Complacency won’t show up on a control chart. But its damage is real. Can AI and systems thinking help us detect it and respond before trust is lost?

As customer expectations evolve, one question remains: Are customers still at the core of your company’s operations?

Back in 1999, a simple but provocative assertion was made: Without customers, everything a company produces is essentially waste. That idea now feels obvious. Yet in 2025, many leadership teams still treat customer experience as a “soft” initiative—secondary to efficiency, compliance, or cost.

However, from a quality perspective, customer-centricity isn’t about branding—it’s about meeting requirements. Quality is the act of delivering what the customer expects—no more, no less.

A 2023 Global Service Study by PwC underscores the challenge: 73% of consumers say experience is vital to purchasing decisions, yet 54% feel most companies need to improve the experience. That gap isn’t just a service failure—it’s a quality failure.

What if we reframed that gap as an opportunity?

Today’s customers want speed and personalization: automation and empathy, innovation and stability. These may seem contradictory—but TRIZ, the Theory of Inventive Problem Solving, teaches us that contradictions aren’t obstacles. They’re signals for innovation. TRIZ reminds us: Don’t choose between competing demands—transcend them.

Beware complacency

Now, let’s talk about a quiet killer of quality: Complacency.

We once had a supplier who proudly displayed the motto, “The Customer Pays Our Bills.”

But over time:
• Performance dropped
• Responsiveness declined
• We moved on

It wasn’t because of price, and it wasn’t because of specs. We left because of perceived indifference—a red flag most quality systems miss.

This wasn’t a sales or price issue. So let’s name it: Complacency is a quality defect. It doesn’t show up on a control chart, but the damage is real:
• Inconsistent service = variation
• Variation = risk
• And unmeasured risk silently compounds

According to Bain & Co., improving retention by just 5% can boost profits by up to 95%. So why don’t more quality systems treat customer retention as a key metric? Why don’t we track trust erosion like we track defects?

In TRIZ terms, our supplier violated the Ideality Principle: The ideal system delivers value without added cost or complexity.

The supplier added friction—and lost trust.

AI: New possibilities, new contradictions

Artificial intelligence can detect weak signals: subtle drops in engagement, early signs of churn, shifts in tone. Algorithms can analyze trends humans find hard to see, surfacing issues before they escalate.

But AI isn’t a silver bullet. Without thoughtful design, it can automate indifference at scale. If our data reflect complacency, so will our model. Just like any quality tool, AI reflects the assumptions behind it.

The real challenge isn’t technical—it’s systemic. Can we use AI not just to optimize transactions, but also to safeguard trust?

In TRIZ terms, AI brings new contradictions. How do we automate empathy? How do we scale personalization without losing human nuance?

These aren’t tech questions—they’re quality questions. So let’s ask ourselves, as quality and operations leaders:
• Do our systems flag early signs of customer drift?
• Are we tracking complacency like any other defect?
• What tools help us uncover the root causes of hidden churn?

Let’s raise the bar and rethink: What does customer-centric quality really look like in our organizations?