Adoption through better experiences, not through capabilities

In Ethan Mollick’s post What Can be Done in 59 Seconds: An Opportunity (and a Crisis), he touches on a subject that is so important that it’s easy to overlook it as a “of course that’s what’s happening” type of thing.

He highlights 2 recent developments that are not as model-capability-focused as most of what has gotten people excited over the last year.

1. Integrating LLMs into existing tools that a billion people use: Microsoft’s Copilot is now integrated into their Office suite

2. Creating and sharing purpose-built GPTs: OpenAI’s GPT Store and teams

His articulation of challenges in getting the most value out of ChatGPT is revealing:

It certainly didn’t help that the ubiquitous chatbot approach to AI hid a lot of its power, which was only revealed after hours of experimentation.

This is a much better way of articulating what I oversimplified (and ranted about) as a usability problem. It is a usability and a usefulness problem, but not simply a “user didn’t know how to complete a task” style of usability. Open-ended chat is amazing if you know how to have the conversation and ask the right questions. I think we can all agree that people are generally not great at doing exactly that in real life, so why would we collectively get better at asking great questions when we’re talking to a bot?

This feels like the next big step in the overall trend of how GenAI becomes more and more mainstream—usable and useful.

The technology came first: LLMs existed for a good while, but only a few people used them as they grew in capability.

The first good UX came next: ChatGPT was a usability breakthrough—for all of chat’s usability shortcomings, it is also the thing that made the technology accessible to billions.

Now comes thoughtful products, experiences, and ecosystems: the overall ease of use is increasing as the tech is being integrated (a la Microsoft), democratized (a la GPT Store), and normalized—not just a select few will be using this in the background of their work, but it will be part of their collaboration in teams and more.

And this growth phase as I’ve described it is completely ignoring how fast the technologies underneath are advancing. That will also increase adoption in more and more scenarios, but will have to follow the same paths to success as any other technology: to meet the fullest potential, everything must be thoughtfully and deliberately designed into peoples’ lives.

Isaac Asimov Asks, “How Do People Get New Ideas?”

Isaac Asimov on the circumstances of creativity from 1959:

It seems to me then that the purpose of cerebration sessions is not to think up new ideas but to educate the participants in facts and fact-combinations, in theories and vagrant thoughts.

His term “cerebration sessions” is a wonderful 1950's term. I might steal that.

And the concept of giving participants “sinecure” tasks to do for payment—making the creativity a less important task—is genius. Getting paid to "think" is probably more common now than back then, but it's still a difficult sell.

Realizing—and destroying—the value of multi-disciplinary collaboration

I think a lot about collaboration. How to do it. When to do it. And most philosophically, why to do it. A former manager of mine, Greg, once put it so plainly that I immediately fell in love with how he wrote it:

Interdisciplinary collaboration is hard by definition. There will be disagreements about what is important. This is exactly the point.

I’ve reused that line (and line of thinking) a lot over the years, and I feel more strongly about that than ever. I’ve used that to set expectations with teams and colleagues. I’ve used it to comfort myself (or others) after a maybe-too-spirited debate in a meeting. That simple three sentences packs a lot into it.

Interdisciplinary collaboration is hard by definition

“Interdisciplinary” is carrying a lot of weight in this first part. Depending on your experience or perspective, you might not think that it is actually very hard. I used to think of the scope of this for myself as collaborating with engineers and user researchers. Then I added product managers into that mix. But I think that’s a designer’s view of the world—those are the most basic and closely related inputs and outputs to a designer’s work product. And in a more mature team or organization, all of those disciplines are generally rowing in the same direction and have some experience working in those types of product teams, so the collaborative cow paths are pretty well worn.

I’ve learned to expand my definition of “interdisciplinary” to mean basically “everyone with a stake in an effort.” Part of my career maturity has exposed me to working hand in hand with data scientists, architects, strategists, marketers, MBAs, PhDs, 24 year olds, 64 year olds, etc. And in a lot of cases, they weren’t experts in working with designers or engineers—and most importantly, they weren’t experienced in a product-driven style of working. So if we’re not all familiar with the same playbook or principles, how do we get moving and determine some ways of working?

There will be disagreements about what is important.

At BCG, when we are assembling a new team, one excercise we do to establish working norms has a series questions that address preferences like communications (Slack, email, phone, etc.) and feedback style (real-time, scheduled, etc). My favorite one is the spectrum of when to collaborate, and I’m typically the most extreme one at the “Start Together” end of the range.

Why is that? Because I want to be “the collaboration guy”? Not really. I’ve just learned that if we don’t start together, it will lead to misaligned everything. Misaligned priorities, information, schedules—even a misaligned understanding of the problem we’re solving. And if we’re not on the same page, we’re going to have two primary problems: collaborative process and collaborative decision making.

Process is important, but I’m not that designer who pontificates on the elegance of the double diamond, etc. There is no “one true way” that I’m arguing for. I’m just trying to realistically make sure our processes are compatible at a minimum and electric as an aspiration. Figuring out the various inputs and outputs on an interdisciplinary team is hard work, but vital.

But doing that hard work starts to reveal what’s important to different people and disciplines. That lays the ground work for better collaborative decision making. And decision making is where collaboration is tested.

This is exactly the point.

If you’re not arguing, if there isn’t any friction in how you make decisions, if everyone is doing what they’ve always done in their own silo’d disciplines—why are you working together? Are you just coordinating and not collaborating? Boring.

When it comes to decision making, that’s where what’s important is on full display. Timelines, budgets, user needs, business goals, etc. Everyone is representing some subset of priorities when you’re making decisions. They might care about their process, their metrics, their boss, or their users. Now, they might not be saying those things, but it’s baked into their recommendations, questions, and solutions. And that’s OK. Acknowledge those differences and talk about them, not just the solution. It’s easy to try and ignore them, though.

I see these symptoms of ignoring or working around those differences flare up in many teams. These are warning signs that I need to invest more time in our collaborative relationship. Things like:

• You find yourself saying “Why are they asking for that?”

  • This is a great flag that someone doesn’t understand others’ processes, and they are expecting something that will plug into their process.

• You feel a general sense of stay-in-your-lane

  • If someone is asking you “Why are you asking for that?” that probably means that they are unclear about the role you are playing on the team, what your responsibilities are, and how they might overlap with theirs. At worst, they don’t want to let you into their process.

• You’re told that others “can handle it” without you

  • When someone says “We can handle that, we don’t need design for that,” they aren’t acting open to collaboration. There can be many valid reasons, too, but it’s worth interrogating. If this is a constant

If the team is not careful, this is where we can squash the value of interdisciplinary collaboration. There is trap where we collectively gloss over the differences, stay in our own lanes, and then just deliver mediocrity.

But do we need to collaborate?

This all doesn’t even get into the need for collaboration: to solve hard problems. I believe that most regular problems have been solved (at least once), and an existing solution gives us a playbook to follow or at least an example to draw inspiration from. A solution from the real world is a great way to align a team to a vision—“it’s like Github, but for XYZ”—and that not only makes collaboration easier, but it also makes solving the problem easier in a lot of ways.

It is the challenges that need expertise that also need collaboration. You can’t have one without the other. I like to think that the solved problems I mentioned above are also generally single-domain or single-discipline problems. Data scientists have their own set of solved problems, designers have design systems ad nauseam, etc. It’s only when we reach the limits of what can be solved on our own that it gets interesting.

So we identify a new problem and it can’t be solved without bringing in an expert in that thing, and then we need to realize that we haven’t worked with them before, and they probably haven’t worked with us before (or someone like us). This is going to be hard.

But that is exactly the point.