Thinking Like AI: A New Approach to AI UX Design

As product makers develop more and more AI features, it’s important to not only think about our human users’ requirements and preferences, but also those of the AI supporting them. To do this, I recently trialled a fun and collaborative method with my product team at Microsoft, a multidisciplinary group of Designers, PMs, and Engineers working on AI features in Microsoft Dynamics 365. I’m excited to share my process here for anyone else who might like to give it a try.

Context

Think about reading this article on Microsoft Design right now.

• Why are you ‘hiring’ the site? • What ‘job’ is it helping you with and what is your goal? • What tasks do you perform to achieve that goal?

• What pain points and challenges do you encounter?

For me, I might say that I hire different blogs to help me with the job of learning because I want to gain an edge in my career. To do this, I frequently check for new articles, add appealing ones to my reading list, read them, and hope I don’t discover halfway through that an article is less helpful than I’d hoped.

Here, I’ve effectively described a job-to-be-done (JTBD), its sub-tasks, and an associated pain point. At Microsoft, this JTBD framework is typically how we think about our business users.  For my team, those are Enterprise Resource Planning (ERP) customers— everyone from accountants to supply chain managers—who ‘hire’ our Dynamics apps to help them do their work. Over the years, we’ve developed interactive persona cards for these various users, allowing team members to get a quick overview or drill into each of their JTBD to understand the corresponding tasks, success metrics, pain points, and more. Using this framework helps us to think holistically about our users and their goals, so we can design features and experiences that allow them to navigate our products as effectively and efficiently as possible.

Of course, like many teams in tech right now, we’re deeply focused on AI features. To find a good fit for AI features, we need to have empathy for our users and identify jobs and sub-tasks where AI might help them, rather than forcing it into the product. However, I think we also need to have empathy for the AI itself. If we want our AI features and autonomous agents to navigate our products as effectively and efficiently as our human users do (leveraging the right data, surfacing the right insights, etc.), perhaps we should also think about AI’s jobs-to-be-done, motivations, pain points and preferences.

If you’re on board, you might be thinking, “how?” JTBD research typically involves interviewing users, mapping out common themes into jobs and tasks, associating different pain points or challenges, and then maybe even validating or refining the results with a large-scale survey. Sure, we could try interviewing Copilot, ChatGPT or other LLMs, but their view is rather limited to what’s already been done—not where we’re trying to go in the coming years.

Data Collection

To overcome this issue, I held a workshop with fifteen colleagues across the ERP suite of products, with representatives from Design, PM, and Engineering. I asked participants to imagine themselves as the AI for our ERP product suite, three years in the future. I played some light background techno music and team members shared gifs of robots in the chat to help get into the mindset. Once ready, I provided the scenario:

_“You are the AI that supported Microsoft’s ERP users for the past three years. Now, you’re sick of it and you want a new job. You decide to apply to be the AI for ERP users at another company. Let’s start by writing the experience and responsibilities section of your CV/resume.”
_
Workshop participants were given 10 minutes and their own individual templates to complete the task. As they worked, I picked out interesting sticky notes and highlighted them in the chat, encouraging people to stay motivated and engaged. Examples included “I automated repetitive tasks” and “I helped users to write better reports.”