Its time to walk the walk with AI

Date:
Sun, 15 Mar 2026 14:00:00 +0000

Description:
AI ambition stalls without strong data foundations and resilience discipline.

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With all of the hype, AI can sometimes feel like old news, with nearly every organization discussing how it is impacting their business . But right now, much of the talk doesnt match up with reality. Most companies wouldnt want to admit this, but they remain stuck in pilot mode.

In fact, research from MITs NANDA initiative found that 95% of AI pilot programs fail, delivering little to no measurable impact. Rather than leaping ahead, most are scrambling to show value, lacking the confidence needed to truly innovate. Rick Vanover Social Links Navigation

Vice President of Product Strategy at Veeam Software. At the heart of this challenge is data . The sheer scale, complexity, and sensitivity of whats needed for AI can be intimidating and even paralyzing. And understandably so. Accessing, managing, and securing data in an AI-driven world is daunting, and existing resilience measures often feel inadequate. Article continues below You may like Championing data leadership: how can data strategy shape AI success? AI blindness is costing your business: how to build trust in the
data powering AI The visibility mirage: Why AI pilots keep stalling between ambition and impact

Yet good data hygiene remains essential, and investing in visibility and resilience from the start is the only way to move forward with confidence. Otherwise, youll be stuck talking the talk, rather than walking the walk. Giving AI a reality check With so much of the conversation around AI focusing on the potential for business transformation, its easy to forget what it all boils down to - data. Generative AI (GenAI), Large Language Models (LLMs), anomaly detection, prediction models, you name it, they are all built on, trained on, and create data.

Its a large part of the reason why were expected to create, capture, copy,
and consume 181 zettabytes of data globally this year alone, 3 times as much as we did 5 years ago. Its hard to conceptualize such large numbers, but essentially, there is far more data that organizations were previously equipped to handle.

AI is also flipping the script on how much data companies can actually use. According to Gartner, 80% of enterprise data is unstructured. Before AI, that meant it was mostly just sitting there, often needing to be stored and protected, but impossible to extract value out of it. With AI, thats all changed. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.

And, its growing exponentially as AI continues to evolve. So today, the real reality of AI is that organizations are struggling to wade through the
growing mountains of data to categorize what theyve actually got. Add an AI pilot program to the top of this pile and it becomes obvious why so many pilots currently fail.

So while organizations would like to say that theyve got a watertight AI policy in place, for most, shadow IT remains a very real issue. Failing pilot programs are holding organizations back, leaving employees to experiment in the background with unauthorized AI tools.

And this will only continue, unless organizations can escape the sinkhole of data to drive real AI innovation. What to read next How to take AI from
pilots to deliver real business value Why agentic AI pilots stall and how to fix them AI governance under strain: what modern platforms mean for data privacy Building on the right foundations AI might be heralded as a new era, but believe it or not, this new era needs to be built on the foundations of the last one. Quite simply, good data hygiene remains good data hygiene, and theres no need to write off your existing resilience measures.

That means continuing to carry out impact assessments on all of your data. Because the first step to sort through the ever-growing piles of data is to understand what youve got. Only then can you identify what data is actually the most integral to your organization, and treat it appropriately.

Gaining that visibility is essential for ensuring the resilience of your data as it continues to grow. Otherwise, if an incident does occur, you wont know what data you actually need to get back up and running, and you wont be able to identify the last known good state.

This cant be a one and done. The flow of data is not stopping anytime soon, and you need to keep a handle on all of it. Its integral that practices like data standardization, data validation, and continuous impact assessments continue to stop organizations from getting buried under the flow again.

Hopefully, most organizations should have these measures in place already, so its less about bringing in brand new methods to unlock AI and more about expanding what youve already got in place. And getting these foundations
right really does need to be the first priority.

Because the truth is, AI can actually help you do this groundwork. You can
use AI to support data classification, improve your data lineage, and strengthen your resilience measures. In a way, your first AI project should simply be to look after your data. Get AI to look after your data, and then your data will look after your AI.

Establishing this sense of control over your data is essential to building
not just the foundations, but the confidence needed to truly innovate with
AI, and to deliver pilot programs that actually work. Dont run before you can walk It sounds obvious, but the solution is simple in principle: start small. You dont need to invent the next great big thing; you just need to prove that your organization can deliver innovation and drive value while balancing control.

Rather than reinventing the wheel, begin with a manageable initiative where
AI can safely add value and demonstrate results. With that under your belt, you wont just build your own confidence, youll also prove to the wider organization that innovation is possible. Then, you can move on to bigger, more transformative applications.

But throughout it all, keep returning to the basics. Ensure that the cost of creation, the performance, and the resiliency of your AI model are all aligned. Otherwise, you wont be able to build business processes around it without jeopardizing your resilience.

At every step, you should be able to explain the processes, and the second
you cant, is when you need to stop and roll it back before it gets out of control.

Starting small is key to overcoming that fear of failure that is holding so many organizations back from not only harnessing the true value of their
data, but also using AI to deliver real, transformative business value.

But we need to carry a small dose of that fear throughout the process to keep that crucial balance between control and innovation to stay resilient. Thats how you finally talk the talk and walk the walk when it comes to AI. We've featured the best AI website builder. This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and
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