AI

The blistering pace of advancement in generative AI leaves companies struggling to effectively implement and measure the technology, while guarding against bias and risk.

Generative AI has created an unprecedented pace of technological change, but complications of choosing AI vendors have grown in equal measure. So tech leaders need to be equipped with the right questions — and be prepared for answers — to help enterprises prosper.

As companies race to add generative models into products and workflows, massive investment to upskill workforces on prompt engineering and ethical AI emerges as a top priority.

Building a new large language model (LLM) from scratch can cost a company millions — or even hundreds of millions. But there are several ways to deploy customized LLMs that are faster, easier, and, most importantly, cheaper.

Data issues are still among the chief reasons why AI projects fall short of expectations, but the advent of generative AI has added a few new twists.

In 2022, the big AI story was the technology emerging from research labs and proofs-of-concept, to it being deployed throughout enterprises to get business value. This year started out about the same, with slightly better ML algorithms and improved data pipelines. But that seems so long ago now. And in the few months left of 2023, change could further offset what we’ve come to expect.