This Moment We’re In, Ep. 3
Velocity is the easy part. Here's what AI actually changes about how product and deign teams actually work.
Product and design work is changing as organizations embrace AI. But how so? Greg explored this question in the third episode of Cindy Chastain’s podcast, This Moment We’re In.
His answers weren’t theoretical, but based on his experience as a fractional design leader, a role that allows him to perceive high-level patterns from a relatively impartial vantage point. This leads to important insights.
I’ll summarize the key ones here, adding my observations. Then, I’ll circle back to Greg’s parting recommendation — a framing that teams should start adopting immediately. Let’s get into it.
Roles are blurring
Traditional team boundaries between product, design, and engineering starting to blur. As Greg put it,
one of the things that was interesting in that particular group was we were moving away from a PM culture and an engineering culture and a design culture, or a design discipline and a product discipline and engineering discipline to a product culture that it was inclusive of all those roles, and that the who was what was situational and based on need.
This has upsides and downsides. On the plus side, team members can step in to perform others’ functions as needed. The result? Sustained velocity.
The main downside: a potential lack of alignment and control. But also, team members may find themselves making decisions outside their area of competence. Roles still bring discernment to the table, which is essential.
As an example, Greg recalled a case in which a PM vibe-coded a design artifact in a week. They were celebrating the fact this could be done at all, when a designer in the team observed they could’ve done it in four hours.
That a tool exists doesn’t mean it will be used effectively.
Two primary uses for AI
Greg called out two distinct modalities for teams using AI:
As a way to increase velocity — i.e., doing the same kind of work, faster. Design is often framed as an internal service function, taking requests issued from other teams. AI can help design orgs provide this service faster.
As a thinking tool — i.e., a medium that allows teams to frame problems, explore possibility spaces, and inform organizational strategy. This includes improving research operations and lowering the cost of producing high-fidelity prototypes.
Greg cited real-world examples of both. In either case, AI accelerated or increased scope. But — and this is critical — it didn’t replace the need for discernment and clarity. We’re squarely in augmentation (rather than automation) territory.
But it was the second modality where there’s most potential. Greg shared an exciting example from a recent engagement, where he worked with a small, distributed team to design a product North Star. Previously, such efforts were constrained by engineering bandwidth, but
That model doesn’t hold up as much anymore, and one of the things we learned in the process of doing this is not only could we move fast, we could insert more things into the process that were hard to argue for under tight time frames that made the product richer. So, when we’re in a sort of divergent phase, we looked at the information architecture of multiple competitors and deeply understood them very quickly, and figured out why they made those decisions.
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Then, we built hybrids of those just because we could. And normally, you or someone would say, “Well, why are you doing that?” And I say, “Well, the juxtaposition of those things might inform us in a way that we haven’t thought about, and there might be an accident of doing that that might surprise us.” So, we did lots of that. And there were a couple of happy accidents in the process, where we were informed by a mashup of something that would have been very difficult and time-consuming to do, that really, you could do in like a couple of hours.
This isn’t just the same kind of work, faster. The ability to move faster changes what kind of work can be done. Designers explore solution spaces by making — “make to think,” as Greg put it. AI lets that happen at a different level.
Think jobs, not features
How can design and product leaders effectively navigate these changes? Greg suggested an insightful reframing: rather than think of the focus of design work as product features, think of the jobs that the product is meant to fulfill:
People never bought features anyway. They only bought outcomes. Features were a way to get to an outcome. But I think the artifact right now is the intelligence that you’re designing. And then, the delivery mechanism is also in flux. So, is our life going to converge on one of the large models and those become almost like operating systems for us? Or is there space for custom products that recognize and have contextual insight about us that make our lives more easy and more straightforward?
Which is to say, the capabilities afforded by AI open new possibility spaces that aren’t well-served by the “feature” framing. We must approach design at a more systemic level — known territory for designers:
At the end of the day, we benefit product teams by understanding it better. And we, as design teams, have been doing this for a long time. Think about journey mapping, journey mapping as a tool, a way to understand how a job was getting completed.
Christensen et al’s “Jobs to Be Done” framework provides an excellent conceptual foundation for this reframing. Check out Greg’s post on Improbable Futures for more on how this plays out for product and design teams.
Key takeaways
As a product or design leader, you must use AI intentionally. This conversation offered three key insights for doing so:
AI isn’t just a way to move faster. It also offers new ways for teams to think, unlocking strategic possibilities.
Humans still need to decide what problems to solve and prioritize those that matter most to the organization and its customers.
Leaders should use the tools themselves. Don’t assume you can delegate hands-on experience to your team. Work with heavy adopters to enable new ways of working.
In the interview, Greg said he wants to disrupt himself. That doesn’t mean starting anew. Instead, it means learning how new technology plus experience unlocks new possibilities. As he noted, our work at Unfinishe reflects that value.
This is an exciting (and unnerving) time. It’s important that seasoned leaders like Greg and Cindy share their experiences implementing AI at scale. If you’re a product or design leader, this conversation is a good investment of your time.
Check out This Moment We’re In wherever you get your podcasts or on YouTube.