New market research commissioned by IBM Corp. has found that almost one-third of IT professionals surveyed globally say their business is now using artificial intelligence with 43% reporting that their company has accelerated their rollout of AI as a result of the COVID-19 pandemic.

While recent advances in the technology are making AI more accessible than ever, the annual survey also found that a lack of AI skills and increasing data complexity are cited as top challenges.

The " Global AI Adoption Index 2021 ," conducted by Morning Consult on behalf of IBM, revealed that while AI adoption was nearly flat over the last year, momentum is shifting as the need for AI has been accelerated by changing business needs due to the global pandemic.

AI, a release stated, is already changing the way businesses operate today, from how they communicate with their customers via virtual assistants to automating key workflows, and even managing network security. 

The "2021 CEO Study " from IBM's Institute for Business Value recently revealed that more than half of CEOs surveyed expect AI to deliver tangible business benefits in the next few years.

Highlights from the "Global AI Adoption Index 2021" include the following:

  • Business adoption of AI was basically flat, but significant investments in AI are planned: Almost one-third of companies reported using AI in their business, similar to 2020 findings . For those deploying or exploring AI, they report it is being driven by multiple pressures and opportunities businesses are facing from the COVID-19 pandemic to advances in the technology that make it more accessible.
  • COVID-19 accelerated how businesses are using automation today: 80% of companies are already using automation software or plan to use this technology in the next 12 months. For more than one-in-three organizations, the pandemic influenced their decision to use automation to bolster the productivity of employees, while others found new applications of the technology made them more resilient.
  • Trustworthy and explainable AI is critical to business: 91% of businesses using AI say their ability to explain how it arrived at a decision is critical. While global businesses are now acutely aware of the importance of having trustworthy AI, more than half of companies cite significant barriers in getting there including lack of skills, inflexible governance tools, biased data and more.
  • The ability to access data anywhere is key for increasing AI adoption: The proliferation of data across the enterprise has resulted in over two-thirds of global IT professionals drawing from more than 20 different data sources to inform their AI. Almost 90% of IT professionals say being able to run their AI projects wherever the data resides is key to the technology's adoption.
  • Natural language processing is at the forefront of recent adoption: Almost half of businesses today are now using applications powered by natural language processing (NLP), and one-in-four businesses plan to begin using NLP technology over the next 12 months.

“As organizations move to a post-pandemic world, data from the Global AI Adoption Index 2021 underscores a major uptick in AI investment,” said Rob Thomas, senior vice president, IBM cloud and data platform.

“A large majority of those investments continue to be focused on the three key capabilities that define AI for business – automating IT and processes, building trust in AI outcomes, and understanding the language of business. We believe these investments will continue to accelerate rapidly as customers look for new, innovative ways to drive their digital transformations by taking advantage of hybrid cloud and AI.”

While adoption is poised for growth, some global businesses are still facing a multitude of challenges when it comes to adopting AI, the release said, and that “persistent barriers across markets and industries highlight the need for continued focus on addressing skills and solutions gaps.”

The survey determined that the top three barriers to AI adoption for businesses are limited AI expertise or knowledge (39%), increasing data complexity and data silos (32%) and lack of tools/platforms for developing AI models (28%).