We’ve been hearing a lot about AI agents and now enough time has passed that we’re starting to see some learnings in industry. Writing in Harvard Business Review, Linda Mantia, Surojit Chatterjee and Vivian S. Lee showcase three case studies of enterprises that have deployed AI agents.

They write about Hitachi Digital and how they deployed an AI agent as the first responder to the 90,000 questions employees send to their HR team annually.

Every year, employees put over 90,000 questions about everything from travel policies and remote work to training and IT support to the company’s HR team of 120 human responders. Answering these queries can be difficult, in part because of Hitachi’s complex infrastructure of over 20 systems of record, including multiple disparate HR systems, various payroll providers, and different IT environments.

Their system, called “Skye,” is actually a system of agents, coordinating with one another and firing off queries depending on the intent and task.

For example, the intent classifier agent sends a simple policy question like “What are allowed expenses for traveling overseas?” or “Does this holiday count in paid time off?” to a file search and respond agent, which provides immediate answers by examining the right knowledge base given the employee’s position and organization. A document generation agent can create employee verification letters (which verify individuals’ employment status) in seconds, with an option for human approval. When an employee files a request for vacation, the leave management agent uses the appropriate HR management system based on its understanding of the user’s identity, completes the necessary forms, waits for the approval of the employee’s manager, and reports back to the employee.

The authors see three essential imperatives when designing and deploying AI agents into companies.

1. Design around outcomes and appoint accountable mission owners. Companies need to stop organizing around internal functions and start building teams around actual customer outcomes—which means putting someone in charge of the whole journey, not just pieces of it.
2. Unlock data silos and clarify the business logic. Your data doesn’t need to be perfect or centralized, but you do need to map out how work actually gets done so AI agents know where to find things and what decisions to make.
3. Develop the leaders and guardrails that intelligent systems require. You can’t just drop AI agents into your org and hope for the best—leaders need to understand how these systems work, build trust with their teams, and put real governance in place to keep things on track.

Matt Ström-Awn makes the argument that companies can achieve sustainable excellence by empowering everyone at each level to take ownership of quality, rather than relying solely on top-down mandates or standardized procedures.

But more and more I’ve come to believe that quality isn’t a slogan, a program, or a scorecard. It’s a promise kept at the edge by the people doing the work. And, ideally, quality is fundamental to the product itself, where users can judge it without our permission. That’s the shift we need: away from heroics at the center, toward systems that make quality inevitable.

The stakes are high. Centralized quality — slogans, KPIs, executive decrees — can produce positive results, but it’s brittle. Decentralized quality — continuous feedback, distributed ownership, emergent standards — builds resilience. In this essay, I’d like to make the case that the future belongs to those who can decentralize their mindset and approach to quality.

Ström-Awn offers multiple case studies, contrasting centralized systems with decentralized ones, using Ford, Amazon, Apple, Toyota, Netflix, 3M, Morning Star, W.L. Gore, Valve, Barnes & Noble, and Microsoft under Satya Nadella as examples.

These stories share a common thread: organizations that trusted their frontline workers to identify and solve quality problems. But decentralized quality has its own vulnerabilities. Valve’s radical structure has been criticized for creating informal power hierarchies and making it difficult to coordinate large projects. Some ex-employees describe a “high school clique” atmosphere where popular workers accumulate influence while others struggle. Without traditional management oversight, initiatives can moulder, or veer in directions that don’t serve broader company goals.

Still, these examples show a different path for achieving quality, where excellence is defined in the course of building a product. Unlike centralized approaches relying on visionary (but fallible) leaders, decentralized systems are resilient to individual failures, adaptable to change, and empowering to builders. The andon cord, the rolling desk, and the local bookstore manager each represent a small bet on human judgment over institutional control. Those bets look like they’re paying off.

OK, so there’s workslop, but there’s also general AI slop. With OpenAI’s recent launch of the Sora app, there going to be more and more AI-generated image and video content making the rounds. I do believe that there’s a place for using AI to generate imagery. It can be done well (see Christian Haas’s “AI Jobs”). Or not.

Casey Newton, writing in his Platformer newsletter:

In Sora we find the entire debate over AI-generated media in miniature. On one hand, the content now widely derided as “slop” continually receives brickbats on social media, in blog posts and in YouTube comments. And on the other, some AI-generated material is generating millions of views — presumably not all from people who are hate-watching it.

As the content on the internet is increasingly AI-generated, platforms will need to balance how much of it they let in, lest the overall quality drops.

As Sarah Perez noted at TechCrunch, Pinterest has come under fire from its user base all year for a perceived decline in quality of the service as the percentage of slop there increases. Many people use the service to find real objects they can buy and use; the more that those objects are replaced with AI fantasies, the worse Pinterest becomes for them.

Like most platforms, Pinterest sees little value in banning slop altogether. After all, some people enjoy looking at fantastical AI creations. At the same time, its success depends in some part on creators believing that there is value in populating the site with authentic photos and videos. The more that Pinterest’s various surfaces are dominated by slop, the less motivated traditional creators may be to post there.

When I read this, I thought to myself, “Geez, this is what a designer does.” I think there is a lot of overlap between what we do as product designers and what product managers do. One critical one—in my opinion, and why we’re calling ourselves product designers—is product sense. Product sense is the skill of finding real user needs and creating solutions that have impact.

