We’ve all been there. Stuck on hold, looping through the same frustrating music, wondering if anyone will ever pick up. In 2025, that experience is not just frustrating. It’s unnecessary.

For years, companies have approached customer support with a simple equation: more tickets means hiring more agents. But this strategy doesn’t scale efficiently. I know, because I’ve tried it myself while leading Customer Success teams at HubSpot, Sprinklr, Adobe, and other industry leaders. No matter how many agents we hired, the same challenges kept surfacing:

• Long wait times: Demand often outpaces hiring, leaving customers waiting.

• Inconsistent service: Quality varies based on agent availability and experience.

• High costs: Scaling through headcount alone is expensive and time-consuming.

Meanwhile, customer expectations have changed. They now expect fast, personalized, and proactive support, yet many businesses are still relying on reactive models built for a time when waiting was acceptable. 

Traditional support models weren’t built for today’s demand. Companies need a scalable, always-on solution. AI-powered customer support offers a way forward.

AI’s Business Impact on Customer Support

Customer churn costs U.S. businesses $168 billion annually and 65% of customers have switched brands due to poor service experiences. The message is clear: Fast, effective support is now a competitive advantage, more than ever before. 

This growing gap between expectations and outdated support models is where AI steps in. AI-driven support is already delivering results:

• Higher customer satisfaction: Banking platform Rho maintained 95% CSAT even as support requests grew 12% monthly, using Maven AGI to improve internal training and speed up resolutions.

• Reduced time to resolve customer issues by an average 60% : Our clients' customer issues are being resolved faster, and human agent productivity has doubled.

These improvements go beyond efficiency gains. AI is transforming customer support into a scalable function that improves both customer and employee experience.

Delivering Intelligent Resolutions

The first wave of AI-powered support focused on basic chatbots. Today’s AI can resolve issues in real time with accuracy and personalization.

At Maven, we’ve helped resolve up to 93% of customer queries autonomously by creating a platform that:

• Pulls real-time data from CRMs, billing systems, and internal knowledge bases to personalize responses,

• Continuously learns from each interaction, improving accuracy and anticipating future needs.

Take a typical billing question: Traditionally, a customer might navigate an interactive voice response (IVR) system, wait 20 minutes for an agent, and still need to repeat their issue multiple times. I went through this last night with xFiniity/Comcast. It's really frustrating. With the advancements in AI and the technology we’ve built, they can receive an instant, personalized resolution - whether it’s a breakdown of charges, a refund option, or a proactive adjustment - without requiring a live agent.

No waiting. No generic responses. No frustration.

Where Human Agents Fit In

AI is reshaping the role of human agents, allowing them to focus on more complex and high-value work. Instead of handling repetitive inquiries, support teams can dedicate their time to problem-solving, relationship-building, and proactive customer engagement. 

Companies like Rho have already seen improvements in both customer satisfaction and agent productivity.

"Our goal is to optimize our team’s focus on high-complexity tasks, where their expertise can truly shine, by minimizing time spent on mundane activities," said Stas Johnson-Chyzhykov, SVP of Operations & Client Experience at Rho.

By offloading routine inquiries, teams are more engaged and better equipped to handle meaningful customer interactions.

The Future of Support: Proactive, Predictive, and Frictionless

The challenge I’ve spent my career tackling has never been more critical, or more solvable. The real opportunity we see with our clients isn’t just resolving issues faster, but anticipating problems before they arise. Our technology is already in the hands of customers, helping them move from reactive support to proactive engagement. Here’s a glimpse:

• Predictive analytics will flag at-risk accounts, enabling teams to intervene before customers consider leaving.

• Sentiment analysis will detect frustration and escalate cases before dissatisfaction turns into churn.

• Automated workflows will resolve common issues, like failed logins or billing discrepancies, before customers even notice.

The companies that embrace AI’s full potential will set the new standard. Those that don’t? They’ll make their customers wait.