AMA: Bloomreach VP, Product Marketing, Divya Mulanjur on AI and Product Marketing

Just like any template or planning doc, the right way to think about shared GPTs is in terms of jobs to be done that continuously update with live context. The most successful setups we have seen are tightly scoped agents built around everyday PMM workflows, such as win-loss analysis, launch plan drafting, competitive intelligence, and analyst relations drafts. One example is a product launch agent my team created in Glean. It is a setup we can replicate for each launch, and because it is connec ...Read More

I cannot overstate how important user experience is, and this is where some of our biggest learnings have come from failure. We worked with a vendor to build a custom AI-driven GTM enablement tool that - had it worked - would've been a game-changer. It combined conversational intelligence with sales play context to deliver deal briefs, objection handling, messaging guidance, and next step recommendations. The folks who worked on creating the engine are some of the best brains in the business, an ...Read More

AI is not a new way for PMMs to help the GTM org. PMM’s core mandate stays the same - understand why customers buy or don't, translate that into clear positioning and GTM strategy, and enable teams to execute. What changes with AI is the frequency, depth, and turnaround time of that work. With AI, PMM can move from being a demand-based function to an always-on GTM engine. A concrete example is how we use AI to analyze every closed deal, every sales conversation, CRM notes, and customer feedback ...Read More

I would try to answer that question by thinking of two categories of work: The first is the work you have always been understaffed for, or rather, cannot scale with human effort. For me, that was analyzing every deal so our insights were credible and informed strategy holistically, and staying on top of competitor movements in a highly saturated market where manual tracking simply does not work. The second is the work you already do, but can be faster and more consistent. Things like deal summar ...Read More

I will probably sound like many other PMMs when I say this, but there is no single AI tool I would recommend in isolation. What matters far more is using AI to capture as much depth of context as possible. One of the most effective ways to do that is through conversational intelligence, because everything else flows from understanding what buyers and sellers are actually saying in real situations. When you pair that with a strong insights layer and lightweight agent orchestration, you can cover ...Read More

By tying it directly to very specific, measurable outcomes that map to real PMM work. One clear example is AI-powered deal analysis. We now analyze 100% of closed deals while increasing coverage by roughly 65%. This is a defined process, with deal insights automatically summarized and pushed into a database as a single source of truth for the business. We are also experimenting with standing up a VoC insights engine that triangulates conversational intelligence, win-loss data, reviews, surveys, ...Read More