So I think people can read this with two lenses:

• If you’re a designer who executes the assignments you’re given, jumping into Figma right away, read this to be more well-rounded and understand the why of what you’re making.
• If you’re a designer who spends 80% of your time questioning everything and defining the problem, and only 20% of your time in Figma, read this to see how much overlap you actually have with a PM.

BTW, if you’re in the first bucket, I highly encourage you to gain the skills necessary to migrate to the second bucket.

While designers often stay on top of visual design trends or the latest best practices from NNG, Jules Walter suggests an even wider aperture. Writing in Lenny’s Newsletter:

Another practice for developing creativity is to spend time learning about emerging trends in technology, society, and regulations. Changes in the industry create opportunities for launching new products that can address user needs in new ways. As a PM, you want to understand what’s possible in your domain in order to come up with creative solutions.

The headline rings true to me because that’s what I look for in designers and how I run my team. The software that we build is too complex and too mission-critical for designers to vibe-code—at least given today’s tooling. But each one of the designers on my team can fill in for a PM when they’re on vacation.

Kai Wong, writing in UX Collective:

One thing I’ve learned, talking with 15 design leaders (and one CEO), is that a ‘designer who codes’ may look appealing, but a ‘designer who understands business’ is far more valuable and more challenging to replace.

You already possess the core skill that makes this transition possible: the ability to understand users with systematic observation and thoughtful questioning.

The only difference, now, is learning to apply that same methodology to understand your business.

Strategic thinking doesn’t require fancy degrees (although it may sometimes help).

Ask strategic questions about business goals. Understand how to balance user and business needs. Frame your design decisions in terms of measurable business impact.

Miquad Jaffer, a product leader at OpenAI shares his 4D method on how to build AI products that users want. In summary, it’s…

• Discover: Find and prioritize real user pain points and friction in daily workflows.
• Design: Make AI features invisible and trustworthy, fitting naturally into users’ existing habits.
• Develop: Build AI systematically, with robust evaluation and clear plans for failures or edge cases.
• Deploy: Treat each first use like a product launch, ensuring instant value and building user trust quickly.

This post has been swimming in my head since I read it. Elena Verna, who joined Lovable just over a month ago to lead marketing and growth, writing in her newsletter, observes that everyone at the company is an AI-native employee. “An AI-native employee isn’t someone who ‘uses AI.’ It’s someone who defaults to AI,” she says.

On how they ship product:

Here, when someone wants to build something (anything) - from internal tools, to marketing pages, to writing production code - they turn to AI and… build it. That’s it.

No headcount asks. No project briefs. No handoffs. Just action.

At Lovable, we’re mostly building with… Lovable. Our Shipped site is built on Lovable. I’m wrapping hackathon sponsorship intake form in Lovable as we speak. Internal tools like credit giveaways and influencer management? Also Lovable (soon to be shared in our community projects so ya’ll can remix them too). On top of that, engineering is using AI extensively to ship code fast (we don’t even really have Product Managers, so our engineers act as them).

I’ve been hearing about more and more companies operating this way. Crazy time to be alive.

More on this topic in a future long-form post.

In a dual profile, Ben Blumenrose spotlights Phil Vander Broek—whose startup Dopt was acquired last year by Airtable—and Filip Skrzesinski—who is currently working on Subframe—in the Designer Founders newsletter.

One of the lessons Vander Broek learned was to not interview customers just to validate an idea. Interview them to get the idea first. In other words, discover the pain points:

They ran 60+ interviews in three waves. The first 20 conversations with product and growth leaders surfaced a shared pain point: driving user adoption was painfully hard, and existing tools felt bolted on. The next 20 calls helped shape a potential solution through mockups and prototypes—one engineer was so interested he volunteered for weekly co-design sessions. A final batch of 20 calls confirmed their ideal customer was engineers, not PMs.

As for Skrzesinski, he’s learning that being a startup founder isn’t about building the product—it’s about building a business:

But here’s Filip’s counterintuitive advice: “Don’t start a company because you love designing products. Do it in spite of that.”

“You won’t be designing in the traditional sense—you’ll be designing the company’s DNA,” he explains. “It’s the invisible work: how you organize, how you think, how you make decisions. How it feels to work there, to use what you’re making, to believe in it.”

Brian Balfour, writing for the Reforge blog:

Speed isn’t just about shipping faster, it’s about accelerating your entire learning metabolism. The critical metric isn’t feature velocity but rather your speed through the complete Insight → Act → Learn loop. This distinction separates products that compound advantages from those that compound technical debt.

The point being that now with AI, product teams are shipping faster. And those who aren’t might get lapped (to use an F1 phrase).

Great reminder from Kai Wong about getting stuck on a solution too early:

Imagine this: the Product Manager has a vision of a design solution based on some requirements and voices it to the team. They say, “I want a table that allows us to check statuses of 100 devices at once.”

You don’t say anything, so that sets the anchor of a design solution as “a table with a bunch of devices and statuses.”

Nate Jones performed a yeoman’s job of summarizing Mary Meeker’s 340-slide deck on AI trends, the “2025 Technology as Innovation (TAI) Report.” For those of you who don’t know, Mary Meeker is a famed technology analyst and investor known for her insightful reports on tech industry trends. For the longest time, as an analyst at Kleiner Perkins, she published the Internet Trends report. And she was always prescient.

Half of Jones’ post is the summary, while the other half is how the report applies to product teams. The whole thing is worth 27 minutes of your time, especially if you work in software.