The rush to AI-enable everything is understandable. No one wants to be the last business to figure out the obvious. Yet, this rapid mass embrace of immature, brittle tools which are frequently not ready for primetime is causing no shortage of heartburn for those in enterprise Information Technology. Three anecdotes serve to underscore the severity of the problem.
- Security / inadvertent exposure of private data. Despite some warnings to not put confidential / private information into ChatGPT, people frequently take confidential data and stick it into ChatGPT. What could go wrong? Well, ChatGPT might leak some of that data due to some services it in turn uses. No doubt OpenAI is a well run, professional organization, with a quick response, but what about all the other Open AI clones there?
- Operations / observability. The current stacks in wide use now aren’t really all that well suited for a new LLM-powered everything world. While there are plenty of monitoring and observability tools out there, the key consideration is in addressing the nuances specific to LLM-powered apps. That as of now is almost non-existent.
- Cost and performance. GPUs are expensive, and sometimes scarce. Training LLMs is cumbersome, complicated and costly. Per Clement Delangue, the CEO Hugging Face: the process of training the company’s Bloom large language model took more than two-and-a-half months and required access to a supercomputer that was “something like the equivalent of 500 GPUs.”
Where’s IT in the Gen AI debate?
MC+A Insight Guest Article. Original Article can be found here. Background The corporate role most frequently missing from conversations about Gen AI is that of the Chief Information Officer. Everywhere else, there is certainly no shortage of glitz and energy about the transformative impact that Gen AI will have — from sales and marketing to support and every other interaction
Generative AI security considerations for today’s enterprise
MC+A Insight Guest Article. Original Article can be found here. Generative AI has captured the imagination of the corporate world from the corner office to the back office. CEOs see bottom-line improvement through automation, and CMOs salivate at the possibility of transforming experiences. To some, generative AI looks like pixie dust that offers the chance to overhaul even the most
Lack of enterprise-grade tools prevent LLM adoption in the enterprise
Enterprises are looking to capitalize on cost savings from AI and automation, but they need to use enterprise grade tools to optimize their Large Language Models.
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