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Skills shortages are the #1 challenge facing IT leaders looking to implement artificial intelligence in 2024. That’s according to research from Confluent, released ahead of this year’s BigData LDN event.
The research, which surveyed over 500 UK IT leaders, explores the top challenges facing IT departments when it comes to adopting and implementing AI.
It found that 68% of IT leaders believe “insufficient skills and expertise” are a challenge when it comes to rolling out AI. More than a quarter (28%) go as far as to say this skills shortage will pose a “major” challenge in the year ahead.
According to Confluent’s data, the UK’s top AI challenges include:
Insufficient skills and expertise (68%)
Inability to integrate data / fragmented systems (66%)
Insufficient infrastructure for real-time data processing (65%)
Poor data timeliness and quality (62%)
Addressing these challenges will be critical, with 79% of IT leaders saying they expect the demand for Generative AI and similar technologies to grow over the next two years.
As a result, many IT leaders are rethinking their approach to data, switching to more real-time processing methods. Over two thirds (68%) say that feeding AI and machine learning (ML) via real-time data streams will be an important part of their strategy in the year ahead, while 29% say it will be their “top priority”.
Commenting on the research, Richard Jones, data expert at Confluent said, “AI has been a marketing buzzword and not a technological reality for most companies — until now. But as AI becomes mainstream for businesses, many find they are still in the early stages of AI implementation. An AI skills shortage, coupled with a lack of infrastructure for real-time data processing, mean businesses are grappling with a complex, evolving technology.
“AI models need accurate, real-time data streams. With data streaming, IT teams can be incredibly agile and adapt in-line with evolving business demands. The adoption of AI can be accelerated by enabling the rapid scaling and deployment of real-time AI applications, while also tracking the data being inputted into AI models — which is fundamental for regulation.”
