AI as Customer Analytics Wingman, Not Delegate

By JP Snow, Principal & Founder at Customer Catalytics, May 8, 2025

This is part 1 of my series featuring real-world case studies on applying generative AI to customer analytics. Want practical, field-tested approaches that deliver immediate ROI and position you ahead of the curve? Subscribe now for weekly insights that turn analytical disruption into competitive advantage.

Series Intro: Customer Creation in the Gen AI Era

Central to my consulting business is the concept of customer creation, succinctly explained in Peter Drucker’s insight that “The purpose of a business is to create and keep a customer.” As a specialist in customer strategy and analytics, I’ve already witnessed firsthand how generative AI is transforming our field.

Since ChatGPT launched just 30 months ago, leaders have been asking if it’s fundamentally changing how customer analytics works. The short answer is: yes. While analytics has used AI for decades in the form of regression models and predictive algorithms, what I’ve found through recent client work is striking: when wielded by skilled practitioners, generative language models don’t replace human insight – they amplify it. These tools apply in numerous and rapidly growing ways that aren’t immediately obvious to everyone. The key difference? Gen AI dramatically shortens the path from question to insight, allowing us to focus more on the “so what” than the “how to.”

In this series, I’ll share what I’ve seen work in our field. Staying current isn’t just about keeping up; it’s about projecting where this field will go and ensuring your career advances with it.

AI as Customer Analytics Wingman, Not Delegate

The most common mistake I see leaders make with generative AI is more conceptual than technical. They assign AI to “analyze my customer data” or “create a strategy,” expecting magic. Like any tool, AI works best when guided by human expertise.

Gen AI excels in four supporting roles applicable to customer analytics:

– Researcher: Finding patterns across vast information quickly

– Code Writer: Generating analytical scripts and queries

– Idea Generator: Offering fresh perspectives when you’re stuck

It should never replace your most important roles as original thinker, expert analyst and strategic conductor.

Best Practices From The Field

Start with Your Own Thinking

Dick Guindon pointed out that “Writing is nature’s way of letting you know how sloppy your thinking is.” A corollary is that if you delegate the strategic part of your analysis to AI, you won’t actually know what you think. As a result, you won’t be able to build on your thinking to extend the insights or expound on them when your consumers want more. It’s the equivalent of taking a robot to the gym to complete your weight training, then wondering why you aren’t getting strong. You need to build your own analytics muscles. Draft your own conclusions first, then use AI to refine—not replace—your thinking.

In a recent client engagement, I needed to synthesize several hours of peer-based benchmarking discussions into a summary report of shared insights and key recommendations. I could have asked a generative AI tool to write the report. Instead, I:

First wrote down my own top themes, take-aways and original insights
Used AI to check for gaps and suggest connections
Outlined the narrative I wanted to present
Leveraged AI’s pattern recognition to strengthen the narrative and check my writing

The result was tighter analysis that remained true to my expertise while benefiting from AI assistance.

Use AI for structure and standards

AI shines when handling routine but necessary tasks:

– Updating outlines as projects evolve

– Converting spoken dictation into structured notes

– Formatting citations and footnotes to specific standards

– Checking content for fit with expected audience needs

This frees your mental bandwidth for what matters most: the insights that drive customer creation.

Coming Soon: The Evolution of AI-Powered Analysis

In my next edition, I’ll share how we’ve quickly progressed through multiple stages of AI-assisted analytics: from writing code to interactive analysis to having AI perform direct analysis—with surprising results in speed, quality, and insight generation.

Until then, try this: Take a customer report you’ve recently produced and ask an AI to play devil’s advocate. What assumptions might be embedded in your analysis? What alternative interpretations exist? The answers might surprise you.

Activate Your Future

Thanks for reading. Leaders work with me to get faster growth through data and scale. My approach is built on what works: Data Decides. Insights Inform. Moments Matter. Systems Sustain. Talent Transforms.

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