Customer Success teams don’t struggle with a lack of data — they struggle with knowing what to do next.

Dashboards are full of signals, metrics, and insights, but too often they sit unused. The problem isn’t visibility — it’s action.

That’s why well-designed alerts are such a powerful bridge. They translate data into movement, ensuring that when something changes in the customer relationship, the right person knows, at the right time, and does the right thing.

Over the last few posts, we’ve explored how customer health has evolved — from static scores to dynamic systems, and now to AI-powered prediction:

• In Why Customer Health Is Broken (and How to Fix It), I discussed why traditional health scores fail to capture real customer dynamics.
• In From Health Score to Health System, we looked at how to turn scores into signal-driven systems.
• And in AI Customer Health Scoring: How to Build It (and How to Avoid Bias), I explained how AI adds predictive intelligence to those systems.

Now, let’s close the loop — because even the best health models fail if alerts don’t trigger the right action.

Customer health has always been at the heart of Customer Success. But in most SaaS organisations, the way we measure it hasn’t kept up with how customers actually evolve.

We started with health scores — simple, single-number snapshots that promised clarity but often delivered false confidence. Then we learned that what really matters isn’t the score itself but the system behind it — a structure of signals, alerts, and playbooks that help teams act in real time.

That evolution took us from monitoring customers to actually managing them. Still, even the best health systems face a new challenge: scale.

When you’re tracking thousands of customers across dozens of products, regions, and behaviours, no human can realistically spot every trend or risk early enough. That’s where AI health scoring comes in — not as a replacement for human judgment, but as an accelerator of it.

AI gives us the ability to move from static, manual interpretation to predictive, data-driven action. Instead of waiting for a red score or a missed milestone, we can now see subtle behavioural shifts — declining engagement, tone changes in communication, or anomalies in product usage — that reveal what’s coming next.

Since moving to Spain, I’ve noticed something striking in contrast to any other country I've lived in: life happens outside, almost all the time. The weather invites people into the streets, and you can find small family-run shops literally on every corner. In the Netherlands, where I lived before, so much of shopping had moved online — out of necessity as much as convenience. Here in Andalusia, though, people still love the ritual of browsing, chatting, and discovering in person. It reminded me that physical shops aren’t just places to buy, they’re experiences in themselves.

This observation connects directly to my work. In my role running Customer HI've ealth for EMEA at ServiceNow, I often speak with retail leaders about the challenges they face. Margins are tight, online competition is fierce, but the shops that stand out are the ones doubling down on customer experience. And again and again, I hear the same question: how do we take the basics of a great storefront and amplify them with AI, so every visit feels personal, seamless, and worth the trip?

Recently, after wandering through some of Jerez’s small shopping streets, I found myself reflecting on that exact connection: the warmth of traditional shopfronts and the modern tools that can make them even stronger.

In recent years, Artificial Intelligence (AI) has moved far beyond being an experimental luxury for big tech. It’s now a core driver of competitiveness for businesses of all sizes and sectors.

Organisations that integrate AI deeply — not just in isolated tools but across strategy, operations, product development, and customer experience — are gaining measurable advantages in speed, revenue, risk mitigation, and innovation.

Here are a few numbers to show how fast things are moving:

• In 2024, 78% of organisations reported using AI in at least one business function, up from 55% the year before.
• The global AI market was valued around $233-294 billion in 2024-2025, with projections to reach between $1.7 and $1.8 trillion by 2030-2032, reflecting a compound annual growth rate (CAGR) of roughly 30%.
• Generative AI alone is expected to unlock $2.6 to $4.4 trillion in economic value
  — across productivity, new products/services, cost savings, etc.

Most SaaS companies track customer health using a simple score — green, yellow, red.

It feels neat and manageable. But here’s the reality:

• A customer can be “green” today and churn tomorrow.
• Scores often lag behind reality.
• They rarely capture the complexity of relationships, adoption, and business context.

Health isn’t static. It’s dynamic, evolving every day. And health scores have limits. 

That’s why we need to move beyond a health score into a health system.

What’s Wrong with Health Scores?