NetApp: Why AI Success Depends on Data Quality

Businesses deploying autonomous AI systems are discovering that one of their biggest challenges has nothing to do with the algorithms themselves. 

It turns out the quality and management of underlying data now makes or breaks whether these systems deliver value or descend into chaos.

Kirsty Biddiscombe, EMEA Business Lead for AI, ML and Data Analytics at NetApp, believes organisations are getting ahead of themselves. 

The storage and data management company, which provides enterprise storage solutions and cloud data services, has clocked 71% higher task completion rates and 99% forecasting accuracy through its own AI implementation. 

But Kirsty insists that businesses need to sort out their data infrastructure first.

While others worry about overhype, she says: “I honestly think that agentic AI has been underhyped.

“Its potential to automate complex or those time-consuming, mundane tasks can save hours of work and dramatically boost productivity.”

Take business travel, she suggests. Autonomous agents could handle bookings, manage delays and coordinate transport based on personal preferences. 

The same logic applies across finance, healthcare, media, telecommunications and retail. But here’s the catch: deployment needs to start small, with specific use cases rather than throwing AI at everything at once.

In this Q&A, Kirsty provides insights on AI’s relationship with the corporate world and how businesses can maximise their productivity by using new technologies.

Please introduce, your role and what you do at NetApp.

I’m Kirsty Biddiscombe and I have the privilege of leading AI business development across EMEA for NetApp.

I spend my days developing go-to-market strategies with partners and customers to cut through the noise of AI adoption.

Not only does that mean figuring out an organisation’s specific needs and stages of AI adoption, but I also work out what’s hype and what’s helpful so that organisations can benefit from practical and scalable AI.

How has AI changed what you do at NetApp in the past few years?

AI hasn’t just changed my role, it has completely transformed how we work as a company and engage with our customers. We’re faster, more efficient and much more accurate now.

In numbers, we’ve seen task completion rise by 71% and our forecasting accuracy improve by 99%.

We’ve also embedded AI capabilities throughout our platforms, which runs quietly in the background, enhancing the productivity and precision delivered across our entire portfolio.

Do you think that some AI technologies – such as agentic AI – have been overhyped this year?

I honestly think that agentic AI has been underhyped.

Its potential to automate complex or those time-consuming, mundane tasks can save hours of work and dramatically boost productivity.

Take business travel as an example: we travel a lot for work, to events, conferences and award shows, and an AI agent could make huge time savings here.

Instead of juggling flight bookings or hotel reservations, an agent could step in and help manage diaries, schedule meetings, handle delays and even coordinate your transport. This could potentially mean having a driverless vehicle waiting for you at arrivals!

This may sound like sci-fi, but it will soon be reality. All of this will be tailored to your preferences too, such as any loyalty schemes that you may be part of and, of course, by checking real-time availability for flight and hotels.

That's just one example though, imagine the impact that this could have across finance, healthcare, media, telecommunications and retail. This technology can unlock hours of productivity and enable healthier, more balanced working lives.

How can businesses get the best out of AI and especially AI agents?

AI shouldn’t be thrown everywhere all at once. It is best to start with the use cases that really matter to your people and customers, as it can help streamline workflows, speed up decision making and free employees from repetitive tasks.

To truly understand how to get the best out of AI, I always start by identifying relevant use cases that deliver maximum impact and can reduce any organisational pressures.

We all know that AI can handle the mundane and repetitive stuff, such as data entry, which allows employees to focus on more strategic or creative work, but it can also protect businesses.

Our AI-powered ransomware solution is a great example as it automatically detects and responds to cyber threats while IT teams focus on higher-value tasks. No one really wants to spend their days firefighting when they don’t have to, and AI can help.

There are so many other ways businesses can utilise AI effectively. The technology’s real-time decision-making capabilities can support organisations seeking to improve their operational efficiency, reducing the stresses or workloads on employees.

This can only foster healthier work environments. AI agents excel at customer service too. They can instantly handle common inquiries, while seamlessly routing any complex issues to human agents when necessary.

How much does data matter when it comes to agentic AI and ROI for businesses?

Data isn’t just important for agentic AI, it is everything for today’s organisations.

With fewer human checkpoints, the quality of an organisation’s data is now the difference between delighting a customer and derailing a process.

Using my travel example, one wrong data point could send you to the wrong airport. You may think, but Kirsty, that can happen anyway?

Well, scale that to healthcare where lives are at stake, and inaccurate data could affect someone’s treatment. That’s why secure, clean and accessible data is the bedrock of successful AI adoption.

How should businesses manage their data?

To make agentic AI work well, businesses need data that’s unified, reliable and fast. This means breaking down siloes, consolidating data from all edge devices, on-premises systems and cloud environments into one, unified infrastructure.

This should be able to scale as a business grows. Firms should also note that AI workloads run on high-throughput, low-latency storage systems.

Add in cloud-native technologies and hybrid deployments, and organisations have a recipe for scalability, security and compliance.

Our solutions integrate data from a diverse range of sources for this very reason, enabling the creation of customised AI applications which can enhance the relevance and accuracy of AI-generated responses.

By implementing these strategies, businesses can effectively manage their data to thrive when it comes to any agentic AI adoption.

What can NetApp do to help businesses and clients to get the best from AI?

We make sure AI is only limited by an organisation’s imagination not their infrastructure, providing comprehensive solutions that focus on hybrid cloud data access, streamlined management and security.

Our native cloud storage services integrate seamlessly with the major providers, and we also eliminate the need to duplicate or move datasets. This allows firms to focus on insights and not waste their time on management.

Built-in ransomware protection, encryption and access controls also safeguard customers against sophisticated threats. We call all of this Intelligent Data Infrastructure.

It unifies file, block and object storage, eliminating silos and supports the next generation of technologies.

An example of this is through our partnerships, with the likes of Nvidia, Intel, Lenovo and Cisco, which provide access to cutting-edge AI technologies and ensures that organisations can be AI-ready.