The question then becomes why, and there are three solvable variables.
Problem 1. The user’s own data is out of reach
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.
Problem 2. There’s no data fact-check
Chatbots hand business users a result with relatively little context for the user to validate it. Showing them the generated code isn’t the answer, since these users aren’t expected to read it. The right response is not signing off on decisions made with data that cannot be checked, but, like programmers shipping code, the pressure to move quickly and deliver supersedes “right” much of the time. And when an auditor or higher-up asks how a figure was produced, “the AI said so” isn’t the answer a business user will want to give.
