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AI value comes from continuous learning, not automation
The companies pulling ahead in AI aren't doing it by deploying more tools. Rather, they're building the data foundations that make intelligence reusable, scalable and reliable.
For most of the last 100-plus years, competitive advantage came from three familiar levers: improving operational efficiency, automating repetitive work and reducing labor costs. AI is quietly retiring that playbook and replacing it with one built around optimized decision-making.
But the organizations pulling ahead are not the ones with the most AI tools. They're the ones rewriting the economics underpinning their AI-driven business decisions.
AI is not just changing the economics of labor. It is also changing the economics of learning. Automation makes today's work less expensive. Learning makes tomorrow's decisions better. Those are different economic engines, and only one of them compounds business value.
For data and analytics leaders, that distinction is the whole job. The key mandate is no longer to deliver useful dashboards and govern data tables for accuracy. Now, it is to build the economic infrastructure of continuous learning and adaptation: the trusted data products, semantic layers and certified measures that help their organizations to learn faster than rivals and reuse what they learn at near-zero cost.
From optimizing processes to optimizing decisions
The industrial-era analytics stack was built to optimize processes: run the plant hotter, route the truck smarter and close the books faster. Those improvements are valuable, but bounded: over time, process optimization runs headlong into the law of diminishing returns.
In the AI era, the opportunity is different. It is about improving decisions. Every customer interaction, pricing call and service ticket is a series of choices that can be made more smartly the next time. Treat each of those decisions as a chance to learn, and the organization stops competing on how fast it executes and starts competing on how well it decides. Faster processes have a ceiling. Better decisions do not.
The economics of reuse
The next lever is reuse. Unlike physical assets, data does not deplete when used. It can serve many use cases at near-zero marginal cost and appreciates in value as it is combined with other data and reused. I call the organizational instinct behind this the Marginal Propensity to Reuse (MPR): how broadly a data asset, model or analytical insight gets applied across the enterprise once it's created.
This is where trusted data products become appreciating assets rather than depreciating projects. A project is built once, for one purpose, and starts losing value the day it goes into production. A data product is built to be reused. In a 2022 article, McKinsey said treating data like a product can deliver new use cases up to 90% faster and reduce total cost of ownership by 30%. Every reuse drives marginal cost down and cumulative value up.
The hidden AI tax
The flip side is economic, too. LLMs do not automatically create reliable organizational knowledge -- they consume it. Point AI at a weakly governed or ungoverned data environment, and it does not fail loudly. It produces confident guesses that don't look wrong, but are.
The result is a hidden AI tax: more hallucinations, more human review, slower adoption, duplicated work and decaying trust in the AI tool. In 2020, McKinsey said data users can spend 30% to 40% of their time searching for data when a clear inventory is not available -- and another 20% to 30% cleaning data when strong controls are not in place. There's no reason to think things have improved since then for organizations with weak data environments.
Every point of that tax is business value AI can't realize because the data foundation won't let it.
The economics of learning
Reuse compounds knowledge across the enterprise; learning compounds it over time. The Marginal Propensity to Learn (MPL) is the rate at which an organization improves from each decision, interaction and feedback cycle.
Historically, organizational learning crawled; it was locked in individual experience and lost when people left. AI can compress that cycle dramatically, but only when humans design for it by capturing feedback, preserving decision rationale and feeding lessons back into the next AI-driven decision.
The AI model does not learn on its own. The organization must build the loop -- and then it learns, too, as AI works more effectively.
Measure value, not activity
None of this shows up on a traditional analytics scorecard. Dashboard counts, data volume and query performance measure operation, not value. The metrics that matter now -- decision quality, decision velocity, data and knowledge reuse, learning velocity and cross-functional adoption -- are today's unmeasured outcomes. No one built them, so no one governs them.
Data teams spent a decade certifying whether metrics are accurate; the next decade belongs to those who can certify whether they are right. Standing up those learning and reuse metrics is data work, not AI work.
The rise of AI economics is not about replacing people with algorithms. It is about turning trusted data into organizational intelligence that helps companies improve with every decision. Industrial economies rewarded scale; AI economies reward learning and reuse. Companies that improve their decision-making faster through data -- and apply those lessons across the organization -- will build competitive advantages that compound. In an economy powered by learning, the organizations that learn from their data fastest wins.
Bill Schmarzo, "The Dean of Big Data," teaches AI-driven innovation at Iowa State University and advises organizations on data science, AI and data monetization. He is a former executive at Dell Technologies, Hitachi Vantara and Yahoo. He has written books on data-driven innovation and applied AI strategy.
