How AI-powered customer service drives growth—not just efficiency

Over the last few years, I’ve spent many hours watching how agents juggle and manage customer issues around the clock. It’s a key element of our AI advisory program at Freshworks, a hands-on approach where we shadow customer support teams to understand their workflows and challenges before implementing AI solutions.

We shadow customer support agents virtually, or go on-site to watch them firsthand. If it’s a call center, we’ll use dual headphones to listen in on customer calls, taking notes on their process. What I often find is that agents spend a lot of time getting up to speed on ticket information before they can even begin addressing a customer's actual problem. Then, by implementing AI tools, we help agents quickly grasp a situation so they can get to solving the issue much faster.

The impact of that extends beyond efficiency—it drives key business outcomes like improved customer satisfaction and retention. In fact, organizations that use AI-powered agent assistance tools see a 35% reduction in handle times and 27% increase in customer satisfaction, according to Gartner research.

These observations reveal a core truth about AI in customer experience: When implemented thoughtfully—in support of people and teams—AI becomes more than an operational improvement, it’s a catalyst for business growth.

Beyond cost savings

The connection between customer experience and business growth isn't new, but AI has dramatically expanded what's possible. Companies that harness AI effectively gain advantages that extend far beyond cost savings.

Consider personalization. Today’s customers expect tailored experiences, but delivering them consistently across thousands or millions of interactions is impossible without intelligent systems. Blue Nile, one of our retail customers, uses our tools to track customer milestones like birthdays and anniversaries. They reach out at these key moments to offer jewelry for special occasions, turning customer data into personalized engagement that drives sales. According to McKinsey, companies with successful AI-driven personalization achieve 40% higher revenue growth than competitors without such capabilities.

But personalization is just the beginning. The most powerful growth driver is how AI transforms support interactions into business intelligence. With consolidated customer data and advanced analytics, patterns emerge that can inform product improvements, reveal new market opportunities, and anticipate evolving customer needs.

One common misconception I encounter is the belief that AI implementation is primarily a technical challenge. In reality, the biggest hurdles are organizational. Successful adoption requires change management from the top down. Leaders must champion these tools and demonstrate their value to frontline teams.

This is why we created our AI advisory program. If our team observes agents handling refund requests, we will document the entire workflow and then show them how our AI tools can streamline the process. The contrast is often striking—tasks that took minutes are reduced to seconds, and agents can focus on building customer relationships rather than navigating complex systems.

Balancing automation with human expertise

Another misconception is that AI will eventually replace human agents entirely. This fundamentally misunderstands how customer relationships work. One retailer I work with has been very clear in our discussions that while they want AI and bots to help with self-service, their customers expect and demand human experiences as part of overall service.

When a customer has a complex problem or emotional concern, no algorithm can replace human empathy and creative problem-solving. The most successful AI deployments don't eliminate the human connection, they enhance it.

Not every company gets it right the first time. Many adopt sophisticated tools without addressing their foundation: data quality and integration. AI is only as good as the information it learns from. Fragmented customer data across multiple systems creates a fundamental barrier to effective AI adoption.

I regularly encounter businesses operating with separate systems for ticketing, chat support, telephony, and knowledge management. Before implementing AI, these companies need to consolidate their customer data into a unified view. Without this foundation, even the most advanced AI will deliver disappointing results. An IDC study found 72% of AI implementation failures in customer experience were attributed to poor data integration and quality issues.

For organizations looking to harness AI as a growth driver, the path forward requires strategic thinking. Start by evaluating your current data environment and customer journey. Identify the points where automation could remove friction and where human interaction adds the most value. Consider working with implementation partners who can accelerate your transformation with proven methodologies.

The real power of AI in customer experience comes from its ability to simultaneously improve efficiency, personalization, and business intelligence. When all three elements work together, the impact extends far beyond the support center. It drives growth throughout the entire organization.

The conversations I'm having with leaders today reflect this broader perspective. They're no longer asking about cost-reduction metrics alone. They want to know how AI-powered customer experience can help them understand their market better, innovate faster, and build stronger customer relationships.

Companies that approach AI with a growth mindset are positioning themselves to thrive, regardless of size or industry. They recognize that exceptional customer experience isn't just about solving problems efficiently—it's about creating connections that drive loyalty, advocacy, and ultimately, long-term growth.

Key takeaways for CX leaders

• Consolidate your data before implementing AI. With 72% of AI implementation failures attributed to poor data integration, unifying your customer data across systems is critical for success.

• Balance automation with human connection. Design your customer journey with clear paths for when AI handles routine tasks and when human agents take over for more complex interactions.

• Focus on business outcomes, not just efficiency metrics. Measure success not just by reduced handle times but by increased customer retention, higher satisfaction, and revenue growth.