Garbage in, AI out. Why 60% of AI projects are set to fail.
Personal views disclaimer: The opinions expressed in this article are entirely my own and do not represent the views, positions, or official communications of my employer or any organisation I am affiliated with. References to third-party products and companies are based on publicly available information and my personal professional observations.
Bad data doesn’t get better when processed. It just gets more elaborate — and at Agentic AI scale, it multiplies.
At NetApp Insight 2025, this image was paired with a Gartner prediction that I haven’t been able to stop thinking about since.
of AI projects will be abandoned through 2026 due to lack of AI-ready data.
— Gartner, Roxane Edjlali, Senior Director Analyst, February 2025
The two reinforce each other perfectly, and together they tell us something that many enterprises are still reluctant to hear.
AI adoption is accelerating — faster than any technology before it
AI is now the go-to initiative for virtually every enterprise. Adoption is rising faster than any technology in recent history, driven not just by investment pressure but by a genuine need to stay competitive. If you are not moving, you will eventually fall behind.
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Bank A — with AI Loan application initial approval in hours |
⏳ Bank B — manual process Same application takes days |
That gap is not just an efficiency difference — it represents real business opportunity won and lost.
The uncomfortable truth: data is what’s missing
With heightened awareness of AI, many organisations have placed it at the centre of their core initiatives. Some have already completed proof-of-concepts or started adoption. These investments are significant, and yet Gartner predicts that 60% of AI projects will be abandoned through 2026 — not because of the wrong model, not because of insufficient compute, but because the data was not ready.
“We can have the most advanced AI infrastructure, top-notch software, applications, and specialists — and still fail. The root cause is always data.”
In the rush to adopt AI, it is the most critical ingredient that gets overlooked. Organisations prioritise the toolset and forget the foundation.
Data: the heart of everything
I have had many meaningful conversations with customers about this very issue. What consistently surprises people is how often data readiness is assumed rather than verified. The image at the top of this post captures it with brutal simplicity — it does not matter how sophisticated your AI layer is. If the data going in is poor quality, the output will be too. Agentic AI simply means bad data, now operating at scale.
This is where NetApp enters the conversation. NetApp has been in the data management space for over three decades. Since AI is largely about unstructured data — files, images, video, audio — their focus on data management, security, protection, availability, optimisation, and accuracy is directly relevant.
What good AI data engineering looks like
Effective AI data engineering means understanding where data lives, keeping it current without unnecessary duplication, enforcing security and classification, and transforming data into a form readily consumable by AI applications. Doing this natively would typically require stitching together a dozen or more different tools and platforms — a level of complexity that stalls most AI initiatives before they reach production.
From what I have seen at NetApp Insight 2025, the NetApp AI Data Engine (AIDE) is one approach that addresses this — making data globally available without duplication across silos, underpinned by ONTAP’s enterprise data mobility and protection capabilities.
Before data is ready for Enterprise AI, the right foundations must be in place — starting with Data Classification. NetApp’s Data Classification service provides over 200 out-of-the-box classifications to identify sensitive information within files. Watch the ONTAP Data Classification demo to see it in action.
Three things to get right for AI success
Standardise your infrastructure
Avoid the rabbit hole of certifying every component of a three-tier architecture for AI. Choose a validated design and start there. Validated reference architectures like Cisco FlexPod with NetApp are a good example of a proven, AI-ready starting point.
Acknowledge tool complexity
No single vendor covers every tool needed for data engineering. A dozen or more tools are typically required, and a lack of internal capacity to manage that complexity will stall AI projects — often quietly and expensively.
Focus on Enterprise AI — not the plumbing
Make your data AI-ready without getting lost in architectural engineering. Leverage platforms and partner ecosystems — such as the NetApp AI ISV partner ecosystem — to offload data engineering tasks so your team can focus on value, not infrastructure.
The ecosystem that completes the picture
While many sessions and articles cover AI deployment and security, very few address how data should be engineered and prepared securely beforehand. Most assume enterprises have already handled this. As a data owner, you need control over access without needing to master the entire AI stack.
Three decades of data management expertise means platforms like NetApp understand where data is, how to keep it current, and how to make it secure and AI-ready. The AI Data Engine is further complemented by technology partners like Diskover, which discovers data in media formats and feeds that intelligence into the AIDE metadata architecture — without copying the data itself.
40%
of AI projects will succeed. Will yours be one of them?
Gartner’s prediction is a challenge, not a verdict. The organisations that succeed will have done the unglamorous work of preparing their data properly. Reach out to your NetApp representative or partner to find out how to get started.
Disclaimer: This article reflects my personal opinions and professional observations only. It does not represent the views or official positions of my employer or any affiliated organisation. All product references are based on publicly available information. Product and company names mentioned are trademarks of their respective owners.
References
1. Gartner — “Lack of AI-Ready Data Puts AI Projects at Risk,” Roxane Edjlali, Senior Director Analyst, February 26, 2025. gartner.com ↗
2. NetApp Data Classification — netapp.com/data-services/classification ↗
3. ONTAP Data Classification Demo — youtube.com ↗
4. NetApp Insight 2025 — “Unleashing GenAI on ONTAP: First E2E AI Data Engine Customer Deployment.” netapp.com ↗
5. NetApp AI ISV Partners — netapp.com ↗
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