- Report requirements 95% of companies have nothing to show for their AI investments
- Only 5% have successfully implemented AI tools in scale
- Workers prefer chatgpt rather than custom AI
New data from Mits Nanda (Networked Agents and Decentralized AI) initiative has claimed that although US companies have invested $ 35-40 billion in generative AI tools, an overwhelming majority (95%) of them has nothing to show for it.
This only leaves 5% of companies successfully implementing AI scale tools with failures accused of AI’s inability to maintain data, adapting and learning over time – not the lack of infrastructure and talent that often reaches the headlines.
Examination of a number of implementations, from off-the-shelf to specially designed systems, found my only 5% of customized AI tools ever when production.
Businesses don’t have much to show for their AI investments
With many executors who now see demos as a little more than science projects, trust in AI initiatives has fallen among business leaders.
The smallest influences were measured across professional services, healthcare and pharmaceutical drugs, consumer and retail, financial services and energy and materials.
Although many companies are struggling to quantify the benefits of their AI implementations, 80% of execs across tech and media expect reduced employment over the next 24 months.
However, the workforce varies varies, where job cuts mostly affect non-core and outsourced roles-exposed 5-20% of such roles already affected.
The study also revealed that workers prefer generic tools such as chatgpt rather than specialized offers, even when driven by the same models.
Knowledge and flexibility of Chatgpt has, in particular, driven Shadow It, with companies that are encouraged to consider work needs and adapt policies accordingly to increase security rather than to ban them completely.
On the flip side, corporate tools are generally seen as more rigid and less efficient despite their typically higher costs.
When looking ahead, it is clear that there is value in a much simpler strategy. Instead of creating complex proprietary systems, fine -tuning widely available tools to comply with the company’s policies can offer much better ROIs while reducing the amount of dedicated AI training workers who may be reducing dedicated AI training workers.
