Posts where AI plays a role in enhancing product management tasks like documentation, feature prioritization, and sprint planning.
AI
When humanity arrives at the doorsteps of these massive technological leaps—whether it’s artificial intelligence, quantum computing, or breakthroughs in energy—the question isn’t just how we innovate, but why and at what cost. I recently read a book by Batya Friedman, founder of the Value Sensitive Design
Product Leadership
We’re witnessing a pivotal moment in the world of computing. It’s not every day that a single breakthrough can redefine our perception of what’s possible. Yet that’s precisely what Google’s Willow chip represents—a quantum leap that promises to reshape how we solve problems, build
AI
With the rapid evolution of generative AI (Gen AI) and AI Agents, companies are rethinking the balance between in-house capabilities and specialized SaaS solutions. Klarna, the global financial services platform, recently severed ties with certain SaaS providers, opting instead to leverage AI to build tailored solutions internally. This decision underscores
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
AI
A few days ago, a friend who’s a lawyer asked me something I’ve been hearing a lot lately: “Is AI going to take my job?” It’s an honest question that many professionals are wrestling with. My response surprised him: “One day, it probably will.” But before he
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
AI
In the world of product development, it’s tempting to chase the latest trends, and AI is no exception. Every day, we hear about how AI is transforming industries, creating new possibilities for automation, personalization, and insight. But as product leaders, it’s our job to separate the hype from
AI
In 1997, chess champion Garry Kasparov took on IBM’s supercomputer, Deep Blue. It was a match that symbolized the rising potential of artificial intelligence (AI). After Kasparov won the first round, many assumed humans still held the upper hand. But then Deep Blue came back to win, marking a