In this guest post, Eric Newcomer, Principal Analyst at Intellyx explores the practical applications and limitations of generative AI.
Generative AI is a game-changing technology. Chat bots seem like magic compared to a traditional static web search. You submit questions in natural human language and receive back complete sentences and paragraphs.
But it isn’t always clear what it is really good for, given limitations such as hallucinations and inaccurate answers, and possible bias. It can be challenging to get through the hype and figure out the best applications for gen AI.
It’s becoming clear that a human has to carefully check the results produced by an AI conversation to confirm whether or not it’s working correctly for a given task.
Consider a set of steps in a human workflow, such as taking and fulfilling a restaurant delivery order, or investigating and remediating an IT incident. Multiple decisions are involved in executing a workflow, such as identifying the correct steps, mapping actions correctly to the steps, aligning to operational business policies, and confirming successful completion. A subsequent step depends on the successful completion of the prior step, and so on. Someone typically confirms each step's results before moving to the next step.
Adding an AI conversation to a human workflow seems natural because a human is already checking and confirming the results, and can also confirm the AI responses and proposed actions. Such an application of AI can significantly improve business process automation.
Gen AI applications
One thing everyone seems to agree on is that gen AI is good at creating summaries, which it can do with a prompt or just via processing a meeting transcription. "Co-pilots” for Zoom, Teams, and Google Meet for example all are capable of translating speech to text and automatica