What does the future of customer experience look like with generative AI?
According to Knowbl’s CEO and Co-Founder, Jay Wolcott, it’s going to critical to understand the risk in implementing AI solutions and the requirements for what “enterprise-ready conversational AI” means.
In this conversation, Jay sheds light on how this innovative technology redefines customer experience, making interactions more seamless, convenient, and efficient.
M.R. Rangaswami: What exactly is “BrandGPT,” and how does it differ from traditional conversational AI technologies?
Jay Wolcott: BrandGPT is a revolutionary Enterprise Platform for Conversational AI (CAI) built leveraging large language models (LLMs) from the ground up. Legacy virtual assistance platforms built upon BiLSTNs and RNN frameworks like the speed, ease, and scalability that LLMs can offer through few-shot learning.
Through the release of this all-new approach, CAI can finally meet its potential of creating an effortless self-service experience for consumers with brands. The proprietary AI approach Knowbl has designed within BrandGPT offers truly conversational and contextual interactions that restrict the limits of Generative AI from uncontrollable risks.
This new approach is driving tons of enterprise excitement for new levels of containment, deflection, and satisfaction across digital and telephony deployments. Beyond the improved recognition and conversational approach, Knowbl’s platform allows brands to launch quickly, leverage existing content, and improve the scalability of capabilities while reducing the technical effort to manage.
M.R.: What emerging trends do you foresee shaping the future of conversational AI and customer experience, and how can businesses prepare for these developments?
Jay: In 2024 we plan to overcome customer frustration with brand bots and virtual assistants, ushering in a new era of effortless and conversational experiences powered by advanced language models.
Brands that embrace LLMs for customer automation early on will establish a competitive advantage, while those who lag will struggle to keep up. Although many organizations are still in the experimental phase of using GenAI for internal purposes due to perceived risks, leading brands are boldly venturing into direct customer automation, reimagining digital interfaces with an “always-on” brand assistant.
We also predict 2024 to be the year that bad bots die. New expectations of AI will lead to frustrated consumers when dealing with legacy bots, and a trend in attrition versus retention will appear.
M.R.: What complexities do multinational companies face when implementing AI-driven solutions, and how can they navigate the challenges to ensure successful adoption across diverse markets?
Jay: Multinational companies encounter a myriad of complexities when implementing AI-driven solutions stemming from the diversity of markets they operate. One significant challenge lies in reconciling varied regulatory landscapes and compliance requirements across different countries, necessitating a nuanced approach to AI implementation that adheres to local regulations.
Additionally, cultural and linguistic diversity poses a hurdle, as AI solutions must be tailored to resonate with the unique preferences and expectations of diverse consumer bases. To successfully navigate these challenges, companies must prioritize a robust localization strategy, customizing AI solutions to align with each market’s specific needs and cultural nuances.
Collaborating with local experts, remaining vigilant of regulatory changes, and fostering open communication with stakeholders is essential for multinational companies to achieve successful AI adoption across diverse markets.
M.R. Rangaswami is the Co-Founder of Sandhill.com