Rapha Danilo

Taking a step back, I think the 1st PM needs to act a lot like a head of product in the early days. The ones I see that do well for their company (and themselves) typically focus on doing 3 things well, where others only do 1-2: PM Execution Typical activities you expect from a PM i.e. research, talking to customers, ideation, roadmap This is foundational, but I think you will likely fail or at least get overlooked for leadership opportunities if you only spend time on this Building the PM playb ...Read More

Here's a few common mistakes I see in no particular order (many I've made myself): Not talking to enough customers IMO you should be talking to at least a couple of customers a week, and listening to more customer calls e.g. with Gong, #ShamelessPlug. But seriously, listen and talk to more customers, it's a gold mine you're sitting on and almost certainly not utilizing enough. Focusing on user personas vs. jobs to be done. This almost always results in a biased, solution-centric view of reality ...Read More

I think at an even higher level we will actually see at least 3 different approaches to AI product management even in the organization structure itself (not necessarily mutually exclusive): Dedicated AI PMs: Absolutely, more and more companies with dedicated AI product managers Possibly with a further separation in focus areas between internal tools vs. customer-facing AI features And/or different focus areas along the AI stack e.g. infra, MLOps, model-level performance vs. more customer-facing ...Read More

The same first principles actually still apply to AI PMs, but with an added dimension of complexity, which is that a generational paradigm/platform shift like AI requires a PM to re-think the benchmarks for what good looks like, consider the new types of outputs/inputs required, and the additional internal communication paths needed to achieve goals. Most similar to being an early PM in mobile 10 years ago, or the early web/internet even before that. What doesn't change: (+) Focus on key outputs ...Read More

The first challenge is actually self-imposed by product teams. Ask yourself: are we implementing an ML feature mostly because of FOMO / not wanting to "fall behind", or because of a first-principled, customer-first assessment of need/opportunity? Assuming we've identified a real need/opportunity for ML in our product, we can ask what type of ML feature best solve the job to be done for our customers. IMO this is the PM's responsibility to own. It's important to involve and partner with R&D a ...Read More

The #1 trait I see the best companies look for now when hiring ANY senior product manager in an area where AI matters is a strong intuition and taste for how AI can be applied in a product. Which comes down to having 1. knowledge of the fundamental AI concepts and 2. strong product sense in order to properly gauge opportunity/risks. Develop a strong intuition around how AI should and should not be applied in your product e.g. Once your understand your user's JBTDs, Gen AI can be great to help yo ...Read More