From the Editor's Desk

How Generative AI Can Support Advanced Analytics Practice

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.

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