Across boardrooms and C-suites, the buzz around AI is deafening. But when organisations move from whiteboard ideas into execution, many hit the same wall: their data is unfit to fuel their AI appetite.
That’s because after decades of fragmented storage strategies, siloed applications, and archiving – often with only compliance in mind – many businesses simply don’t even know what data they have, let alone know how to convert their data into an asset.
AI isn’t plug and play
Archives were once dismissed as little more than a data repository. Thanks to AI and analytics, what was once seen purely as a cost burden is now revealing unexpected value. Buried in years of data that was locked away are powerful insights – into customer behaviour, market movements, and operational inefficiencies- that can drive real revenue growth.
But here’s the catch: it’s only a gold mine if IT can dig effectively. That means delivering trusted, accessible data ready for AI to exploit. Otherwise, it remains just another data storage cloud with very little potential. And, without proper oversight and governance, those organisations looking to use data for AI risk being non-compliant or even exposing sensitive information.
Businesses racing to scale AI often find themselves bogged down by a lack of visibility where data is stored, and unclear governance policies that stall innovation and increase risk. This isn’t just an operational woe. Poor-quality, misclassified, or duplicated data can introduce bias, security risk, and reputational damage. Even the most advanced model in the world can’t compensate for feeding it non-compliant or inaccurate data. That’s why AI readiness today is more about fixing the data foundation than fine-tuning the models.
Facing the archive challenge
Feeding archive data into AI and analytics engines is no small feat—especially when you factor in the need to comply with a myriad of regulations and laws. The problem starts where traditional backup solutions were used as an archive, which are notoriously ill-suited for modern demands. You can’t easily delete data, search it, or structure it. That fragmentation makes it nearly impossible for IT teams to gain a unified view or use the data.
And that’s not just an operational headache—it’s a governance nightmare. You can’t just dump backups or legacy archives into an AI model. Some data must be masked—think financial records for example. Other data may be off-limits altogether. But figuring out what’s safe, and what’s not, takes time and visibility that most teams don’t have.
Even if you do crack access, the battle’s not over. Archive data isn’t AI-ready by default. It has to be cleaned, shaped, and formatted—a task traditional tools struggle with. To add to that, they’re expensive and slow to boot.
From application-centric to data-centric archiving
It’s time to stop treating archiving as the end of the data lifecycle. Instead, organisations must change to a data-centric model, one that makes data an asset rather than locking it away.
For too long, businesses have used point solutions to manage data in silos, one tool for email, another for database records, another for cloud content. But AI doesn’t care where data lives. It needs to be curated, complete, and trusted datasets regardless of format or origin.
That’s why building a modern archive that provides data governance can help businesses unlock their AI and analytics potential. These platforms can help accelerate the deployment of critical AI-powered use cases across sectors, from finance to HR and healthcare –all while adhering to strict data governance and regulatory requirements.
A governed, cloud-native archive lets organizations ingest and control data across their entire estate, structured and unstructured, legacy and modern, and deliver it securely to the AI and analytics platforms that need it. This is especially important in regulated industries, where the cost of getting AI wrong, through bias, data leakage, or non-compliance, can be significant.
Need to retire or delete data on request? No problem—IT can pinpoint and remove precisely what’s needed.
The road ahead
AI isn’t slowing down, and neither are the risks associated with poor archiving practices. Businesses need to get proactive about governing their legacy and regulated data if they want to get the most out of AI.
This doesn’t mean ripping and replacing every system overnight. It means adopting a strategic, archive-first approach. Decommission what you don’t need. Centralise what you do. Govern everything.
The companies that do this well won’t just reduce their risk and technical debt, they’ll gain a real edge in the AI race. Because at the end of the day, AI is only as smart as the data it’s given. And it’s time we started treating data not as a burden to store, but as an asset to activate.
