Using Research to Inform AI Innovation
The goal of this project was to research user pain points, evaluate the current state of the product, and identify opportunities to integrate AI that would reduce customer issues and ease the load on the customer success team. I uncovered key challenges, mapped AI-driven opportunities to improve both the editor and marketplace experience, and collaborated with development to create an AI opportunity matrix that was presented to stakeholders to guide next steps.
Role
Senior Product Designer
Duration
4 Months
Tools
Mural
Google Docs
Deliverables
User Research, Competitive Analysis, Tool Evaluation, Thematrix Table, Pain Point Solution, Next Step Recommendation, Stakeholder Reports, and AI Opportunity Matrix.
Overview
This project leverages deep user research, heuristic evaluation, and quantitative data to shape a long-term vision for how AI can transform the TreeRing editor and market place tools, enhancing user experience, empowering employees, and positioning the platform for future growth.
Why TreeRing Needed AI Now
As TreeRing’s yearbook editor neared its 15th anniversary, the platform was showing its age. Slow performance, outdated functionality, and a cumbersome creation process left many users frustrated and dissatisfied. What should have been a creative and exciting project of building a yearbook too often turned into a time-consuming struggle marked by lagging tools, unreliable features, and broken layouts.
Over time, these recurring issues compounded. Customers began relying on third party software to complete even basic design tasks. Parents and editors reported blurry or distorted images, cropped faces in final prints, and grid tools that failed to snap into place. These problems not only frustrated users but also created costly ripple effects including higher support call volumes, expensive reprints, and sales representatives avoiding certain features altogether when demonstrating the product to schools.
TreeRing needed AI now because the existing editor was no longer keeping pace with modern user expectations. Customers wanted faster, smarter, and more reliable tools that reduced tedious tasks and gave them confidence in the final product. For sales, an updated AI driven feature set offered a chance to re energize demos and strengthen retention in a competitive market. For customer service, it meant fewer repeated issues and less reliance on costly rework.
In short, AI was not just about innovation. It was about solving long standing frustrations, rebuilding trust with users, and giving TreeRing a clear path forward to modernize its platform for the next generation of yearbook creation.
Setting the Stage for AI Integration
Before jumping into solutions, it was important to establish a clear foundation for how AI could realistically support both users and the business. The research revealed recurring frustrations: slow performance, clunky photo workflows, and a lack of guidance that left many customers turning to third party tools. At the same time, sales and customer service teams stressed the need for features that not only streamlined creation but also rebuilt user trust and confidence in the editor.
With these insights, I began framing AI not as a novelty but as a strategic enabler. The goal was to define where automation, intelligent assistance, and guardrails could make the biggest impact without overcomplicating the experience. By mapping user journeys, evaluating pain points against feasibility, and aligning with business priorities, I created a shared understanding of where AI should integrate first.
This groundwork positioned AI as a solution to real user problems, a driver of efficiency for customer service, and a competitive differentiator for sales. It also set expectations across teams, ensuring that design, development, and stakeholders were aligned on both the immediate opportunities and the long term vision for AI within TreeRing’s platform.
Research
Research was the core of this project and ultimately the most important phase. It provided the foundation for identifying the strongest opportunities for AI and shaping the strategy for how they would be executed. Throughout this stage I applied standard UX research methodologies, including user interviews, evaluation of the editor tool, exploration of key themes, and the merging of design insights with NPS feedback and support data. From there I generated thematic matrices that uncovered patterns and informed clear recommendations, which guided the strategy moving forward.
Uncovering User Pain Points Solution Table Through Interviews
Between May 20 and June 20, I conducted in-depth interviews with customer service representatives and sales team members to uncover the realities of how TreeRing is pitched, supported, and used day to day. By observing live sales demos and yearbook creation workflows, I captured recurring frustrations in real time, from features that sales avoided due to poor performance to customer reliance on third party tools for basic design tasks. Customer service teams added another layer of insight, highlighting patterns in support calls, points of confusion, and the need for clearer guidance. These sessions not only revealed where the editor was breaking down but also directly shaped the AI integration strategy by pinpointing opportunities for automation in photo formatting and layout generation, while reducing the support burden. The research became the bridge between understanding user pain points and designing AI-driven solutions that would modernize the platform.
