AI
MIT research revealing that 95 % of enterprise Gen AI projects fail to generate significant business value has become a frequently cited data point in the discussions around a potential“ AI bubble” and growing scepticism about the effectiveness of LLMs.
Squaring this finding with the reality that billions of people now use LLMs in their everyday lives presents a paradox. While LLMs have clearly transformed the consumer web browsing experience, that impact has yet to translate into broad enterprise value. This gap should be seen as a lag rather than an absolute incompatibility, especially given that the factors holding back enterprise value are increasingly understood and diagnosable.
The woes of enterprisescale transformation Anybody who’ s ever been involved in rolling out a new technology in an enterprise environment will attest to the fact that enterprise transformation is rarely straightforward. Back in 2017, for example, a Gartner analyst claimed that as many as 85 % of big data projects at the time were failing. The underlying challenges remain consistent today and are especially relevant to enterprisescale rollout of LLMs. Change is difficult in general, but it is particularly complex in enterprise environments where strong risk aversion can slow adoption. Successful transformation often hinges on effective employee education and change management.
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