Today’s guest is Sanjay Mehta, Head of Industry Commerce for LucidWorks. Sanjay joins Seth Earley and Chris Featherstone to the discuss the rapidly evolving hype of generative AI and how it can be applied to your industry.

Organizations are taking a cautious approach to Generative AI — the Large Language Model (LLM) powered ChatGPT-like applications that have burst onto the technology and consumer scene. Increasingly, the C-suite is trying to factor in how LLMs and Generative AI will be part of their digital transformation roadmaps.

Attrition is a constant challenge that keeps contact center executives awake at night. While the usual suspects like low pay, repetitive tasks, and high-stress levels are often blamed for high attrition rates, there are some less obvious reasons that contribute significantly to this issue. In this blog post, we'll delve into the three unobvious reasons why contact center attrition is high and how you can tackle them head-on.

Organizations are taking a cautious approach to Generative AI – the Large Language Model (LLM) powered ChatGPT-like applications that have burst onto the technology and consumer scene. Increasingly, the C-suite is trying to factor in how LLMs and Generative AI will be part of their digital transformation roadmaps.

Are you considering trying out one of the many new generative AI tools? The potential rewards are compelling, but it's essential to understand the basics of generative AI and the risks to avoid when incorporating it into your business.

Most contact center leaders know what contact center optimization is through experience. At some point in their career, they’ve had to double down on strategies or tactics to lower costs, improve agent performance, or redesign their customer experience. However, with new technology advancements, now is a great time for leaders to revisit what optimization looks like in the new era of contact centers. This refresher aims to reinvigorate your approach to optimization, ensuring your contact center not only meets but exceeds performance expectations in an evolving landscape.

Generative AI has caught fire in the industry – almost every tech vendor has a ChatGPT-like offering (or claims to have one). They are claiming to use the same technology – a large language model (LLM) (actually there are many Large Language Models both open source and proprietary fine-tuned for various industries and purposes) to access and organize content knowledge of the enterprise. As with previous new technologies, LLMs are getting hyped. But what is generative AI? 

The underlying principles of Artificial Intelligence have been evolving over decades. Recent advances have created nothing short of a revolutionary breakthrough in information management. Generative AI is in the public consciousness and corporate applications are promising but require certain guardrails and decision-making policies and processes. While “governance“ is a term that brings to mind bureaucratic structures with little practical on-the-ground application, a correctly designed decision-making framework driven by business process/outcome measures and KPIs provides a critical component of data analytics and AI programs.

Artificial intelligence (AI) is increasingly hyped by vendors of all shapes and sizes – from well-funded startups to well-known software brands. Financial organizations are building AI-driven investment advisors1.