Evaluating the Current State of the Editor and Competitive Analysis
I conducted a full heuristic evaluation of the TreeRing editor, drawing on my UX background and prior research to measure latency, document bugs, and capture usability challenges throughout the workflow. I also ran a competitive analysis to understand how other tools in the market were addressing similar needs, since many TreeRing users were already relying on third party apps to complete their projects. What I found was telling: the editor was extremely slow, often underperforming or breaking during the yearbook creation process, which led to dissatisfaction and extended time spent on even simple tasks. In contrast, smoother tools like Canva allowed me to work more efficiently side by side, highlighting just how outdated and cumbersome TreeRing’s experience had become and underscoring the urgency for modernization through AI-driven solutions.
Revealing NPS Feedback and Support Data Findings
Because TreeRing’s editor had been in use for nearly 15 years, recurring annoyances were mounting, and it was critical to understand exactly why users were dissatisfied in order to prioritize fixes and identify where AI could make the biggest impact. Out of 618 detractors in the 2024 school year, 337 left written comments that revealed systemic issues with TreeRing’s editor, from platform instability and lagging (35 percent) to poor usability (21 percent) and missing features (14 percent), with additional frustrations around production, portrait setup, graphics, and ordering. By combining LAD tagging, BERTopic clustering, and keyword scans, I was able to confirm these themes across both NPS and support data, proving the problems were widespread and deeply embedded in the product. This analysis not only exposed where the tool was breaking down today but also created a clear roadmap for where AI could bring the most value, such as stabilizing photo uploads, automating layouts, and providing clearer guidance through guardrails and fallbacks. These insights directly informed the AI integration strategy, ensuring the redesign targeted the root causes of user dissatisfaction while laying the foundation for a more reliable and modernized platform.
Generating The Thematic Matrix
My thematic matrix brings together insights from multiple research sources, including LAD tagging of NPS comments, BERTopic theme modeling, and interviews with sales and customer service teams. By cross referencing these datasets, it reveals recurring pain points, the themes that matter most, and the user groups most affected. This synthesis validates which challenges are systemic and highlights where improvements and AI integration will deliver the greatest outcome.
Through a mix of data analysis and interviews, I uncovered that slow performance and system lag were the most pressing issues, consistently cited across every source and severely undermining user trust. Alongside this, users reported frustrations with editing layouts, managing orders, handling portraits, and relying on limited tools, with Chief Editors and parents feeling the greatest strain. These recurring challenges, paired with broader usability gaps like confusing onboarding and weak search, made it clear that the platform needed a modern, AI-driven approach to streamline workflows and rebuild confidence.
This thematic matrix triangulates insights from tagging, clustering, and user feedback to validate recurring platform challenges, highlight who is most affected, and create a clear roadmap for prioritizing redesign efforts and AI integration.
Strategy
The next phase of the project focused on design execution, where I produced annotated wireframes, high fidelity mockups, and a prototype to define the product vision in detail. These deliverables established a single source of truth for all teams, eliminated ambiguity, and streamlined the development handoff. By anticipating edge cases early, they also provided a solid foundation for technical documentation and ensured the product could be built with consistency and efficiency.
Creating The Pain Point Solution Table
To move from research into actionable design, I built a solutioning table that combined qualitative insights from user interviews with quantitative findings from the Business Analyst report. This table validated the earlier research by revealing strong consistency across all data sources, including NPS comments, support cases, and interviews, confirming that the pain points we identified were not isolated but systemic. By aligning qualitative and quantitative insights, the table reinforced the reliability of each issue, highlighted recurring patterns, and surfaced both AI and non AI solution opportunities. With this foundation, the design team was well positioned to make informed decisions around tool enhancements and AI recommendations that would directly address user concerns and guide the redesign strategy for the current fiscal year.
Addressing What Needs To Be Done Now
Through interviews, support data, and research analysis, it became clear that many of TreeRing’s biggest challenges could be addressed without waiting for AI. Parents, editors, and administrators were all struggling with friction in different parts of the platform, and each frustration chipped away at user trust and added to the burden on customer support.
