Podcast Summary: Experts of Experience
Episode: This One Thing Will Generate 400% More Customer Data
Release Date: February 19, 2025
Host: Lauren Wood
Guest: Michael Mao, Senior Vice President of Innovation Strategy at Salesforce
Presented by: Salesforce Customer Success
Introduction
In this episode of Experts of Experience, host Lauren Wood delves deep into the transformative impact of generative AI on customer experience (CX) with Michael Mao, Senior Vice President of Innovation Strategy at Salesforce. The conversation explores how businesses can effectively harness emerging technologies like AI and GPT to exceed customer expectations and generate substantial customer data.
Guest Background
Lauren Wood introduces Michael Mao, highlighting his pivotal role in founding Gartner's CRM practice and his two-decade-long experience in helping organizations enhance customer support and service globally. At Salesforce, Michael focuses on developing innovative strategies that leverage AI to drive business growth and elevate customer experiences (Timestamp [00:52]).
The Evolution of AI in Customer Experience
Lauren’s Perspective on AI:
Lauren begins by discussing the general perception of AI, noting that while predictive AI had limited engagement, the advent of generative AI, exemplified by ChatGPT, has captured widespread attention. She emphasizes that generative AI should be seen as an evolutionary step rather than an endpoint in AI development (Timestamp [02:38]).
Michael’s Insights on Generative and Agentic AI:
Michael expands on this by distinguishing between predictive and generative AI. He introduces "agentic AI," which not only understands language but can also take actionable steps, such as tracking orders and completing forms on behalf of customers. This advancement aims to substitute mundane tasks and enhance operational efficiency (Timestamp [02:38]-[05:59]).
Michael Mao: "If I trust you, I will pour out 400% more information, accurate data about myself than if I don't trust you." (Timestamp [00:52])
Common Misconceptions and Pitfalls in AI Implementation
Lauren’s Query:
Lauren raises concerns about the paradox of AI in CX—while it offers great efficiencies, improper implementation can degrade customer experience. She asks Michael to shed light on common misconceptions and mistakes businesses make when integrating generative AI (Timestamp [06:58]).
Michael’s Example of Failure:
Michael recounts a case where a conversational AI bot failed due to siloed application and inaccurate data sources. The bot’s inability to access the correct information led to irrelevant and privacy-violating responses, highlighting the critical importance of data integrity and integration (Timestamp [06:58]-[09:08]).
Michael Mao: "Conversational bot... didn't connect with the right information. It also violated privacy and a whole bunch of rules that would get you shut down in Europe for GDPR." (Timestamp [06:58])
The Crucial Role of Clean Data
Lauren’s Emphasis on Data Quality:
Lauren echoes the sentiment that "garbage in, garbage out," stressing the need for clean, reliable data to ensure AI systems function correctly (Timestamp [09:08]).
Michael’s Solution with Data Cloud:
Michael discusses Salesforce’s Data Cloud, which acts as a data ingestion engine ensuring data accuracy and compliance. He provides an example of a healthcare client who utilized clean email data to streamline their generative AI processes, resulting in significant efficiency gains and improved customer interactions (Timestamp [09:08]-[13:32]).
Michael Mao: "Data is always being produced. And if you can get just like water, clean water, clean data, you can do amazing things." (Timestamp [02:38])
Building and Maintaining Trust with Customers
Lauren’s Focus on Trust:
Lauren highlights trust as the foundation for customers willingly sharing their data, which in turn enhances their experience and loyalty (Timestamp [21:11]).
Michael’s Approach to Trust:
Michael underscores Salesforce’s commitment to trust, advocating for ethical use of data and ensuring AI interactions respect customer privacy. He explains that trust not only fosters data sharing but also builds a stronger, more personalized customer relationship (Timestamp [19:04]-[22:41]).
Michael Mao: "If I trust you, I will pour out 400% more information, accurate data about myself than if I don't trust you." (Timestamp [00:52])
Empowering Employees Through AI
Lauren’s Observation:
Lauren notes that AI can significantly improve employee satisfaction by automating repetitive tasks, allowing staff to focus on more meaningful work (Timestamp [27:31]-[32:15]).
Michael’s Advocacy:
Michael agrees, emphasizing that AI should lift cognitive burdens from employees, enhancing their productivity and job satisfaction. He provides examples of how Salesforce leverages AI to free up employees from mundane tasks, thereby improving overall service quality and employee morale (Timestamp [29:38]-[32:15]).
Michael Mao: "This is one of the best use cases for AI... use AI to improve the lives of our employees, because that then gets transferred to the customer." (Timestamp [29:38])
Balancing AI Automation and Human Interaction
Lauren’s Inquiry:
Lauren probes into how organizations can determine when to use AI versus human interaction, especially in emotionally charged or complex situations (Timestamp [33:45]-[41:05]).
Michael’s Guidelines:
Michael advises that interactions involving emotional intelligence (EQ) and complex decision-making should involve humans. He differentiates between tasks best handled by AI and those requiring human empathy and understanding. For example, routine inquiries can be automated, while crises or nuanced conversations should be managed by human agents (Timestamp [38:45]-[41:05]).
Michael Mao: "If there is an emotional component to this interaction, humans should be the ones to interact with the customer." (Timestamp [38:45])
Encouraging AI Experimentation and Strategic Implementation
Michael’s Strategy:
Michael encourages organizations to experiment with small AI use cases while ensuring robust data management. He categorizes adopters into innovators (Type A) who embrace risk for early advantages, and pragmatists who seek balanced, incremental implementations. This approach helps mitigate risks while scaling AI initiatives effectively (Timestamp [41:15]-[44:54]).
Final Thoughts and Leadership Advice
Michael’s Key Advice:
In his concluding remarks, Michael emphasizes the importance of leadership in fostering a customer-first culture. He advises CX leaders to ensure executive buy-in for AI initiatives and to build environments that prioritize employee well-being and customer satisfaction. Leadership commitment is crucial for aligning organizational efforts towards sustained success (Timestamp [53:20]-[57:06]).
Michael Mao: "If you're working for a company where your executive leader is not down with this initiative. Find a new job. Get out of dodge." (Timestamp [53:20])
Notable Quotes
- Michael Mao: "If I trust you, I will pour out 400% more information, accurate data about myself than if I don't trust you." (Timestamp [00:52])
- Michael Mao: "Don't be creepy." (Timestamp [19:04], [21:11])
- Lauren Wood: "Garbage in, garbage out." (Timestamp [09:08])
- Michael Mao: "Service is not a department, only customer service is us. It is what we live for." (Timestamp [00:00], [27:00])
- Michael Mao: "This is one of the best use cases for AI... use AI to improve the lives of our employees, because that then gets transferred to the customer." (Timestamp [29:38])
Conclusion
This episode of Experts of Experience provides a comprehensive look into how generative and agentic AI can revolutionize customer experience by generating substantial customer data, enhancing personalization, and empowering employees. Michael Mao’s insights emphasize the importance of clean data, building trust, balancing AI with human touch, and strategic leadership in successfully implementing AI-driven CX initiatives. Listeners gain valuable strategies for navigating the evolving landscape of customer experience powered by advanced AI technologies.