One of the reasons programmers “got” agentic AI first, in the sense that it could meaningfully alter their productivity and effectiveness, is because code could be checked. They turn agents loose on a function, read the diff, run the tests, and decide whether to keep it.
One of the reasons programmers “got” agentic AI first, in the sense that it could meaningfully alter their productivity and effectiveness, is because code could be checked. They turn agents loose on a function, read the diff, run the tests, and decide whether to keep it.
The folks who work with data all day long sit one step back from the same door. People in business and financial operations roles outnumber software developers in the US by about six to one, and they’ve thus far picked up a small fraction, at best, of the productivity gain that engineering has absorbed. Yes, they can describe the analysis they want, which is what requires their specialized knowledge. What comes back to them is a number or a chart, or maybe two hundred lines of code, none of which shows an analyst how the results were produced.
The output, in other words, cannot automatically be assumed to have used the right pieces of data, combined it correctly, and finally computed with the right formulas. Or maybe the agent missed a bunch of the data. The result is a black box, which isn’t something that should breed confidence.
The question then becomes why, and there are three solvable variables.
A business user’s data sits in spreadsheets, files in SharePoint, applications such as Salesforce, and data platforms such as Databricks, Snowflake, or BigQuery. Getting to it means filing a request with a data platform team whose responsibility is governing everything. Of course, as just about any data platform engineer will tell you, the team’s headcount is undersized for the requests it receives, and what the business user gets as a workaround is often a copy of a copy. Maybe it’s a CSV that was pulled a couple of months ago, or a workbook that has been handed off four times with untracked changes. Then when an agent goes to work on that data, the business user has something fast… working on a shadow version of the truth. The data lineage is long gone before the agent takes its crack, so no one is really sure where certain numbers came from, and the trust has been lost. Not good regardless of use case, and probably a career event if you’re at a financial institution.
In context
- Topic: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Source: InfoWorld
- Published: 12/10/2026
Continue reading at the original source →
Excerpt published automatically by the site radar. The full text belongs to its publisher and is linked above.
Why it matters
The conversation about artificial intelligence has become noisy, and that makes it harder to see what matters. What matters is rarely the model. It is which concrete process ends up cheaper, faster or more reliable, and who is accountable when the answer comes out wrong.
The first thing I look at is not model capability but the data feeding it. Almost every project I have seen fail did not fail on the algorithm. It failed because the data lived in three systems with three different definitions and nobody wanted to be the one to fix that.
What usually goes wrong
The point where it usually breaks is the handover to operations. The team that built the pilot knows how to read the results; the team receiving it does not. Without training and written criteria, people start accepting everything the model says or ignoring it entirely, and both are equally expensive.
What to watch
- Which concrete process is touched and how it is measured before and after; with no baseline there is no result, only opinion.
- Who reviews the model outputs and how often — the point at which AI becomes auditable.
- Cost per query at real volume, not pilot volume: the variable that produces the most surprises.
How I read this entry
I would use it for a talent conversation rather than a purchasing one. The capability that needs installing is not operating a tool, which changes every six months, but framing the problem well and judging the answer. That one stays in the house.
This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
Comments
No comments yet. Yours can be the first.
Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
