New research commissioned by IBM has found that about 42% of enterprise-scale organisations (over 1 000 employees) surveyed have AI actively in use in their businesses.
Early adopters are leading the way, with 59% of responding enterprises already working with AI intending to accelerate and increase investment in the technology.
Ongoing challenges for AI adoption in enterprises remain, including hiring employees with the right skillsets, data complexity, and ethical concerns continue to inhibit businesses from adopting AI technologies into their operations.
“We’re seeing that the early adopters who overcame barriers to deploy AI are making further investments, proving to me that they are already experiencing the benefits from AI,” says Rob Thomas, senior vice-president of IBM Software. “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.
“We see organisations leveraging AI for use cases where I believe the technology can most quickly have a profound impact like IT automation, digital labour, and customer care.
“For the 40% of companies surveyed stuck in the sandbox, I am confident 2024 will be the year of tackling and overcoming barriers to entry like the skills gap and data complexity.”
Highlights from the “IBM Global AI Adoption Index 2023,” conducted by Morning Consult on behalf of IBM, include:
Over the last several years, AI adoption has remained steady at large organisations surveyed:
* Today, 42% of IT professionals at large organisations report that they have actively deployed AI while an additional 40% are actively exploring using the technology.
* Additionally, 38% of IT professionals at enterprises report that their company is actively implementing generative AI and another 42% are exploring it.
* Organisations in India (59%), the UAE (58%), Singapore (53%), and China (50%) are leading the way in active use of AI, compared with lagging markets like Spain (28%), Australia (29%), and France (26%).
* Companies within the financial services industry are most likely to be using AI, with about half of IT professionals within that industry reporting their company has actively deployed AI. Thirty-seven percent of IT professionals within the telecommunications industry state that their company is also deploying AI.
The majority of surveyed companies actively deploying or exploring AI have accelerated their rollout or investments in the past 24 months:
* 59% of IT professionals at companies deploying or exploring AI indicate that their company has accelerated their investments in or rollout of AI in the past 24 months.
* China (85%), India (74%), and the UAE (72%) are the markets most likely to be accelerating AI rollout, while businesses in the UK (40%), Australia (38%) and Canada (35%) were the least likely to accelerate the rollout.
* Research and development (44%) and reskilling/workforce development (39%) are the top AI investments at organizations exploring or deploying AI.
Easier to use AI tools and the need to reduce costs and automate processes are driving AI adoption among surveyed companies:
* Advances in AI tools that make them more accessible (45%), the need to reduce costs and automate key processes (42%), and the increasing amount of AI embedded into standard off the shelf business applications (37%) are the top factors driving AI adoption.
* For IT professionals, the two most important changes to AI in recent years are solutions that are easier to deploy (43%) and the increased prevalence of data, AI, and automation skills (42%).
* The AI use cases driving adoption for surveyed companies currently exploring or deploying AI are not limited, but cut across many key areas of business operations: Automation of IT processes (33%); Security and threat detection (26%); AI monitoring or governance (25%); Business analytics or intelligence (24%); Automating processing, understanding, and flow of documents (24%); Automating customer or employee self-service answers and actions (23%); Automation of business processes (22%); Automation of network processes (22%); Digital labour (22%); Marketing and sales (22%); Fraud detection (22%); Search and knowledge discovery (21%); Human resources and talent acquisition (19%); Financial planning and analysis (18%); and Supply chain intelligence (18%).
The same set of barriers are keeping the next wave of surveyed companies from benefiting from AI:
* The top barriers hindering successful AI adoption at enterprises both exploring or deploying AI are limited AI skills and expertise (33%), too much data complexity (25%), 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%).
Generative AI poses different barriers to entry from traditional AI models:
* Data privacy (57%) and trust and transparency (43%) concerns are the biggest inhibitors of generative AI according to IT professionals at surveyed organisations not exploring or implementing generative AI.
* 35% also say that lack of skills for implementation are a big inhibitor.
Among surveyed organisations, AI is already having an impact on the workforce:
* One-in-five organisations report they do not have employees with the right skills in place to use new AI or automation tools and 16% cannot find new hires with the skills to address that gap.
* Among companies citing AI’s use to address labor or skills shortages, 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%).
* Only 34% are currently training or reskilling employees to work together with new automation and AI tools.
The need for trustworthy and governed AI is understood by IT professionals, but barriers are making it difficult for surveyed companies to put into practice:
* IT professionals are largely in agreement that consumers are more likely to choose services from companies with transparent and ethical AI practices (85% strongly or somewhat agree) and say being able to explain how their AI reached a decision is important to their business (83% among companies exploring or deploying AI).
* But, with many companies already deploying AI facing multiple barriers in the process, well under half report they are taking key steps towards trustworthy AI like reducing bias (27%), tracking data provenance (37%), making sure they can explain the decisions of their AI models (41%), or developing ethical AI policies (44%).