“What has happened with the GenAI and Chat-GPT rollout in November 2022 is that it has made AI accessible.” Somya Kapoor, CEO and Co-Founder of TheLoops
Just one year ago, if you posed a question on AI to the general public, you may have received blank faces and stares. Flash forward to today, and AI has become synonymous with Chat-GPT, record breaking levels of productivity and even the adoption of AI Copilots.
In the world of customer support and CX, common knowledge of AI is no exception.
AI-powered tools and platforms like TheLoops now help support teams automate repetitive tasks, freeing up their time to focus on more complex and customer-centric work. And AI isn’t just reshaping support but also ushering in a new era of growth and efficiency.
However, with that growth comes a little thing Matt Beran of InvGate calls, FUD.
Fear. Uncertainty. Doubt.
- What will happen to the role of the Support agent?
- What skills are required to work with AI?
- Where does one start when evaluating a swarming sea of AI solutions?
To help quell this FUD, particularly as more and more CX (Support and Success) teams receive mandates to embrace AI from the C-Suite down, the Support Driven Community hosted Matt and our Co-Founder and CEO Somya for a Fireside chat discussion “AI – Your Co-Pilot, Not the Captain – Navigating Efficiency Together.”
Here’s a quick recap from Somya and Matt’s chat with three key takeaways.
Takeaway #1: View AI as an Augmentation Tool, Not a Human Replacement
Matt and Somya both acknowledged that back in 2016 with the rise of chatbots, similar concerns of job loss arose across Support teams.
Initially, the focus with chatbot implementation was on cutting costs and providing customers the opportunity to self-serve. However, just a few years later in 2019, Gartner reported that self-service solutions like chatbots only solved 9% of customer queries without needing to loop in a human. What chatbots managed to achieve, in parallel to ticket deflection and driving self-service, was familiarizing Support leaders with one type of AI: NLP.
These days, Somya shared, “Support leaders are open to AI–it just used to take them 9 to 12 months to actualize their investment. Now with GenAI, this investment is justified from the CFO.”
Matt weighed in on how early adopters of AI have shifted from apps and predictive analytics to Chat GPT followed by Somya’s observation on AI for Support and CX: it’s no longer a question of, “do I use it”–it more like, “how do I use it?”
What will set the tone for adoption in 2024 and years to come?
AI Acceptance. Support professionals must come to see AI as a productivity tool that augments human capabilities rather than a direct threat to employment. This is an important mindset shift as it paves the way for AI integration without creating unnecessary resistance among staff.
Somya shared, “If you’re still questioning AI, you may be in that zone where you resisted even going to the cloud. AI is not going to take away your job–it’s actually going to give you a seat at the table.”
Matt calls hesitancy around AI or any tech adoption, “the conscious objector.” He also acknowledges that lots of Support teams and leaders are at differents points of the AI Adoption-journey:
-Let’s try everything
-Let’s experiment
-No way are we ever going to try any of this stuff
Listen for more on this at 4:51 of the interview.
Speaking of AI acceptance and adoption, our friends over at Intercom covered a few of the new roles AI has created for Support on their blog. Read more on those here.
Takeaway #2: What Are The Actual Productivity Gains of AI Anyway?
One of the biggest promises of AI in Support has been AI-driven productivity gains. Somya shared how AI is helping her move away from being inundated with manual tasks and data analysis to quickly and efficiently understanding summaries of calls, and so much more.
According to the Bureau of Labor Statistics, average labor productivity growth in the United States was 1.4% per year during the 12 years before the COVID-19 pandemic (2007–2019). However, now with GenAI, one case study alone showed that Customer Support productivity increased up to 25%. On average, AI enabled workers are 68% more productive.
Somya explained why.
“AI takes on the IQ elements of service and support to help agents provide EQ. Summarization from the chat that has happened helps agents meet the customer where they are. You can ask them what’s their kid’s name? What’s their favorite flavor of coffee? You are now able to spend less time in discovery and more time in actual support.”
Somya expanded on the concept of Copilots, stating, “In addition to boosting productivity, AI Agent Copilots collaborate with agents by offering summaries of similar interactions. As an agent, Copilots provide a comprehensive, real-time view of behavior, sentiment, and past product interactions, and even offer prediction of escalation—all seamlessly integrated into my workspace.”
Check out our Linkedin post on how AI Copilots and chat are different.
On the productivity piece, Matt added one final benefit: start to segment functions by, “this is clearly something AI should do, this is clearly something a human should do and in your question areas, you should probably have both.”
Takeaway 3: Data-Driven Wisdom Is Your Support Team’s Best Friend
Where Support historically was viewed as a cost center, thanks to the rise of AI, Support is being viewed as a competitive advantage. Somya shared that CEO’s are asking for agent scores and they want to know how well your organization is performing.
Matt was curious about AI for CX ops specifically–how do you train the data? Or train with data so that it doesn’t have bias or a predetermined notion?
Somya’s answer is contextualization is key: “If you’re not going to have any contextualization, it’s garbage, in garbage out. You want AI that understands and maps to a common data model, we call it a CX data model. The AI needs to understand business logic, customer data, ticket data, usage, logs and alerts.”
Another data-driven advantage that Matt brought up was driving collaboration with other departments, including the C-Suite as well as product and engineering teams. “AI can take the tribal knowledge from Support and give it heft….my opinion and conjecture can can actually be backed up by numbers and customer insights.”
On the aspect of training, Somya and Matt discussed how critical it was to have AI with real-time learning for ongoing improvement. Systems should not just learn once and stop. Instead, they should be in a constant state of learning and adaptation. This has far-reaching implications for efficiency and effectiveness within your support team.
While these were the top three takeaways, there were many more.
Somya and Matt cover the debate of build vs buy, the benefits of automation and it being support’s time to shine.
Watch and listen to the full Fireside chat of AI Your Copilot, Not the Captain here.
