Image generated using DALLe
Image generated using ChatGPT's DALL·E. See my post on the process here.

The Impact of Large Language Models in Experience Design

The field of experience design is undergoing a massive evolution with recent advancements in language language models. If you haven't seen some of the headlines, Judd Antin's article describes the severe impact of layoffs on User Experience Research (UXR) teams, highlighting that the layoffs were not merely due to economic downturns but a result of a decline in business value to companies. The piece ends with a call to action for UX Researchers to adapt and embrace new methologies and perspectives that align their work more closely with business outcomes.

There is also Robert Fabricant's piece in Fast Company which delves into the recent challenges and shifting landscape for a generation of designer leaders who previously enjoyed significant expansion of design's role and responsibilities in various business sectors. He points out this era appears to be ending as evidenced by significant layoffs in design and the elimination of senior design positions at major companies like IBM, Expedia, J&J, and downsizing at firms like Ideo. Whether this is a breakup between business and design or a generational shift in leadership is still to be determined. The current situation though, raises questions about the future of design leadership, the role of design in business, and how new models of design leadership will shape the professional practice amongst the current economic downturn and advancements in AI which are changing the skills demanded in design and leadership.

The field of design has been shifting for sometime with advancements in techology. If you look at tools, for example, Figma, it didn't just improve collaboration speed with engineers and other stakeholders, it increased the level of automation available to generate high-fideltiy UX. Another example is Marvin, which can acts as an AI assistant for UX research and democratizies access to research and insights in a way that previously took at lot more effort to organize, tag, and share at the same scale (if that was possible at all without a small army).

The rise of AI will lead to significant changes in the way experience designers work, how teams are built and managed, and also the perceived value of design by the business. I believe experience design will undergo a resurgence from this foggy period in time, as teams learn to harness the power of these emerging tools to reskill and augment their roles in new ways, and as quality assurance becomes increasingly important with new AI products being released to market.

I have begun my own journey of learning on how to best utilize these tools as an experience designer and leader. This space in my portfolio is dedicated to project work related to large language model's impact on design, some of the limitations, and also hopefully a few tips and tricks. Enjoy!

Large Language Model (LLM) Projects

In making LLMs useful to augment tasks you have to make sure you're using it to solve the right problem. To do this you should ask why you are pursuing using Gen AI, the value you expect it to bring to your major business goals, how you will measure success, and what use cases could maximize the value to you if you add it to your tool kit. Below are some of the use cases I am currently exploring:

Use Case Business Goal Example Success Metrics
Augment content with visuals generated from DALL·E Task and process automation reducing operational costs Productivity (more story points per sprint or time spent on value-added tasks)
Stakeholder satisfaction
Generate code directly from designs (Blog in progress) Improved quality and adherence to designs
Faster design and development cycles
Reduction in design and dev cycle time
Reduction of items caught in QA environments or design reviews
Prompt engineering to augment non-visual design tasks (Blog in progress) Faster design cycle times Productivity (more story points per sprint or time spent on value-added tasks)