- Global enterprise spending on custom and off-the-shelf AI software will reach $64 billion by 2025, a new Forrester report projects. The category is set to rise from $33 billion in 2021.
- Growth in this category will outpace that of broader software spending, with compound annual growth rates of 18%, compared to software's 12%. AI software spending will make up 6% of total spending on software by 2025.
- Three key categories will drive spending in this subset of software: AI-infused software, AI maker tools, and AI-centric software.
Implementing AI has now become strategic for the majority of global enterprises, said Mike Gualtieri, VP and principal analyst at Forrester. Though companies can hire teams of data scientists to build bespoke applications, they are increasingly using AI that's infused in business applications from software vendors.
"It is far easier to get AI in the next upgrade of your accounting system or supply chain planning system than to build a bespoke AI application," Gualtieri said in an email.
Part of the challenge of baked-in AI is fragmentation. Businesses now have software solutions wrapped around each critical process, and with more AI capabilities comes the potential for a disjointed approach to the technology.
Aside from the on-board AI capabilities, companies will still need AI platforms to build AI applications that are differentiated for its customers and operations, said Gualtieri.
"It's not unlike software," he said. "Enterprises both build and buy software."
The boom in AI software comes with another challenge: having enough prepared talent to manage it. Hiring alone won't bridge that gap, calling businesses to create internal upskilling programs.
With more dollars flowing to AI software, CIO and other IT executives need to have a clear strategy to execute — instead of haphazardly plugging AI into every company process.
"Tech leaders must get involved in any data science and/or AI platforms purchase decisions because they understand the full scope of tech operations," Gualtieri said. "Data science teams' scope is too narrow. They must invest in AI platforms.