Feature
The Gist
- AI Implementation. Generative AI has democratized AI's accessibility, revolutionizing both product development and consumption patterns.
- Operational excellence with AI. Rigorous data analysis and employee engagement have elevated chatbot and AI implementations, resulting in significant gains in customer satisfaction and operational efficiency.
- Ethical and transparent AI. As AI continues to integrate deeper into business operations, maintaining ethical standards, quality control and transparency remains paramount.
Editor’s note: Shri Nandan caught up with us before taking on her new role at Comcast. Also, did you know Nandan will be one of the featured speakers at CMSWire's inaugural THRIVE conference in Atlanta in December? Check out more on Nandan's presentation.
Shri Nandan, VP of AI Experiences at Comcast, understands the transformative power of Artificial Intelligence (AI). Its evolution mirrors significant technological shifts in the past, like the internet boom of the '90s. She recognizes the nuanced transitions of AI's journey, its real-world applications in chatbot implementations, its impact on operations, employee and customer experiences and the ethical considerations surrounding its use.
Those considerations are crucial when it comes to things like implementing an AI chatbot for successful conversational outcomes with customers. Nandan has been involved in those implementations in past roles and is looking forward to working with AI now and in the future.
As Nandan explains in her Season 4, Episode 2 conversation on our CX Decoded Podcast — check out the full transcript and more on the conversation — the AI revolution is here, and its profound impact on various business functions is undeniable. As with any technological advancement, its integration comes with challenges and opportunities. The key lies in understanding its potential, leveraging its strengths, and being vigilant about the ethical and quality aspects.
Here are some other takeaways about AI implementations from our conversation with Nandan:
AI Implementation: Generative AI Vaults Tech into Mainstream
AI has progressed from a hype cycle to a more practical, generative phase, making it accessible and impactful across industries. This transition has particularly affected the way products are both developed and consumed.
“I think the mid-'90s, when we saw the internet boom, that was similar,” Nandan said. “... And then about 10 years ago, we had the AI hype cycle, starting with predictive AI. And now I think of it as the second AI hype cycle where we have generative AI; it's just made AI available to everybody. It's not like AI didn't exist. It's always been around."
Robots have been in play forever. Now, it’s just become more “approachable and available.” Therefore, it’s affecting our lives as people who produce products and as people who consume products in amazing ways, she added.
“It's an amazing transformation. It's a really great time to be a product manager,” Nandan added.
Related Article: AI & Next Best Action: Transforming Decision-Making
Chatbot AI Implementation: Metrics, Operational Impact
As for implementing an AI chatbot as a product and customer experience leader, Nandan said that involves having strong success metrics, a continuous need for improvement and a keen eye on operational impact.
Specifically, that means:
- Success metrics: Through rigorous data analysis and iterative improvement, Nandan in the past achieved a 95% query resolution rate with an AI chatbot, which significantly impacted customer satisfaction and operational efficiency.
- Iterative improvement: The journey from a 65% to a 95% resolution rate required a dedicated business intelligence team and continuous data analysis to understand and cater to customer needs better.
- Employee engagement: Involving the contact center staff from day one in the chatbot development helped ensure the tool would be seen as an aid rather than a threat to job security.
- Efficiency gains: Implementing AI, including the use of tools like GitHub Copilot for coding, led to time savings and improved productivity among teams.
- Revenue uplift: Insights derived from chatbot data helped in identifying new revenue streams, like targeting customers interested in refinancing, which was reflected in an uptick in revenue.
“You don't have to overthink it,” Nandan said. “You just sort of categorize the customer problems and say these are all the problems we need to solve and put the answers in there. And then the AI picks it up. But we didn't get there overnight. It took us about a year to get to 95%. It's not overnight, and even then we are constantly tweaking, we're looking at the data. And we're tweaking the data that we're feeding into the language models. ... It's doable. It's entirely possible.”
Advice for AI Implementation: Get a Strong Business Intelligence Team
Harnessing the power of AI, especially in customer-centric operations, requires more than just cutting-edge technology. At the heart of successful AI integration lies the ability to decode and make sense of vast amounts of data.
Here’s how Nandan suggests getting started:
- Start small: Nandan advises starting small, perhaps with simpler AI applications, and then gradually building up based on the learnings and outcomes.
- Engage multiple stakeholders: It's important to engage various stakeholders including marketing, IT, contact center staff and compliance teams right from the inception of AI projects.
- Focus on business outcomes: Ensuring that AI initiatives are driven by clear business objectives like revenue generation, customer satisfaction or operational efficiency is recommended.
While many might assume that mere access to AI tools and data would suffice, the reality is far more nuanced. Nandan sheds light on the critical importance of a dedicated and adept business intelligence team in the AI journey.
“You need a good business intelligence team,” she said. “You need to have people whose brains work in that way, who look at numbers and derive insights. And I cannot do that. I cannot look at data and have it speak to me in that way. So I have a really strong team of business intelligence folks who are constantly mining the data from the AI chatbot.”
Analyze the data. Mine it. Get into the heads of customers.
“And that way from day one, there's no scope for delay,” Nandan said. “And I think that's something that we should have understood sooner, and it became a bit of a challenge. But now that we get that we're sort of getting smarter about how we look at the data and how quickly we react to it.”
Related Article: How Generative AI Improves Customer Experience Metrics
Employee and Customer Experience Impact of AI Implementations
The ability of customer service agents to spend more time with high-value customers, thanks to AI handling routine queries, can lead to a better experience for both employees and customers, according to Nandan. The quality of interactions often gets overshadowed by the quantity of calls handled. However, truly transformative customer experiences are rooted in the depth and authenticity of these interactions.
Reflecting on the impact of AI tools in the customer service realm, Nandan underscores the profound difference made by allowing service agents more time per call.
“I think it has reduced stress levels a lot,” Nandan said of AI chatbot implementations. “I was mentioning the fact that they can stay on the call with one person for about 20 minutes. And that makes a huge difference to the employee and to the customer. The more time you can give them and solve their problems — instead of saying I'm sorry, you'll have to come back in two days or let me put you on hold or let me transfer you to another person.”
A contact center agent that gives personal attention to one individual for that long makes a difference in the life of both the customer and the employee.
“We're seeing the data that proves that as well,” Nandan said. “More and more of the calls that are being handled by the agents are getting contained. They're solving problems. They are arriving at a solution that helps everybody. So I think it's definitely been a positive experience for employees.”
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AI Implementation: Ethics, Transparency and the Future of Customer Experience
Ethical considerations around AI, especially in terms of transparency with consumers regarding AI usage and ensuring compliance with regulatory standards, are essential. With AI-generated code or responses, ensuring adherence to compliance, quality and ethical standards is crucial.
Nandan discusses the practical aspects of implementing AI in enhancing customer and employee experiences, with real-world examples of challenges faced and solutions devised. She also provides insights into the potential future directions of AI integration in customer experience domains, along with advice for practitioners looking to embark on or further their AI journeys:
- Voice chat integration: Looking forward, the integration of voice chat with AI, along with enhancing human-like interactions, is seen as a potential area of growth.
- Co-piloting & auto-piloting: Experimentation with conversational co-piloting and auto-piloting where AI assists in solving customer issues more interactively is on the horizon.
AI implementation needs to be done with an ethics checklist in mind.
“Every time you use something that's generated by a machine, you have to think about ethics and quality and the effect it has on the customer,” Nandan said. “I don't think you can just write AI code and then just leave it alone there too do whatever it wants. I think there's a lot of monitoring and handling that needs to happen.”
