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Generative artificial intelligence could radically transform the business landscape, opening new doors to innovation in product design, customer service, medical discoveries, and much more.
The threat of incurring “technical debt” is real. Selecting the wrong component parts, fitting them together incorrectly, or not anticipating future developments can each increase the risk of making obsolescent investments that result in upstream disruption and downstream chaos. Organizations will abandon custom-built tooling, particularly proprietary LLMs, due to the costs of maintaining them,. This makes sense: for complex technologies, buying is usually far more cost-effective than building.
One way to determine the security of an LLM lies in how it’s hosted. If an LLM is connected to and trained on internal data and then exposed to outsiders via a customer-support chatbot, bad actors can query and extract all that data.Technology leaders must be confident they have full visibility, monitoring, and governance over all generative AI work.
Without clear answers, confidence will remain out of reach. It’s critical for technology leaders to create a single repository and production environment for all generative AI within their organizations to achieve the appropriate level of monitoring and transparency.Building a generative AI chatbot isn’t terribly difficult. Making sure it’s providing the right information is a lot harder.
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