For parents, the experience often felt confusing and fragmented. Dashboards lacked clarity, navigation between the marketplace, editor, and purchasing flows was disjointed, and mobile access was unreliable. As a result, parents missed custom pages, made duplicate purchases, or abandoned tasks altogether. By redesigning the dashboard, simplifying navigation, and creating mobile-first workflows with persistent confirmations, the process would feel more seamless and intuitive.
Submission and printing workflows were another major source of stress. Users frequently missed deadlines, misunderstood submission status, or made last-minute errors because critical steps were unclear. A step-by-step submission flow with visible deadlines, localized time zones, and real-time progress tracking would reduce confusion, cut down on reprints, and build trust in the process.
Editors and administrators also felt limited by outdated tools. Tasks like tagging, indexing, and layout adjustments had to be repeated manually, alignment tools often failed, and print previews did not reflect the final output. Introducing bulk actions, reusable components, accurate previews, and reliable alignment would save time and create more professional results.
Finally, usability issues cut across the entire platform. Onboarding was one-size-fits-all, UI bugs were persistent, autosave and undo functions were missing, and notifications were scattered. Tailored onboarding, contextual education, and a unified design system would give users confidence from the start, while fixing long-standing bugs and improving communication would reduce dependency on support.
These recommendations represent a path forward that addresses pain points uncovered in research while strengthening the foundation of the tool. They provide immediate, tangible improvements that modernize the experience today, while also creating the stability and consistency needed to support larger AI-driven innovations in the future.
Recommending AI For The Editor Tool
As I moved deeper into the project, it became clear that AI had the potential to do more than just patch existing pain points. It could fundamentally reshape the TreeRing experience. Users were struggling with slow workflows, confusing tools, and repetitive tasks, and the research made it obvious that automation, smarter guidance, and predictive features could ease much of this frustration. My goal became not only to modernize the platform but also to reimagine how users interact with it through AI.
One of the biggest challenges users faced was simply getting started. Parents and editors often stared at a blank page, unsure of how to arrange photos or worried about making mistakes that would show up at print. Here, AI could step in as a design partner, suggesting layouts, autofilling pages with best fit photos, and surfacing real time alerts before problems snowballed. Users would still be able to personalize and override choices, but instead of spending hours building from scratch, they could refine and polish, shifting from frustration to creativity.
The same principle applied to photo management. Editors wasted countless hours tagging, centering portraits, and cleaning up uploads, while many parents turned to third party apps just to prepare images. By introducing facial recognition, automatic background removal, and AI driven tagging, the tool could handle the tedious work behind the scenes and free users to focus on storytelling. Smarter search and filtering would also make it easier to find the right content without wrestling with clunky file structures.
Printing and submission were another consistent source of stress. Deadlines felt vague, confirmation steps were unclear, and mistakes often were not caught until it was too late. AI could monitor books in the background, highlight unresolved issues, send personalized countdowns, and even predict which projects were at risk of being late. This proactive approach would reduce last minute chaos, save money on reprints, and give users confidence that they were on track.
Parents in particular stood to benefit from a guided AI builder for custom pages. Many did not know where to start or abandoned the process entirely. By surfacing the builder earlier, recommending layouts, and giving real time feedback when content was missing, AI could make the experience simple and reassuring, helping more parents finish pages successfully while reducing support requests.
Finally, AI could scale the platform’s ability to support users. A chatbot trained on past tickets could instantly resolve common questions, while context aware prompts would detect when users were stuck and provide help in the moment. For editors, automation could simplify heavy workflows like index creation, bulk photo management, and duplicate detection, making it possible to handle large volumes of content with ease.
Together, these ideas represented more than feature fixes. They told a story of how TreeRing could evolve into a smarter, more supportive platform. By embedding AI into critical workflows, the tool could transform from a slow, outdated editor into a modern companion that helps users every step of the way. This vision not only addressed today’s frustrations but also set TreeRing up for long term innovation and scalability.
Suggesting Next Steps
To address long-standing user frustrations and prepare TreeRing’s platform for the future, I developed a set of recommendations grounded in research, interviews, and quantitative data. These steps were mapped against the yearbook creation workflow and aligned with a seasonal schedule so improvements would reach users when they needed them most.
