In an earlier article, we spoke about the two-pronged approach for UX practitioners. Now, let's deep dive into the first approach and see, how AI assistance can be leveraged to optimize and accelerate an ideal UX design workflow. This article is not about the tools available in the market, rather it's more about the design methods. Tools change! (Tools: Gen AI websites, Figma plugins, etc)
I believe that we need to use AI — only if the model can do an above-average job in terms of output, and in circumstances where speed is paramount from a UX delivery standpoint. Else, there's no point in seeking AI assistance. The whole idea of AI assistance should be business-purpose driven or by complementing core-skills of a UX practitioner, and not be any other measure. So, use AI wisely and seek its assistance only when you strongly think and feel that — it can significantly empower you, in your work outcomes.
Disclaimer: I haven't tested these ideas. However these ideas are based on the capabilities of generative AI and my understanding of its integration in UX design and design management.
Here are 9 ways to deploy AI in your design workflow.
- Workshop Planning
- Brainstorming Ideas
- Evidence-based Design
- Visual Assets
- Visual Correction
- UX Writing Assistance
- Storyboarding
- UX Quotient Check
- Design Advocacy
Now, let's explore the 9 ways
1. Workshop Planning & Facilitation
Design thinking facilitation is one of the few core skills that has not been impacted by generative AI. (This skill needs human intervention, collaboration, lots and lots of planning and doing) However certain elements of this activity can be assisted by AI.
For example, you can deploy AI to plan your schedule, brainstorm ideas for your session, outline various design methods, choose the appropriate thinking techniques for your workshop, and generate various sesion material to suit your specific business context. I think the planning peice can be done by AI — all else must be driven by competent design-thinking facilitators.
2. Brainstorming Ideas
You can generate various ideas and explore design direction in terms of both interaction and visual design. Even before design, you can have a dialogue with AI to see what kind of user experience would suit for a given business context, market, and a specific user group. This would also enable you know what alternative products and services are doing in your domain. It's more or less an advanced search, reason with you, summarize key information and enable you to take better design decisions, early on. (Several companies have one-person UX team — for them, it's a boon, as AI can act as a peer)
3 .Evidence-based design documentation
When you need to take a final call with the plethora of options and design patterns available for a given solution, one can take AI's help to identify the reasoning behind choosing a specific design pattern or control for a given user interaction. (You can document the reasoning summaries for further presentation with product management or other stakeholders) Evidence-based design is not new for a seasoned UX practitioner, however with AI we can easily collate all this reasoning, accelerate our decision making, the design delivery itself, and the required documentation to advocate our choices.
For example, you need to design a contextual menu in a desktop interface. The objective of the UX practitioner is to find the most appropriate design solution from the various ways, one can design this menu. A clear reasoning and documented response as to why we have zeroed in on a particular menu pattern is — is what this point is all about. Ideally one arrives at this by thinking about the business, research, user context, objectives of the interaction element, overall usability best practices in the market or domain. In the pre-AI era, we had to do a time consuming manual search, find HCI research papers or articles to quote evidence, and formulate our own reasoning of a pattern's usage. Now AI: our team member can help us think faster and move forward.
For beginners in design, such assistance is superb, as it helps them show evidence as well as, learn how-to design, in this entire process.
4. Visual Assets
Generative AI can create some interesting visual assets at tremendous speed. I understand that the quality is subjective here. However if you want to quickly whip out images, icons, and other visual assets, it's a great advantage. Especially, visual design is quite a unique skill when compared to other skills in the UX spectrum, and not everyone has that keen visual sense, and the ability to churn out work at speed and on-demand. Once again, AI saves the day.
This is even more relevant with one-person UX teams, where you're delivering work across the UX spectrum — and seriously, you need accelerators.
Now, several UX practitioners also support marketing and brand efforts for their products in terms of websites, sales landing pages, microsites, and other sales-enabling interfaces. This is where visual asset generation can really help to accelerate the sales and user adoption process of your products and services.
5. Visual Correction
Visual correction of stock images, videos, iconography, and several more areas where correction can be done by the system and not manually by designers — that takes forever. As UX practitioners, we can focus on better things, that add higher value to the table. (Everything can't be equally important — in work, and life)
6. UX Writing Assistance
Well, not every UX practice / team has a "UX Writer" as a dedicated role. Very few companies really understand the the power of UX writing and why they need to hire professionals in this area. What about companies that don't hire writers? These companies hire UX designers — and expect them to write as well.
Now, with generative AI, every UX practitioner can also provide UX writing, accompanied with their design solutions. This is helpful for designers who are not proficient or inclined to writing. AI can help with basic things like labels, nomenclature, naming your call-to-actions, tool tips, error messaging, notifications, coach marks, tone analysis, setting the tone, design documentation, product guides, play books, user FAQs, and several other areas of UX design, that demand a high proficiency in writing.
If you're a professional UX writer, it's a totally different story. (I'm not going to comment and compare experts with generative AI capabilities. It's for the experts to figure out)
7. Storyboarding
This is plain and simple. Ask AI to generate storyboards for the various simple and complex user scenarios / user flows and save yourself a great amount of time. Obviously, you can enhance the generated storyboards and use them in workshops, team meetings, and client discussions. Storyboards are easy to comprehend, and this makes you an interesting storyteller. Storytelling is a core-skill in the business-of-design and the design-of-business.
8. UX Quotient Check
Ask AI to perform an analysis of your design and get a check on the UX quotient. I'm speculating on this one, as I'm not really sure if this capability exists, at the moment. Once you input your files, AI can do a design-review of sorts, and present you a score on the UX quotient of your design solution. (May be areas like Usability, Influence, Trust, Accessibility, Inclusivity, etc. can be scored and a detailed UX-quotient-report can be generated for the UX practitioner to act upon)
9. Design Advocacy
Well, advocacy is not really part of the overall design workflow, however it is — and should be — every UX practitioner's second job. Thought leadership content like blogs, podcast ideas, your social channels, internal newsletters, and training material can be developed at great speed. However, on the leadership-content front, I think originality and building a unique point-of-view — for you, your team, and your organization really matters. So, one needs to know when to seek AI assistance, and when to sweat it out to create original content, that can build both internal and external influence for your UX practice.
Conclusion
Finally, the point of AI assistance is to save-your-time and enable you to focus on high-value areas like design strategy, omni-channel thinking, design ethics, talking to your users, design-thinking workshop facilitation, collaborating with internal teams and your client stakeholders, delivering game-changing training programs, creating original thought-leadership content, learning and upgrading your skills, and most importantly, delivering your design solutions driven by excellence — addressing user, brand, and business objectives.
It's for the UX practitioner to decide when and where to seek AI's support, based on their unique job requirements and an awareness about their core-strengths, skills, and goals.
Originally published on LinkedIn on March 10, 2025