Brand intelligence: surfacing hidden patterns to prevent repeat mistakes
- My role
- Lead designer
- Responsibilities
- End-to-end UX & UI, discovery, mapping, design system
- Collaborators
- QA production, production leads, creative directors, customer teams, engineering, PMs
- Timeline
- 3 months
Overview
Our production teams were making repeated mistakes, despite having brand guidelines. One team proposed a solution — a new brand guidelines hub — but I questioned whether that would solve the real issue.
After revisiting the research, I reframed the problem and proposed a smarter approach: surface brand-related insights directly from real project data (feedback, revisions, emails). This concept became the new direction we aligned on as a team.
The problem
Despite having access to brand guidelines, our production teams were still making the same brand mistakes — often only caught late in the process, resulting in repeated revision rounds and low client satisfaction.
The goal
Make sure everyone working on a brand — designer, QA, or creative lead — had access to the most relevant, up-to-date brand information at every stage of the creative process.
Context
This project was handed to me after the initial solution — a revised brand profile — had already been decided on. I was tasked with mocking up the UI. While I pushed back on the value of it, I agreed to move forward in order to have visuals to centre the discussion around.
Reframing the problem
Hesitation from the team around the value of an updated brand profile gave me room to pause and reframe. Instead of assuming ‘we need better guidelines’, I took a step back and asked:
When do mistakes happen, and what context is missing in that moment?
After reviewing internal workflows and project data, I realised the issue was timing and visibility, not a lack of documentation.
The concept
What if we used the data we already collect — revision comments, emails, and phone calls — to surface common brand issues before they happen?
I mocked up a concept that:
- Flagged recurring issues automatically (e.g. tone, logo use, framing)
- Integrated directly into production tooling
- Used AI to cluster and highlight patterns over time
User feedback
After reviewing the initial concept with users, we identified adjustments that would improve the output and its usefulness:
- Let teams mark an issue as helpful for this concept, or not
- Insights should be reviewed and summarised by a creative director, then shared with designers, to avoid ambiguity
- It should be easy to dismiss or hide irrelevant insights, to reduce noise
- Teams wanted to see a history of revision insights at any time, including those marked not relevant
- We’d need to keep tweaking the AI prompt as the feature was used, to ensure the themes stayed accurate
Outcome
With a new iteration of the concept, I shared it with the PMs, the original designer, engineers, and stakeholders. The feedback was positive and the value was clear, from both a business and a user perspective. This resulted in:
- Buy-in to shift direction toward a smarter, insight-driven solution
- Alignment of two teams around a more scalable, embedded way to prevent brand errors
- The revised approach now being scoped for implementation
Reflection
This was a reminder that the first solution isn’t always the right one — and that sometimes your real value as a designer is in helping teams slow down, reframe, and re-align.