A recent global study conducted by S&P Global Market Intelligence, commissioned by WEKA, has revealed that the adoption of artificial intelligence (AI) is on the rise. However, the study also highlights several challenges that hinder firms from implementing AI successfully at scale.
The research, which surveyed 1,516 AI/ML decision-makers and influencers across the Asia-Pacific, Europe, Middle East and Africa, and North America regions, found that the rapid advancement of generative AI has presented data infrastructure and sustainability challenges. This has required organizations to rethink how they store, manage, and process data.
Nick Patience, senior research analyst at 451 Research, explains that the rise of data and performance-intensive workloads like generative AI has forced organizations to reevaluate their data architectures and establish long-term scalability. Having a modern data stack that efficiently supports AI workloads and hybrid cloud deployments is crucial for achieving enterprise-scale and value creation.
Despite the growing adoption of AI, the study revealed that only 28% of respondents have reached enterprise scale with their AI projects. However, 69% of respondents reported having at least one AI project in production, highlighting the increasing use of AI to drive significant business value and create new revenue streams.
The study also identified data management as the most significant obstacle to AI/ML deployments, with 32% of respondents citing it as a challenge. This outweighed concerns over security (26%) and compute performance (20%), suggesting that many organizations’ current data architectures are ill-equipped to support AI initiatives effectively.
To overcome these challenges and accommodate AI workloads across various infrastructure venues, companies need to leverage a modern data architecture that can handle significant data challenges and requirements. Liran Zvibel, CEO at WEKA, compares the use of outdated data management approaches to powering a state-of-the-art electric vehicle with 1990s battery technology. By building a data stack designed to support AI workloads seamlessly, organizations can position themselves as leaders in AI adoption and disruptors in their industry.
What does the study reveal about AI adoption?
The study shows that AI adoption is increasing among enterprises and research groups.
What challenges do organizations face in scaling AI adoption?
According to the study, organizations struggle with data infrastructure and AI sustainability challenges, particularly in relation to the rapid advancement of generative AI.
What is the most significant obstacle to AI/ML deployments?
The study found that data management is the most frequently cited challenge in deploying AI and machine learning (AI/ML) technologies.
How many organizations have reached enterprise scale with their AI projects?
Only 28% of respondents in the study reported reaching enterprise scale with their AI projects, indicating that scaling AI adoption remains elusive for many organizations.
How are organizations using AI to drive business value?
The study reveals that 69% of respondents are using AI/ML to develop new revenue drivers and create business value, shifting AI from a cost-saving lever to a revenue driver.