Disfluency

In last week's post about the Underpant Gnomes, one example point was

"Connect your documents and data to an AI chatbot so you can talk to your data."

For example, you could ask your AI, "Hey, what were last quarter's sales numbers in Europe?" and get a nice report without manual clicking and digging. This seems like an obvious use case for Generative AI. Why wouldn't you want to make your data more accessible?

Because easier isn't always better. In Smarter, Faster, Better, Charles Duhigg writes about a concept he calls disfluency:

Sometimes, deep insights can only develop if you have to wrestle with the data. He gives the example of teachers who had access to all sorts of fancy dashboards about student performance. It wasn't until they ditched all that for handwritten notes on stacks of index cards that they made important discoveries about how to help each student.

To use AI to its fullest potential, we'll want to dig deeper and think beyond using it to make access to raw information easier and instead use it to generate insight:

• Don't just dig up numbers for me that I could easily dig up myself. Tell me what I'm missing!

• Don't just summarize a document. Tell me what's actionable.

• Don't just list customer complaints. Identify trends and suggest fixes to improve satisfaction.

• Don't just extract key metrics from reports. Flag emerging patterns that signal future opportunities or risks.

• Don't just compile meeting notes. Highlight unresolved issues and recommend next steps for resolution.

Figuring out how to connect the raw information to the actual insight is the crucial Phase 2 in using AI productively.