Jonathan Rosenberg, CTO and Head of AI at Five9 shares more on Five9’s latest AI enhancements with pointers on what modern marketers should do when deploying new AI powered martech as new users:

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Hi Jonathan, tell us about yourself and more about your role at Five9.

My position as CTO and head of AI at Five9 combines two roles into one. As head of AI, I run the engineering and operational teams that build our Genius AI portfolio, which is inclusive of organically developed and inorganically acquired teams. I also develop our product and technology vision around AI, guiding the company in the direction we want to go when it comes to AI solution development.

As CTO, I oversee our overall technology direction with a strong focus on our next-gen capabilities. I have a long history in the telecommunications space, having worked in the industry for 30 years, including stints as CTO for Webex and Skype. I’m also the lead author of the SIP protocol, which has been foundational for VOIP.

We’d love to hear about Five9’s latest AI enhancements and how it’s enabling marketing and customer facing teams?

An interesting new feature of generative AI is its ability to analyze data in the contact center in a way that is relevant for marketing. The contact center has always stored excessive amounts of raw data – including call recordings and transcripts – which provide valuable insight into what customers were saying and experiencing. Companies previously had difficulty extracting this data but thanks to GenAI, they can now tap into this information.

Five9 recently launched a product called AI Insights, which mines data from contact center conversations and produces beneficial insights for marketers. Thanks to AI Insights we can now understand the top reasons for customer queries, as well as customer satisfaction and sentiment. This gives us another way to measure the voice of the customer, without the requirement or hassle of conducting surveys. This is just the tip of the iceberg of GenAI advancements, as these products grow more intelligent, and are able to provide even more thorough insight.

What about today’s state of CX in B2B SaaS needs a complete overhaul?

Although strides have been made in this space, personalization in the CX space still requires much work. Martech tools for outbound emails and SMS have always held personalization capabilities. Where personalization capabilities start to taper off is when we want to engage a chatbot, and the experiences they provide are not sufficiently personalized to accomplish our goals or even satisfy the customer query.

This is where we see GenAI being the most powerful. By feeding it contextual data, whether in the form of call, chat, email, CX journey history, real-time API data pulled from CDPs, CRMs, or other customer data repositories, chatbots can have every source of information about the customer to help turn a lead into a closed opportunity.

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Can you talk about some of the most interesting ways in which leading SaaS brands have used AI powered martech and AI powered CX solutions to drive business goals. What key learnings from these references would you leave us with?

One of Five9’s customers was a financial services vendor, who ran into a problem in which some of their own customers were misbilled. The business wasn’t initially sure how big the scope of the problem was, but after reviewing AI Insights they saw a huge uptick in connections into the contact center related to this billing problem.

Thanks to the valuable context provided by AI Insights data, the marketing team decided to proactively outreach to customers on the issue.

What are some of the ground level pointers that marketers and CX teams should keep in mind when deploying new AI tools for the first time?

When using generative AI tools, marketers and CX teams should first and foremost ensure that the models implemented can be tailored through the ingestion of contextual data. This is essential for personalization and for customizing the experience towards the customer query. Marketers should look at contextual data as the lever that optimizes the behavior of generative AI. The more powerful that lever, the better the results.

Secondly, make sure that your AI tool is engine agnostic. This means that the technology itself can make use of a variety of different underlying Large Language Models (LLMs) brands. We’re in an era of rapid change and innovation when it comes to AI, and the last thing you want is to be locked into one LLM or vendor, especially as the space evolves to deliver better results in price and performance.

Third, and of utmost importance – you must carefully manage hallucination. It’s easy to get caught up in the hype and excitement of generative AI tech, but hallucination is real, and when GenAI is exposed directly to consumers, it introduces risks that need to be managed. When looking for tech or tools in the generative AI space, make sure the vendors provide resources that help you prevent hallucination risk.

Five of the biggest myths surrounding AI and the five most exciting things around AI that you’d like to highlight here before we wrap up?

As AI is a constantly evolving space, it’s normal to believe the rumors or hype that comes with the territory. At this point, the top myths I’ve learned to dispel:

1. When you use GenAI, your input automatically trains the model. This is false. The model learns as more data is collected, and humans are still very much needed to augment
2. When you ask a consumer chatbot a question, it always provides the answer by looking it up on the Internet. This is also false. Like all generative AI tools and LLMs, ChatGPT uses information stored in its model through its training
3. GenAI’s application to CX is just about chatbots. Tons of applications exist in the space to drive excellent CX, including analytics, agent guidance, next best action, real-time transcription, and post-call summarization and knowledge answers
4. CX Vendor AI products are all the same. This is incorrect, I promise! Over time, as vendors build value and technology evolves, they will create increasing differentiation for their AI models
5. Hallucination cannot be prevented. Although we cannot fundamentally stop AI hallucination, there are techniques to reduce occurrences (and risk), including tapping into technologies like RAG.

The generative AI space is constantly transforming, bringing lots of exciting possibilities to CX operations and companies! Personally, I’ve been closely watching the following:

1. GenAI has finally given us the technology we need to deliver on promises the CX industry has been making for decades
2. GenAI will enable us to deliver hyper-personalization
3. Contextual data is the new gold in the era of generative AI
4. GenAI democratizes AI and allows low-cost implementation and self-service like we’ve never seen before
5. CX is one of the industries with the greatest opportunities for transformation due to generative AI

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