Are most AI projects destined to fail?
The current era of AI projects could provide the lessons we need to make the most of the technology going forward
How much has your company invested in AI? Are you finding it a revolutionary technology, or still waiting for the promised gains to materialize?
As businesses seek to unlock productivity gains from AI, investment in the technology has continued to rise. All across the world, firms are buying AI products, undergoing digital transformation in preparation, or even fine-tuning AI to suit their bespoke use cases.
But how much of this is going to pay off? And what do you do if your AI project fails on its own terms?
In this episode, Jane speaks to James Hodge, chief strategy advisor, EMEA at Splunk, to explore where we’re going wrong with AI.
Highlights
“I think there are 100% domain issues. One of the big predictions out there at the moment is where we're using lots of general, large language models, we’re going to see a big shift towards domain specific language models, more reasoning models coming out, and even small language models, when we realize, ‘Actually, I don't need to go and train something as large for the task I actually want to design it for.’”
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“I think first realize it's not a failure, it's a learning experience. I think especially in industry, when we talk about project failure, it's the words ‘failure’, ‘waste of money’, ‘no values returned’, yes but actually what did we learn from that experience? What caused us to get to a point where we said, this is no longer right for us, and we're going to pull the project, stop the project, and start to go and break that down into those buckets?”
“In three to five years, no business will be able to be operationally efficient and attractive to the market without the use of AI technologies. Because it will allow you speed to market, the ability to be able to be adaptive and responsive. And what we're really talking about is business resilience, which comes from understanding and knowledge.”
Footnotes
- Why are so many AI projects destined for failure? Inexperienced staff, poor planning, and a shoehorned approach to agile development are all stifling innovation
- AI projects are faltering as CDOs grapple with poor data quality
- ‘Frontier models are still unable to solve the majority of tasks’
- "The big obstacle isn't anything technical": Dell CTO John Roese on why companies are failing on AI adoption
- Why agentic AI will be a huge money-spinner for big tech providers
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