I’ve been thinking lately about the collision of the old and the new, the late and the soon. Maybe it’s because I just had a birthday, or because in this corner of the world we recently went from a winter-like blizzard to a summer-like swelter in less than a week.

Or maybe it’s because of AI.

A natural response to rapid change is to retreat into the familiar. When pressures of work or the world stretch my limits, I find comfort in objects made with craft and care. Among the items that soothe my soul are a well-seasoned cast-iron pan that’s great for omelets; the Japanese-made guitar my mom bought for my dad in the 1970s; a favorite pair of leather boots; and this morning’s cup of rich, black coffee whose beans I ground myself, served in a custom-made pottery mug, a gift from a friend.

While I revel in the craft behind these objects, I also appreciate the miracles of technology that conjured each of them. I ground those beans and brewed that coffee with electronic gizmos that I paid too much for. The cast-iron pan was undoubtedly designed with clever CAD systems and state-of-the-art manufacturing methods. I’m sure that the bootmaker’s top-tier digital marketing makes my boots popular and possible.

“Respect for the craft, wonder at the possible, caution for the risks” is how we software product pros might incorporate technology and methods made possible by Artificial Intelligence. We can and should love our product craft: the research, the users, the writing, the collaborating. But that shouldn’t prevent us from adopting new capabilities to accelerate that work, improve our output, offer more value. And at the same time, the promise of new capabilities shouldn’t mask their risks, either.

None of this is in conflict. All are possible at once.

With the advent of AI-enabled tools and methods, the garden we product professionals dwell in has gotten undeniably richer—but also more treacherous. (And yes, that sentence’s em dash is all mine, and isn’t it weird and a little sad that I think I need to say so?). There’s new fruit to harvest, and it’s delicious, and new pests to eradicate, and they’re stubborn. Sometimes, the best tool for the job is the old shovel we’ve carried and maintained for decades. Sometimes it’s not.

Pull on your favorite boots and finish off that cup of artisanal coffee. There are new things to learn. And the new ways need your old skills.

On to the Garden,

Around the Garden

A conversation with the self

Check it out: I’ll Know It When I Build It, by Christina Wodtke, the Elegant Hack

Consultant and entrepreneur Christina Wodtke explores something I hadn’t considered: product-making with AI is closer to the act of drawing than to traditional software engineering.

Her argument: before AI, software-making required you to carefully plot out the work ahead of you, and then to execute the thing you planned. But AI’s ability to respond incrementally means you can work like an artist works on a drawing: starting with rough forms, seeing what works, erasing lines, discarding, restarting, adding new, building up, all while witnessing the thing you are making evolve in response.

She describes the act of drawing as a conversation with the self and a thinking process made visible. The same can be said for other creative efforts. For example, I often have profoundly enjoyable experiences when working alone on creative writing projects. I dump, draft, delete, refactor, rethink, trim, tighten. Heck, I’m doing it while writing this Pollinator! Writing in conversation with myself is a deeply satisfying way to work, and it results in the best final output.

Wodtke’s idea is that AI makes that kind of rhythm available for software product development, at least up to the level of the prototype. To be clear: she’s not saying AI is great for the making of art (that’s a separate discussion); she’s saying AI can apply deeply human, non-AI art-making processes to the making of software.

I think Wodtke has hit on something here.

The unreplicateable

Check it out: The Human Touch in Product Management: What AI Can’t Replace, by Eddie Pratt, Product Focus

Eddie Pratt from Product Focus writes like a product manager thinks. He implicitly gets that the heart of the job is applying judgment and decision making to build a value-generating system. Like many in the profession, he’s wrestling with how to apply AI capabilities while retaining human judgment and the value of personal encounters with users and teammates. I like his framing in this article of “what AI can’t replace.”

He starts the piece by describing an AI-assisted customer research effort that resulted in an outcome that surprised him: the value of the human sources.

His description of “something that AI could never replicate” is inspiring.

