The AI landscape has changed a bit since I wrote What Is Real AI? back in 2021. The advent of GenAI has enabled a new wave of dubious AI sales pitches. Here’s one that crossed my desk recently:
We’ve identified some key GenAI opportunities at PermaPlate … forecast revenue and claims across [products] and adjust staffing monthly.
This sounds like a good idea, except – it’s not a GenAI application. It’s a standard forecasting exercise that everybody does already. If I did want to switch to a learning model – even a deep neural net, which is architecturally similar to an LLM – it would still not be GenAI.
The thing to remember is that GenAI “generates” things, like blog posts and deepfakes. My favorite learning models, going back to AI-Based Risk Rating, are all quantitative in nature. Here again, there are plenty of good statistical methods. Even if you prefer to use a learning model, you may not choose a neural net.
Neural nets have a problem with explainability. They’re basically a black box. That’s why credit bureaus, which must be able to explain their ratings, use a two-step approach. They use AI for exploration and feature engineering, but then they put the features into a more-transparent logistic regression model.
I might consider GenAI in a forecasting application, to deal with unstructured data. On the other hand, I would ask why the data is unstructured. We did this exercise as a POC here at PermaPlate. I wrote a little program that would read a service contract, and then answer natural-language queries.
Which coverage did the customer select and does it include roadside assistance?
It was a cool demo, but – if you want coverage details available for automation, it makes a lot more sense to store them in machine-readable form, at origination time. And what kind of automation might that be? Well, it might be “agentic.”
Agentic AI means that the AI has “agency,” in the sense that it can make decisions and do things in the world. Cool, huh? We give AI agency by equipping it with tools, in the form of software APIs.
Imagine asking ChatGPT to organize your next trip. It can’t, because it’s trapped inside your web browser. But if you invoke ChatGPT as part of an agentic workflow, with interfaces to the airlines and hotels, it can actually book the trip.
Agentic workflows often divide the work among tool-using LLMs, with a mastermind LLM directing the others. For systems that don’t have APIs, the agent can use Robotic Process Automation to operate the system’s user interface – just like you would at the keyboard. It’s not surprising that UIPath, one of the leading RPA vendors, has moved into Agentic AI.
Here is a short list of the latest AI techniques:
- Large Language Model (LLM) – Like Grok and ChatGPT, these are AI models that can read and write (and plan, and execute).
- Generative AI – Broad class of AI models that can create things, including LLMs but also diffusion models for video and other media.
- Deep Neural Net (DNN) – Core technology behind GenAI, and many other learning models, as in my earlier article.
- Retrieval Augmented Generation (RAG) – As the name implies, GenAI “augmented” by the ability to find and read your documents. See Unguided RAG for Text Comparison.
- Robotic Process Automation (RPA) – Not AI, but frequently used by Agentic AI. As I wrote in Applied AI for Auto Finance, you can derive a lot of efficiency from RPA alone.
- Agentic AI – AI agents that can make decisions and act autonomously.
Now that you know the lingo, you can choose the right tool for the job – or your AI sales pitch. I, for one, will not be using GenAI to predict claims volume … but I may use Agentic AI to dispatch the technicians.
