Heading

When Generative AI (GenAI) burst into the scene in early 2023, it quickly became apparent that there was something distinctly different about it compared to its predecessors. The buzz was immediate, and it wasn't just hype. The technology brought a new level of language understanding and conversation quality that made the classic AI bots seem almost archaic.  

If you've ever interacted with an old-school classic AI, you know the drill—rigid, sometimes frustrating, and often only semi-helpful. The AI would pick up on keywords but would fail to grasp the nuanced, human elements of the conversation, leaving you with an experience that felt, well, robotic.

Take, for example, a simple request like, “I want to reset my password.” Classic AI might handle this with ease. But throw a more complex scenario its way— “I’m stuck at an airport due to a delayed flight; I need to submit an invoice due to a deadline, and I need help resetting my AR system password”—and you'll find it fumbling, tripping over its circuits trying to make sense of the situation. Despite all the claims about advanced data-set training and entity extraction capabilities, the conversation would often break down in the face of complexity.

Then came GenAI, with the most famous example being ChatGPT, which most people got their first taste of in 2023. Suddenly, the game changed. These new AI bots seemed to understand conversations on a much deeper level. The interactions felt more natural, more human-like, and less frustrating. The bots could handle more complex sentences, retain the context over longer conversations, and even provide follow-up questions when necessary. This wasn’t just an incremental improvement—it was a revolutionary leap forward in conversational AI.

But translating this new level of conversational understanding into enterprise products? That’s a whole different ball game. While many enterprises rushed to integrate what they believed were GenAI solutions, they often found themselves dealing with products that, despite the branding, lacked the fluidity and depth of true GenAI. This is where Rezolve.ai steps in with a concept we like to call “Conversational Elasticity.”

What is Conversational Elasticity?

Conversational Elasticity refers to a bot’s ability to stretch and adapt its responses in real-time, just like a human would in a conversation.  

Just like you would empathetically respond to a friendly ‘Hey! I need to talk to you about something,’ (Problem discovery and response), offer a tissue if someone is sobbing (performing an action), or tell directions to a stranger when they seem to be lost (knowledge-based answers), that's what Conversational Elasticity does for AI interactions. It allows the AI bot to handle complex, nuanced conversations without breaking down or losing context. This includes, but is not limited to, answering questions, pulling out knowledge articles/PDFs/reports, etc., and even executing processes (like employee onboarding, software installation request, and so on). All the users must do is talk – and the problem shall be solved!

Here are some key characteristics that define high Conversational Elasticity for GenAI bots;

1. Understanding Complex Sentences and Requests

Classic AI could manage straightforward requests, but GenAI with high Conversational Elasticity can tackle much more intricate scenarios. Whether it’s a user juggling multiple tasks or dealing with a complicated issue, the bot can parse through the complexity and provide a meaningful response.

2. Ability to Ask Follow-Up Questions

Does this conversation with a chatbot sound familiar to you? - “My laptop isn’t working,” only to be met with silence or a non-sequitur response? A bot with Conversational Elasticity would instead know to follow-up, like “What seems to be the issue?” This allows the conversation to move forward naturally, mimicking how a live human agent might interact (which even the GenAI bot can summon if required).  

3. Retaining Context in Long Conversations

Conversations aren’t always quick exchanges. Sometimes, they require a series of back-and-forths, especially in scenarios like employee onboarding or processing a complex service request. High Conversational Elasticity means the bot can maintain the context of these longer interactions, ensuring that the user doesn’t have to repeat themselves or clarify earlier points.

4. Adapting to Changing Conversation Directions

Humans are unpredictable. You might start a conversation about one thing and then pivot to something entirely different. A bot with high Conversational Elasticity can smoothly transition between these topics, keeping the conversation cohesive and relevant.

5. Seamlessly Bringing in Human Help

Not every problem can be solved by a bot, no matter how advanced. But when an AI bot with high Conversational Elasticity senses that a situation is beyond its capabilities, it can seamlessly hand over the conversation to a human agent without disrupting the flow.

6. Empathy and Understanding

While true human empathy might seem like a lofty goal for AI, Conversational Elasticity allows bots to mimic realistic empathetic responses. If a user expresses frustration or stress, the bot can acknowledge these emotions, making the interaction feel more human and less transactional. This also helps reduce L1 escalations and enables provision of more personalized and real-time support.  

7. Sensing High-Risk Situations and Escalating Appropriately

In certain scenarios, the stakes are high, and a wrong response could have serious consequences. Bots with high Conversational Elasticity can detect these situations and escalate them to human agents immediately, ensuring that the user’s needs are met with the appropriate level of care.

Why Should You Care About GenAI’s Conversational Elasticity?

If you’re an enterprise looking to implement AI solutions, this concept is crucial. It’s the difference between offering your customers a frustrating, robotic interaction and providing a seamless, human-like experience that can handle complexity, adapt to change, and respond with empathy.

Moreover, when you are investing in new technology to uplift your business operations, ROI considerations become very important. Earlier, we had explained the ROI considerations of investing in a GenAI-based employee support in our eBook. Our analysis very clearly showed that low capability chatbots usually lead to low adoption and hence – low ROI. For outdated IT support chatbots, user adoption can be as low as 5% and may peak at 20%.  

But with modern GenAI solutions like Rezolve.ai, companies not only see a ticket deflection of 25-35% in just 3 months of implementation, but also 55-80% ROI within 2 years of implementation.

This is simply because conversational AI interfaces have a very high user adoption rate and pave way to amazing ROI for enterprises – both in terms of operational cost savings and enhanced employee experience and productivity.  

Conversational Elasticity is the future of AI-driven customer interactions. By embracing this concept, enterprises can ensure that their AI solutions are not only functional but also truly transformative, offering experiences that delight rather than frustrate. And in today’s fast-paced digital landscape, that’s something you absolutely should care about.

Learn how you can implement a conversational GenAI loved by hundreds of top companies across the globe for employee support and ITSM.

[Book a demo for Rezolve.ai now]