Our Top 5 GenAI Articles of 2024

2024 was a real doozy. If you emerged from the generative AI haze with your sanity still intact, then we salute you. 

This year, we saw early GenAI use cases like chatbots and copilots, we saw data teams introducing open table formats into their lakehouses, we saw data products grow in popularity more than ever before, and we saw everything in between. 

Maybe next year, we’ll see data teams taking their AI-ready data and, well, doing something with it. (Maybe). 

To commemorate this year in data and AI, we looked back on some of the most popular GenAI-related articles we published in 2024 – and some of our team’s favorites, too. 

Let’s take a stroll down memory lane and see what we were up to in the world of AI this past year. 

1. 5 Hard Truths About Generative AI for Technology Leaders

This article stirred up some buzz when we published it, and it quickly became a favorite among the team as well. With so much talk about GenAI and how to use it (and not use it), Barr Moses, CEO and Co-founder of Monte Carlo, finally put pen to paper to debunk some of the most common myths around GenAI development.

When it comes to hype and trends, sometimes you just need a good reality check – and these were five hard truths everyone needed to hear.

2. Building Ethical AI Starts with the Data Team – Here’s Why

Continuing down the path of approaching generative AI development realistically, Barr published this article as both a reminder and a call to action to data teams: ethical AI starts with them.

GenAI is nascent, and we’re all still developing the ethics that will govern it as it continues to scale. But, as Barr reminds us, we’ll have to start thinking critically about the role data teams play in how we develop AI applications if we want to realize the full potential value of ethical AI. 

3. The 2024 State of Reliable AI Survey

We can talk about generative AI until the (virtual) cows come home, but sometimes you just need to get into the numbers behind it all. This year, we wanted to know exactly what’s happening with AI, how it impacts data quality, and what data professionals are doing about it, so we took a survey with our friends at Wakefield.

What did we find? Well, the results speak for themselves. While responses indicated that nearly 100% of data teams are actively pursuing AI applications, the majority (68%) said they weren’t completely confident in the quality of the data that powers it. We all clearly have some work to do when it comes to feeling confident in the reliability of our GenAI models. 

4. Generative AI Use Case: Using LLMs to Score Customer Conversations

Luckily, we did see some pretty exciting GenAI use cases this year, starting with this one by the team over at insurance start-up AssuranceIQ. 

The team built an LLM-based product to structure unstructured data and score customer conversations for developing sales and customer support teams – and they did it with data quality top of mind. This exciting use case just goes to show that AI is a data product, just like any other data product, and maintaining high-quality data is essential more than just RAG or fine-tuning models. It’s also essential for initiatives just like this: measuring LLM outputs and the outputs of other features that could end up in new, additional predictive models.

5. How WHOOP Built and Launched a Reliable GenAI Chatbot

Last but certainly not least, this GenAI use case was a favorite among the MC team (and beyond!). 

Fitness wearable company WHOOP leveraged GenAI to power an in-app feature called WHOOP Coach, then repurposed the underlying infrastructure to help internal team members access the data via a self-serve chatbot. And, not only did they implement the chatbot – they implemented an all-new data quality framework alongside it using data observbaility to maintain its reliability. 

What can we say, we’re suckers for a good self-serve data product, especially when it’s powered by GenAI (and backed by data quality). 

What’s next for GenAI in 2025?

As we look into our crystal ball towards next year, one thing’s for certain: GenAI will continue to be a hot topic. And whether you’re all in on GenAI or you’re still feeling skeptical, we can all count on its continued development. 

Data leaders will need to determine ways to make GenAI valuable for their businesses, and true value is built on reliable, trustworthy data products. And that’s where Monte Carlo’s data observability platform can help. 

To learn more about how data observability can bring trust into your GenAI data products next year and beyond, talk to our team.

Our promise: we will show you the product.