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Transform Customer Support with AI: Improve Agent Productivity by 35%

Webinar | 06.19.2023 | Michael Zhang, Chyngyz Dzhumanazarov

GPT Improves Support Agent Productivity by up to 35%.

 

In a recent webinar, industry experts discussed the transformative impact of artificial intelligence (AI) on customer support. The session featured Michael Zhang, an entrepreneur and founder of Artisana AI, along with Chyngyz Dzhumanazarov, CEO of Kodif. A key takeaway from the discussion was a significant statistic: AI can increase support agent productivity by 35%.

 

Quick Recap

The Kodif webinar featuring Michael Zhang (founder of Artisana AI) and Chyngyz Dzhumanazarov (CEO of Kodif) explored AI’s transformative impact on customer support operations. The discussion traced the evolution of language models from their 1950s origins to modern GPT technologies, highlighting how AI now serves as a co-pilot for support agents rather than a replacement.

A Stanford-MIT study involving Fortune 500 companies and over 5,000 support agents revealed that AI can increase agent productivity by 35%. The webinar emphasized how AI tools provide consistent outputs that help new agents perform at higher levels, streamline workflows, reduce handle times, and improve resolution rates. Key considerations for successful AI implementation, including data readiness and compliance with security standards, were also discussed as essential factors for maximizing the benefits of these technologies.

AI Boosts Customer Service Efficiency

 

The Evolution of AI in Customer Support

Historical Context of Language Models

The journey of AI in customer support has evolved significantly since the inception of language models in the 1950s. Breakthrough developments in 2017-2018 led to the introduction of advanced models like GPT-3, which have reshaped the landscape of customer interactions. As highlighted in the webinar:

“GPT-3 marked a pivotal moment in how AI can engage with users” (Zhang, 01:51)

Looking ahead, GPT-4 is anticipated to further enhance these capabilities.

 

The Stanford-MIT Study: Real-World Impact

Research Methodology and Findings

The Stanford-MIT study examined the real-world impact of AI on customer support, involving participation from Fortune 500 companies and studying over 5,000 customer support agents. The study compared the effects of AI in real-world deployments versus controlled lab settings. Key findings revealed that AI significantly improved productivity and customer satisfaction metrics.

First Real-World Study of GPT-3’s Impact

 

AI as a Co-Pilot: Enhancing Human Capabilities

Complementing Human Agents

AI acts as a co-pilot for customer support agents, enhancing their capabilities rather than replacing them. The technology streamlines workflows, reduces handle times, and improves resolution rates, making agents more efficient. Zhang noted:

“AI allows agents to focus on complex queries while handling routine inquiries seamlessly” (Zhang, 05:50)

 

Leveling Up Support Team Performance

Training and Consistency

One of the most notable impacts of AI is on the training of new support agents. AI tools provide consistent outputs, enabling even inexperienced agents to perform at a high level. Dzhumanazarov emphasized that:

“AI assistance not only boosts individual performance but also enhances the overall skill level across the team” (Dzhumanazarov, 06:10)

AI can turn any complex manual workflow into a single click

 

Implementation Considerations

Key Factors for Successful Deployment

Implementing AI in customer support requires careful planning. Organizations must ensure data readiness and adhere to compliance and security standards. Best practices for deployment were discussed, emphasizing the importance of a strategic approach to maximize AI’s benefits.

 

Putting It into Practice

✅ Implement AI as a co-pilot for agents rather than a replacement

✅ Focus AI deployment on handling routine inquiries to free up agents for complex issues

✅ Ensure all AI solutions comply with security and compliance standards

✅ Develop metrics to measure the productivity increase from AI implementation

✅ Balance technological capabilities with human insight for best results

 

Watch the webinar now:

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