Posts focused on actionable use cases for managing product lifecycles and creating effective roadmaps.
AI
How AI Agents Are Reshaping Modern Work—and What You Should Do About It When Humans Manage AI Agents (Not Just People) A profound shift is unfolding in product organizations: teams are moving from humans managing humans to humans managing AI agents. While GPT’s forthcoming “PhD Quality AI Agent”
AI
Imagine you’ve just been asked to integrate AI into your product—one that needs to do it all: predict user churn, handle legal questions, parse medical data, and even produce marketing copy. You might initially look at a single, general-purpose Large Language Model (LLM) and think, “This should cover
AI
Remember the not-so-distant past of telecommunications. There was a time when making a phone call meant planning your day around finding a phone booth, ensuring you had change in your pocket, and hoping the person you were calling was near a phone. Fast forward to today, and telecommunications are no
Tools & Technologies
The past few years have made one thing abundantly clear: tech companies, like many industries, go through cycles of expansion and contraction. In this context, we often talk about two distinct phases—lean times, where resources are constrained and companies must focus on doing more with less, and buffered times,
AI
AI development, unlike traditional software engineering, isn’t a straight line. Models need to adapt to new data, user behaviors, and shifting business conditions. In fast-paced environments, agility becomes more than just a buzzword—it’s a necessity. The key to success in AI is to build systems that can
Product Lifecycle & Roadmaps
When it comes to product roadmaps, most of us are used to thinking in linear terms. We develop features, launch them, and then move on to the next big thing. But with AI, the roadmap looks a little different. Unlike traditional software features, AI systems don’t just get built
AI
Building AI-powered products isn’t just about understanding the technology—it’s about bridging the gap between product goals and technical execution. To get AI right, product leaders need to collaborate effectively with data scientists. It sounds simple, but in practice, these two groups often speak different languages, and misalignment