Where new tech fits in, and the GenAI use case checklist

If Press-Release Driven Development based purely on hype is not the way to go, what does a sensible approach to exciting new tech look like? A shortcut for thinking about GenAI uses cases comes at the bottom of this post, but first, some background.

If you look around your organization, you will no doubt find a whole bunch of problems. Things you'd like to be different. For each problem, answer this: Why hasn't it already been solved? Why is it still a problem?

Most of your answers will have nothing to do with tech.

• It's not that much of a pain.

• There's no political will to tackle this issue / it would step on someone's toes

• We don't trust that a technological solution would actually stick.

For those problems, once the non-technical objections have been removed, the technical solution is often quite simple.

In some cases, the answer might be:

• We looked into it and found it costs too much to solve the problem.

In a very few select cases, the answer might be:

• It's just not technologically possible right now.

These last two answers are good pointers for problems where new technology can make a difference. It might lower the cost of solving a problem compared to previous tech or enable solving the problem in the first place.

Example with GenAI

Generative AI and large language models (LLMs) are exciting because they plausibly promise to address both the cost and feasibility issues. It is (often) cheaper to run inference on a well-prompted LLM than to perform custom training on a concrete model, and for some tasks, it's the only way to solve them at the required level.

The GenAI use case checklist:

• Is it beyond simple rules-based automation or pattern recognition?

• Does it require an understanding of non-standardized input material?

• ⚠️ Does it not require expert-level taste and judgment?

These three criteria put the LLM right in the middle of a spectrum between a standard computer program (or classical machine learning) on one side and a human expert on the other side:

• If you can get away with the simpler program, this is absolutely the way to go. It will be cheaper to run and more reliable, too.

• If you absolutely need an expert's judgment, offloading it to an LLM will come with nasty surprises.

• For those problems that fall into the middle, absolutely go ahead and give GenAI a try. Or talk to us and we’ll try it with you!