While businesses race to deploy AI in 2026, 84% are failing before they even start. A recent IDC and NetApp study found that the vast majority of firms believe their data storage is not optimised for AI. This is causing businesses to stumble at the starting block.
From streamlining workflows to taking on the burden of repetitive tasks, AI has the potential to bring value to every business. How we capitalise on these capabilities, however, is not as simple as just deploying AI models and leaving them to their own devices. There is always preparatory work to do first. The most important facet of this work is around data.
It is well understood by now that high-quality data is crucial when implementing AI across business operations. Yet businesses are struggling to get data right. This is holding them back from using AI in a meaningful and effective way.
More is less
One of the main mistakes when dealing with data for AI is prioritising quantity over quality. This mindset creates a whole range of issues for businesses, showing that, for AI at least, less is sometimes more.
Large volumes of incomplete or inconsistent data create ‘noise’ that confuses and distracts AI models. Businesses don’t want to ask for one data point and have AI wade through spreadsheets of unrelated information in mixed formats. This will only slow down analysis and obscure any valuable insights that could accelerate go-to-market timelines.
On top of this, all this surplus data needs to be stored somewhere. When dealing with large quantities of data, it is often siloed and scattered. This effectively hobbles attempts to add AI to a workstream.
Imagine looking for a specific book in a library spanning many floors and rooms where books are randomly shelved. You might never find it. Now imagine the same collection arranged in a single room with a systematic approach to how it is ordered.
For AI, your data infrastructure is like a library which it has to navigate. This is the very problem most businesses face. They are forcing their models to search the chaotic library and expect instant success.
Enterprise data is complex and messy. Pipelines, which are ill-equipped to store this type of information, create long-term maintenance problems. It makes systems fragile and hard to scale, never mind govern. This underlines the findings from our global Enterprise AI maturity research with IDC. That shows that eight in ten are not extracting the value out of their AI that they could be.
Setting AI up for success
To solve these issues, businesses need to focus on two things. First, that their data is high quality, and secondly, that it is unified. This means that the data is standardised and consistent across all sources, so that it can be accessed and analysed coherently. This requires infrastructure that can standardise diverse types and formats of data into something much more coherent.
When data is unified, AI’s capabilities improve exponentially. In a unified system, AI no longer has to query multiple systems separately as it can access everything simultaneously. In practical terms, this translates to duplicates being identified and removed at speed. It reduces noise and improves accuracy.
This also resolves inconsistencies. Standardising formats and definitions so that data follows consistent structures, naming conventions, and business rules enables AI models to interpret information correctly. Together, these improvements provide AI with cleaner, more reliable inputs. That allows it to generate insights with greater speed and precision.
This makes data (and in turn the AI operating on it) significantly more governable. Unified data means businesses can clearly track data lineage. This enables them to understand exactly where data originated and how it has been used by AI models. This visibility is crucial for enforcing privacy requirements and controlling who can access what information.
Looking back on the ransomware attacks last year, which saw unprecedented volumes of confidential customer data leaked, the importance of this cannot be understated. Sensitive, regulated data only being available to authorised entities is an imperative for responsible business practices.
Strong governance also means businesses can better audit and justify AI-driven decisions. For businesses, these capabilities translate directly into increased trust and transparency. 57% of UK consumers distrust brands using AI. Responsible usage can give businesses a clear competitive edge.
Building AI that can scale with you
Beyond this, unified data systems address sustainability. The urge to hoard data in hopes of improving AI capacity has environmental consequences which often aren’t addressed. A significant portion of enterprise data is only used once and never accessed again. However, it remains stored, powered and maintained.
92% of UK businesses acknowledge this environmental impact of ‘single-use data’ but struggle to tackle it. Over a quarter of businesses expect their data footprint to grow up to 50% due to AI projects.
When 38% of data remains unused, conducting a data audit to cut down on unneeded information eases storage capacity. This, in turn, costs businesses less and leads to more sustainable operations.
Data only needs to be stored if it is directly relevant. It should be stored in clean, unified systems which help AI achieve its full potential, facilitating proper governance models for future innovation and scaling. If businesses want their AI usage to be efficient, effective, sustainable and scalable for the future, they must adopt a quality over quantity mindset and avoid those chaotic data libraries.
NetApp is the intelligent data infrastructure company, combining unified data storage, integrated data, operational and workload services to turn a world of disruption into opportunity for every customer. NetApp creates silo-free infrastructure, harnessing observability and AI to enable the industry’s best data management. As the only enterprise-grade storage service natively embedded in the world’s biggest clouds, our data storage delivers seamless flexibility. In addition, our data services create a data advantage through superior cyber resilience, governance, and application agility. Our operational and workload services provide continuous optimisation of performance and efficiency for infrastructure and workloads through observability and AI. No matter the data type, workload, or environment, with NetApp you can transform your data.
