Titled ‘Architect an AI Advantage’, the report surveyed nearly 400 IT leaders from the UK and Ireland. While it noted increased investments in AI, it also pointed out that many businesses are neglecting crucial areas that could impact their ability to achieve successful AI outcomes. These areas include low levels of data maturity, potential weaknesses in networking and computing resources, and critical ethics and compliance issues. Additionally, the report identified considerable gaps in strategy and understanding that could negatively affect future returns on investment (ROI).

“It’s unsurprising that our research reported that 94% of businesses are planning to increase their AI budgets this year,” said Matt Armstrong-Barnes, chief technologist for AI, Hewlett Packard Enterprise. “However, what may be surprising is that businesses are investing in AI without first taking a holistic view of the technology and how to implement it. Diving in before considering whether they are set up to benefit from AI and who needs to be involved in its roll-out will lead to misalignment between departments and fragmentation that limits its potential.”

Acknowledging Low Data Maturity

Strong AI performance that impacts business outcomes depends on quality data input, but the research shows that while organisations clearly understand this – labelling data management as one of the most critical elements for AI success – their data maturity levels remain low. Only a small percentage (6%) of organisations can run real-time data pushes/pulls to enable innovation and external data monetisation, while just 29% have set up data governance models and can run advanced analytics.

Of greater concern, fewer than 6 in 10 respondents said their organisation is completely capable of handling any of the key stages of data preparation for use in AI models – from accessing (57%) and storing (51%), to analysing (54%) to processing (52%). This discrepancy not only risks slowing down the AI model creation process, but also increases the probability the model will deliver inaccurate insights and a negative ROI.

Provisioning for the end-to-end lifecycle

A similar gap appeared when respondents were asked about the compute and networking requirements across the end-to-end AI lifecycle. On the surface, confidence levels look high in this regard: 92% of IT leaders believe their network infrastructure is set up to support AI traffic, while 83% agree their systems have enough flexibility in compute capacity to support the unique demands across different stages of the AI lifecycle.

Gartner expects “GenAI will play a role in 70% of text- and data-heavy tasks by 2025, up from less than 10% in 2023,” yet less than half of IT leaders admitted to having a full understanding of what the demands of the various AI workloads across data acquisition, model training and monitoring might be – calling into serious question how accurately they can provision for them.

Ignoring cross-business connections, compliance, and ethics 

Organisations are failing to connect the dots between key areas of business, with over a quarter (28%) of IT leaders describing their organisation’s overall AI approach as “fragmented.” As evidence of this, over a third (38%) of organisations have chosen to create separate AI strategies for individual functions, while 38% are creating different sets of goals altogether.

More dangerous still, it appears that ethics and compliance are being completely overlooked, despite growing scrutiny around ethics and compliance from both consumers and regulatory bodies. The research shows that legal/compliance (15%) and ethics (14%) were deemed by IT leaders to be the least critical for AI success. In addition, the results showed that almost 1 in 4 organisations (20%) aren’t involving legal teams in their business’s AI strategy conversations at all.

The fear of missing out on AI and the business risk of over confidence

As businesses move quickly to understand the hype around AI, without proper AI ethics and compliance, businesses run the risk of exposing their proprietary data – a cornerstone for retaining their competitive edge and maintaining their brand reputation. Among the issues, businesses lacking an AI ethics policy risk developing models that lack proper compliance and diversity standards, resulting in negative impacts to the company’s brand, loss in sales or costly fines and legal battles.

There are additional risks as well, as the quality of the outcomes from AI models is limited to the quality of the data they ingest. This is reflected in the report, which shows data maturity levels remain low. When combined with the metric that less than half of IT leaders admitted to having a lack of full understanding on the IT infrastructure demands across the AI lifecycle, there is an increase in the overall risk of developing ineffective models, including the impact from AI hallucinations. Also, as the power demand to run AI models is extremely high, this can contribute to an unnecessary increase in data centre carbon emissions. These challenges lower the ROI from a company’s capital investment in AI and can further negatively impact the overall company brand.

“If business continue their current approach to AI, it will adversely impact their long-term success,” continued Armstrong-Barnes. “They must adopt a comprehensive end-to-end approach across the full AI lifecycle to streamline interoperability and better identify risks and opportunities. Considering AI – especially GenAI – is data, power, time and resource intensive to deploy and maintain, businesses need to take the necessary steps and lay the groundwork for their deployments so they don’t run before they can walk.”