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Nikhil
Rai
Senior Global and Industry Marketing Manager
Blackberry QNX
Nikhil leads global product and industry marketing at BlackBerry QNX, the embedded software powering software defined systems across automotive, industrial and machinery. He brings 18 years of international experience across big tech and startups, spanning the US, Europe and Asia, with a background that runs unusually deep on the technical side: embedded software, simulation, EDA, PLM and systems engineering. Before QNX he was Senior Director of Product and Solutions Marketing at Jama Software, through its 1.2 billion dollar acquisition by Francisco Partners. He is a senior IEEE member and a published author on systems engineering and live traceability, a 40 under 40 Innovator for product and solutions marketing, a regular speaker at embedded systems conferences in the US and Germany, and a venture studio judge at MIT. That's about 130 words. Two notes: I've taken the acquisition figure and the award straight from his own bio without verifying either, so worth a sense check before it goes on the site. And I dropped the metaverse and blockchain publications, since they pull attention away from the fireside topic. Say the word if you want them back in.
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27 October 2026 15:45 - 16:15
Fireside chat - Small Team, Big Output: Scaling PMM with AI in the Loop
Most product marketing teams are under-resourced and over-requested, and the honest answer to "can you also do this?" has been yes for far too long. AI has not changed that maths so much as raised the stakes: teams that use it well are quietly shipping at a scale that used to need three more headcount, and teams that don't are still writing the same one-pager by hand. This fireside gets into what that actually looks like day to day. Which parts of the PMM workflow genuinely hand off to AI, which ones fall apart when you try, and how the prioritization conversation with sales and product changes once capacity stops being the limiting factor. Expect specifics on where it's gone wrong as well as where it's worked, and a candid view of what still needs a human in the loop. Key learnings: - A practical split of the PMM workload into what to automate, what to assist and what to protect - How to rebuild your prioritization criteria when output is no longer the bottleneck - The quality controls that keep AI-assisted work from sounding like AI-assisted work