Beyond Tokenomics: Why Datanomics Is the Missing Half of the AI Economics Equation

We've spent the last two years increasingly focused on optimizing the economics of inference or tokenomics: How many tokens can a model generate per second? How quickly can the model respond? What is the cost per million tokens?

These questions matter. As enterprises move from experimentation to production AI, efficiency at the model layer becomes critical. But there is a bigger economic equation that is often overlooked. Before an AI system generates its first token, enterprises must solve a much harder problem: How do we transform massive volumes of fragmented enterprise data into trusted, governed, AI-ready intelligence?

The stakes are significant. Research from MIT found that roughly 95% of enterprise generative AI pilots deliver no measurable return, despite an estimated USD30-40 billion in investment. And Gartner predicts that through 2026, organizations will abandon 60% of AI projects that are unsupported by AI-ready data.

This is where the next AI battleground will emerge. I call this datanomics.

Why datanomics is not just tokenomics plus

Tokenomics measures the efficiency of AI output generation. Datanomics measures the efficiency of preparing the foundation that makes those outputs valuable.

For enterprises, the journey from raw data to AI-ready data involves:

• Discovering data across multiple environments
• Understanding data quality and relevance
• Removing duplication and noise
• Protecting sensitive information
• Applying governance and access controls
• Building reliable pipelines for retrieval and AI agents

The reality is that enterprise data was not created for AI. It exists across applications, databases, file systems, data lakes, SaaS platforms, edge environments, and increasingly, private and hybrid cloud environments. The sprawl is real: the average organization now runs more than 100 SaaS applications, according to data compiled by BetterCloud and Statista, each one a potential silo.

The challenge is not simply having data. The challenge is making data trusted, accessible, and usable by AI systems. According to a Gartner survey of data management leaders, 63% of organizations either do not have, or are unsure whether they have, the right data management practices in place to support AI.

Most AI factory discussions today focus on: tokens per second, time to first token, and cost per million tokens. These are important measures of AI performance. But they start too late in the journey.

A more complete AI factory scorecard should include metrics such as:

• Cost to AI-ready data (per TB): How much time, engineering effort, and operational cost does it take to transform one terabyte of enterprise data into trusted, governed data that AI applications can use? This is not a trivial number: Gartner estimates that poor data quality alone costs organizations an average of USD12.9 million per year.
• Time to AI-ready data: How quickly can new data sources become available to AI systems?
• Trusted data coverage: What percentage of enterprise data meets the quality, governance, and security requirements needed for AI?

These metrics will become increasingly important as organizations move from AI assistants and copilots toward autonomous AI agents.

Why agents make datanomics even more important

The rise of agentic AI changes the equation. Traditional AI applications often operate within defined boundaries. AI agents, however, are designed to reason, plan, access information, and take actions across enterprise workflows.

This increases the importance of data quality and governance. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. McKinsey's research points to a root cause: while nearly two-thirds of enterprises have experimented with agents, fewer than 10% have scaled them to deliver tangible value — and eight in 10 companies cite data limitations as a roadblock.

An AI agent with access to inaccurate, outdated, or poorly governed information does not become more intelligent. It simply becomes faster at making unreliable decisions. The future of enterprise AI will not only depend on better models. It will depend on better data foundations.

Sovereign AI is fundamentally a datanomics challenge

Sovereign AI discussions are often framed around where models run or where infrastructure is located. In Deloitte's State of AI in the Enterprise research, 77% of leaders said the location of AI development is a key factor when choosing new technologies, with nearly USD100 billion expected to be invested in sovereign AI compute by 2026.

But true sovereignty goes deeper. It requires organizations to maintain control over:

• Where enterprise data resides
• Who accesses the data
• How you govern the data
• How AI systems use the data
• How you can trust and audit those data-driven AI decisions

Yet research from Accenture suggests most organizations still treat this as a compliance exercise: only 15% have made AI sovereignty a CEO or board-level priority, even as geopolitical uncertainty pushes a majority toward sovereign solutions. The AI factory of the future is not just a place where models run. It is an operating model where data, infrastructure, models, security, and governance work together.

Shifting from tokenomics to datanomics

The first wave of enterprise AI optimization was about reducing the cost of intelligence generation. The next wave will be about reducing the cost of intelligence preparation. The organizations that win with AI will not necessarily be those with the largest models or the most GPUs. They will be the organizations that can most efficiently transform their enterprise data into trusted intelligence.

Tokenomics will continue to matter. But datanomics may determine who can scale AI successfully. The next generation of AI factories will not just optimize tokens. They will optimize the entire journey from data to decisions.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/wildpixel