Artificial Intelligence (AI) has become a buzzword, promising an industry breaking change. Large enterprises, in particular, have been keen on using AI to gain a competitive edge. A recent study by IBM, which surveyed 8,500 IT professionals globally, sheds light on the current state of AI adoption in large enterprises, the challenges they face, and the opportunities that lie ahead.
Current State of AI in Enterprises
The study reveals that approximately 42% of large-scale organizations are actively using AI in various aspects of their operations. What’s more, 59% of these organizations have plans to increase their investment in AI. This trend is a testament to the growing recognition of AI’s value in improving efficiency, reducing costs, and enhancing decision-making.
Interestingly, smaller companies with 1,000 or fewer employees are less likely to adopt AI. This discrepancy can be attributed to the financial resources and technical expertise required to implement AI effectively. Larger enterprises often have more substantial budgets and access to specialized talent, making AI adoption more feasible.
The main drivers behind the expansion of AI in enterprises are the availability of better development tools, the promise of cost reduction, and the automation of various business processes. These factors make AI an attractive proposition for organizations looking to stay ahead in a highly competitive landscape.
Challenges in AI adoption
Despite the enthusiasm for AI adoption, the IBM study identifies several significant barriers that organizations encounter. Limited AI skills and expertise within their workforce pose a formidable challenge. The field of AI is rapidly evolving, and finding professionals with the right skill set can be a daunting task.
Data complexity is another hurdle. A staggering 63% of organizations use more than 20 data sources for AI and analytics. Managing and integrating this diverse and voluminous data is a complex undertaking.
Integration difficulties, high implementation costs, and a lack of development tools for AI models also hinder adoption. The integration of AI into existing systems and processes is essential for its success. High upfront costs can be prohibitive for some organizations, especially smaller ones.
Generative AI and open source technology
There is a growing interest in generative AI and open-source technologies among large enterprises. Generative AI, which focuses on creating content such as images, text, and music, has gained traction for creative applications and content generation. Companies are evenly split between using in-house AI solutions and open-source technologies. This reflects the diversity of approaches organizations are taking to meet their AI needs.
In response to the challenges they face, large enterprises are channeling their investments into two key areas: research and development (R&D) and workforce upskilling. R&D efforts aim to create innovative AI solutions tailored to the specific needs of the organization.
Despite the challenges outlined, industry experts, such as IDC, are optimistic about the future of AI adoption in enterprises. They predict a significant increase in enterprise spending on AI, forecasting a rise from $16 billion to $143 billion by 2027. However, readiness for AI deployment remains relatively low, with only 14% of organizations feeling fully prepared.
One critical aspect that often goes unnoticed is the infrastructure readiness required for AI deployment. According to Cisco’s research, most enterprise networks are not currently equipped to handle the demanding workloads that AI entails. To use the full potential of AI, substantial upgrades in data center GPUs (Graphics Processing Units) and improvements in network throughput and latency are necessary.