The Efficiency Trap: Why AI Alone Won’t Drive Growth

From automation to personalization — where the real growth lies.

AI for Efficiency or Growth: What's the Trend?

Recently, someone asked me: “Do you think companies today are using AI more for efficiencies or for data insights?

Without hesitation, I said, efficiencies.

It’s the fastest, most measurable win: automate a task, trim a timeline, lower a cost. And to be clear, there’s nothing wrong with that. Efficiency matters. But I can’t shake the feeling that many teams are stopping there, missing the deeper opportunity to use AI for personalization, customization, and customer experience. That’s where the real growth is.

Why Everyone’s Still in Efficiency Mode

Leaders want transformation, but they start with triage. Under the hood, most organizations are still wrestling with messy data, archaic systems, and disconnected platforms. You can’t personalize at scale when your customer data lives in silos, half of it missing context or history. So efficiency becomes the easy story, and personalization gets pushed to “someday.” It’s not lack of vision. It’s infrastructure.

The Efficiency–Layoff Loop

AI has become both the solution and the scapegoat. Companies chase efficiency, achieve short-term savings, reduce headcount, and end up relying even more on automation. With smaller teams, there’s less energy for experimentation or collaboration.

It’s a self-reinforcing cycle — one that keeps teams optimizing instead of evolving.

The Efficiency Loop: why many teams get stuck optimizing instead of evolving.

"Ironically, companies chasing efficiency become less competitive in customer experience, which is where the real growth lies."

From Efficiency to Experience

Efficiency is the starting line, not the finish.

Phase	Focus	Example	Mindset
Efficiency	Automation & savings	Chatbots, content generation	“Do more with less”
Enablement	Data & insight	Predictive analytics, integration	“Make smarter decisions”
Experience	Personalization & growth	Adaptive journeys, orchestration	“Deliver unique value continuously”

What Personalization at Scale Really Means

• Real-time next-best-action models

• Dynamic content and messaging

• Automated orchestration

• Feedback loops that learn over time

The Data Readiness Gap

My posts about data readiness have gotten a lot of nods and validation. Everyone agrees the data mess is real. But if we all know that clean, connected data is the prerequisite for AI-powered growth, why aren’t more companies tackling it? Maybe it’s bandwidth, maybe short-term ROI pressure. Still, I believe the companies that fix their foundation now will win later.

Efficiency is survival. Personalization is growth.

The Moment the Narrative Will Shift

At some point, the market will stop rewarding “we implemented AI” and start asking, “did it make the experience better?” That’s when the story changes, and when businesses built around data readiness and human-in-the-loop systems (like mine) will be ready.

Closing Thought

AI shouldn’t just make us faster. It should make us smarter about the humans we serve.