The News:
TTEC Holdings, a provider of CX technology and services, announced an expanded suite of AI solutions incorporating the use of generative AI and large language models (LLMs) in its CX solutions. These solutions have been developed based on demand from its client relationships, and through the company’s AI Center of Excellence for Customer Experience.
The latest solutions from the Center include an Enhanced Customer Experience AI Readiness Assessment & Roadmap; the use of private LLMs and managed services that are trained on clients’ proprietary data sets to drive engagement; a voice-based AI Engagement and Digital Channel Accelerator designed to support omnichannel customer journeys; generative AI Empowered Associate, a solution to augment associate performance through conversational knowledge bases, automated contact summaries, and AI-based real-time coaching to improve speed to proficiency, lower average handling time (AHT), and increase customer satisfaction (CSAT); and advanced AI analytics, which include models designed to improve the customer experience through micro customer segmentation and real-time customized journeys. See the complete press release from TTEC here.
TTEC Holdings Announces Expanded Suite of Generative AI-Enabled CX Solutions
Analyst Take:
TTEC Holdings is the latest technology company to announce the incorporation of generative AI and LLMs into its suite of CX solutions. In addition to the use of generative AI to augment associate performance and the use of a private LLM to drive customer engagement, TTEC announced a comprehensive AI readiness assessment and roadmap, advanced AI analytics models designed to improve CX through micro customer segmentation and real-time customer journeys, and the development of voice-based AI engagement messaging with intelligent routing capabilities.
TTEC joins a host of other CX technology platforms and toolset vendors in making generative AI and LLMs available, with the goal of improving traditional contact center agent metrics through agent empowerment.
In March 2023, Salesforce announced Einstein GPT, a new generative AI technology that combines OpenAI’s enterprise-grade ChatGPT with Salesforce’s private AI models to deliver relevant and trusted AI-generated content across sales, service, marketing, commerce, and IT interactions at scale. Microsoft that month also unveiled CoPilot, which uses the power of LLMs sitting alongside Microsoft 365 apps like an assistant, and appearing in the sidebar as a chatbot, allowing users to summon the tool through natural language. Meanwhile, Creative Virtual indicated it would also use ChatGPT technology and vector matching to provide limited and controlled use of the technology, and other companies are similarly rolling out these types of tools. And conversational AI platform provider LivePerson revealed earlier in May its suite of generative AI and large language model solutions in a flashy, comprehensive launch.
Empowering agents via the use of generative AI and LLMs is perhaps the most compelling use case that can be deployed today. Because these models are trained on vetted and approved company data, there is significantly less risk that any content generated will be wildly inaccurate. Furthermore, by restricting the technology to live agents, as opposed to letting the general public access the technology directly, there is less of a risk for prompt abuse.
That said, organizations seeking to use the tools from TTEC or any other vendors must still take several steps to ensure that the technology provides the benefits they are seeking, which generally revolve around improving agents’ competency, efficiency, and speed. Training human agents in how to interact with the model is paramount, so they can work efficiently and not waste time interacting with these models instead of assisting customers. For managers, it will be critical to also monitor agent interactions with these generative AI and large language model tools to spot instances of potential prompt abuse.
As these tools are rolled out, it will be instructive to see how the use of generative AI and LLMs impact agent KPIs, such as time to service or product proficiency, average handle time, customer satisfaction, and overall customer experience. In addition to these hard metrics, it will be worth assessing whether the launch of these tools has a positive or negative impact on employee experience and job satisfaction, two factors which can have a strong influence on the experience that is delivered to customers.
Author Information
Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
