Let’s say your company spent millions on AI talent and infrastructure. Six months later, the models still produce nonsense because nobody fixed the data problem first.

IT leaders everywhere share this mindset — 72% believe their data infrastructure can support AI today. The reality? Most can’t even handle basic real-time queries. So how do you get AI-ready?

The confidence gap that kills AI projects

HPE research reveals a dangerous disconnect — organisations feel prepared, yet only 7% can run real-time data operations. Just a quarter manages advanced analytics successfully.

43% are already deploying AI enterprise-wide on shaky foundations. When expectations crash into reality, boards lose faith and budgets evaporate.

Your data lives everywhere except where AI needs it

Data scientists waste months hunting information scattered across departments. Marketing’s customer insights hide in one silo, product telemetry in another, financial metrics somewhere else entirely.

34% of companies admit their data sits isolated in separate applications. Request a complete dataset and you’ll get fragments, if you’re lucky.

The hidden maturity crisis

Fewer than six in ten organisations handle basic data preparation properly. They can’t access, store, or process information reliably.

Real-time data pushes? Forget it — 93% lack this capability entirely, making responsive AI impossible.

Breaking down the accessibility barrier

Global data visibility starts with knowing where everything lives. Map your data landscape completely before attempting AI deployments.

Shared data models remain rare — only 37% have centralised business intelligence. Without equal access across teams, AI becomes a privilege for the few.

Storage built for yesterday won’t power tomorrow

Legacy systems scale capacity but choke on performance when data volumes explode. AI workloads demand both massive storage and lightning-fast access.

Shared-nothing architectures hit walls when you need shared-everything flexibility. Modern AI requires disaggregated systems that scale performance and capacity independently.

The architecture decision that matters

Hybrid cloud spreads storage wherever data originates — edge sensors, core systems, cloud platforms — no single point of failure, no performance bottlenecks.

A third of IT leaders chose private cloud for AI data, 30% picked hybrid. Public cloud’s latency makes it unsuitable for serious AI workloads.

Automation keeps momentum alive

Data-first businesses are 3.5 times more likely to automate protection completely. Your policies should classify and sort information before it pollutes AI training sets.

AI can even clean its data — algorithms spot inconsistencies and duplicates that humans miss. But only if the foundation is solid first.

The competitive reality check

Data-first leaders beat competitors to market 13.5 times more often. While others struggle with basics, they’re already extracting intelligence from information.

Every day you delay proper data preparation is a day competitors pull further ahead. In the AI race, data excellence determines winners and losers.