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Nikhil

Rai

Senior Global Product and Industry Marketing Manager

Blackberry QNX

Nikhil Rai leads global product and industry marketing for QNX foundational products and industrial automation, shaping thought leadership, go-to-market strategy, positioning, and messaging for software powering millions of embedded devices across mission-critical, safety, and physical AI applications. He brings more than 18 years of international experience across technology companies and startups in the United States, Europe, and India. He was previously Senior Director of Product and Solutions Marketing at Jama Software (acquired for 1.2 billion dollars), Director of Product Marketing at PTC and Midaxo, and built his product strategy & marketing career at MathWorks. An IEEE Senior Member and a published author on various prominent systems engineering and tech journals, Nikhil is also a 40 Under 40 Innovator in product and solutions marketing, a regular speaker at embedded systems conferences in the United States & EU. Based in Boston, he is passionate about the positive societal impact of technology and the convergence of various complex technologies

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27 October 2026 16:15 - 16:45

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