Enterprise Case Study
Customer-Centric AI: Co-designing Industry Solutions
Navigating Uncertainty and Leveraging User-Centered Design to Explore AI Solutions for Australian Industries Facing Data and AI Adoption Challenges
40+
Interview Hours
20+
Team Members
300+
Ideas
4.5/5
Desirability Score
Project Details
Client
Major Telco and Major Software Firm
Duration
9 months
Skillset
AI Enabled Service Design and Research Product Design Gen AI Concept Testing with Prompting & Synthetic Data Human-Centered AI Ethics
Tools & Stakeholders
Tools
Miro Adobe XD SharePoint Confluence JIRA Excel PowerPoint MS Teams CoPilot Gen AI
Key Stakeholders
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Product Owners and Product Managers
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Scrum Leads
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AI Experts
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Data Stewards
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Data Scientists
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Software Developers
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Data Engineers
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AI Leadership
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Data and Reporting Professionals
Case study details
In the competitive Data and AI landscape during 2023, I joined an innovation team at a leading telecom company to explore AI solutions for Australian industries.
Our mission: tackle data complexities and AI adoption challenges for Australian industries.
Challenge: With only high-level insights into our target audience and facing strong competition, the project was complex and daunting, especially as I was new to AI.
Despite this challenge, I led my team through problem definition, user research, and prototyping, focusing on user needs. This laid the foundation for collaboration with AI experts, data scientists, and engineers from our major telco and major software firm. By the end, our synergy turned challenges into opportunities, establishing a strong product-user-market fit with promising AI use cases.
"By the end, our synergy turned challenges into opportunities, establishing a strong product-user-market fit with promising AI use cases."
Over the course of a few months, my team and I navigated the complexities of AI adoption within Australian industries. Following the double diamond approach, I guided us from initial ambiguity to a clear, user-centered solution. Each phase in the project is loosely represented by the double diamond process image below. The major phases can also be seen in the figure below, with each phase building on the insights from the previous stage.
Here’s how the journey unfolded:
1. Initial Planning and Scoping:
In my first week, I met with the Product Owner and Product Manager to understand the project’s goals, stakeholders, and resources. I created a Design Brief outlining how I could support the team. Although I was initially nervous about my limited AI knowledge, I immersed myself in AI research, consulting experts and reading extensively (I do love learning :-)). While I maintained my primary focus on user needs, I also spent time to ensure I understood the business context and AI-related insights to meaningfully contribute from day one. Ensuring alignment between design work and business outcomes is something I prioritize in every project.
2. Kick-off problem discovery
Following our initial consultations, it became evident that the team had some understanding of the issues but lacked a clear mission and specific goals for our AI solutions. This ambiguity was one of the key reasons they sought a Service Designer—to help untangle these complexities and refocus on the customer. To address this challenge, I facilitated a series of workshops using MIRO. During these sessions, we defined key questions to explore, drafted problem statements that our product could address, mapped stakeholders, assessed customer knowledge gaps related to the problem statements, and envisioned desired outcomes. These workshops provided a solid foundation for the subsequent phase: research and discovery aimed at understanding our target users and the broader market for the AI product we were developing.
As insights from the problem discovery workshops emerged, the design brief and plan were iteratively updated. The screenshots showcase sections of the design brief and the problem discovery workshop layout, all created in MIRO.
3. Research and Discovery
Phase 1: SME & Thought Leader Interviews
Following the problem discovery phase, I identified several knowledge gaps, particularly around customer needs for an end-to-end AI solution. As my team had already done several interviews with Subject Matter Experts and Thought Leaders, I built on that work, synthesizing all interviews, including looking at research from firms like Omdia and Gartner. While this provided a broad view of the challenges Australian industries face with AI, the insights weren’t specific enough to guide our project. We needed more actionable insights.