Here’s his list of what AI can’t replace:

• Deep Customer Understanding.

• Cross-functional Collaboration.

• Adaptive Learning.

It’s probably incomplete, and it may be unique to Pratt’s own context, but it’s a great start.

Optimism from O’Reilly

Check it out: How we bet against the bitter lesson: skills and the knowledge economy, by Tim O’Reilly

Tim O’Reilly of O’Reilly Media is a legendary spreader of technology information and insights. Here O’Reilly confronts something called the “bitter lesson” from Rich Sutton: the idea that, over time, methods powered by sheer computational force tend to outperform approaches shaped by human knowledge.

O’Reilly asks if new methods and tools like Agent Skills (a standard for intelligent agents to use instructions to do things more accurately) mean we can bet against this bitter lesson. He isn’t definitive in his answer, but he offers the possibility that we can, if we recognize AI as a social and cultural technology, more akin to language than to machine.

Why does this matter for product managers and teams? We’re bombarded with pleas to work faster, learn new tools, race against and with our replacements. We might be about to learn the bitter lesson: the robots will win out. But there’s a parallel story where learning from each other and extending our value in a knowledge economy (through domain knowledge, judgment, taste) offers advantages that pure scaling can’t yet match. The yet there is critical. Perhaps our challenge is to extend the yet.

How Delightful

Check it out: The Power of Product Delight in Tech Products, from Nesrine Changuel (YouTube video)

I recently attended a webinar that featured French product leader Nesrine Changuel on the topic of Product Delight. Changuel developed her theory of delight in product over a long career working on products at Spotify, Google, and Microsoft—and picking up a Ph.D. along the way.

Her approach is rigorous and systematic. Product managers can inject delight in their products by segmenting users by motivators, which are a combination of functional and emotional drivers.

Some other lessons from her talk:

• Delight is not aesthetics, and it is not gamification.

• Delight is a business driver. For example, delighted customers are 50% more likely to stay.

• The classic double-diamond model can be reframed to start with Motivators.

• A good portfolio mix looks like this:

  • 50% low delight features (functional drivers only)

  • 40% deep delight features (functional + emotional drivers)

  • 10% surface delight features (emotional drivers only)

To get started, use her motivator grid, available via Changuel’s newsletter.

I found her talk, well, delightful. Thanks to Nesrine Changuel for her work, and to ProdPad for hosting the webinar.

Outside the Box

Pollinator HQ is housed within the offices of Solution Design Group, a custom software development consultancy based in Minneapolis, Minnesota, USA. This website, the Minnesota Natural Resource Atlas, might primarily be interesting to fellow Minnesotans, but man it is fun. It’s from some wizzes at the The Natural Resources Research Institute at the University of Minnesota. The website offers cool data geographic visualizations of lakes, rivers, forests, and similar resources. It’s mostly for academic purposes, but the rivers & streams map alone, veined red and blue with waterways, will set a river-lover’s heart a-flutter.

Check it out at https://mnatlas.org/.

About the Pollinator

• The Pollinator is a free publication from the Product practice at Solution Design Group (SDG). Each issue features an opening reflection and a curated digest of noteworthy content and articles from across the internet’s vast product community.

• Solution Design Group (SDG) is an employee-owned digital product innovation and custom software development consultancy. Our team of over 200 consultants and other technology and business professionals includes experienced software engineers, technical architects, user experience designers, and product and innovation strategists. We serve companies across industries to discover promising business opportunities, build high-quality technology solutions, and improve the effectiveness of digital product teams.

• The Pollinator's editor is Jason Scherschligt, SDG's Head of Product. Please direct complaints, suggestions, and especially praise to Jason at [email protected].

• Why The Pollinator? Jason often says that as he works with leaders and teams across companies and industries, he feels like a honeybee in a garden, spending time on one flower, moving to another, collecting experiences and insights, and distributing them like pollen, so an entire garden blooms. How lovely.