Trucking and logistics companies already keep excellent customer metrics when it comes to the transport of goods. Apply that same rigor to your digital products and services and you start to build an insurmountable moat around your business as well as happier, more self-sufficient customers. Here is how to do that.

Utility companies are already spending the most they ever have on expanding the grid to meet growing demands. Their digital and mobile experiences however are not getting the same treatment. Here’s how utilities companies can improve their mobile apps and websites to meet modern customer expectations.

Regional grocery stores can’t compete with the Walmarts of the world on budget when it comes to building digital experiences. But you can learn from them. This article explains how understanding your shopper’s behavior helps inform which bets to make in your digital shopping experiences and how those bets can lead to significant increases in sales per shopper if you can help them to no “forget the milk.”

‍ ‍Digital product development is crucial for the industrial manufacturing sector. Digital products are fundamentally different than physical ones. This article explains why that is and how to adapt your physical manufacturing product knowledge to digital products.

As AI continues to change the world on a weekly basis, it’s nice to see industry thought leaders revising their original POVs on what makes for successful product management. Here is Marty Cagan’s recent list of 10 reversals and how each one is a learning topic at Sense & Respond Learning.

The common consensus now is that “judgment” and “product sense” are the valued skills in product managers. These activities are work. And they are rarely accounted for in the budget. That’s the last 30% of getting a product across the finish line, especially with you newly AI enabled teams.

Product management is evolving quickly with the integration of AI. What will it look like in the next 6-12 months? Here’s our take on what stays the same, what changes and how PM evolves into the next century of work.

It seems project management is a dying profession, if you work in tech. The reality couldn’t be further from that. Project Management as a profession is growing in industries that have deterministic projects. In tech, where uncertainty is high, product managers do the bulk of the work. What happens to the project management work in tech teams when there are no project managers?

Product management training at scale cannot be done solely by AI or self-paced video libraries. Your context, your challenges and the human side get lost. Here’s how to train 200 to 2000 product manages in a scalable way that actually delivers results to your bottom line.

As evidence piles up that AI productivity does not yield clear ROI, here is one simple tactic product managers can use to make sure what they’re building is meaningful for the company.

Most leaders adopt AI tools-first and automate the wrong things. A recap of our session with Barry O'Reilly on the Traits, Tasks, Tools sequence.

87.7% of product teams have adopted AI. Only 36% say it's improving how they build. The gap isn't a tooling problem — it's a judgment problem.

AI-native SAFe is an oxymoron. You can’t bolt on AI words and expect a rigid process to miraculously change to fit the new technology. Try this instead.

Human oversight catches AI's mistakes. Human judgment decides whether the work was worth automating in the first place. One matters more.

An agent that writes a flawless PRD for a feature nobody wanted hasn't helped you. It's helped you waste time more efficiently. Why "AI agents across the lifecycle" solves the wrong problem.

AI quietly broke the metrics we trust. Ben Yoskovitz on what to measure when your product runs on tokens, and why power users now cost you more.

Some companies see AI as a cost-cutting miracle. Others, take a look at the work being automated and use the newly found manpower and bandwidth to generate new revenue streams. Here’s how IKEA came up with an “extra” $1.3BB.

Choosing your AI tool is far less important than knowing how to deliver real value with it.