Case Study: Designing an AI-Driven Product with Strategic Ownership
“In complex AI systems, clarity is not a bonus—it’s the core feature. Product Designers must lead the charge, not just in how things look, but in how they think and work.”
Project Overview
In this case study, we examine a team that has recognized the potential of Machine Learning (ML) and Artificial Intelligence (AI) to refine and enhance a longstanding methodology for forecasting product order volumes. By leveraging AI and ML, the team can achieve more precise ordering based on those forecasts, while also gaining the capability to monitor market prices and receive insights on how to adjust orders to minimize costs. Historically, this team has relied on Excel for manual and meticulous user input, maintaining continuous communication among members and adhering to a process honed over several decades. While this approach has been effective, they have now realized that integrating AI and ML can significantly enhance their workflow by handling larger datasets at a faster pace and generating profound insights aimed at maximizing efficiency, reducing costs, and driving business growth.
Role of the Product Designer
The Product Designer in this role helped initiate a significant project aimed at transforming a long-standing, Excel-based process into a web-based application. This endeavor required not only a user-centered design approach but also an understanding of how AI and ML can be applied effectively to realize the project goals and meet the users needs.
To achieve this, the Product Designer addressed the following considerations:
What processes were being followed?
How did users use Excel for data entry and forecasting?
Which identified business processes needed to be maintained, which could be enhanced with AI, and which could be replaced entirely?
Where could AI and ML introduce efficiencies and savings, and provide valuable insights?
Were the identified AI efficiencies and insights aligned with user expectations?
How would the design help users provide inputs easily, and act upon AI-generated outputs and insights?
How would the design help users enhance their work efficiency and enable them to focus on more strategic and human-centric tasks?
At this stage, and before any of the AI models and algorithms were developed, the Product Designer assumed a strategic role in defining the foundational framework for the data the AI models would use and the outputs and insights they would generate for user consumption and action.
By adopting a product owner's mindset and taking on a strategic role in determining the user requirements as they pertained to the AI models, the Product Designer shaped the product through the following actions:
Identifying key stakeholders through close collaboration with the project manager
Gaining an understanding of the current business process and the value the project proposes to achieve
Developing interview scripts designed to deepen understanding of:
The current business process and how Excel is used to generate forecasts
The ideal product vision and how it aligns with the user needs and expectations, particularly as it applies to the use of AI and ML
How the product could add value to users' day-to-day work
Scheduling and facilitating interviews with stakeholders.
Maintaining an openness to any additional insights gained during the interviews such as including additional stakeholders, and exploring other areas of the business as necessary.
Collecting and synthesizing feedback from the stakeholder interviews.
Establishing a framework based on the feedback analysis for user personas, user journeys and user flows.
These activities enabled the Product Designer to acquire deep insight into the current business process, the stakeholders and users and their roles, and most importantly, insights into the AI and ML user needs and what outputs would be expected .
Research & Discovery
Using the insights gained from stakeholder interviews, the Product Designer was able to:
Identify user personas based on the roles and users discussed during the interviews.
Conduct additional workshops to further refine the identified user personas.
Discover new opportunities for additional user personas not previously identified and develop them further.
Develop user journeys for key user personas or those with the most critical needs.
Create user flows that outline the application's essential features, associated screens, inputs, and outputs.
Refine user flows, enhance identified features, and ascertain any missing components and data.
Develop an information architecture (IA) aimed at providing easy and intuitive navigation that prioritized productivity and ease of navigation between various sections of the application.
The outcome of this process was a well-defined product roadmap and vision that provided a clear framework for technical teams. Data scientists, engineers, back-end and front-end developers could use this roadmap, along with the user needs and technical requirements identified, to begin designing and developing AI models. This structured approach ensured that the AI models were not only functional but also optimized to enhance the user experience.
Designing with Clarity and Logic
The research and discovery phase provided essential insights, enabling the Product Designer to conceptualize how the application would meet users' needs and allow the AI models to generate the necessary outcomes. The user flow diagrams established an information architecture that formed the basis for the features the application offered and the overall user experience. With the information architecture now established, wireframes and mockups were created to facilitate discussions with users regarding the design direction.
Wireframes and mockups enabled stakeholders and end users to understand how input is provided into the AI models and what the output would look like.
This stage was crucial in the product design process as it helped establish the foundation for robust AI models that would directly meet the users' needs.
The wireframes and mockups were refined based on feedback from users and stakeholders through recurring reviews and workshops.
Wireframes, mockups, and user flows helped the Product Designer build a prototype that:
Assisted data scientists, engineers, front-end, and back-end developers in visualizing user input and the generated output in the application.
Assisted data scientists and engineers in understanding the requirements for data input and output processing, and design algorithms to meet these requirements.
Illustrated the detailed interactions necessary to enable users to calibrate their inputs into AI models.
Facilitated collaboration between back-end and front-end developers with data scientists and engineers to design and build APIs that support the flow of data and insights from AI models.
Allowed running usability testing sessions with end users to validate the design and iterate based on the feedback received.
The development of a prototype represented a significant milestone and underscored the strategic role of the Product Designer played in guiding stakeholders and users through discovery sessions, workshops and design reviews. The Product Designer's efforts in understanding data input and output requirements, and visualizing them clearly as part of a prototype, enabled technical teams to design AI models and algorithms that met those requirement. This hybrid mindset adopted by the Product Designer, functioning as an intermediary between business strategy and technical execution, was pivotal in fostering collaboration among product, engineering, and data teams, ensuring a clear understanding of the product vision and roadmap.
To drive the success of this AI-enabled product, the Product Designer delivered a strategic and structured design process that included:
User personas, journeys, flows, and an information architecture that defined core behaviours and ensured the experience aligned with user needs.
Interactive prototypes, refined through multiple rounds of usability testing and stakeholder input.
Detailed interactions clearly outlining user inputs and the data required to support model performance.
Insight-driven visualizations, which shaped how AI outputs were presented and guided model design.
An end-to-end product roadmap, mapping the full product vision while enabling the extraction of an MVP and a plan for iterative, future releases.
Lessons Learned
In this case study, the product designer started their work by identifying users' needs and pain points related to an existing business process. Stakeholders and users wanted to explore how AI and ML could introduce savings and efficiencies into their business.
The designer's responsibilities included identifying and analyzing the problem, and integrating ML and AI solutions through extensive collaboration with stakeholders, product managers, engineers, and data teams. This collaboration was crucial for the designer to articulate the product vision using a user-centered approach while also providing comprehensive insights into the data engineering efforts needed to optimize AI model outcomes.
Designing and developing AI and ML models for a product is a time and resource-intensive process. Therefore, it is essential for organizations to ensure these resources and efforts are invested in a manner that maximizes benefits and potential gains. In this case study, the Product Designer’s role was vital in establishing the product vision and roadmap, and in helping the various project teams understand and acheive this vision.
