According to Gartner, worldwide IT spending is projected to total $5.1 trillion in 2023 – that’s an eight percent increase compared to 2023 spending.
So what’s driving this uptick in spending? AI and automation – though not as much as you’d think.
“In 2023 and 2024, very little IT spending will be tied to GenAI,” John-David Lovelock, Distinguished VP Analyst at Gartner, said in a press release on the topic. “However, organizations are continuing to invest in AI and automation to increase operational efficiency and bridge IT talent gaps. The hype around GenAI is supporting this trend, as CIOs recognize that today’s AI projects will be instrumental in developing an AI strategy and story before GenAI becomes part of their IT budgets starting in 2025.”
In addition to the interest and investment in AI – Garnet said cloud price increases will drive additional software and IT services spending.
“The software and IT services segments will both see double-digit growth in 2024, largely driven by cloud spending,” the press release states. “Global spending on public cloud services is forecast to increase 20.4% in 2024, and similarly to 2023, the source of growth will be a combination of cloud vendor price increases and increased utilization.”
But despite the spending news, Gartner notes many CIOs are experiencing change fatigue – causing them to hesitate when it comes to investing in new projects and initiatives, “this is pushing a portion of 2023’s IT spending into 2024, a trend that is expected to continue into 2025,” Gartner writes.
“Faced with a new wave of pragmatism, capital restrictions or margin concerns, CIOs are delaying some IT spending,” Lovelock said in the release. “Organizations are shifting the emphasis of IT projects towards cost control, efficiencies and automation while curtailing IT initiatives that will take longer to deliver returns.”
Driving Investment in AI for IT
While generative AI is all the talk, there are other AI tools companies are investing in to spur better customer experiences – especially when it comes to IT and self-service.
Growing in popularity, conversational AI chatbots are a great alternative to the often-clunky traditional chatbot experience. In fact, a study from TeamDynamix and CIO.com found that when conversational AI chatbots are used, more than 61 percent of users could effectively resolve their issues without having to put in a ticket or contact support – that’s vs. only a 35 percent resolution rate when traditional chat is used.
So, what is the difference between conversational AI chatbots and traditional chatbots? Traditional chatbots aren’t inherently smart. They lack the “brain” or natural language processing (NLP) that conversational AI chatbots have.
Instead of understanding a person’s intent when a question is asked – a traditional chatbot is programmed to follow a very linear path and any deviation from that path (usually due to the inputs from an end-user or customer) can cause that chatbot to fail. And when a chatbot fails it’s often a frustrating experience and adds to the ticket volume of your IT service desk.
According to that same market study, few companies are satisfied with their current chatbots. The study found that traditional chatbots have a Net Promoter Score (NPS) of negative eight—far from a ringing endorsement—and 65 percent of those surveyed said their solution is not widely adopted by their customers. The addition of conversational AI, however, makes a huge difference— using a conversational AI chatbot increased the adoption rate of the overall chat solution from 16 percent to 50 percent.
When you combine automation, self-service and conversational AI – you can have a positive impact on IT resource drain and improve the experience of your customers and end-users.
Conversational AI chatbots are intelligent software applications that can simulate human conversations and perform tasks such as answering questions, providing information and performing transactions.
Conversational AI leverages natural language processing and understands intent. While they do need to be trained, with conversational AI you can facilitate more complex conversations and resolve issues through actions vs. the traditional chatbot’s question/answer limited dialog path.
Because conversational AI chatbots understand intent, they are much more effective when it comes to assisting customers and end-users. For example, when using traditional chat if you say “My password broke” it would likely respond with something like “I’m sorry, I don’t understand that. Please type your issue again,” and continue down that path with a final response that might give you links to 3-5 FAQ articles that it thinks might help resolve the issue – leaving it to the customer to further seek out a solution to the password problem.
When you have conversational AI, if you were to say “My password is broken” it can infer your intent and know that what you mean is your password isn’t working and needs to be reset. The response from the chatbot is then something like, “I can see you’re having issues with your password. I’ve sent a reset password link to the email on file.”
On the backend, the chatbot can pull the email address needed from the system where it’s stored and automatically send a password reset email – resolving the issue within the conversation in seconds versus sending the customer links and having them do the work to request the link.
Want to learn more about conversational AI and all it can offer versus traditional chat? Check out our market study: State of Chatbots and Conversational AI.