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The potential benefits of AI are huge, but organizations need to plan carefully and implement responsibly
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It’s hard to ignore the impact that AI has had on business in the past few years. Huge advancements in generative AI, LLMs and deep learning have made artificial intelligence more accessible than ever, while continued progress and refinement in data analytics and machine learning have raised business intelligence to new heights.
In 2023 Accenture research highlighted that nearly three quarters (73%) of businesses were prioritizing AI over all other digital investments, while in H2 2024 a McKinsey survey reported that 78% of respondents said their organizations used AI in at least one business function. For businesses that have yet to fully embrace AI, there is a real fear of being left behind, but those huge advancements we’ve seen in recent years just make investing in AI today even more compelling.
As with any investment an organization makes – whether that be infrastructure, manpower or technology – a focus on AI needs to deliver a positive business outcome. This outcome should be defined at the outset. Only then can you understand what kind of AI workloads you’ll be running, and what kind of platform you’ll need to run them. The days of tinkering with AI to see what it can do are over. Businesses in 2025 are looking to AI as a tool to deliver tangible benefits.
Michael Corrado, Senior Worldwide Marketing Manager for AI and Private Cloud at HPE, stresses this point – “You want to focus your first use cases where you know you’re likely to get great business value at low risk, things that are hindering profitability through inefficiency or anywhere a manual process is slowing you down.”
But Corrado also explains that those defined use cases then need to be carefully examined, “From there, you’ll need to consider technical feasibility, resources, potential risks, scalability, and how it aligns with key performance indicators.”
That might sound like a lot to consider but with proper planning each of those core pieces should naturally fall into place.
There’s an understandable level of fear surrounding AI and the jobs that it could replace, but it’s important to remember that it’s also creating jobs. It’s up to organizations to build in the human resource requirements for any AI implementation, whether that be completely new roles or retraining existing staff to work in new ways.
According to the McKinsey survey, 19% of respondents indicated that their organizations would likely be reskilling over 50% of their workforce due to AI over the next three years. If you don’t want your workforce worrying about their jobs while you build out your AI strategy, communicate with them and highlight the opportunities that this next step of digital transformation will bring.
It’s also important to ensure that any AI platforms are built and used responsibly. And also to work out what that means.
First and foremost, it means investing in adequate AI governance and compliance teams, especially if you’re planning to feed or train your AI on any personal data. This isn’t a box-ticking exercise: security and compliance issues can cause significant financial and reputational damage for a business. Any processing of sensitive data by AI needs to be sewn up tight.
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Here’s another glib statement that people make, often without thinking about it: that data is valuable, but that value can only be realised when genuine insight can be extracted from data. AI has given us a unique opportunity to turn that problem into an opportunity.
The historical big data problem hinged on the fact that businesses were collecting and hoarding excessive amounts of data, but didn’t know how to leverage it effectively.
AI can deliver the tools to fully extract the value from your data, but it needs to be clean and structured. Even if your ultimate goal is AI-driven data governance, initially that task will need to be implemented and managed by people who understand the desired business outcomes.
“Where data quality is bad, you get a bad outcome, no matter how good your machine
learning model is,” says Chad Smykay, AI Chief Technology Officer at HPE.
According to Smykay, data needs to be prepared, cleansed and organized before it can be leveraged by your AI workloads, but it’s also vital that the data itself is relevant to the business use case being addressed: “That means people who understand how the organization will extract value out of AI need to be the ones that set the rules of the road.”
AI can be a highly effective tool for businesses, even a transformational tool, but it requires a significant investment in hardware, software and services. Huge amounts of compute power from both CPU and GPU hardware, along with fast and highly structured data fabric to ensure maximum efficiency for LLMs are a given, but how your business implements this powerful IT infrastructure can be nuanced.
Are you looking for a fully on-premises private cloud solution in your organization’s own data centre, or does a hybrid cloud option offer better RoI? Should you be looking at more general server hardware that can also run AI workloads like HPE’s ProLiant range, or something specifically tailored to HPC and AI like HPE’s Cray XD servers? Or does the flexibility and scalability of an ‘as-a-service’ solution like HPE’s GreenLake provide a more cost-effective route to your AI aspirations?
When it comes to AI, as with so many things, one size does not fit all. Finding the right solution for your organization and the specific business outcomes it’s looking for is vital, and choosing the right partner can be just as important. If you are unsure about where to begin, HPE’s Private Cloud AI is the “quick start” for your use case needs. HPE can provide all the hardware, software, infrastructure, security, advice and support to help your business navigate its AI journey, and ultimately, realise its business goals.
