- Report 95% of companies have nothing to show for their investments in AI
- Only 5% successfully deployed AI tools on a large scale
- Workers prefer chatppt to personalized AI
The new data from the NANDA (network agents and decentralized) initiative said that although US companies have invested 35 to 40 billion dollars of generative AI tools, an overwhelming majority (95%) of them have nothing to show.
This leaves only 5% of companies that have managed to deploy large -scale AI tools, with failures attributed to the inability of AI to keep the data, to adapt and to learn over time – not the shortage of infrastructure and talent which often reaches the headlines.
By exploring a range of deployments, standard systems with specially designed systems, MIT found only 5% of personalized AI tools reaching production.
Companies are not much to show for their investments in AI
With many leaders now considering demos as scientific projects, confidence in AI initiatives has decreased among business leaders.
The smallest impacts have been measured in professional services, health care and pharmaceutical products, consumers and retail services, financial and energy services and materials.
Although many companies find it difficult to quantify the advantages of their AI deployments, 80% of technology and media leaders expect reduced hiring over the next 24 months.
However, the impacts of the workforce vary, the deletions of jobs mainly affecting unrelated and outsourced roles – around 5 to 20% of these already affected roles.
The study also revealed that workers prefer generic tools such as Chatppt on specialized offers, even when they are fueled by the same models.
The familiarity and flexibility of Chatgpt in particular have motivated the adoption of shadow computing, companies have asked to consider the needs of workers and to adapt policies accordingly to increase safety rather than prohibiting them completely.
On the other hand, corporate tools are generally considered more rigid and less effective, despite their generally higher costs.
For the future, it is clear that there is value in a much simpler strategy. Rather than creating complex owner systems, adjusting the tools widely available to respect the business policies could offer much better king while simultaneously reducing the quantity of dedicated AI training workers.
