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After Low-Code: The Case for Code-First Analytics.

BI tools had their moment. AI-driven development has surpassed them in cost, quality, and speed. The only reason to keep building traditional reports is fear of change.

March 2026 · 5 min read
01

The Verdict Is In

Power BI, Tableau, Qlik. These were serious tools. Mastering DAX formulas in Power BI is genuinely difficult. Complex Tableau calculations rival the logic you find in data science. Qlik's associative engine required real expertise to use well. Nobody built enterprise reporting on these platforms by accident. It took skill, years of it.

That is what makes this hard to hear. AI-driven development has surpassed these tools on every axis that matters. Cost. Quality. Speed. Building a polished, enterprise-ready data application now takes less time with AI than it takes to build the equivalent in Power BI. The output is better. It is version-controlled. It is testable. And it belongs to you, not to a vendor's proprietary format.

The same is true for the rest of the low-code stack. Power Apps. Power Automate. UiPath. Appian. OutSystems. These platforms existed because writing production code was slow and expensive. AI removed that constraint. The abstraction layer is no longer worth the trade-offs.

AI-driven development has passed BI and low-code on every axis. Cost. Quality. Speed. The debate is over.

The only organisations still building net-new BI reports and Power Apps are the ones too cautious to make the switch. Not because the technology is not ready. Because the decision has not been made.

02

The Migration Is Easier Than You Think

The biggest objection is always the same. "We have hundreds of reports. The logic is complex. No one understands how half of them work. Even our most senior developers cannot follow what someone else built."

That is exactly why AI is the right tool for this job.

BI platforms already store their logic in parseable files. Power BI has .pbix and .bim files. Tableau has .twbx, .twb, and .tds, all XML. Qlik Sense has .qvf files, .qvs load scripts, and JSON metadata. Every formula, every ETL step, every measure. It is all in there, structured and extractable.

Point an AI agent at these files and tell it to do a full analysis. It will map every measure, trace every dependency, and document the entire logic chain faster than any human could. Tell it which pages you actually use in the visual layer. Hidden pages do not get rebuilt. Dead measures that lead nowhere get flagged and removed. What is left is a clean specification for exactly what needs to exist in the new system. Then you use AI to build it. Even for extremely complex reports, the full cycle from analysis to working application typically takes a few hours.

The confused, undocumented report that no one dares touch becomes a fully understood blueprint in hours.

The same applies to Power Apps and RPA bots. Every Power App is a JSON definition. Every UiPath workflow is XAML. These are not black boxes. They are structured files that AI agents can read, analyse, and convert into clean, maintainable code. The bot that took three weeks to build in UiPath and breaks every time the UI changes? An AI-built script does the same job, runs faster, and you can actually debug it.

03

What to Build Instead

The replacement is not another BI tool. It is not another low-code platform. It is a code-first data application. Custom-built, AI-assisted, deployed as software.

The difference matters. A BI report lives inside a vendor's runtime. A Power App lives inside Microsoft's ecosystem. A data application lives in your infrastructure. You control the data layer, the logic, the interface, and the deployment. When something breaks, you fix it in code. You do not file a support ticket and wait.

For the underlying data platform, the choice depends on scale. Small and mid-size organisations should look seriously at Supabase. It is Postgres-native, open source, and handles more than most people expect. Larger organisations with genuinely massive data volumes may benefit from Databricks. But do not default to "big" infrastructure because it feels safer. Most companies do not need it.

If you do not believe this is real, go see it. We built an enterprise analytics platform using exactly this method. No BI tools. No low-code. Just AI-assisted code on modern infrastructure.

See it in action →

04

The Role Is Not Dead. It Is Evolving.

This is not an argument against BI professionals. It is the opposite.

BI analysts sit closer to the business than almost anyone in a technical role. They understand what decisions get made, what data drives those decisions, and what the output needs to look like. That knowledge is more valuable now than it has ever been.

What is changing is the tool. Instead of expressing logic through DAX, calculated fields, or visual workflows, a BI analyst works with AI to build the application directly. They describe what they need. The AI writes the code. They review, adjust, iterate. The cycle is faster, the output is better, and the analyst stays in control of the business logic the entire time.

The same shift applies to the citizen developers who built Power Apps and the process analysts who configured RPA bots. Their domain knowledge did not lose value. The platform they used to express it did. They now have a better one.

The BI professionals who make this shift will thrive. They already have the hardest part. The domain knowledge. The tooling just caught up.

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