By Bryan Kirschner, Vice President, Strategy at DataStax\n\nYears 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:\n\nIn traditional operating models, scale inevitably reaches a point at which it delivers diminishing returns. But we don\u2019t necessarily see this with AI-driven models, in which the return on scale can continue to climb to previously unheard-of levels.\n\nBy 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.\n\nSome long-established companies moved aggressively to capitalize on the opportunity this presented: John Deere\u2019s investment in autonomous farming is one example. Their big, hairy, audacious goals for scale\u2013500 million acres served by digital tools to complete multiple value-creating activities by 2026\u2013illustrates an important point.\n\nBuilding 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\u2019s 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.\n\nMake decisions that enable\u2014not constrain\u2014 scale\n\nEven if your organization isn\u2019t yet as \u201call in\u201d on AI as John Deere, you share the same market context. The \u201cviable and valuable\u201d horizon for the scale of AI is vast and continues to expand.\n\nThe 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.\n\nIf you feel any responsibility whatsoever to wrestle with whether or not your organization \u201ctruly\u201d needs limitless scale for handling data in the \u201cage of AI,\u201d let me offer some words that may relieve you of that burden: it\u2019s in no way up to you. Whatever you conclude won\u2019t actually matter.\n\nStrategy guru Roger Martin wisely advises that \u201cStrategy is centrally about compelling the thing you don\u2019t control \u2014 your customers \u2014 to take actions you wish they would take.\u201d \n\nBest-of-breed technologies to enable GenAI are here, now\n\nThe 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\u2019s ingenuity. Failing that, it will be driven by competitors and aspiring disruptors.\n\nGenerative AI seals the deal. Conversational customer interaction, for example,\u00a0 gets better when trained on more data from those domain-specific interactions\u2013and even better when trained on data from the individual user\u2019s prior interactions. And it\u2019s somewhere between likely and certain that the number of \u201ccustomer\u201d interactions won\u2019t 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.\n\nThe good news is that committing to a limitless data stack is not just an easy decision to make\u2013it is also an easy intention to fulfill. The best-of-breed technologies are open source and available as a service, to all.\n\nIf you intend to play to win in our rapidly emerging era of superabundant AI, you will never regret choosing to draft \u201cscalability\u201d onto your team.\n\nLearn how DataStax provides a scalable foundation for generative AI projects.\n\nAbout Bryan Kirschner:\n\nBryan 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.