The old way of creating reports is almost cliché, but only because it remains so pervasive.
It’s a familiar scene: A teammate pings you at 4:57 pm asking for a last-minute report. The data and business logic you need live across 10 spreadsheets, in five Microsoft Teams threads, and in an email from two months ago that you can’t seem to find.
But that was the old way. What happens when you use AI for the same situation?
Let’s find out.
What working with AI often looks like
Your stakeholder pings you, asking for a report based on a massive tax reconciliation spreadsheet.
This spreadsheet is a beast, chock-full of tabs, formulas, and data that’s been copied and pasted from several enterprise data sources.
“Sorry for the last-minute ask,” they say, “but can you just throw AI at this?”
You go to your LLM prompt library, select a robust prompt, and input it into Claude, along with the spreadsheet.
Four seconds later, you get over 1,700 lines of Python code. Somewhere inside, there appear to be all the data transformations, calculations, and visualizations you need to build your report.
But there’s a hiccup.
Your stakeholder remembers that your tax jurisdictions change four times a year and wants to ensure that you can make any necessary changes.
Sure, you think, that shouldn’t be a problem. I can probably find that line of code somewhere …
Also, there are three subsidiaries. Someone else handles those taxes, so you’ll need to filter those out.
Finally, your stakeholder remembers that your CFO will want to sign off on this and that your auditor is coming tomorrow. They’ll both want to see the logic behind your report.
Suddenly, parsing through and validating hundreds of lines of AI-generated code seems far more difficult and time-consuming than you’d hoped.
VURA: The missing piece
While AI can bring incredible levels of automation and speed, those are only force multipliers when directed strategically. “I can get an infinite number of PowerPoints out of the AI systems if I want that,” Ethan Mollick recently told me during our executive exchange. “It may even be good content, but if it doesn’t serve the purpose you need it to, the productivity gains become a trap.”
Ethan’s point is that more isn’t always better; bringing four hundred PowerPoints to a sales call won’t help you close a deal. Likewise, instantly generating hundreds of lines of Python is unlikely to help your CFO feel confident in your AI’s vibe-coded report.
For an AI workflow to be trusted, it has to be Visible, Understandable, Repeatable, and Auditable, or VURA. You need to know what’s happening at every step of the process: where the inputs came from, how business logic was applied, and whether the outputs were correct. So, how can you accomplish this?
The transformation and business logic layer
Let’s try a different AI-powered workflow. Same situation and model. Only this time, we’re going to add a visual transformation and business logic layer.
First, we go into Claude and type up a prompt, but instead of Python, we ask for an Alteryx workflow.
We open our workflow in Alteryx, and instead of hundreds of lines of AI-generated code, we see a visual canvas showing the entire tax reconciliation process.
It’s still an AI-generated workflow, but now, anyone in the organization can inspect it. They can see what data was used. Your analysts and domain experts can validate the logic. And you can add governance and repeat the process.
Suddenly, AI-generated workflows become far more trustworthy and scalable, giving you a foundation for enterprise intelligence.
The future of enterprise AI workflows
AI tools that can’t adapt when the business changes have short shelf lives, and rebuilding from scratch constantly drains tokens, time, and energy. Endless iterations create endless chances for inconsistencies and errors.
With a visual business logic layer, the people who know your business best — your business analysts, sales professionals, finance team, and more — can apply their expertise to your AI workflows and validate its outputs. They can see what’s happening at every step of your AI workflows so that every process is Visible, Understandable, Repeatable, and Auditable.
Speed and reliability are no longer mutually exclusive. Now, you can bring AI’s power and your business experts together to create something fast and reliable, the intelligent solution you need to create scalable business value.
Learn more: See how Alteryx One can help you build AI workflows your business can trust. Or, watch a live workflow demo to see Alteryx in Action.
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