4 Ways Corporate Call Centers Are Using AI Today
Enterprises are clamoring to find real, practical uses for AI. I speak with enterprise executives daily, and most have AI FOMO—they all want to know what we’re hearing from other companies about how they are really using AI, and if they're getting any return on their AI investments.
Unfortunately, data shows that most enterprises are failing with AI. A new MIT research study found that 95 percent of enterprise AI projects fail. The ones that succeed tend to have some traits in common:
- They purchased specialized tools rather than building tools themselves.
- They selected tools that could be deeply integrated and adapt over time.
- They empowered line-of-business managers—not just central AI labs—to select tools and make decisions.
One area that’s making great strides with AI is corporate call centers—in-house call centers with 50 or more agents. Here are four ways they are using AI today.
Role playing customer service scenarios for agent training purposes. AI is great for training because it can present an infinite number of scenarios and tailor responses to the conversation. Training can be broad, for entire teams, or personalized for agents struggling with a specific aspect of their jobs, such as explaining product features clearly. Real-time agent assist and co-pilot products, for example, that provide customer contact agents and/or sales reps with real-time support, including suggested prompts and responses, actions, edits to tone, and more, are ideal when interacting with customers on phone, email, text, and/or chat.
QA calls with human agents. AI can be used to listen to real calls and provide feedback for agents and managers about how best to handle certain scenarios. For example, automated QA / QM tools that utilize natural language processing (NLP) and AI to automate the quality assurance of every interaction between customer service agents and customers on all channels can be beneficial. These tools help identify potential issues, improve agent performance, and ensure consistency in service delivery. These tools include automated scorecards and coaching tips for individual agents but also focus on the broader management of customer service quality across an entire team, department, or organization.
Handling Tier 1 customer support inbound calls. AI agents that are indistinguishable from humans can be easily trained to handle a company's most common inbound support calls. Consumer lending company Sunshine Loans had a high call abandonment rate—approaching 30 percent—and human agents were having trouble keeping up with the 700,000 loan applications coming in by phone each month. The company implemented Voice AI agents, and now 70 to 80 percent of inbound calls are completely handled by AI, and its call abandonment rate is down to 6 percent. After about one month, Sunshine Loans was able to replace 100-plus offshore agents with 24/7 AI coverage.
Making outbound sales calls. Voice AI quality has progressed to the point where companies can use AI rather than human agents for outbound sales calls as well. One example is performance marketing agency Inbounds.com, which delivers quality leads at scale to clients across the service industries. Using Voice AI for these calls helped the company achieve 3x the profitability per lead, 72 percent faster campaign deployment, and a 25 percent improvement in call success rates compared to human agents. Best of all, Inbounds.com can scale client campaigns without having to scale its call center headcount.
Voice AI is getting better by the day. If you haven't explored options recently, I urge you to see what's out there. This is an area that’s ready for low-risk AI disruption, and we are seeing companies reap the benefits already.
Bing Wu is the cofounder and CEO of Retell AI, an enterprise-grade AI voice platform built for corporate call centers. A former tech PM at TikTok and a serial entrepreneur, Wu is passionate about creating AI that sounds human, works reliably, and transforms how enterprises engage over the phone.