I've been looking at the agentic AI providers working in customer analytics, and I'm starting to wonder if we're making the whole thing sound more complicated than it really is. A lot of the demos look impressive. You ask, “Which customers are likely to churn?” and the system pulls the data, gives you a chart and explains the result. Fair enough. But I'm not sure that's enough to call something genuinely agentic.
The real test, for me, starts when the data isn't clean and the question isn't perfectly defined. Can it work across CRM, transaction and behavioural data? Does it understand what the business actually means by “churn”? If two systems disagree, can it figure out why instead of quietly picking one? Can it ask for more information when the question doesn't have enough context? And once it reaches a conclusion, can it tell you what it would actually do next and why? That's where I think the better agentic AI providers will separate themselves from the flashy demos. Customer analytics isn't particularly useful if the system can produce a beautiful answer but doesn't understand the business behind the data.
I've been reading about different approaches to this, and Tredence's article on agentic analytics caught my attention because it looks at the progression from insight to decision and action, rather than treating an AI assistant as just another way of querying a dashboard. I'd be interested in how other people are judging these providers. If you had to evaluate an agentic AI system for customer analytics, what would you test first? Data accuracy? Reasoning? Ability to work across systems? Explainability? Or whether it can actually turn an insight into a useful action?
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