I’ve recorded voice-over narration for my ProductCamp Boston 2024 session, Flying Blind On a Rocket Cycle: Customer-Centered Product Strategy For Machine Intelligence.  Originally presented live for a cozy in-person audience, the driving (*cough* – the Italian word for driver is ‘pilota’…) concept behind this case study format was recreating the experience of doing product strategy for an emerging enterprise technology space in real time, by offering the audience a sort of co-pilot’s (*cough* – not a GenAI co-pilot…) view through the windshield for shared learning purposes.

This particular story of building out new analytics products, categories, and portfolios happened a while back in tech terms, in the pre-transformer era of AI, when deep learning was new.  In addition to being strongly enterprise focused, unlike much of the readily available material on product strategy, it’s timely on more than one level thanks to abundant parallels with our current AI-focused moment.  

Recall there was considerable debate at the time around the core concepts, expressed via rhetoric like ‘machine learning is just glorified statistics’.  (Search for this, if you’re feeling nostalgic for richly sardonic memes and gifs focused on IRML, often shared via pre-lapsarian social platforms.)  Now we’re having a very public global debate about whether GenAI is [also] a new and transformative general purpose computing paradigm, built on probabilistic computing, or just a stochastic parrot that’s been wildly scaled up in terms, of compute, data, etc. for short-term gains.  The rhetoric now takes what used to be technical perspectives at heavily niche conferences – NIPS before Zuck, anyone…? – and makes them the subject of public conservations about the stock market, featuring well-known AI academic and industry leaders and tech CEO’s of all types talking live on leading business television programs during investors’ liquid lunch windows.

Yes, as you may be thinking, these are in fact explicitly product strategy conversations happening out loud – with a hefty dollop of geo-politics… – which I’ll come back to another time.

For easy listening — we went quite fast in the first-run live talk — I’ve added bit more depth to the narration, and shared the recording in two parts.  [And I think I’ll add a concluding / bookend segment that summarizes the business strategy perspective, given the close linkage to product strategy thats’s set up at the beginning of this story.]

Thanks for listening, and please share your perspectives.

Part 1 

Part 2 

For reference, here’s the original session description:

“Using the product strategy cycle as a guide, this session shares a case study on the growth and evolution of B2B product portfolios driven by machine intelligence for a leading SaaS product maker.  This case study reviews a series of new product efforts; outlines the methods, tools, and practices that powered opportunity assessment, product discovery, and strategic planning; traces the evolution of product portfolios; and considers business outcomes from building and growing a portfolio of new analytics products and services for Oracle over the course of several years.”

This case study illustrates and demonstrates:

• Crafting customer-centered product strategies for new machine intelligence / AI / ML technologies
• Building and evolving customer-centered products and portfolios, and new product categories
• Establishing effective, innovative, customer-centered product strategy capabilities and practices for emerging spaces