How enterprises can improve ROI on AI investments 

In this guest post, Sarju Raja, country manager at AvePoint UK, sets out the steps enterprises can take to ensure their investments in artificial intelligence technologies pay off.

We’re at the start of widescale artificial intelligence (AI) adoption, and it’s impossible to say where exactly this new technology will take us.

Earlier this year, AvePoint’s own research showed that 80% of organisations had plans to grow their use of AI, and today almost two-thirds of executives described AI as a revenue driver.

However, continued budgetary pressures mean that many firms are looking for ways to make their AI investments go even further without increasing spending.

AI is a powerful tool, but it’s not a magic wand. To get the most from the technology, organisations need to optimise their data, make targeted investments in training and enablement, and employ effective information management strategies. And here is how to do that.

Implement strong information management to clean your data

In the AvePoint study, IT professionals found organisations with strong information management policies are 1.5 times more likely to see the full benefits of AI.

While the link between strong information management and AI success might not be obvious at first, it makes sense when the need for AI systems to have high-quality data is considered.

In fact, this is the crux of the “garbage in, garbage out” hypothesis surrounding AI. Without an optimised and secure bedrock of data to power high-quality outputs, AI tools like chatbots will deliver worse output that might even hurt productivity.

According to the report, companies with more mature information management strategies reported stronger resource optimisation, enhanced decision-making, and improved efficiency and productivity.

So, to improve an organisation’s information management strategy quickly, it is important to start properly tagging and optimising existing data repositories. And it is never too late to set up automatic content tagging and other data governance frameworks that fall under the information management umbrella.

Proper resource allocation

Despite lingering economic uncertainty, global IT spending is expected to grow in 2024. Gartner projects, for example, that global spending will rise by about $200bn in 2024 – that’s an 8% increase on 2023.

But even as spending rises, the broader economic climate is putting pressure on organisations to get more from their investments. Achieving this with AI can be done by investing in AI-related programs and tools that are likely to have the greatest impact. Instead of distributing licences for AI tools broadly across an organisation, for example, IT leaders should allot licences to the professionals and departments most likely to use them to great effect. Once licences are allotted, leaders should monitor use and redistribute them in cases where they are not making an impact. By investing strategically and constantly monitoring output, an organisation will go further with AI.

In addition to optimising resources and licence allocation, leaders should invest in training and development to ensure their people can use AI safely and efficiently.

By investing in training on an ongoing basis, organisations can keep their workforce informed about security and productivity best practices, which helps their employees use AI efficiently and safely.

By expanding training programmes and refining resource allocation, organisations can help themselves succeed in the AI era. But failing to do so, could put an organisation at a serious disadvantage.

Optimise storage

According to Gartner, spending on cloud storage exceeded $500bn in 2023, and is predicted to clear $600bn in 2024. As leaders face continued pressure to save on cloud storage costs, they should look to improve their storage optimisation with automated data classification, while scanning for inactive, redundant and obsolete items.

This has three benefits: it improves the performance of AI tools by optimising the data that fuels them, lowers data storage costs by automatically detecting and removing redundant and unnecessary data, and mitigates security and other related risks.

After organisations have eliminated unwanted data and optimised the data they want to keep, they can delete and reorganise what is left to save money and improve efficiency.

With 200+ zetabytes of expected cloud storage by 2025, organisations cannot afford to ignore the costs or risk associated with data hoarding.

In addition to savings on storage, optimisation also helps improve employee output by making it easier to find information.