By Bryan Kirschner, Vice President, Strategy at DataStax
One of the major findings of our recently released State of AI Innovation report was how bullish managers and technical practitioners were about generative AI enhancing, rather than threatening, their careers.
A key reason why I think they’re right is generative AI’s ability to operate in useful ways using content that people already produce–or could produce quite easily.
I use the word “content” rather than “data” here deliberately. All AI thrives on data, but generative AI applications can readily be built against the documents, emails, meeting transcripts, and other content that knowledge workers produce as a matter of course.
This is made possible by a process called “retrieval augmented generations,” or RAG. RAG provides large language models (LLMs), which comprise the foundation of generative AI apps, with contextual content and data in real-time from corporate databases. (Here’s a more detailed explanation of the importance of RAG.)
Interrogate ‘all that you’ve done before’
There’s an individual use case and a (potential) enterprise use case that provide glimpses of how proprietary content can fuel powerful AI-driven outcomes.
The first is technologist and consultant Luke Wroblewski’s “Ask Luke” personal assistant. It enables people–including Wroblewski himself!–to ask questions against the 2,000-plus articles, 100-plus videos, and three books (and more) that he’s produced in his career.
Here’s how he describes the benefit of Ask Luke’s robust response to a usability question: “It’s not hard to see how the process of looking across thousands of files, finding the right slides, timestamps in videos, and links to articles would have taken me a lot longer than the ~10 seconds it takes Ask Luke to generate a response. Already a big personal productivity gain.”
As someone who has also been in this line of work a long time and values paying it forward by sharing what I’ve learned with others, being able to instantly and easily interrogate “all that you’ve done before” is a very compelling idea.
But above and beyond just saving time and (for example) getting new hires up to speed faster, generative AI offers some intriguing opportunities to raise everyone’s game–if you play your cards right as an organization.
Gain a better understanding your audience
I’ve been a long-time fan of Amazon’s “working backward from the customer” approach—specifically, the mock press release.
The “customer quote” in particular invites the right kind of “outside-in” conversation: I’ve seen an example red-lined with the question, “would a customer really say this?”
It’s a powerful mechanism for pivoting people from crafting reactions that “sound great” internally with hopes of getting a green light toward something that “rings true”–and for provable reasons–coming from the audience that, at the end of the day, matters most.
This practice starts to look even more exciting with generative AI in the mix. Using RAG, a generative AI agent could read the corpus of mock press releases and real comments and reactions from customers on (for example) social media, as well as reviews and press coverage, and then provide meaningful guidance.
What teams or segments outperform or underperform? For certain audiences, is there a tendency to over- or under-shoot? By looking at consumer reaction to competitive or adjacent products, a genAI agent could enter the mix by producing what it would think a customer would say from an “outside-in” perspective–the point being not to replace the judgment of product managers, but to spin up a richer dialogue that would previously have been infeasible.
AI keeps getting better. You should, too.
This brings us to the strategic implications.
Most companies don’t do prospective press releases, but any given company might create some other form of content that’s unique fuel for generative AI. Most individuals don’t create as much content as Wroblewski, but many business units or functional organizations do.
It would be foolish to bet against generative AI’s capabilities continuing to get better. It would be wise to bet on people coming up with ingenious applications of those capabilities, using the content they already produce or could easily start producing.
As our survey showed, people are excited about the potential. Now’s the time to back them up with the permission to experiment and an architecture that’s ready and able to take all their good ideas into production without skipping a beat.
Learn more about DataStax.
About Bryan Kirschner:
Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.
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