The generative AI revolution is sweeping through industries, and 70% of businesses are currently exploring the potential of this technology, according to a Gartner poll. They are eager to utilize the technology to enhance customer experience, bolster retention rates and drive revenue growth.
While generic LLMs like GPT have potential, their limitations dampen their effectiveness in the context of contact centers. One of the fundamental challenges lies in their lack of specificity and control, which hinders their ability to provide accurate and contextually appropriate responses. These models also often struggle to distinguish right from wrong answers, leading to potential inaccuracies and confabulations. In the world of AI, these inaccuracies are often referred to as "hallucinations" – a term that underscores the risks associated with using generic models in business settings.
Another area where LLMs fall short is their inability to effectively engage in spoken human conversation and their navigation of real-world environments. These limitations become evident in contact centers, where the ability to understand nuanced customer inquiries and provide tailored responses is crucial.
Contact center leaders eagerly await the emergence of AI models that strike just the right balance between generative power and intuitively precise control, paving the way for transformative customer interactions in the future.
Enter Observe.AI and its 30-billion-parameter Contact Center LLM and new Generative AI Suite for boosting agent performance.
Observe.AI’s proprietary LLM is trained on a domain-specific dataset of hundreds of millions of customer interactions. This enables it to support a diverse set of AI-based tasks that are highly specific to contact center teams.
Observe.AI’s new Generative AI Suite leverages this LLM to enhance agent performance before, during and after customer interactions – through capabilities like surfacing real-time answers, automated call summarization and automated coaching notes that drive agent self-improvement.
Knowledge AI saves the time agents spend manually searching knowledge bases and FAQs by providing answers to customer questions – increasing first call resolutions and reducing AHT
Auto Summary automatically and consistently captures interaction summaries in multiple formats – structured, unstructured and entities – eliminating after-call work, allowing the agent to focus on the customer, and improving the quality and consistency of notes
Auto Coaching automatically generates and serves up coaching notes for agents on the spot as soon as a customer interaction ends – driving immediate skills improvement, better CX and faster feedback for performance improvement in addition to regular supervisor-assisted coaching.
“By leveraging a domain-specific LLM, we’re able to drive deeper trend analysis, more accurate call summarization, and in-context question answering while ensuring degrees of control, calibration, and privacy that are simply not possible with generic models,” said Vache Moroyan, Senior Vice President of product at Observe.AI.
The future of generative AI in contact centers may look promising as Observe.AI introduces this solution for transformative customer interactions and improved agent performance.
Edited by
Alex Passett
