Next: Agent behavior ->
Foundational AI agent exploration work — Klook
Foundational content design and UX research work. Sep 2024 - Jun 2025
Portfolio note: Working on conversational AI wasn’t just a one-off project, but iterations on content and conversational design over various features. I’ve documented some of that process here, with more to share in interviews.
Building the foundations
While working on Klook’s first B2C AI features, I conducted desktop research, user interviews and usability tests for each one, to sharpen and clarify these UX questions:
- What are users’ mental models at each step of a trip planning and booking process?
- How do users perceive AI at each step of the booking process for different products, e.g. reading hotel reviews vs. comparing tours?
- What tone and persona should Klook’s conversational AI adopt? How might it cohere with Klook’s existing brand voice?
- What user problems might actually be solved by AI?
Early explorations: AI reviews summary feature
As the lead UX writer on Klook’s first consumer AI feature, I conducted user interviews with an internal panel to find out how users perceived an AI reviews summary for hotels, and mental models surrounding AI more generally. This was intended to shape design direction and product decisions.
I designed the prototype with content, and had help from the designer to refine the interactions. Click into the first image to watch the prototype — simple, but effective for usability testing.
Insights: What I found was that while users find review summary for hotels extremely helpful, this needed to be tempered by users’ booking phase.
- In the first phase of travellers’ booking process, most are in what I called an “objective phase”. They will be focusing on the facts like location, beds, amenities, rooms etc, to narrow down hotels that fit their needs. In this phase, travellers were more likely to value the help that AI. could provide in helping them “sift through” and “narrow down” their choices.
- However, to come to a final decision, users go through a 2nd phase, which I called the “subjective phase”. At this time, users would be relying on reading “authentic” reviews from other travellers. This prioritization of authenticity led them to be skeptical of the completeness of AI review summaries.
Challenge: Making a case for product naming — pushing back against product managers and designers, who wanted to emphasize that the feature used “AI”, as they perceived that this would impress users.
Applying UX insights to content design
I found from research that when users are in the “subjective phase”, an AI summary becomes less helpful and in fact obstructive. I shared this with product and design, which made for a clearer content rationale than any of our gut feelings.
Based on the insights above, coupled with the fact that the reviews summary would be situated within the reviews section — squarely in the “subjective phase” of users’ decision-making, I went with a softer content approach. I focused content concepts and copy on reassurance and highlighting the benefit of the feature.
Two content design solutions I implemented
Content contributions included
Hypothesis: Users are likely to feel safer and more reassured by the module if it speaks to how they can benefit from it and their concerns about authenticity, rather than trying to flex Klook’s use of AI.
Pushback from designers and product managers: Believed that highlighting “AI” would impress users and make them more likely to use the product. While user testing shifted their mindsets, they still felt that it would be ideal to launch with a mention of “AI” somewhere.
Move the mention of “AI” into the subtitle, and leading instead with a product name that highlighted the benefits to users.
Similarly, in the subtitle, instead of making bold, unsubstantiated claims of “real guests reviews”, I used “real reviews” instead, to directly speak to the concerns about authenticity that users faced in the subject phase.
I supported this solution with competitive analysis from competitors, such as Trip.com, Expedia and Tripadvisor, which made it more compelling.
Hypothesis: There were too many numbers on the screen, each carrying a different meaning. There were the rating (e.g. 4.5/5), number of reviews (e.g. 274 reviews), “1/5” and “10 reviews”. The first two were fine, as they fit into users’ mental models of hotel reviews. The latter two caused confusion as users weren’t used to seeing it, and without significant affordance, it was hard to grasp. Moreover, this information is not significant enough to users’ decision-making to motivate them to dig into what those numbers mean.
Simplify the information on the screen, remove extraneous numbers to avoid obfuscating the key information on the page. I presented the findings and my hypothesis to product and design, as well as the content solution. They were convinced by the findings, and updated the design and product logic.
Content contributions included
Hypothesis: Users would be more reassured with some facts about how AI is used, and what goes into the reviews summary.
Simplify the blurb, focus on delivering the key information that users need to know.
Don’t overly humanize this section (e.g. “hi, i am”) — save the conversational elements for a conversational interface. This blurb should focus on delivering facts, rather than getting friendly with the user. Product and design were willing to try this, and agreed that conversational elements be saved for a conversational interface (more to come in the next projects).