All news articles
Lorikeet News Desk
Apr 10, 2025
TL;DR
CS teams face challenges with repetitive tasks, and while AI is seen as a potential solution, fears of job loss keep teams from integrating it.
They're fighting back the whole concept of customer success, because they think it's a human-driven interface that cannot be replaced through AI. They're going through their own fear motion right now.
Manil Vasantha
VP of Customer Support and CEO/COO | Custx.ai
Arduous, repetitive tasks plague customer success teams—from entry level team members to CCSOs and VPs of CS. AI is being looked at increasingly as the tool that could free teams to do their best work, but fears of job loss once tasks are relinquished to AI are keeping teams from fully integrating AI into their workflows.
Manil Vasantha is a VP of Customer Support at one of the major Enterprise cloud software providers, and CEO/COO of customer service consultancy Custx.ai. He believes AI automation is no longer a luxury but a timely necessity for the future of customer support and customer success teams.
Fight or flight: For many customer success teams, automation remains a controversial topic. Many are still in "fight or flight" mode, clinging to the belief that customer success is inherently so human-driven that truly incorporating AI into workflows will cause problems.
"They're fighting back the whole concept of customer success," Vasantha explained, "because they think it's a human-driven interface that cannot be replaced through AI. They're going through their own fear motion right now."
Vasantha calls for leaders to help drive changes amongst teams, "Most of these initiatives need to come through CCOs, for example, that drive this initiative across the spectrum of customer success and customer support. They have to go after the low-hanging fruits: reducing the cost of supporting customers so that ultimately translates to cost savings to the customer and better service for them."
Human touch in automation: Despite the push for automation, Vasantha acknowledges that full automation is still a long way off, therefore calming fears of job displacement. "There’s no IQ concept in AI," he says, noting that the technology still struggles with understanding complex elements like language, context, and regional differences. To this end, "you have to add human touch" even in automated systems in order to maintain the personal connection that customers expect.
There's a lot of churn in customer success... With churn, every time you get a new person coming on board, are you training the customer success folks six to nine months and you're costing the customer down the line because that new person is not ready to support your customer.
Manil Vasantha
VP of Customer Support and CEO/COO | Custx.ai
The need for speed: Vasantha advocates for the fast-tracking of automation within customer success to counter the hiring churn. "There's a lot of churn in customer success. It's proven to become unmanageable in terms of cost," Vasantha explains. "With churn, every time you get a new person coming on board, are you training the customer success folks six to nine months and you're costing the customer down the line because that new person is not ready to support your customer. In order to ensure consistency to your customers, you have to ensure that there is some level of automation that gives them a consistent onboarding experience or a consistent first time user experience."
By automating aspects of customer support, companies can save costs and improve efficiency, allowing them to focus on what truly matters—providing better service to customers.
Knowledge bases: Speaking from a place of giving advice to those fresh to a company and wanting to set up successful systems, Vasantha swears by building a data collection process and internal knowledge bases. "I will not invest in building out my tech team if I don't have a knowledge base. Without it, I'm just wasting my time and money." Giving an example he adds, "If a case comes in, it needs to go into one or two or three buckets at the most," he explains. These categories are: whether a solution already exists in the knowledge base, whether it's a product bug, or if it’s a documentation issue.
"If you put a hard rule like that," Vasantha states, "your knowledge base and the quality of knowledge base is immediately increased." Building this knowledge base, however, takes time—18 to 24 months—but is vital for streamlining customer support operations and improving automation.
