Don Schuerman, CTO and Vice President Marketing and Technology Strategy, Pegasystems. Read Don Schuerman's full executive profile here.
It’s easy for those of us in the tech industry to fall under the spell of the"shiny object" trap. And when you have a buzz as loud as generative AI has created this year, you can’t help but be mesmerized by the possibilities. Now, tech execs in every industry are suddenly under pressure from all sides to implement a generative AI strategy, sending them scrambling to add some GPT magic to their tech stack.
At the enterprise, AI can help a customer service representative predict what product or service a customer would be interested in based on patterns in their service and purchase history. In some instances, those predictions can be recalculated in real time based on what the customer says during a call or clicks online.
The type of data you choose to train your models on is based on different business needs. In some cases, training AI models on publicly available commodity data is perfectly fine. Many of these models—such as GPT-3.5 or GPT-4 large language models —come already trained. Optical character recognition models are trained on a wide range of data so they can recognize all sorts of characters, words and languages.
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