By Bryan Kirschner, Vice President, Strategy at DataStax
Years before the meteoric adoption of ChatGPT made AI top of mind for just about everyone, the authors of Competing in the Age of AI had already pointed out something every business leader should ignore at their peril:
In traditional operating models, scale inevitably reaches a point at which it delivers diminishing returns. But we don’t necessarily see this with AI-driven models, in which the return on scale can continue to climb to previously unheard-of levels.
By the time they wrote this (2020), the theoretical economics of AI at scale had found a happy match with best-of-breed data technologies advanced enough to deliver massive, smart, distributed real-time digital systems that would have been pipe dreams not long ago.
Some long-established companies moved aggressively to capitalize on the opportunity this presented: John Deere’s investment in autonomous farming is one example. Their big, hairy, audacious goals for scale–500 million acres served by digital tools to complete multiple value-creating activities by 2026–illustrates an important point.
Building out a technology architecture scaling to multiple services applied to 500 million acres is a bold bet on using technology to drive growth. But at the same time, it’s really only the first stage of their journey. To put a fine point on it: accepting limitations that (for example) would result in the infrastructure starting to fail at 501 million acres would be an utterly foolish business decision.
Make decisions that enable—not constrain— scale
Even if your organization isn’t yet as “all in” on AI as John Deere, you share the same market context. The “viable and valuable” horizon for the scale of AI is vast and continues to expand.
The easiest decision at the intersection of business and technology strategy you will ever need to make about AI is to commit to ensuring your tech stack will never constrain the scale you can achieve.
If you feel any responsibility whatsoever to wrestle with whether or not your organization “truly” needs limitless scale for handling data in the “age of AI,” let me offer some words that may relieve you of that burden: it’s in no way up to you. Whatever you conclude won’t actually matter.
Strategy guru Roger Martin wisely advises that “Strategy is centrally about compelling the thing you don’t control — your customers — to take actions you wish they would take.”
Best-of-breed technologies to enable GenAI are here, now
The scale of data behind AI that will compel customers to take action in the future is well beyond what you can currently do or even imagine. It will be driven by the fierce pace of evolution of best-of-breed technology like the Apache open-source data ecosystem. It will be driven by the ways that AI leaders like Netflix and Uber raise the bar for consumer expectations. Ideally, it will in part be driven by your organization’s ingenuity. Failing that, it will be driven by competitors and aspiring disruptors.
Generative AI seals the deal. Conversational customer interaction, for example, gets better when trained on more data from those domain-specific interactions–and even better when trained on data from the individual user’s prior interactions. And it’s somewhere between likely and certain that the number of “customer” interactions won’t be limited by the number of flesh-and-blood customers you have, but rather multiplied by the number of AI agents they have working on their behalf, too.
The good news is that committing to a limitless data stack is not just an easy decision to make–it is also an easy intention to fulfill. The best-of-breed technologies are open source and available as a service, to all.
If you intend to play to win in our rapidly emerging era of superabundant AI, you will never regret choosing to draft “scalability” onto your team.
Learn how DataStax provides a scalable foundation for generative AI projects.
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.
