Want AI that works? Fix your data

Picture this: a company pours millions into an AI project, only to see the initiative falter. The reason often isn’t the model or the team, but the underlying data feeding it. Even the most advanced AI will struggle when data is inconsistent, incomplete, or – critically – not delivered as a consumable, governed product.

 

Semarchy’s 2026 AI Readiness research highlights that while 97% of enterprises now invest in AI (up from 75% in 2025), not all AI adopters are equal, and not everyone has built strong data foundations; 51% still cite data management as their biggest challenge. This disconnect reveals that amid the hype over advanced models, the industry treats data as raw material to clean rather than a product to deliver.

 

 

The oldest rule still applies

"Rubbish in, rubbish out" remains true, but the framing misses the point. The issue isn’t just that data needs cleaning before AI can use it, but that data isn’t being delivered as a trusted and ready-to-use data product - a governed, reusable data asset with clear ownership and measurable qualities - in the first place. When incomplete, outdated, or poor-quality data feeds AI models, they inevitably mirror those weaknesses and will amplify errors and bias.

 

Building AI on this shaky foundation is like constructing a skyscraper on shifting sand; the structure may look impressive, but it won’t stand for long.

 

 

Why "Fix the data first" isn’t enough

The traditional approach - audit data, clean it, hand it to AI - assumes data is static, but we know AI workloads are dynamic. They evolve incredibly fast and require semantic context to interpret meaning, not just schema to retrieve fields.

 

When organisations treat data as something to fix rather than deliver, they create a gap between what data teams produce and what AI teams need. So then, what is the right strategy?

 

 

The unsung hero behind successful AI

Master Data Management (MDM) is central to sustainable AI success. MDM systems bring order to chaos, unifying, standardising, and governing core data assets before algorithms are trained or deployed. Yet as corporate data volumes explode, MDM strategies must modernise, and it’s now about making data work like a product, delivered through repeatable processes that ensure quality and trust.

 

A data product approach transforms how organisations handle data delivery. Rather than treating data as a technical deliverable, it means solving specific business problems with data that has clear ownership, defined users, and measurable value. This ensures data is valuable, intuitive, and is accessible for AI initiatives.

 

What matters is that AI isn’t a special case - it’s just another consumer. When data is delivered as a product with built-in governance, semantic context, and standardised interfaces, AI accesses it through the same trusted channels as your BI tools and business users.

 

Modern data delivery also requires collaboration between IT and data teams and business stakeholders through ongoing feedback loops. As AI needs evolve rapidly, this iterative approach keeps processes aligned and transforms data management from a technical exercise into a business enabler. Data that’s clean, consistent, governed, and optimised for AI consumption is only then AI-ready.

 

 

Steps to prepare data for AI success

Preparing for AI doesn’t mean fixing everything at once. It means changing how you deliver data.

 

Start with a detailed data audit that catalogues data sources, ownership, and quality to identify high-risk areas impacting specific AI use cases. Then ask the critical question: Is this data delivered as a product, or just extracted as a dataset? If the latter, you’ve identified the real problem.

 

Take a retailer preparing for AI-driven inventory forecasting. During the audit, the business discovers delayed or incomplete sales records from specific stores. Rather than pushing ahead, a cross-functional team then identifies gaps and implements solutions, resulting in cleaner inputs and smoother AI deployment.

 

From there, adopt a product-driven approach and first define what you’re delivering – what entities does it contact, what’s the semantic model, who owns it, etc. Build governance in from the start so that data lineage is baked into the foundation. Make sure it’s accessible to all consumers (e.g. through APIs for systems, endpoints for AI agents), ensuring that everything is governed consistently. Maintain it like software with version control and automated testing, and remember that monitoring is vital so that issues are caught well before they impact performance.

 

When large-scale issues are widespread, companies should consider temporarily halting AI initiatives while they address underlying data. This will help with preserving budget and preventing downstream failures (as well as retaining internal and external trust). It’s not embarrassing for things to go wrong, but it is to continue when you could choose to improve.

 

 

Building better AI by getting the basics right

AI isn’t magic; it’s a mirror. It reflects the quality and coherence of the data you feed it. Strong data foundations make sure AI doesn’t just work, but that it works well.

 

Organisations that treat data as a strategic asset rather than a technical necessity will turn AI investments into real business impact with sharper decisions, better customer experiences, and new revenue opportunities.

 

In the end, AI success isn’t about having the most advanced model. It’s about delivering the most trustworthy data - as a product, not a project.

 

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Craig Gravina is CTO at Semarchy

 

Main image courtesy of iStockPhoto.com and agsandrew