Artificial intelligence has been in development for decades, but it’s only recently that the tech industry has set high expectations for its role in digital innovation.
After the AI boom in 2023, more attention has been focused on this technology, with companies gearing up to fully embrace AI this year.
However, understanding the value AI brings to the bottom line is still a challenge for startups and enterprises.
Tech leaders have been addressing concerns within their teams about being caught up in a hype cycle that may not result in significant returns.
One major reason for the delayed emergence of AI’s value is the high operating costs associated with running big data and AI projects, from computer chips to cloud computing infrastructure.
Recently, SQream, a New York-based data analytics acceleration company, released a report titled, “2024 State of Big Data Analytics: Constant Compromising Is Leading to Suboptimal Results”, highlighting the challenges of cost control in analytics and AI projects.
Rising costs threaten AI projects
While AI holds promise for intelligent product development and data-driven decision-making, the reality is that achieving these gains comes at a high cost, as shown in SQream’s report based on a survey of data management professionals.
Despite generous budgets, costs for cloud analytics continue to rise, leading to project failures and compromising AI initiatives.
As data systems move to the cloud, managing large data sets becomes costlier, and AI queries require significant computational power.
High costs and lack of budget were cited as the main challenges faced by companies in 2023, hindering the success of AI projects.
The report suggests that using GPUs can significantly impact data analytics and AI goals, offering efficiency gains and closing the gap between costs and benefits for enterprises.
