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AI-Driven Workflow Optimisation for Titan's Watch Faces Design

CONTEXT

The project aimed to create a design system workflow using AI for watch face generation. Key outcomes included AI prompt engineering, a comprehensive design assets library and a robust workflow, aligning design, development, marketing, and product teams.

4

Designers

50

Days

2

Brands

20+

Products

20k

Watch faces

THE PROJECT BRIEF

The initial brief for the project was to design 2,000 implementable watch faces for two brands and over 15 products within 50 days. However, after conducting primary research and qualitative interviews with stakeholders from the development and marketing teams, the brief was redefined to:

Create a seamless design system workflow that synergizes the efforts of the design, development, and marketing teams to generate watch faces using AI and Extensive assets library.

THE PROCESS

The process we followed for this project was not as linear or straightforward as it may appear. However, here is my best attempt to represent all the steps involved in a linear timeline.

Anatomy of AI- Designed watch faces

Assets From the Design Assets Library

AI generated Image

The Challenge

Design Assets

The initial brief for the project was to design 2,000 implementable watch faces for two brands and over 15 products within 45 days. However, after conducting primary research and qualitative interviews with stakeholders from the development and marketing teams, the brief was redefined to:

=

AI generated Image

+

Watch face

Two Brands 

Screen sizes

Types of Interactions

Screen Resolutions

Data display
Ablities

Different USPs
to promote

Different briefs
based on products

Vector
conversion

prompt Engineering

Default watchfaces

Festive, event,
occasional watchfaces

Cloud watchfaces

Image implementation
capabilities

Image
manipulation

THE CONCERNS

Here are some key concerns and pain points of all the stakeholders in their own words

THE SOLUTION

The proposed solution involved integrating the following four pillars of the new optimised design system.

The New Workflow

Design Complications Index (DCI)

Screen Recording 2024-06-18 at 10.24.27 PM (1).gif

Design Assets Library (DAL)

Brief, Feedback & Approval formats

THE NEW WORKFLOW

The Design Complications Index (DCI)

The Design complication index is a comprehensive catalogue detailing various types of watch faces and the parameters required to create them, this pillar of the new design system sets clear expectations for the design, Development Marketing teams during the initial stages of the project

The Design Assets Library (DAL)

The Design Assets Library is created to provide designers with a standardised set of design elements. This ensures consistency across all projects, streamlines the design process, and allows designers to work more efficiently by reusing established components along with AI to create watch faces seamlessly.

THE OUTCOME

Here's a glimpse of the watch faces crafted using AI and our streamlined workflow, now enjoyed by Titan and Fastrack smartwatch users. This project not only delivered innovative watch faces but also showcased how AI can drive efficient and prolific project execution for the organisation.

Series 1

Series 2

Series 3

MY LEARNING FROM THE PROJECT 

1. Convincing Higher Management to Adopt New Technology 

In this project, I learned that simply explaining the benefits of new technology like AI isn't always enough to convince higher management. I initially sent an email requesting the adoption of an AI image generation tool. However, it was only when I created and presented sample watchfaces that the management truly understood the potential. This visual demonstration proved far more effective than words alone. As a result, they not only approved the use of the AI image generation platform Midjourney but also encouraged the entire organization to leverage AI for increased project efficiency and pace.

2. Practical Use of AI in Design workflows and systems

While AI has proven to be a valuable tool, it’s clear that human intervention is still crucial, especially when ensuring the visuals meet audience preferences. Currently, AI cannot create design assets that require precise dimensions, geometry, and vector formats, such as indices, dials, hands, and numbers. Therefore, while AI can generate creative concepts and initial designs, human designers are essential for refining these elements to meet technical and aesthetic standards.



3. Integrating AI with Design Systems and Workflows

The integration of AI into established design systems and workflows can enhance flexibility and efficiency. Design systems provide a robust structure for designers to maintain consistency and optimize workflows within an organization. AI can enhance these systems by generating variable elements like images and text, and by using machine learning to recognize and categorize content. This integration adds value to both design workflows and systems, but only if designers see AI as a tool and make it an integral part of their process.

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