This is a high-level recap of TheLoops Fireside Chat series, “Real Leaders, Real Improvements: What It’s Like To Have AI Transform CX Processes,” featuring Lakshmi Rao, Director of Support at Sauce Labs and Support leader Colin Crowley. In this blog, you’ll learn how Support leaders are leveraging AI to drive efficiency and improve quality, how processes are being redesigned without too much effort and how this all plays a part in improving customer retention.
AI is changing customer support operations overnight, offering greater opportunities for agent efficiency, improved support quality, and access to real-time customer insights. Yet as fast as this area is developing, many leaders still find themselves postponing the new frontier, unsure of how to effectively implement AI beyond the legacy of chatbots.
On the other hand, early adopters of AI for CX ops are vocal and encouraging their peers to get on board given all of the advantages they are seeing. In fact, we recently hosted a fireside chat featuring two experts who have embraced various use cases and love the results they are seeing. In their own words, they have “merely scratched the surface” but do express concern for peers who haven’t even started yet.
To help kick things into gear for you and your team, here are 3 key points Lakshmi Rao, Director of Support at Sauce Labs, and Colin Crowley, a CX thought leader and executive, offer as valuable guidance for those navigating the emerging landscape of AI for support operations.
AI Is Moving Fast, Delayed Adoption Means More Strain For Your Team
According to the 6th edition of Salesforce’s State of Service report, “Administrative work, internal meetings, and other mundane tasks take up nearly two-thirds of agents’ workday.”
The same report goes on to say that, “58% of agents at underperforming organizations toggle between multiple screens.” With this data in mind, its no wonder why AI adoption in customer support has evolved from chatbots and deflection to becoming the cornerstone ensuring agent efficiency and productivity.
These very points are the reason why Colin encourages support leaders to finally break away from the notion of AI being synonymous with chat when it comes to customer support.
“When people in Support hear AI, they often think ‘chatbot,’ but AI is so much more than that,” Colin noted. “Too much focus on AI-driven chatbots, which primarily automate basic customer inquiries, limits you to only a fraction of AI’s potential in the CX landscape. While chatbots serve as an entry point, their capabilities are just the tip of the iceberg in the broader context of AI-driven support operations.”
Lakshmi echoed this same perspective, sharing her experience with the new ways she has used TheLoopsAI to overhaul CX operations at Sauce Labs. “I was amazed at how much you can actually do with AI once you begin exploring its broader applications. The more we use it for Support operations, the more use cases we find and our Success and Engineering teams are benefitting as well.”
Here’s a sample overview of what Lakshmi and her team are tapping into specific to AI for CX operations:
- Sentiment analysis and impact scoring
- Automated knowledge base generation
- Topic, tagging and trend analysis
- AI Agent Copilot
- Real-time reporting and insights for Managers
- Cross-team collaboration
These use cases and applications demonstrate that AI is not just about deflecting tickets; it’s about enhancing your entire support ecosystem.
“We’re even able to mitigate future escalations and incidents and so on,” Lakshmi shared.
Visit TheLoops ROI calculator to see how new levels of efficiency can impact your annual support costs.
Diving Deeper: Measuring The Impact of AI on Support Processes
Another hot topic Colin and Lakshmi agreed upon is AI’s ability to influence and change how support is now measured, bringing a newfound blend of efficiency gains, quality improvements and impact on revenue into the mix all at once.
When combined, AI Agent Copilot and AutoQA are providing Support leaders with the ability to minimize tradeoffs and uncover a clearer pathway to cost savings.
As this video clip below from Colin points out, “Historically, there has always been more data when it comes to efficiency. Every platform will help you understand how many interactions your agents have per hour, and track the response time, but measuring qualitative data had been difficult and limiting. Surveys have low response rates and selection biases, not to mention the manual lift of traditional QA. AI now makes QA data more reliable, and it puts a science behind the metrics of quality while ensuring more standardization in your QA evaluations as well.”
AI Agent Copilot is providing Sauce Labs with a key advantage, guiding agents in real-time to resolve cases with recommendations.
”AI Agent Copilot gives you a continuous feedback loop that your agents can rely on,” she explained. The recommendations from historical cases, current case resolutions and other areas provides ongoing guidance to agents as they handle complex cases, reducing the need for managers to constantly intervene with corrective or delayed actions after the fact.
The impact on support efficiency has been substantial. “It really is helpful in having that continuous response to an agent as the interaction is ongoing. It saves me a lot of time so that I don’t have to do audits and time intensive analysis.”
Lakshmi also speaks to AI’s ability to accelerate data analysis further across customer data and other insights, making it more actionable. “Not only does AI give us the ability to analyze large sets of data in real time, it does so with a very high level of accuracy.”
