About 42% of enterprise-scale companies report having actively implemented artificial intelligence in their businesses, and of those, 59% have accelerated their implementation or investments in the technology, according to IBM's 2023 Global AI Adoption Index. of 1,000 employees.
“More accessible AI tools, the push toward automation of key processes, and increasing amounts of AI integrated into out-of-the-box business applications are the primary factors driving the expansion of enterprise-level AI,” said Rob Thomas, vice president senior at IBM Software, in a statement. “We see organizations leveraging AI for use cases where I believe the technology can have a profound impact more quickly, such as IT automation, digital workforce and customer service,” he said.
Although the research revealed that 40% of companies surveyed remain “stuck in the sandbox,” Thomas said he is confident they will overcome barriers such as the skills gap and data complexity this year.
What is driving AI adoption?
The main factors driving AI adoption are:
- Advances in AI tools that make them more accessible (45%).
- The need to reduce costs and automate key processes (42%).
- The growing amount of AI integrated into standard enterprise applications (37%).
The majority of companies surveyed (59%) that are actively implementing or exploring AI have accelerated their implementation or investments in the last 24 months. The top AI investments for organizations exploring or implementing AI are in research and development (44%) and workforce reskilling/development (39%).
For IT professionals, the two biggest improvements to AI in recent years are tools that are easier to deploy (43%) and the greater prevalence of data, AI and automation skills (42%).
Financial services is one of the most mature industries in adopting AI, followed by telecommunications, an IBM spokesperson told TechRepublic.
More statistics from the IBM Global AI Adoption Index 2023
The research also found that:
- More than a third of enterprise IT professionals (38%) report that their company is actively implementing generative AI and another 42% are exploring it.
- Organizations in India (59%), China (50%), Singapore (53%) and the United Arab Emirates (58%) are leading the active use of AI, compared to lagging markets such as Spain (28%), Australia (29%) and France (26%).
- Companies within the financial services industry are most likely to use AI, and about half of IT professionals in that industry report that their company has actively implemented AI. Within the telecommunications industry, 37% of IT professionals say their company is implementing AI.
The main use case of AI is automation
The AI use cases that are driving adoption by surveyed companies currently exploring or implementing AI span many key areas of business operations. In particular, automation is the main use case in several areas, including:
- IT processes (33%).
- Processing, understanding and flow of documents (24%).
- Customer or employee self-service responses and actions (23%).
- Business processes (22%).
- Network processes (22%).
Other areas where AI is used include:
- Security and threat detection (26%).
- AI monitoring or governance (25%).
- Analytics or business intelligence (24%).
- Digital work (22%).
- Marketing and sales (22%).
- Fraud detection (22%).
- Search and discovery of knowledge (21%).
- Human resources and talent acquisition (19%).
- Financial planning and analysis (18%).
- Supply chain intelligence (18%).
The main barriers to the use of AI
Forty percent of companies surveyed are exploring or experimenting with AI but have not implemented their models. The main barriers preventing deployment include limited AI skills and experience (33%), too much data complexity (25%) and ethical concerns (23%), the company said.
Generative AI poses different barriers to entry than traditional AI models, the report notes. For example, IT professionals at organizations surveyed that are not exploring or implementing generative AI reported that concerns about data privacy (57%) and trust and transparency (43%) are the biggest inhibitors of AI. generative. Another 35% also said a lack of implementation skills is a big inhibitor, according to the report.
How to address AI barriers to entry
An IBM spokesperson said: “Companies need to set AI strategies that clearly define the problems they want to solve, ensure they have the right data in the right place to drive those results, bridge skills gaps by selecting the right people and tools.” of appropriate automation. and incorporate AI governance from the beginning of its adoption process.”
For some organizations, the best approach might be to start small and specific. “In 2024, we expect business leaders to begin analyzing and testing AI on a case-by-case basis rather than in broad strokes,” the spokesperson said, “assuming the technology is the right tool to solve each problem. “Businesses using AI for the first time will use off-the-shelf AI assistants built for specific business needs.”
The impact of AI on the workforce
Among organizations surveyed, one in five reported that they do not have employees with the right skills to use new automation or artificial intelligence tools, and 16% cannot find new employees with the skills to address that gap.
Companies using AI to address labor or skills shortages said they are turning to AI to reduce manual or repetitive tasks with automation tools (55%) or automate customer self-service responses and actions (47%). %). Only 34% said they are training or reskilling employees to work together with new automation and artificial intelligence tools.
The importance of trusted and governed AI
According to the IBM report, IT professionals understand the need for governed and trusted AI, but the aforementioned barriers make it difficult for the companies surveyed to put it into practice.
For example, research found that IT professionals generally agree that consumers are more likely to choose services from companies with transparent and ethical AI practices (85% strongly or somewhat agree). They said the ability to explain how their AI arrived at a decision is important to their business (83% among companies exploring or implementing AI).
However, a surprising finding was that although many companies already implementing AI face multiple barriers in the process, far less than half reported that they are taking key steps towards trustworthy AI, such as:
- Reduce bias (27%).
- Tracking the origin of the data (37%).
- Making sure they can explain the decisions of their AI models (41%).
- Develop ethical AI policies (44%).
Reducing bias starts with governance, the IBM spokesperson said. “To realize the full potential of AI and reduce bias, data and AI governance tools are essential to scale models while maintaining fairness, transparency and compliance,” the spokesperson said.
“Without these safeguards, AI results can be biased, discriminatory, or sometimes just plain wrong,” the spokesperson added. Without the use of governance tools, AI can expose companies to various data privacy issues, including leakage of private and sensitive data or copyright infringement. Organizations are also at risk of legal complications and ethical dilemmas, so incorporating governance from the beginning can help avoid problems later.
IBM Survey Methodology
IBM said the survey was conducted in November 2023 among a representative sample of 8,584 IT professionals in Australia, Canada, China, France, Germany, India, Italy, Japan, Singapore, South Korea, Spain, United Arab Emirates, United Kingdom United States, United States and Latin America.
