensure that AI-driven products are not just smart but also fair, accessible, and truly user-friendly for everyone. The question isn't whether we can afford to invest in this work, it's whether we can afford not to.
Let's talk about something that's changing the game for all of us in digital product design: AI search. It's not just a small update; it's a complete revolution in how people find information online.
Today's AI-powered search tools like Google's Gemini, ChatGPT, and Perplexity AI aren't just retrieving information they're having conversations with users. Instead of giving you ten blue links, they're providing direct answers, synthesizing information from multiple sources, and predicting what you really want to know.
This raises a huge question for those of us creating digital products: How do we design experiences that remain visible and useful when AI is deciding what users see?
AI Search Is Reshaping How Users Find and Interact with Products
Users don't browse anymore: they ask and receive. Instead of clicking through multiple websites, they're getting instant, synthesized answers in one place.
The whole interaction feels more human. People are asking complex questions in natural language, and the AI responses feel like real conversations rather than search results.
Perhaps most importantly, AI is now the gatekeeper. It's deciding what information users see based on what it determines is relevant, trustworthy, and accessible.
This shift has major implications for product teams:
If you're a product manager, you need to rethink how your product appears in AI search results and how to engage users who arrive via AI recommendations.
UX designers—you're now designing for AI-first interactions. When AI directs users to your interfaces, will they know what to do?
Information architects, your job is getting more complex. You need to structure content in ways that AI can easily parse and present effectively.
Content designers, you're writing for two audiences now: humans and AI systems. Your content needs to be AI-readable while still maintaining your brand voice.
And UX researchers—there's a whole new world of user behaviors to investigate as people adapt to AI-driven search.
How Product Teams Can Optimize for AI-Driven Search
So what can you actually do about all this? Let's break it down into practical steps:
Structuring Information for AI Understanding
AI systems need well-organized content to effectively understand and recommend your information. When content lacks proper structure, AI models may misinterpret or completely overlook it.
Key Strategies
Implement clear headings and metadata – AI models give priority to content with logical organization and descriptive labels
Add schema markup – This structured data helps AI systems properly contextualize and categorize your information
Optimize navigation for AI-directed traffic – When AI sends users to specific pages, ensure they can easily explore your broader content ecosystem
LLM.txt Implementation
The LLM.txt standard (llmstxt.org) provides a framework specifically designed to make content discoverable for AI training. This emerging standard helps content creators signal permissions and structure to AI systems, improving how your content is processed during model training.
How you can use Optimal: Conduct Tree Testing to evaluate and refine your site's navigation structure, ensuring AI systems can consistently surface the most relevant information for users.
Optimize for Conversational Search and AI Interactions
Since AI search is becoming more dialogue-based, your content should follow suit.
Write in a conversational, FAQ-style format – AI prefers direct, structured answers to common questions.
Ensure content is scannable – Bullet points, short paragraphs, and clear summaries improve AI’s ability to synthesize information.
Design product interfaces for AI-referred users – Users arriving from AI search may lack context ensure onboarding and help features are intuitive.
How you can use Optimal: Run First Click Testing to see if users can quickly find critical information when landing on AI-surfaced pages.
Establish Credibility and Trust in an AI-Filtered World
AI systems prioritize content they consider authoritative and trustworthy.
Use expert-driven content – AI models favor content from reputable sources with verifiable expertise.
Provide source transparency – Clearly reference original research, customer testimonials, and product documentation.
Test for AI-user trust factors – Ensure AI-generated responses accurately represent your brand’s information.
How you can use Optimal: Conduct Usability Testing to assess how users perceive AI-surfaced information from your product.
The Future of UX Research
As AI search becomes more dominant, UX research will be crucial in understanding these new interactions:
How do users decide whether to trust AI-generated content?
When do they accept AI's answers, and when do they seek alternatives?
How does AI shape their decision-making process?
Final Thoughts: AI Search Is Changing the Game—Are You Ready?
AI-powered search is reshaping how users discover and interact with products. The key takeaway? AI search isn't eliminating the need for great UX, it's actually making it more important than ever.
Product teams that embrace AI-aware design strategies, by structuring content effectively, optimizing for conversational search, and prioritizing transparency, will gain a competitive edge in this new era of discovery.
Want to ensure your product thrives in an AI-driven search landscape? Test and refine your AI-powered UX experiences with Optimal today.
