When AI makes customer experience feel personal

We’ve all felt the frustrating side of automation — irrelevant marketing emails, clunky digital workflows, or robotic customer service that leads you in circles. Too often, technology is used to reduce costs or speed up interactions at the expense of real connection.

But every so often, a company gets it right. Instead of relying on automation to do more for less, some organizations are using AI to do something far more powerful — to elevate the customer experience, making it more human, not less.

In an era where personalization often means little more than inserting a first name into a template, the real opportunity lies in using AI to deliver helpful, timely and emotionally intelligent experiences. That means understanding what the customer needs — before they ask for it — and responding in a way that feels effortless.

A tale of two messages

A colleague recently shared two vastly different experiences he had with financial institutions, which he detailed in LinkedIn posts (here and here). 

In the first, he received a bland, automated message informing him he no longer meets a minimum balance requirement. It was a trigger-based alert — impersonal and irrelevant within a day, as market fluctuations corrected the issue. He joked about the absurdity of receiving daily messages that flagged and un-flagged his account depending on market volatility.

In contrast, a message from Citizens Bank about renewing a student loan was designed with such care and intelligence that it felt like magic. The communication anticipated what he needed, guided him through the process seamlessly and did it all without unnecessary steps. 

Whether AI was involved or not, the takeaway was clear: some companies are using technology to create experiences that feel intelligent and human. Others are stuck in the era of template-driven alerts that barely acknowledge the person on the other side of the screen.

There are fewer genuine excuses for tone-deaf communications in an era of intelligent CX. Why do so many still fall short while others shine? The distinction happens right from the start.

Dig deeper: Why relying on AI won’t improve the customer experience

Avoiding the shiny object trap

Martech has long been a land of shiny objects. Now, with AI, there are even more — and brighter objects — to distract us. Two common pitfalls emerge:

• Cool doesn’t mean relevant: Some AI uses are flashy but misaligned with real customer or business needs.
• Ambition without focus: Spreading effort across too many initiatives can lead to underwhelming results — or worse, failure.

Despite rising awareness of AI’s potential, many companies abandon data-driven CX when faced with complexity, poor alignment or unclear objectives and ROI.

Begin with purpose, not platform

Companies that succeed with AI begin not with technology, but with purpose. They ask foundational questions: 

• What real business or customer problem are we solving? 
• What is the intended outcome?

AI works best when it is applied to issues at the crossroads of customer need and business value, such as:

• Reducing churn.
• Improving onboarding.
• Deflecting calls via smart self-service tools. 

Instead of chasing outlandish demos, these companies tie each use case directly to measurable outcomes. Success must be clearly defined and tracked, whether it’s reducing call center volume, boosting conversion rates or improving net promoter scores.

Know your customer first

Automating a broken process only magnifies its flaws. Teams must understand where customers struggle before rolling out AI-based messaging or chatbots. For instance:

• Did service calls involve frequent service outages, poor documents or other journey bottlenecks? 
• What issues result in customers leaving? 

Take the case of a telecom company that launched a chatbot to handle service issues. On paper, it was a smart move. But the company hadn’t done the work to understand why customers were reaching out in the first place.

Many were calling about frequent service outages specific to certain ZIP codes — a detail the chatbot wasn’t equipped to address. Instead, it offered generic troubleshooting tips, adding to customer frustration. Call abandonment rates increased, as did the number of people demanding to speak to a live agent.

The lesson? If you skip understanding your customer, AI can quickly become a barrier instead of a bridge. That matters, because while consumers are optimistic about AI, their trust must still be earned. According to Zendesk:

• 59% expect generative AI to transform customer experiences within the next two years — yet many remain wary. 
• More than six in ten consumers (63%) worry about bias in AI algorithms, highlighting growing concerns about fairness and accountability. 

CX leaders share this concern: 

• 74% say transparency is vital.
• 77% feel personally responsible for data protection.

Without clear guardrails, companies risk eroding trust before they’ve even begun.

Dig deeper: AI improves customer service only when it supports humans, not replaces them

Smart, phased adoption over full-scale bets

The reality is that most effective AI in CX initiatives don’t require rocket science but readiness. Companies that succeed often start with modest use cases. A smart prioritization matrix helps:

• X-axis: Confidence in the use case (based on solid customer insight).
• Y-axis: Implementation complexity (based on data and tech availability).

Priority use cases are high-confidence and low-complexity. Examples include recommendation engines for ecommerce or send-time optimization for emails.

Avoid overcommitting 

Start with a few early wins to build credibility and momentum based on reliable customer service data. AI needs data — but start with what you have. A quick audit can help prioritize the right use cases by answering:

• What inputs are required?
• Is the data available, reliable and accessible?
• Can it be updated frequently enough?

If not, treat data preparation as a priority — don’t rush to automate this particular use case.

Based on available data, one AI initiative may involve simple sentiment detection on support tickets or optimized email send times based on user behavior. Delivering quick and measurable improvements before graduating to more advanced personalization engines builds confidence and political capital for bigger investments.

Dig deeper: 5 areas where businesses need to improve their customer experience

Measure, iterate, govern

Start with a minimum viable model (MVM). Test on small, relevant segments. Measure performance clearly and iterate. Once there is a clear line of sight to a successful experience, scale. 

Finally, set up a set of metrics that will help you govern the AI-driven automation process. Meaningful AI deployment demands continuous visibility, such as tracking: 

• Conversion lift.
• Call deflection rates.
• Cost per contacted customer.
• Net promoter score changes.

The real difference maker

At their core, the most impactful AI-driven CX journeys share these traits: 

• They stem from deep customer insight.
• They begin small.
• They blend AI and humans.
• They scale responsibly. 

As AI becomes more embedded, the issue no longer revolves around if it will change CX — it’s how thoughtfully companies deploy it.

When Citizens Bank sends you a communication that doesn’t feel like a template, that knows just what you need and when you need it — and does it without getting in the way — that’s what it looks like when technology makes experiences more human, not less.