How Smart Design Systems can improve business processes?

In big organizations, the number of digital products online, ready for production or being in a development phase, often take overwhelming dimensions. Sometimes, the execution of these projects is assigned to specific departments, occasionally executed in-house or outsourced to vendors, which most of the time generate working silos where everything tends to be disconnected. If we follow up these efforts closely we might discover a chaotic outlook trying to find consistency over relevant subjects such as user experience patterns or uniformity in visual and technical aspects.

The regular design style-guidelines, un-thought for digital purposes, will not be enough to tie up the complexity of multiple digital products created to fulfill diverse types of objectives, at this point, everything is becoming hard to handle and only one thing is clear, the lack of governance in this matter will bring a negative effect over the customer experience and user’s perception of the brand, harming and diminishing the effectiveness of all these efforts.

A new paradigm

Considering the design system’s evolution and their current state of the art, many professionals in the field are starting to look seriously at their benefits. The design systems are not just beneficial for designers, but to the whole chain of digital production, enabling more room to make emphasis on the user experience design, releasing the burden of repetitive tasks, due to the UI it’s already predetermined through component libraries. Design systems can lift the efficiency, saving lots of time, reducing production costs, promoting the code reusability, delivering consistency, quality, and control over any user interface design. All these are hefty arguments to consider them as a serious paragon of value into the digital transformation scene at any mid-large scale organization that is looking to improve their performance.

DesignOps through a data-driven approach

The creation of a comprehensive design system is not a common task and needs to be lead with a special sense of strategic vision, understanding that this will impact and play a fundamental role in the orchestration of multiple products, this could emerge as the backbone of a DesignOps framework, in order to amplify thus its strategic value in the organization.

A data-driven strategic approach seems to be the correct way to crank-up this journey, this will allow the teams to get granular control over all the assets and components generated for the system. Adding from an early stage feature engineering in order to yield a structured and labeled dataset that will contribute later on to the integration of AI features that can boost its use and adoption.

The Next level

Many verticals have emerged introducing artificial intelligence as a game-changing factor, disrupting the models to make them better and more efficient. How to move a design system to its next level utilizing data and AI to introduce new features that can elevate its performance and experience, is what I would like to show in a couple of examples below.

NLP Assistant

The first feature that I want to introduce it’s an NLP Assistant (Natural Language Processing) that can deliver quick answers about all design system assets stored in the dataset. The assistant is able to provide information about components, guidelines, fonts, colors, previews, download links, disambiguate confusing questions, and boost designers and developers workflow drastically, they don’t need to dive into hundreds of pages to find the information that they need, now the assistant it’s providing a straightforward interaction with the whole component catalog, just chat with him.

Visual Recognition

The second feature was based on the computer vision field, using Visual Recognition we’ve generated a model that can identify 16 different classes of the essential UI elements, this feature helps whenever someone needs to know the availability of a specific component, just doing drag & drop using a screenshot of the element, the system will run a convolutional network model to recognize the submitted image and provide an answer checking the similar items available in the catalog.

Transferring all the design properties and specs from our Design System in order to build a robust and labeled dataset will be the fundamental step towards the implementation of smart features, this will be the starting point to take advantage of the AI technology in this field.

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