That’s the word from a recent survey of 7,502 IT executives and professionals around the world, commissioned by IBM’s Watson group. Overall, 35% of companies now report using AI in their businesses – up from 31% a year ago, with an additional 42% exploring the technology. It’s being applied through off-the-shelf solutions such as virtual assistants, as well as being embedded in existing business operations – especially IT processes.  The irony, of course, is that the people charged with building out AI-driven applications and systems – IT teams – need AI the most to support their efforts. This isn’t totally surprising, as AI development and implementation makes things much more complex, requiring greater levels of automation.  About half of organizations are seeing benefits from using AI to automate IT, business or network processes, including cost savings and efficiencies (54%), improvements in IT or network performance (53%), and better experiences for customers (48%).  Another 30% of IT professionals say employees at their organization are saving time with new AI and automation software and tools, particularly in fields such as IT itself – where skills shortages are common. AI is helping organizations address skills gaps, for example, by automating tasks for skilled workers, or by using AI-assisted learning or employee engagement.   The most advanced AI adoption is happening in areas such as IT operations, security and threat detection and business process automation. One-third of companies are already using AI to automate their IT processes – AIOps – which helps preserve application performance while also making resource allocation more efficient. A majority of IT professionals at large companies are using it to drive efficiencies in IT operations (ITOps) (54%) compared to just 40% at smaller ones.
Use cases for AI include the following:  

Automating IT operations  32%Automating IT or software asset management    32%Activity monitoring   29%Automating customer care experiences   28%Automating business workflows    27%Real-time inventory management    26%5G services    25%Supply chain efficiency and resiliency   24%

The leading inhibitors to successful AI adoption for businesses include limited AI skills, expertise or knowledge (34%), the price is too high (29%), lack of tools or platforms to develop models (25%), projects are too complex or difficult to integrate and scale (24%), and too much data complexity (24%). AI transparency is also a concern. Four in five respondents cite being able to explain how their AI arrived at a decision as important to their business. Actions currently being taken by IT professionals include safeguarding data privacy as the step they are taking to ensure their AI is trustworthy and responsible. A majority of IT professionals report their company is drawing from over 20 different data sources to inform their AI, BI and analytics systems.