Designer

The Designers Role

The Designer focuses on uncovering real customer problems and creating user-centric solutions that are functional, usable and visually appealing. Designers conduct customer research to understand behaviours, needs and motivations, and they design experiments to evaluate the effectiveness of the solutions they are building. The role spans the entire product lifecycle, from ideation through to delivery and iteration based on user feedback and product performance.

Product Risk

The designer has a core responsibility for validating the Desirability risk — do customers really want this product? — and the Usability risk — can customers use this product to solve their problems? They also provide input into the Third-party incentive risks, Feasibility risk and Viability risk.

Key Responsibilities

The whole Stream Team works on these activities together using the teaming model — there are no handovers between roles. The list below describes the activities the Designer leads and brings the most expertise to, but the team wins or loses on the outcome together and every team member participates in every step.

1. Preparing Research: Working with the other Stream Team members to identify the research objectives, target audiences and developing a research plan with the necessary materials to conduct the research.

2. Conducting Primary Generative Research: Designing and executing various primary user research methods such as interviews, surveys, diary studies, and field studies to gather meaningful data.

3. Conducting Secondary Generative Research: Designing and executing various secondary research methods such as reviewing analytics data, sourcing industry reports and running competitive analysis.

4. Synthesising Insights: Analysing the collected data to derive actionable insights. This involves identifying patterns, pain points, and user needs that can inform product decisions.

5. Communicating Research: Sharing the insights with the rest of the team in a way that is easy to understand and actionable.

6. Prioritising Opportunities: Working with the Stream Team to identify the most promising opportunities to pursue.

7. Generating Solutions: Engaging in brainstorming sessions and workshops to generate a wide range of ideas and solutions that address identified user needs and product objectives.

8. Prioritising Solutions: Comparing and contrasting potential solutions to rank and select the most promising solutions for further development.

9. Identifying Assumptions: Listing out the underlying assumptions behind proposed solutions, based on the dimensions of desirability, usability, viability and feasibility.

10. Prioritising Assumptions: Determining which assumptions are most critical to the success of the solutions and should be tested first.

11. Designing Experiments: Developing structured tests and experiments to validate the key assumptions and learn more about the users' behaviours and needs.

12. Running Experiments: Assessing the outcomes of experiments and prototype tests to make informed decisions about which solutions to pursue or iterate upon.

Key Artifacts Produced

1. Research Objectives: A document outlining the research questions that need to be answered.

2. Jobs to be Done: A document outlining the jobs that users are trying to accomplish and the progress they are trying to make.

3. Research Plan: A document outlining the research methods, the target audience, and the timeline for the research.

4. Interview Guide: A document outlining the questions that will be asked during user interviews.

5. Interview Snapshots: A document summarising the key insights from user interviews.

6. Research Repository: A collection of all the research artifacts produced by the Team.

7. Customer Journey Maps: A visualisation of the flow of each persona / job when interacting with the product.

8. Opportunities: The potential areas of focus that the team is considering based on the insights gathered.

9. Opportunity Solution Tree: A visual representation of the opportunities that the team is pursuing and the solutions that are being considered.

10. Unvalidated Solutions: A comprehensive list of potential solutions generated before validation, aimed at addressing the identified needs and objectives.

11. Assumptions: A detailed list of the assumptions underlying each proposed solution, which are critical to its success and need to be tested.

12. Experiment Plan: Documented strategies for testing the assumptions and viability of solutions, including methodologies, variables, and expected outcomes.

13. Prototypes: Interactive or static models of solutions created to explore, test, and validate ideas with users and stakeholders.

14. Validated Assumptions: Analyses and findings from conducted experiments, providing insights into the effectiveness and feasibility of the proposed solutions.

15. Invalidated Assumptions: Clarity on the solutions that were tested and did not meet the desired outcomes, helping to refine the design direction.

Why combine research and design?

A common misconception is that the designer's role is to make things look pretty. However, the designer's role is much more than that. The designer is responsible for ensuring that the solutions being developed solve real customer problems and are functional, usable and visually appealing. This involves a deep understanding of the underlying problems, the ability to create innovative solutions and the skills to create quick prototypes to test and validate them.

Working with AI

AI changes the designer's work in three ways:

1. Research synthesis is faster. Clustering interview snippets, finding themes across hundreds of support tickets, summarising survey free-text. Work that previously took days can be done in hours. The designer's value moves from doing the synthesis to interrogating it: spotting where the AI summary has flattened a nuance, or where a small but important counter-signal has been averaged out.

2. Prototype fidelity gets cheaper. AI-assisted design tools generate plausible UI quickly, which makes it tempting to skip exploration. Resist that pull. The value of generating multiple solutions before converging is unchanged. Use AI to broaden ideation, not to short-circuit it.

3. AI-native interactions are now part of the design space. When the product itself uses AI, the designer is also designing the model's behaviour, the tone, the failure modes, and the signals that tell the user what just happened and why. This requires new evaluation methods (see running experiments) that go beyond traditional usability testing.