One area her team has totally been able to redesign and improve with rapid speed is the tagging of interactions.
“Before AI, we relied on customers, who were opening a case, to tell us which topic or which product line the case was for,” Lakshmi noted, “but there were a lot of discrepancies. Now with TheLoops AI handling that for us, we can focus on other strategic tasks such as using Copilot to improve our knowledge base by editing TheLoops AI generated articles.”
Read more on how Sauce Labs obtained six figures savings, reduced escalations by 30% and increased agent efficiency by 25% here.
As Lakshmi pointed out, “We now have insights in real time, not as a reaction to something that has happened, across a broad data set. And our teams are aligned. Whereas before, everyone had their own tooling and data, and language, now our Support and Success teams work more proactively and speak the same language. We prevent customer churn before it becomes a risk and we’re also no longer relying on someone telling us exactly why a specific account needs attention. I think it’s fair to say prevention is the better than cure in these situations.”
As an added benefit, the real-time nature of each insight is crucial especially when it pertains to customer data. Colin suggests that managers remember this impact AI for CX ops has on data analysis not just for their team, but for the company at large.
”One of the first things that always comes to mind when I think of leveraging AI is just the multiplicity of data that Support has under our feet, especially when you’re moving towards conversational channels. And I’m surprised more leaders don’t think this way. The wealth of data we have can inform strategic decisions across various departments including product enhancements, training opportunities for the team, voice of the customer and also minimize reliance on manual reporting and spot gap analysis.”
On Choosing the Right Use Cases And AI Vendor
With a sea of AI vendors available to choose from, here’s how Colin and Lakshmi suggest you select the appropriate partner.
- Scope of AI Technologies
First, its essential to consider the breadth of AI solutions a vendor offers. Colin doubles down that you want a partner with a wide range of AI capabilities.
“It’s important to have technology from a partner who is providing what’s required today in the here and now but that also knows whats needed in the future and is developing that now.”
Point solutions offer more of a start and stop approach, not to mention multiple implementations. However, a broad suite and platform like TheLoops AI allows companies to address various needs within their support operations, ensuring you can scale and expand use cases over time without needing to switch companies, start from scratch and invest more time ramping up the learning curve.
- Robust Reporting and ROI Justification
Another crucial factor is a vendor’s ability to provide robust reporting capabilities. As Colin points out, “One of the big questions you’re always going to get is, ‘How are you justifying the ROI investment’? And that can be one of the trickiest things for CX leaders to explain to the C-Suite. So having a really robust reporting system is a key component of that.”
- Collaboration and Flexibility
Lakshmi emphasized the importance of a collaborative relationship with whomever you choose. Speaking from her own experience, she shares, ”What you want to look for is someone who is an extension of your team, who sees where you’re going with this, what’s the end goal and what is the savings, both in cost and time, and works with you to that point.” Hear more from her in this quick video.
She emphasized the need for Support leaders to find vendors who are flexible, willing to experiment, and able to evolve their solutions based on the unique needs of the organization.
- Future Vision, Relationship Focused With Community Support Available
Colin highlighted the importance of a vendor’s future vision: “The field is changing so quickly. You want to make sure that any vendor you’re dealing with has a clear future vision of where they’re going, how they plan to get there, and why they’re going in that direction. Because you’re starting a relationship in a fast moving space, and as you’re trying to move, you want to make sure that the vendor is moving with you.”
He also stressed the value of community support: “AI provides a great fertile ground for learning a lot from peers and sharing use cases and sharing success stories, because again, there’s a lot of newness going on where you can’t just grab a white paper and learn everything that you need to know. So that community component through a vendor can be really important.”
Lakshmi echoed this as well, appreciating how TheLoops, “helps us and led us to think differently about AI adoption based on experiences with other users.”
Community and peer to peer learning is a core component of our Fireside chat series. See our latest speaker line up and schedule on TheLoops event page.
Wrapping Up
The insights from Lakshmi and Colin reveal AI’s potential in CX operations, extending a world of adoption far beyond chatbots. From AI Copilot technology to AutoQA and cross-functional analytics, AI is reshaping customer support into a much more productive, streamlined, data-driven function.
Successful adoption requires choosing the right vendor, considering factors like comprehensive solutions, and knowing how others are scaling in the space.
By implementing AI for CX operations, you’re not just improving current processes—you’re future-proofing your CX operations. As Colin and Lakshmi have emphasized, don’t delay–begin your AI journey to ensure you emerge as a leader in delivering exceptional customer experiences.
Curious to see how TheLoops can help? Schedule a call with our team today.
