Design+GenAI: Redefinition

Over the past year the only things designers seem to be able to talk about when it comes to generative AI is exactly how soon it will take all their jobs. For a profession that prides itself on “out of the box” thinking, seems like a lot of folks are not only in the box, but cowering in one of its corner hoping the future passes them by.

Back in 1988 I was the first designer at my design firm to use a computer to design. Until then all our design work was based on hand-crafts; pencil and paper, marker sketches, ChartPak lettering, photostats. Hand skills that, at the time, were critical for designers to be successful. I used Aldus Freehand on a Mac II to go from initial concept to production art, but throughout the process I had to trace over the printouts with markers and colored pencils to make the design directors “comfortable” with what they were looking at. When it comes to their tools, change has always been hard for designers. But nevertheless its inevitable. With that in mind, while the buzz is calming down, GenAI is not going away.

For decades designers have been complaining they are relegated to just “drawing picture” or “making things pretty”, given GenAI is very handy for those tasks its time for designers to step-up, to show what design means. With that in mind, this is the first of a 5 part series of how design + genAI can be used to drive dramatic improvements to the user experience and overall design of products and services.

Part 1. Comprehensive view of user behaviors.

You would be hard pressed to find a company that does not have a fully instrumented platform, with the ability to monitor click-streams, comparing user journeys based, and tracking end-to-end behavioral metrics for all their products. However, the volume of data generated by this level of instrumentation can be overwhelming. Especially since in most companies there is more than one tool being used to capture the data between marketing, sales, product, and support. And in the case of enterprise solutions, instrumenting a suite of products used by different stakeholders, such as sales & fulfillment, manufacturing, distribution, etc. the task can feel all but impossible. 

Enter GenAI. 

Behavioral Overview
Leveraging various methods of instrumentation data in conjunction with purchase history, sales data, marketing campaigns, etc., GenAI can assist with the difficult task of integrating data from across different tools and contexts helping to analyze how different user groups behave over time either with the same product, or across products in your portfolio. Providing you with critical insights into user on-boarding, learning, adoption, decision making, etc. GenAI can be used to normalize and standardize data to identify critical touchpoints for locking in engagement and driving growth.

Closing the Say/Do Gap
You can take sentiment analysis to the next level by combining user feedback with behavioral insights to help close the “say/do” gap that has persisted since the earliest days of user research. Finally with GenAI you can contextualize how users say they “say” about a feature or product, and what they actually “do” with that product or feature, to define and prioritize improvements. 

Correlation across products
For enterprise products generative AI can extract and correlate the data from each of the products in your suite. By connecting user ID, data tags, asset IDs, etc. GenAI can produce behavioral patterns across the suite, identifying how users complete a set of tasks, or they use a range of solutions within the suite to work with others on solving more complex problems. Again leveraging metadata such as user ID, account information, various asset ID’s, time stamps, etc. you can use GenAI to model, refine and improve workflows, increase cost effectiveness and improve accuracy. Going a step further, if you extract the organizational model from an HCM suite, your GenAI can model the various parts of your company collaborate, allowing you to have insight to how the entire organization uses the full suite of products, identifying potential skill gaps, policy improvements, data lineage, etc. with the goal of increasing productivity, compliance, and efficiency. 

Engagement
Building on engagement, GenAI can be used in a self-service context to increase adoption and solidify retention, making recommendations to expand adoption of new features or complementary products by making them part of the suggestions offered in response to the users questions and  tasks. These recommendations can be created by ingesting employee training material, policies, etc, and then integrated into the workflows employees use to complete their tasks. 

Summary
By leveraging GenAI capability to integrate data from multiple touchpoints, and analyze behavioral patterns, you can create a richer set of insights. And when combined with good old-fashion human creative problem solving and generative thinking, you can deliver more impactful innovations across all your products and internal tools. Design+GenAI allows you to optimize user engagement, increase retention, and deliver a competitive advantage with your user experience, leading to better product outcomes and business success.

Stay tuned for Part 2: Personalization