You Ask, I Answer: Monetizing Data for Generative AI?

You Ask, I Answer: Monetizing Data for Generative AI?

In today’s episode, I tackle questions from the audience on leveraging AI to generate revenue. From using generative tools to create more content to building conversational interfaces, there are ways associations can capitalize on these emerging technologies. However, we have to be mindful of the risks, as AI still makes mistakes and can “hallucinate” false information. Join me to explore opportunities and potential pitfalls when implementing AI to drive monetization.

You Ask, I Answer: Monetizing Data for Generative AI?

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

Today’s episode of you ask I answer was recorded in front of a live studio audience at the digital now conference in Denver, Colorado, in November 2023.

The session title was appropriately you ask I answer live generative AI q&a.


Great, thank you.

Alright, so like I was announced, this is completely informal.

This it’s it’s a wild west anything goes you have a question about anything you’ve seen or heard today, I’ll do my best to answer it.

If not, I will make something up and tell you that too.

So who has a question that they would like to tap? Yes, person in green.

Wait, gotta wait for the microphone.

Yes, that’s okay.

I’m too.

So I am already just feeling a lot of pressure from a deep software stack that I would like to slow down.

So the thought of making that software stack bigger with AI is something that’s intimidating to me.

And I just like to hear your thoughts on keeping your software stack manageable while also incorporating AI into what you’re doing.

Yeah, so in terms of managing your your Mar tech stack, a big question is going to be who your stack is centered on.

Because pretty much like if you if you’re a Microsoft shop, if you’re a Google shop, if you’re an Amazon shop, you’ve already got a vendor that’s probably already been through the vendor approval process and stuff.

And so that would be the place I would start.

Pretty much every tech company has an AI stack.

So Amazon has AWS bedrock and Sage maker, Google has duet and GCP, Google Cloud, Microsoft, of course, has Azure.

So everybody’s got something.

So if you want to try to at least avoid adding vendors, that would be the place I would start.

That’s also probably a good place to start because you’re going to have less issues with compliance if you’re working within your existing tech stack vendor because they’ve got an offering that chances are in somewhere in the terms of service, there’s also like saying, here’s how we protect your data and so on and so forth.

That’s the first place.

It will grow.

That’s really kind of unavoidable, particularly for some of the point solutions where there isn’t a an off the shelf model.

So the hey, Jen, video generation, that’s a point solution right now.

There are ways to engineer a similar system, but you got to be super technical to do it.

So you’ll probably have to use that vendor until one of the big tech companies offers that internally and stuff.

So, yep.

Next question.

Right there in the front.

Right, I guess.

So I was in a session on package applications and we were talking about monetization.


And so we’re all you know, association type organizations.

Seems like there’s a lot of ways to leverage AI for process efficiency, improved, you know, products and services, user experience.

Are there specific things that associations should be looking at that could potentially generate revenue, either directly or indirectly from applications? How do you make money right now? Membership, publications, meetings, that kind of stuff.


So immediately you think about publications, right? So publications are locked into a format, right? It’s a thing.

You can use the tools that exist right now to broaden the scope of what those things are.

So like if you want, there’s Google Cloud has a really good text to speech API.

One of the things that I’ve done with my past books I’ve written is I said, okay, drop this in, turn it into an audio book and you get machine read stuff, but it is now in a format that is that you can listen to instead.

So even just taking the existing content library you have and making it multimedia is a way to increase the value of the product and you can either sell it separately or you can bundle in as a value add.

That’s the first thing that comes to mind.

Second thing that comes to mind is if you’re using the tools for content creation, by default, you can just create a lot more stuff with their assistance.

Of all the tools that are out there for long form content creation, probably the best one right now is Anthropix Claude because it has a very large context window, aka memory.

It can process 60-ish thousand words at a time.

So you could give it, say, a journal publication and say, hey, I want you to come up with four more articles based on this theme from copying this style with these style citations and just amplify the amount of stuff you can create.

The third area, which is a lot more risky, is enabling conversational interfaces to your content.

If you build that fine-tuned model, someone could have a conversation about what your association does.

I would be careful with that just because the models themselves can still say really funky things at times.

Depending on the model you use, it can hallucinate.

It can just outright make shit up.

Monetization, like I said during the whatever thing, turning your data into one of those plugins that you can then sell access to that as a service is another option.

That’s a more technical option too, but it’s a way of leveraging all the data you have, particularly if you have a lot of public facing data.

You can make a conversation agent or a plugin for language models that specifically talks about that thing.

It can either be access to the data set itself, it can be access to the machine readable version of that, or it can be a plugin for a model.

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