Phase 2: User Interviews
To deepen our understanding, I collaborated with the team to develop a research plan targeting specific industries, aiming to capture insights into both leadership perspectives and user experiences. Together, we crafted a strategy to examine the end-to-end journey, from purchase and usage through to troubleshooting and support, focusing first on understanding the product experience and the problems it could solve. Our goal was to capture a holistic view of the end-to-end experience, although first up was to understand the product experience and what problems it could solve. However, gaining access to senior leaders proved challenging, and attempts to form a Customer Advisory Board took longer than anticipated (something that I have worked on earlier in order to engage customers in research and discovery, see the Customer Advisory Board Research case study for more details). Fortunately, our Product Owner secured new partnerships, allowing us to pivot to interviews with data and AI professionals at an enterprise company. This shift was a breakthrough. The actionable insights from these professionals provided clarity on key pain points and opportunities, helping guide our next steps. I led the research, developed interview guides, conducted the interviews, always ensuring someone from the product team was present as well, and synthesized the findings, ensuring the project continued to move forward.
Phase 3: Workshops & User Journey Mapping
With sufficient insights gathered, I organized workshops to map user journeys so we could get a better understanding of the biggest pain points/opportunities for innovation across our target users work. This phase was particularly enjoyable for me, as it involved real-time collaboration with our team and users. Through empathy exercises and experience mapping, we refined our scope and identified new target users. I adapted our methods based on participant preferences—whether through Miro boards or a mix of interviews and workshops—to keep participants engaged and comfortable. This flexibility paid off, resulting in deeper insights and more enthusiastic engagement from participants. One even said, "Yeah, your project is really fun—I like participating in the workshops. Please keep me involved!"
The screenshots below highlight various artifacts I created to guide the team through collaborative exercises during the research and discovery phases, including research planning, interview guides, empathy persona- and journey mapping. Many of these artifacts were developed in MIRO, but I also used PowerPoint, Word, and Excel—focusing on choosing the best tools for the task based on what we had access to and what would make collaboration as seamless as possible for the team.
4. Define
I synthesized insights from our research. I focused on making insights visible and actionable, moving beyond traditional reports, for example, I facilitated team collaboration through HCD workshops and created a SharePoint site with key findings and resources. This also resulted in the co-creation of personas and journey maps. As being part of an agile team, I also consistently brought the customer's perspective into discussions, influencing technical decisions where users would be impacted.
The screenshot is an example of an empathy and experience rating workshop that I designed and facilitated. During this workshop, our target user rated the experience in touch points along their journey. This helped us understand where the biggest pain points were across the journey.
5. Ideation and Prototyping:
Building on the insights we gathered; I led brainstorming sessions to map out future product experiences. Our developers, who also participated in ideation workshops, then explored the future product ideas from a technical perspective, while I tested them using generative AI models. As a curious learner and always trying to find out what's the best options available, I delved into methods like Wizard of Oz validation and AI Fairness 360 to better understand the user experience and how to validate our solutions.
Additionally, I researched game-thinking concepts to get ideas on how to make our potential product more engaging.
All this led to the creation of storyboard scenarios, which I developed in close collaboration together with the Product Team based on the feasible ideas our team had tested. Storyboards proved to be a quick and powerful way to validate desirability with our potential user groups. This approach also allowed us to focus on validating the logic behind the experience rather than getting bogged down with detailed wireframes, which often according to my experience, shift participants’ attention to the interface.
The validation through storyboards paved the way for the creation of low-fidelity wireframes, which I developed based on the ideas deemed most valuable.
