The high dependency on the contact-center model proved to be a challenge with sudden increases in volumes. The hotline center’s labor resources were scarce, especially against the backdrop of various government-imposed restrictions during pandemic. Customer access to a dedicated human interface with tele-support required much longer wait times during this period.
While enterprise-wide adoption of generative AI (gen AI) remained challenging, Towngas started exploring ways to successfully implement these technologies to gain significant competitive advantage.
Dependencies to be provisioned using gen AI aimed to:
- Provide a seamless customer experience and services in real time without any delays in order placement
- Automate contact center workflows with AI insights
- Maintain consistent quality through monitoring and feedback mechanisms
Towngas faced business challenges such as labor shortages and high turnover rates, resulting in the need to reskill existing tele-agents or to quickly onboard new agents—steps that entailed huge time and cost commitments. With the pandemic, there was also the substantial increase in call wait-times when handling service appointments because ofgovernment-imposed restrictions.
There were also language barriers. Maintaining consistency and quality across channels and agents arose as a new government requirement for contact call centers.
The technical challenges included data security and privacy concerns, and the difficulty of integrating different systems and channels to provide a seamless customer experience.
Towngas needed to optimise its digitalization efforts to achieve an all-inclusive solution by bringing automation to its CRM functions. The company aimed to serve its existing customers through a single platform built on AI to provide a single version of the truth to the contact center team.