- Many AI models are not as effective as they are marketed to be, reports allegations
- 95% of the companies investigated have seen very little influence from their LLMs
- Specialization is the key to successful AI -adoption
New research from Mits Nanda initiative has claimed that the vast majority of Genai initiatives trying to drive rapid revenue growth are ‘falling flat’.
Of these samplers, 95% of companies implementing generative AI are “delivering little or no measurable influence” on profits and losses.
It seems to be an all-or-nothing game, as the 5% of companies that benefit from generative AI distinguishes Sig-Pose is primarily, says the lead author, Startups led by 19 or 20-year-olds who have seen the ‘Jerk to $ 20 million revenue in one year’.
It seems that the key to success with AI models is specialization. Successful implementation is about choosing ‘One Pain Point’ and performing this well and carefully collaboration with companies using tools.
Specialized suppliers are successful around 67% of the time, but internally built models only succeed about a third so often. Very regulated sectors such as the financial industry see many organizations building their own AI systems, but the research suggests that companies are much more likely to failure when they do.
When line leaders are authorized to drive the adoption, they see success because they are able to choose tools that can adapt over time.
Assignment is also important as most Genai budgets are dedicated for sale and marketing -but the biggest ROI was seen in the back -Office automation.
This is not the first time that research has suggested that AI models are not working as they should. A significant number of companies have introduced redundancies of workers at lower levels and brought in AI systems – but over half of UK companies that replaced workers with AI regret their decision.
Material benefits of these models are increasingly difficult to find, and security risks associated with the models relate to organizations -as well as AI models that make ESG targets much more difficult to reach.