Performance was one of the biggest challenges. A 15-year-old tool showed its age with lag, crashes, and slow responses during editing, uploading, and submitting. I explored two paths forward: a gradual refactor to modernize core flows, or a full rebuild using modern frameworks with autosave and real-time previews. While both would reduce friction, a rebuild offered the greatest opportunity to meet expectations and deliver a future-ready platform.
The parent journey also needed urgent attention. Parents frequently got lost between the editor, marketplace, and purchasing flows, leading to missed pages or duplicate orders. Redesigning the dashboard, simplifying navigation, making the experience mobile-friendly, and overhauling submission workflows with clearer steps, localized deadlines, and real-time tracking would reduce confusion and costly errors.
Editors and administrators required stronger tools to work efficiently at scale. Bulk actions, reusable components, reliable alignment, and accurate print previews would save time and improve quality. Onboarding needed to shift from one-size-fits-all to role-specific guidance with contextual help and embedded education. Fixing UI bugs, improving consistency, and adding essentials like autosave and undo would rebuild trust in the system.
AI and automation added further value by reducing repetitive work and providing smarter workflows. Features like auto-tagging, background removal, duplicate detection, and AI-guided layouts could streamline creation, while proactive alerts and chatbots would prevent missed deadlines and ease the burden on support.
To deliver improvements in a structured way, I proposed a season-ahead roadmap: summer for editor and admin tools, fall for submission redesigns and AI layouts, winter for parent dashboards and mobile-first updates, and spring for predictive support and workflow automation. This phased approach ensured continuous progress and alignment with the natural rhythms of yearbook creation.
By defining next steps across design, project management, and development, each team had clear ownership and a roadmap for delivery. From heuristic evaluations and annotated wireframes to scoped JIRA epics and technical assessments, the process gave stakeholders confidence while keeping the user at the center. This strategy positioned TreeRing to resolve critical pain points, modernize the platform, and deliver a more cohesive and user-friendly experience, while laying the foundation for advanced AI integration.
AI Opportunity Matrix
After spending time with users and analyzing how the platform is used day to day, I uncovered significant opportunities for TreeRing to integrate AI in ways that directly support the business. AI not only reduces overhead by streamlining management and lowering the demand on customer service teams, it also boosts productivity and efficiency across all user groups. By bridging the yearbook editor with the marketplace, the platform can create new opportunities for upselling and cross-selling, turning a once outdated tool into a driver of revenue growth while simultaneously reducing operating costs. This dual impact of efficiency and profitability positions AI as both a user experience improvement and a long-term business strategy.
When I began mapping out where AI could make the biggest impact, I wanted a way to connect user pain points with business goals in a format that everyone on the team could easily understand. That is how the Yearbook and Marketplace AI Opportunity Map came to life. This framework visually illustrated not just where AI could fit into the TreeRing platform, but also how feasible each idea was and what kind of return it could bring. High impact opportunities stood out quickly as the same frustrations users voiced in interviews, like speed, usability, and confidence, aligned directly with areas that could improve conversion, retention, and long term trust. By showing how AI could bridge the yearbook editor and marketplace into one seamless experience, the map revealed a clear path to both reducing customer reliance on third party tools and opening new revenue streams for the business.
When I analyzed the high impact, low feasibility quadrant, I recognized an opportunity to bridge user needs with business growth. I positioned these features as more than usability improvements, but as strategic investments that would modernize TreeRing’s platform while reducing support costs and increasing retention. By framing solutions like smart photo selection, user activity tracking, auto generated captions, smart previews, and automated layouts in terms of both feasibility and value, I built a business case for AI that balanced user efficiency with profitability. My role was to ensure that design insights translated into clear strategic direction, showing how AI could move the platform away from outdated workflows and third party reliance toward a unified, scalable, and revenue generating experience.
In summary, my research and design strategy uncovered clear pathways for TreeRing to increase revenue by integrating AI in ways that both modernize the platform and directly address recurring user pain points. By streamlining core workflows, introducing intelligent automation, and reducing friction across the editor and marketplace, I positioned AI not only as a tool for efficiency but as a driver of retention, upselling, and new customer acquisition. These improvements help rebuild user trust, prevent churn to competitors and third party tools, and create market awareness of TreeRing as a forward thinking, AI powered solution. More than feature upgrades, this approach represents a strategic shift where design and AI integration fuel long term business growth, reduce operational costs, and elevate the overall customer experience.
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