Why AI Labs Want Real Business Workflows

The next frontier is understanding how work actually gets done.

For years, the artificial intelligence industry has been hungry for information.

Books, websites, academic papers, code repositories, and public conversations helped build increasingly capable language models. The internet became the raw material for a new generation of technology.

But there's a difference between knowing how to describe a task and knowing how to complete one.

An AI model can explain how to resolve a customer complaint. Can it navigate a complicated refund request, identify a billing discrepancy, coordinate with the finance team, and determine when a manager needs to approve an exception?

That's a different challenge.

And it's one reason real business workflows are becoming an increasingly interesting category of AI training data.

From knowing things to doing things

The first wave of generative AI made it possible to produce convincing text, images, and code. The next wave is focused on systems that can carry out work.

These systems, commonly called AI agents, are designed to move beyond answering questions. They interact with software, retrieve information, make decisions, and complete sequences of actions.

That ambition creates a new data problem.

Traditional training material can teach a model what a customer support ticket looks like. But an agent responsible for resolving tickets needs to understand what happens between receiving a request and closing the case.

It needs context, judgment, and a sense of what successful execution looks like.

Research from Anthropic provides a useful indication of where enterprise AI is heading. Its September 2025 Economic Index found that 77% of observed business API usage followed automation patterns, compared with roughly half of consumer usage. That figure describes usage of Anthropic's API, not the entire AI market, but it illustrates how businesses are already delegating tasks to models.

Source: Anthropic Economic Index, September 2025

The value is in the steps between the beginning and the end

Consider a software consultancy that has spent years developing applications for clients.

Its historical records might include project requirements, engineering tickets, technical discussions, code revisions, testing results, and final resolutions.

Individually, these records contain useful information.

Together, they can tell a much richer story.

A customer reports a performance problem. An engineer investigates. The team debates several possible causes. One proposed solution fails. Another works, but introduces a new complication. The team adjusts its approach, deploys a fix, and confirms the outcome.

The finished code reveals what worked.

The workflow reveals how people figured it out.

That distinction matters for training and evaluating AI systems intended to perform complex tasks. Connected histories can expose decisions, dependencies, exceptions, and outcomes that isolated documents often leave out.

For an AI agent, the challenge isn't simply generating a plausible answer. It's choosing appropriate actions as circumstances change.

What a real business workflow looks like

Imagine a managed IT services provider handling a customer's network outage.

A simplified record might say:

Issue: Network unavailable. Resolution: Reconfigured firewall.

A connected workflow could show considerably more.

Illustrative example, not an actual licensed dataset.

The connected version could help researchers construct realistic training tasks, evaluate an agent's decisions, or identify failure modes.

Not every record would be suitable for model training. Some would contain confidential customer information, credentials, personal data, or other restricted material. Any commercial use would require appropriate rights, safeguards, and data preparation.

But the broader principle is compelling: the history of completing work can be valuable in ways that the finished output alone is not.

Why the ordinary business could matter

When people imagine valuable AI datasets, they often think of technology giants, research institutions, or enormous consumer platforms.

Yet much of the world's practical business knowledge sits inside less visible organizations.

A regional accounting firm may have years of documented reconciliation processes.

A logistics company may have extensive records of shipment exceptions and operational decisions.

A software development agency may have thousands of engineering tickets and associated project discussions.

A customer support provider may have detailed histories of escalations, resolutions, and quality reviews.

These organizations don't necessarily think of themselves as data companies. Their records exist because employees needed to do their jobs.

But that is precisely what makes the records interesting.

They document how work unfolds under real constraints, including incomplete information, competing priorities, and unexpected problems.

A particularly useful dataset might not come from a famous company. It might come from a specialized business that has consistently documented the same category of work for a decade.

More data isn't always better data

For AI developers, volume is only one part of the equation.

A large archive of disconnected messages might be less useful than a smaller collection of complete, well-organized task histories.

Several characteristics can make workflow data more useful:

Continuity. Records cover the same processes over meaningful periods, making it possible to observe how tasks evolve.

Connected context. Tickets, discussions, decisions, and outcomes can be linked together.

Clear results. Researchers can determine whether an action succeeded, failed, or required additional intervention.

Realistic complexity. Workflows contain exceptions, handoffs, and competing considerations rather than only idealized examples.

Usable rights and governance. The organization has a lawful basis to provide the information for the intended use, with appropriate contractual and privacy protections.

That final consideration is essential.

A company possessing records does not automatically mean it owns all the rights necessary to license them. Client contracts, employee information, confidentiality obligations, intellectual property, and privacy law can significantly limit what is commercially available.

The opportunity is not simply to export a company's systems and sell the contents. It's to determine whether specific information can be lawfully transformed into a useful, appropriately governed dataset.

The evaluation opportunity

Training is only one potential application.

As AI agents become more capable, developers also need better ways to test whether those agents can handle real work.

OpenAI's 2025 research on computer-using agents highlighted the gap between performing individual interactions and reliably completing complex tasks. Its computer-using agent achieved a 38.1% success rate on the OSWorld benchmark, compared with 72.4% for humans under the reported evaluation conditions.

Source: OpenAI, Computer-Using Agent

Those figures are a historical benchmark result, not a measure of today's frontier capabilities. Their significance is in the challenge they illustrate: successful computer use requires navigating sequences of actions, not merely producing fluent responses.

Realistic business workflows can potentially help developers design evaluations around tasks such as troubleshooting a technical incident, processing an exception, or coordinating a multistep customer request.

In that setting, the data provides more than examples. It can help define what competent performance actually means.

A new market for institutional knowledge?

The growth of AI agents raises an intriguing economic question.

Could years of accumulated business operations become a new category of licensable intellectual assets?

Possibly, although the market remains difficult to generalize.

Dataset value depends on buyer demand, quality, uniqueness, documentation, licensing rights, and the cost of preparing the information. There is no universal price for a historical ticket archive or a collection of project discussions.

And many businesses will find that their most sensitive records are better used internally than licensed externally.

Still, the underlying shift is worth watching.

Businesses have traditionally viewed operational records as tools for compliance, reporting, customer service, and internal knowledge management.

AI introduces another possible use: helping machines learn how complex work is performed.

That creates potential opportunities for data providers, licensing specialists, privacy infrastructure companies, and organizations with distinctive operational histories.

The next data frontier is operational

The internet helped AI learn how people communicate, explain, and create.

The next challenge is teaching AI systems to navigate the messy reality of getting things done.

That means understanding why a support case was escalated, why an engineering fix was rejected, why a financial discrepancy required investigation, or why a routine process became an exception.

Much of that knowledge has never appeared on the public internet.

It lives in the systems businesses use every day.

And as AI moves from generating answers to executing work, those records could become an increasingly important part of the data economy.

The next valuable AI dataset may be sitting inside a company that has never considered itself part of the AI industry.

About Feed The AI

Feed The AI tracks the companies, capital, datasets, and commercial infrastructure powering the artificial intelligence economy.

We also explore responsible data partnership opportunities for organizations interested in understanding whether their historical operational data has commercial licensing potential.

Interested in exploring a data partnership? Contact joe@feedtheai.com.