Breakthrough analytics are just around the corner, as innovators bring AI and XBRL together.

AI is an incredible tool for turning data into ideas – but good ideas need good data. XBRL brings accuracy to the table while AI brings efficiency, adding up to a revolution in business analysis. XBRL data strengthens AI models and enables them to generate deeper, better insights, while smart AI assistance is also set to enhance the XBRL reporting process.

Left in the past, or essential for the future?

The big question we hear is: “Why do we need XBRL if we have AI?”

It is a valid question. If AI can apparently understand and analyse any data we feed it, why bother with the process of tagging reports with XBRL to produce structured digital data? In other words, why do we need to tell AI what we mean if it can figure things out without our help?

But in fact, far from losing relevance, XBRL is even more essential in the age of AI. XBRL is not the past of digital reporting – it’s the future. Why is that?

AI needs XBRL for best results

AI models are capable of ingesting huge amounts of data and spitting out business-relevant insights in a way that seems almost like magic. But the power of AI really depends on the quality of the data it consumes – and XBRL is AI superfood.

XBRL provides structured data, where each piece of information comes with a computer-readable digital tag attached. It tells AI what each data point means and how different facts are related. This structured data is what AI models need.

Of course, you can feed AI unstructured data, but that means that the AI is forced to guess what the data means before it can analyse it. Sometimes it gets it right, sometimes it gets it almost right, and sometimes it gets it disastrously wrong. Just as with conventional analysis, the “garbage in, garbage out” rule applies.

On the other hand, XBRL data is some of the best possible food for AI analysis. Structured data, tagged at source, takes the guesswork out of the equation, enabling AI to generate more, deeper and more accurate insights.

XBRL creates trust and value

What would happen if we stopped tagging corporate reports? Competing AIs deployed by different users, such as data aggregators and major analysts, would each interpret the raw data independently. The result? Multiple versions of reality, all slightly distorted in different ways, and no completely clear and accurate picture.

Digital reporting prevents this chaos. XBRL reports provide a single, trusted, company-verified source of truth for all users, including regulators, analysts and investors. Management accountability is critical in ensuring the quality and consistency of the data and optimising its value.

The world is not short of data. Data is cheap – but good data is priceless. Users want reliable, decision-useful insights. And that means that high-quality XBRL reports have an incredibly high value for AI analysis. Quite simply, XBRL disclosures offer an analytic goldmine.

As AI becomes a more and more important part of analysis, that value will only increase. Demand for XBRL data will continue to rise, while its impact grows. On the other hand, issuers will increasingly find that, for users, data that is not structured is not relevant.

All of this means that the need for authoritative, high-quality and well maintained XBRL taxonomies is more profound than ever. These provide the digital definitions that underpin digital reporting and enable data comparability.

The age of AI is here – and in the AI era structured data is not just relevant to performance analytics, it is indispensable.

Smart tools for smart tagging

The great news for corporates is that AI is not only transforming analysis. Smart AI tools are also poised to streamline the digital tagging process, making it more efficient and cost-effective. The future we’re stepping into is one of human-machine partnership: AI as the diligent aide, tackling the grunt work of data tagging, while management provides crucial oversight, handling complexities with nuanced human judgment. Creating high-quality, authoritative XBRL reports will get faster, easier and more reliable.

New horizons

XBRL and AI work incredibly well together. In combination, they look set to unlock analytic possibilities unlike anything we’ve seen before, reshaping corporate reporting and propelling us toward new horizons. At the heart of it all, structured data remains the global currency for decision-useful analysis.


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