Avoid these 3 common pitfalls when using AI to improve customer experience

Agentic AI is rapidly reshaping how companies engage with customers. But in the rush to deploy, many brands are encountering costly detours. For CMOs and CX leaders, avoiding three common missteps can make the difference between scalable success and underwhelming results.

1. Don't let your AI operate in a vacuum

AI only delivers meaningful results when it understands how your business actually works—and that means understanding your applications. Your customer journey doesn't live in a deck. It lives in your CRM, ERP, service platforms, and workflow tools.

A single support issue might touch four or five different systems. If AI doesn't have access across those touchpoints, it will deliver fragmented, inconsistent experiences that frustrate customers and slow down your teams.

The solution? Ensure your AI agents are embedded in the same application environment where customer interactions actually happen. This preserves context, shortens resolution time, and creates more cohesive brand experiences.

2. Avoid "rip and replace" thinking

Many companies assume that AI requires massive infrastructure overhauls—moving all data into a centralized lake before adding intelligence on top. While that approach can work in theory, it often leads to unnecessary spending and painful delays.

What's often lost is context: when data is stripped from the systems where it was created, you lose the behavioral signals and process connections that make personalization and automation possible.

Instead, consider application-native AI that builds on your existing investments. This approach avoids costly migrations and enables you to apply intelligence directly to real-world workflows. It's faster, more cost-effective, and far more aligned with how your teams and customers actually operate.

3. Speed matters—But so does strategy

While some organizations are still forming committees to explore AI ethics and frameworks, others are already putting AI into action to resolve service issues instantly, deliver personalized recommendations, and anticipate customer needs in real time.

This doesn't mean you need to leap before you look. The key is to start small but strategic. Identify customer-facing use cases where AI can deliver immediate value—like improving response times or personalizing engagement—and build from there.

It's about finding the right balance: move with urgency but stay grounded in the needs of your customers and teams. A well-executed AI agent today can free up time, reduce frustration, and deliver the kinds of experiences your customers are already starting to expect.

Focus on what really matters: Customer outcomes

The most successful AI strategies aren't driven by technology for technology's sake—they're driven by customer needs. Long wait times. Repetitive service requests. Inconsistent information. These are the friction points that AI is best equipped to solve.

When you deploy AI with context—within the systems your teams already use—you get more than operational efficiency. You get better service, stronger relationships, and brand loyalty that lasts.

CMOs today don't need to choose between speed and strategy—they need both. By staying focused on real-world impact and avoiding these common mistakes, marketing and CX leaders can turn AI into a powerful competitive advantage that delivers results across every touchpoint.