This screenshot illustrates examples of the storyboard concepts we used to validate the desirability of feasible ideas. While I can't share the actual ones due to confidentiality, these mock-ups represent the approach we took. These simple storyboards were incredibly powerful in gathering quick feedback, helping to guide our direction. They also informed the design of the initial detailed UX wireframes in an interactive prototype that I designed in Adobe XD, which showcased the key interactions for the product experience. Unfortunately, I don't have the wireframes available. Although I may make them up when I got time. Also need to figure out what concepts to apply as I cannot use the actual concepts due NDAs. Stay tuned.... :-)
How Gen AI Supported My Design Process
Not only did our team develop new and innovative AI solutions for the project; we also embraced Gen AI tools throughout the design process itself. By integrating generative AI into every stage of our workflow, I accelerated research, enhanced collaboration, and brought fresh perspectives to our design decisions. This approach helped us move faster, uncover deeper insights, and deliver more user-centered outcomes.
There are different modes of using AI: automating tasks, offloading entire processes with Agentic AI, or augmenting your work. In this project, I primarily used AI to augment my design process—acting as a team member, mentor, and thought partner. This back-and-forth dialogue with Gen AI challenged my thinking and allowed me to explore new possibilities at each project phase.
While the fundamentals of human-centered design remain, AI is transforming how we collaborate and solve problems—spanning channels and even empowering independent AI agents. I am also actively exploring Agentic AI tools, such as Lindy, for independent task execution and automation in design operations—making this practice future-ready and continually evolving as new tools and capabilities emerge.
How I Applied Gen AI Across the Project:
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Landscape Framing & Planning: Used Gen AI to research and summarize industry reports, rapidly defining customer needs for research planning and testing.
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Customer Research: Leveraged AI to generate interview guides, synthesize qualitative data, and draft insights—always with human oversight and review.
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Journey Mapping & Ideation: Created personas, mapped journeys, and ran workshops, with Gen AI providing ideas and summarizing workshop outcomes.
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Concept Testing & UX: Used AI for drafting documentation and summarizing research insights, while manually designing the UX screens.
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Validation & Storytelling: Applied LLMs to validate logic, challenge assumptions, and transparently document where and how AI was used in the process.
This is a continuously evolving area of practice for me - I regularly integrate new AI tools and methods as they become available, further refining my design process and maximizing project impact.
My efforts
The data from this project (as shown in the accompanying image below) indicate the effort I brought to delivering meaningful results for the team.
While confidentiality limits the details I can share about the insights and AI use cases we developed, I would be happy to discuss the approach and process in more depth if needed.
The above efforts were crucial in driving actionable insights and aligning the project with user needs and business goals.
While confidentiality limits the details I can share about the insights and AI use cases we developed, I would be happy to discuss the approach and process in more depth if needed.
KEY OUTCOMES OF THE INDUSTRY IQ PROJECT:
My work, leadership and close collaboration with the team led to the following key results for the project:
Customer Insights
I conducted 40+ hours of interviews with stakeholders and potential users, deepening our understanding of customer needs and AI use cases. This helped us define a strong customer value proposition and align our solutions with user expectations.
Business Impact
The project started with 3 rounds of in-depth research, followed by collaborative workshops to map journey maps, create personas, and ideate on opportunities, resulting in over 300 innovative ideas. These insights were used to assess the feasibility of product concepts, which were then tested through storyboards. The team achieved a desirability score of 4.5/5, demonstrating strong customer interest and potential for significant time savings. This validation of product concepts directly influenced our strategic direction, leading to the design of detailed wireframes that translated these insights into effective, actionable solutions, ultimately driving key business outcomes.
Personal Growth and Development
This project was a major contributor to both personal and team growth. I’m humbled to have learned so much from collaborating with my team, stakeholders, and users throughout the process. By continuously learning and applying Human-Centered Design practices, we collectively enhanced our skills and refined our approach to AI-driven innovation. This experience deepened my understanding of diverse perspectives and strengthened our team’s ability to deliver meaningful solutions.
Disclaimer: Due to confidentiality, specific details or insights about the service or product cannot be disclosed. While my case studies focus on my contributions, I acknowledge that these achievements would not have been possible without the collaboration of my dedicated team and stakeholders. I am honored and humbled to have worked alongside such fantastic people on these projects.