The glare of attention on generative AI threatens to overshadow advanced analytics. Companies pouring resources into much-hyped large language models (LLMs) such as ChatGPT risk neglecting advanced analytics and their proven value for improving business decisions and processes, such as predicting the next best offer for each customer or optimizing supply chains.

The consequences for resource allocation and value creation are significant. Data and analytics teams that our team works with are reporting that generative AI initiatives, often pushed by senior leaders afraid of missing out on the next big thing, are siphoning funds from their budgets. This reallocation could undermine projects aimed at delivering value across the organization, even as most enterprises are still seeking convincing business cases for the use of LLMs.

However, advanced analytics and LLMs have vastly different capabilities, and leaders should not think in terms of choosing one over the other. These technologies can work in concert, combining, for example, the reliable predictive power of machine learning-based advanced analytics with the natural language capabilities of LLMs.

Considering these complementary capabilities, we see opportunities for generative AI to tackle challenges in the development and deployment phases of advanced analytics — for both predictive and prescriptive applications. LLMs can be particularly useful in helping users incorporate unstructured data sources into analyses, translate business problems into analytical models, and understand and explain models’ results.

In this article, we’ll describe some experiments we have conducted with LLMs to boost advanced analytics use cases. We’ll also provide guidance on monitoring and verifying that output, which remains a best practice when working with LLMs, given that they are known to sometimes produce unreliable or incorrect results.

Applying LLMs in Predictive Analytics

Predictive analytics lies at the heart of processes that are increasingly data-driven for many companies. It’s rare to find a marketing department that isn’t discussing shifts in customer churn predictions and how to react, or commercial teams that aren’t considering how to boost next month’s sales in response to a dip that’s been forecast by predictive analytics. We see opportunities to expand the impact of such approaches by tapping LLMs in the following ways to increase the variety of data used to train and execute models or better communicate with business stakeholders who use predictive analytics outputs in decision-making.