Generative AI Can Help Deliver Exceptional CX

Generative AI can transform the workplace, but businesses need to deliver a safe, ethical and empathetic CX.

Brett Weigl, Senior Vice President

November 15, 2023

4 Min Read
customer experience

The recent explosion of generative artificial intelligence (AI) into the public's hands saw people around the world beginning to explore this technology and the impact it could have on their lives. As generative AI rose in popularity, many businesses turned to their channel partners to learn how they could implement AI across their operations to increase efficiencies and unlock new growth opportunities.

And for good reason, too — the potential value of AI is skyrocketing. According to a recent report from McKinsey, AI technology could add nearly $4.4 trillion to the global economy each year. Additionally, the study identified four business functions that could derive the most value from generative AI — customer operations, marketing/sales, software engineering and R&D.

With so much potential still left untapped, AI adoption and innovation will continue to proliferate across organizations. However, it's vital these organizations learn how to safely and ethically implement generative AI — and other forms of this powerful technology — to revolutionize their workplace and transform roles. When AI is implemented effectively, businesses can empower their employees to deliver superhuman performance, resulting in hyper-personalized customer interactions, higher productivity and improved employee engagement.

How to Implement Generative AI Effectively and Responsibly

As business leaders begin leveraging generative AI capabilities, partners will play a key role in communicating the importance of implementing AI securely and responsibly. Business leaders must ensure the content generated by large language models (LLMs) is accurate, relevant and appropriate, or they may risk losing their customer trust and loyalty.

Here are three key truths partners should communicate when helping businesses implement AI.

Establish Trust with Customers: End users want personalization that doesn't intrude on their privacy. The way LLMs arrive at decisions is often seen as a "black box," resulting in consumer distrust about how data is gathered and stored. It's essential for businesses to have a clear understanding of the inputs and data used to train the model. This includes data sets that are not in the public domain and any relevant information that resides across a company's systems.

Additionally, it's vital to provide AI model explainability to understand how and why the model arrived at its decision. This transparency will allow businesses to remain in control and ensure their model makes decisions aligned with their desired outcomes.

Communicate the Power of AI: Integrating generative AI in contact center workflows can give agents a co-pilot that predictively suggests what they can do next for their customer, spanning content, workflow, intent and more. Many contact centers are already using generative AI to support their employees and enhance bots, boosting their ability to orchestrate stronger customer experiences. With its advanced natural language processing capabilities, generative AI enables organizations to better detect topic, sentiment and tone from customer conversations. This allows contact center employees to easily extract insights for more seamless self- or assisted-service experiences.

Generative AI also offers the ability to quickly find answers in knowledge bases, enabling bots or agents to highlight the most relevant responses for customer requests. Additionally, AI technology can help employees generate content and summarize conversations, saving workers valuable time in creating customer follow-ups, sales communications and more.

While generative AI has demonstrated its great potential to help people save time and improve efficiency, it's not a replacement for human decision-making. As employers look to incorporate more automation within their workforce, it's important they understand that human feedback is a crucial element needed to train AI. Organizations shouldn't implement generative AI on a whim. Partners can help businesses identify where AI and automation is needed by analyzing the customer service needs of their end users.

Establish Clear Ethical Guidelines and Resources: As with any form of AI, ethical considerations, such as bias, abuse and privacy, should be scrutinized before using generative AI.

One critical component in building out ethical guidelines is that AI needs to be supervised so businesses can ensure the quality and accuracy of the customer and employee experience. Human oversight is critical to protect the accuracy of content generated by the model and guarantee it meets brand voice, legal and compliance requirements. Within these guidelines, it's also important to prioritize training models based on unbiased data to ensure privacy design principles are incorporated into the development process and avoid producing misinformation. Additionally, businesses should actively work to reduce bias in their models and maintain transparency around how it is making decisions.

The Future of AI in The Workplace

Despite the potential of generative AI to transform the workplace, partners must encourage businesses to put proper safeguards in place to account for its limits.

Business applications of generative AI are here to stay and have huge benefits in delivering better customer and employee experiences when executed correctly. In the workplace of the future, employees will not only be assisted by AI but also will assist AI as it becomes a more independent form of self-service.

Leaders shouldn't arbitrarily rush into adopting generative AI because of the hype. Instead, they should work with their partners to deliberately consider each step on their AI journey to ensure their business is delivering a safe, ethical and empathetic customer experience.

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About the Author(s)

Brett Weigl

Senior Vice President, Genesys

Brett Weigl is senior vice president and general manager for digital, AI and journey analytics at Genesys. An enterprise SaaS product management leader with more than 20 years of experience, he oversees the company's digital-first solutions for complete customer experience and AI across digital and the contact center. He previously worked at Salesforce Service Cloud and ExactTarget. You may follow him on LinkedIn or @Genesys on X.

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