AI Data Data Science

Trust, Two Truths, and the Coming Agent Swarm

Picture a typical workday.

You’re in a meeting. Someone asks the typical question: “So, how was revenue last month?”

You pull up your dashboard and respond, “Looks like we’re up 5%.”

The CFO then opens his laptop, checks his numbers, and says, “Well, from what I’m seeing, we’re down 2%.”

And from that moment on, the meeting stops being about the organization’s next decisions and turns into a comparison game.

Are we using the same date range? Order date or payment date? Gross or net revenue? UTC or local time? How exactly are “real users” defined? Are we looking at the finance mart or raw invoices?

This happens all the time in healthy, well-run companies. Two analysts can produce two clean reports and still disagree. The same metric gets defined differently across teams, and that knowledge never gets consolidated in a single place.

This is often the real problem in analytics. We’re not dealing with “bad data” in some abstract sense, but with a lack of shared meaning. This hidden cost is what you could call the trust tax: the price you pay on every important decision just to prove the numbers are real.

Now here’s the twist. In 2026, we’re moving from dashboards to AI agents and AI-driven analytics. And if two humans can generate two versions of the truth, an agent can generate twenty – fast, confidently, and on demand.

AI won’t fix the inconsistent definitions. It will scale them. And without a stronger foundation in place, you may get answers more quickly, but you’ll spend even more time arguing about which one to trust.

Self-service BI didn’t fail. We just skipped the boring part.

For years, all data professionals talked about was self-service BI and data-driven decision-making.

An endless stack of tools, including Tableau, Power BI, and Looker, was introduced to help explore data and move faster. But even with this technology, the industry kept running into the same problem.

Tool vendors gave everyone access to the library, but the books weren’t organized. Access was democratized, but meaning wasn’t.

So people did what they’ve always done: They created local definitions based on what they needed.

For marketing, “active user” meant someone who visited the website.
For product, it meant someone who completed an action.
For finance, it meant someone who paid.
And for support, it meant someone who hasn’t churned.

Everyone was right – within their own silo.

But we’ve now entered the era of AI, and companies believe that AI will solve their problems, even if the foundational work is skipped. They think they can just put an LLM on top of the chaos and it will magically understand their business logic and align everyone’s definitions.

Spoiler alert: It won’t! AI won’t make the cracks disappear – it will widen them. And worse, it will sound perfectly reasonable while doing so.

The trust tax is not just a metaphor

Though much of this may sound like organizational and behavioral factors, it shows up in very tangible economic terms.

Many studies have recently highlighted the financial impact of poor data quality. Gartner estimates it costs organizations an average of USD 12.9 million per year. According to Thomas C. Redman, the cost to the U.S. economy is USD 3 trillion annually.

In 2025, Alteryx also reported that 76% of analysts still rely on spreadsheets as their primary tool for cleaning and preparing data, revealing that even in the AI era, the most common safety net is still an Excel export.

The trust tax shows up as manual work whose sole purpose is to validate the numbers. And beyond the money, there’s also a loss of confidence in data systems and slower, more fragmented decision-making.

Why AI can’t answer “simple” business questions

People ask: “Why can an agent write a poem but not calculate my churn rate?” Because a poem is a single output, whereas a business metric is a chain.

A typical report rarely boils down to one query. It’s often a process involving 10 to 15 steps – from identifying the right tables to computing ratios. And even if an agent is 90% accurate at each step, the probability of getting the entire chain right drops to around 35%.

What you end up with is a system that is often plausible, sometimes correct, and occasionally catastrophic. And that assumes “90% per step” performance is even realistic in your environment.

In real enterprise settings, text-to-SQL gets significantly harder. Benchmarks have evolved for a reason. Newer evaluations, such as Spider 2.0-style environments, reflect messier, more realistic conditions, characterized by larger schemas, multi-step reasoning, and hidden assumptions. Performance declines accordingly. 

Lack of trust isn’t a result of AI not being smart enough. It comes from organizations lacking a shared contract for meaning.

The missing contract: A semantic layer

If you want AI to stop guessing, you need to give it something solid to anchor to: a contract.

In analytics, that contract is the semantic layer, which functions as the official dictionary of your business. Definitions like “revenue,” “active user,” and “gross margin” are formally defined in code, each with explicit rules, covering filters, time and currency logic, exclusions, and more.

Instead of letting an agent query raw tables like t_sales_v2_final, invoice_line_items_2021_backup, or prod_users_all_time, you point it to something that reflects business reality.

This isn’t about making data prettier. It’s about removing ambiguity – and that’s precisely the role of a semantic layer. 

Next week, in a second part, we’ll look at what this means in practice. How the role of data analysts is evolving, what a reliable AI analytics stack looks like, and why semantic infrastructure is becoming one of the key parts of modern data systems.

To be continued…

About Databao

Databao is a new data product from JetBrains that helps data teams create and maintain a shared semantic context and build their own data agents on top of it. Our goal is to provide an AI-native analytics experience that business users can trust, enabling them to query and analyze data in plain language.

Databao’s modular components, the context engine and data agent, can run independently, either locally or within your existing infrastructure, using your own API keys.

We are also inviting data teams to build a proof of concept with us: we’ll explore your use case, define a context-building process, and grant agent access to a selected group of business users. Together, we will then evaluate the quality of responses and the overall value.

TALK TO THE TEAM