To successfully deploy and scale AI, firms need to understand the full scope of opportunities – and challenges – that lie ahead
The meteoric rise of GenAI has ushered in a new age of disruptive innovation. Just eighteen months after ChatGPT blazed a trail for the technology, thousands of businesses are now using it to unlock fresh insights, automate mundane tasks and create new content. Even firms that haven’t yet embraced AI can’t deny its impact; we are clearly on the cusp of a technological shift that will rival – and potentially exceed – the introduction of the internet or mobile devices.
“You can’t ignore it,” says Steven Huels, general manager of the AI business unit at Red Hat, a leading provider of enterprise open-source solutions. “It’s not going to go away. It’s going to have far-reaching impacts on your business, your competitive nature, [etc.]”
Indeed, Gartner estimates that 85% of enterprises will have used GenAI application programming interfaces (APIs) or deployed GenAI-enabled applications by 2026. Nevertheless, many organisations are struggling to get projects into production quickly – and crucially cost-effectively. Often that’s because they lack the talent, partners or tools to successfully enhance their applications with AI.
The lack of alignment between rapidly evolving tools can lower productivity and complicate collaboration between data scientists, software developers and IT operations, for instance. Complex administrative processes may further undermine efforts to scale AI deployments. While popular cloud platforms seem to offer the scalability and attractive toolsets enterprises need, they often come with a significant degree of user lock-in, which can limit architectural and deployment options.