Organisations are failing to understand the computing and networking demands across the artificial intelligence life cycle, with fewer than half of IT leaders admitting to having a full understanding of what the demands of various AI workloads might be. At the same time, 44% of IT leaders believe their organisations are fully set up to realise the benefits of AI, a report commissioned by Hewlett Packard Enterprise showed.
This represents a gap in organisational strategies, such as a lack of alignment between processes and metrics, resulting in consequential fragmentation in approach, according to the report.
While global commitment to AI shows growing investments, businesses are overlooking areas that can affect successful AI outcomes, including low data maturity levels, possible deficiencies in networking and compute provisioning, and vital ethics and compliance considerations. Disconnects in strategy and understanding could adversely affect future returns on investment, the report showed.
“Findings clearly demonstrate the appetite for AI, but they also highlight very real blind spots that could see progress stagnate if a more holistic approach is not followed," said Sylvia Hooks, vice president, HPE Aruba Networking. "Misalignment on strategy and department involvement—for example—can impede organisations from leveraging critical areas of expertise, making effective and efficient decisions, and ensuring a holistic AI roadmap benefits all areas of the business congruently,” she said.
Acknowledging Low Data Maturity
Research showed that while organisations consider data management as one of the most critical elements for AI success, their data maturity levels remain low. Only 7% of organisations can run real-time data pushes/pulls to enable innovation and external data monetisation, while just 26% have set up data governance models and can run advanced analytics.
Fewer than six in 10 respondents said their organisation is completely capable of handling key stages of data preparation for use in AI models, from accessing (59%) and storing (57%), to processing (55%) and recovering (51%). This discrepancy risks slowing down the AI model creation process and increases the probability that the model will deliver inaccurate insights and a negative ROI.
Provisioning For 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. Of the IT leaders, 93% believed their network infrastructure is set up to support AI traffic, while 84% said their systems have flexibility in compute capacity to support the demands across different stages of the AI lifecycle.
Yet, less than half of IT leaders admitted to having a full understanding of what the demands of the various AI workloads across training, tuning and inferencing might be, calling into question how accurately they can provision for them.
Ignoring Cross-Business Connections, Compliance And Ethics
Over a quarter (28%) of IT leaders described their organisation's overall AI approach as “fragmented.” Around 35% of organisations are creating separate AI strategies for individual functions, while 32% are creating different sets of goals altogether.
Research also showed that legal/compliance (13%) and ethics (11%) were deemed by IT leaders to be the least critical for AI success, despite growing scrutiny around ethics and compliance from consumers and regulatory bodies. Also, 22% of organisations aren't involving legal teams in their business's AI strategy conversations.
Fear Of Missing Out And Business Risk Of Over Confidence
As businesses move quickly to understand the hype around AI, without proper AI ethics and compliance, they run the risk of exposing their proprietary data. Businesses lacking an AI ethics policy risk developing models that lack proper compliance and diversity standards, which may result in negative impacts to the brand, loss of sales or fines and legal battles.
When low data maturity levels are combined with the metric that half of IT leaders lack full understanding of the IT infrastructure demands, there is a risk of developing ineffective models, including the impact from AI hallucinations.
Also, as the power demand to run AI models is high, this can increase data centre carbon emissions. These challenges lower the ROI from a company's capital investment in AI and can negatively impact the overall company brand, the study showed.
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