IBM: Barriers impact enterprise AI adoption, but value of tech is significant

Greater availability of development tools, the desire to reduce costs, and automation are driving enterprise adoption of AI. But challenges such as employee skillsets, data complexity, and ethical concerns remain barriers to widespread adoption of the technology.

These are a few of the core findings in a new study published by IBM, which surveyed 8,500 IT professionals worldwide to determine the deployment of AI in enterprise organizations with more than 1,000 employees. The study found that about 42% of enterprise-scale organizations already have AI actively in use, with 59% of those organizations planning to grow their use and investment in the technology going forward. Companies with 1,000 or fewer employees are less likely than larger companies to be adopting general AI and generative AI, the study found.

“More accessible AI tools, the drive for automation of key processes, and increasing amounts of AI embedded into off-the-shelf business applications are top factors driving the expansion of AI at the enterprise level,” said Rob Thomas, senior vice president of IBM Software, in a statement. “We see organizations leveraging AI for use cases where I believe the technology can most quickly have a profound impact like IT automation, digital labor, and customer care.”

AI is contributing to multiple facets of organizational operations, with IT process automation and marketing being the most popular applications. IT Professionals are at the forefront of AI usage at their companies and note the importance of being able to build and run AI projects wherever their data resides. Confidence in these capabilities is high, as most IT professionals are confident that their company has the right tools to find data across the business, the study found.

Many of those companies already exploring or deploying AI have accelerated their rollout of AI in the past two years, with “research and development” and “workforce upskilling” emerging as top investment priorities, IBM stated. In the dynamic landscape of generative AI, companies are increasingly utilizing open source technology, with an even split in use between in-house and open-source technologies, IBM stated.

Among companies citing AI’s use to address labor or skills shortages, the study found that they are tapping AI to do things like reduce manual or repetitive tasks with automation tools (55%), or automate customer self-service answers and actions (47%).

The study found that the top AI use cases include:

• Automation of IT processes (33%)
• Security and threat detection (26%)
• AI monitoring or governance (25%) 
• Business analytics or intelligence and  automating processing; understanding; and flow of documents (24%)
• Automating customer or employee self-service answers and actions (23%) 
• Automation of business processes; automation of network processes; digital labor; marketing and sales; and fraud detection (22%)

On the obstacles side, challenges such as limited knowledge, a lack of AI development tools, and high costs hinder adoption, IBM stated. In the context of generative AI, additional obstacles emerge, including data privacy concerns and a persistent shortage of implementation skills, IBM stated.

The top barriers hindering successful AI adoption at enterprises both exploring or deploying AI are limited AI skills and expertise (33%) and too much data complexity (25%) among them. For example, most organizations (63%) are using 20 or more data sources to inform AI, BI, and analytics systems according to IT professionals surveyed.

Additional barriers include ethical concerns (23%), AI projects that are too difficult to integrate and scale (22%), high price (21%), and lack of tools for AI model development (21%), according to the IBM study.  

Industry watchers see huge potential for AI technologies. IDC, for example, says enterprise spending on generative AI services, software and infrastructure will skyrocket over the next four years, jumping from $16 billion this year to $143 billion in 2027. However, the vast majority of companies aren’t ready for it. Just 14% of organizations surveyed in Cisco’s recently published readiness index said they are fully prepared to deploy and leverage AI-powered technologies.

In particular,  Cisco found that most current enterprise networks are not equipped to meet AI workloads. Businesses understand that AI will increase infrastructure workloads, but only 17% have networks that are fully flexible to handle the complexity.

“23% of companies have limited or no scalability at all when it comes to meeting new AI challenges within their current IT infrastructures,” Cisco stated. “To accommodate AI’s increased power and computing demands, more than three-quarters of companies will require further data center graphics processing units (GPUs) to support current and future AI workloads. In addition, 30% say the latency and throughput of their network is not optimal or sub-optimal, and 48% agree that they need further improvements on this front to cater to future needs.”