Conversational AI vs. Generative AI: Which solution will turbocharge your contact center’s performance and help you achieve your CX goals? Worldwide, the evolution of artificial intelligence has unlocked new waves of productivity for business leaders and teams.
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While the impact of advanced AI algorithms can be felt everywhere, it’s particularly prominent in the contact center. In the last year alone, we’ve lost count of the number of contact center, CRM, and CX software vendors introducing new AI capabilities for customer service teams.
Though ChatGPT, Microsoft Copilot, and even solutions like NICE’s Enlighten AI suite are driving focus to the rise of generative AI, it’s not the only intelligent tech making waves. Conversational AI is also emerging as a critical part of contact center success.
The question is, which of these two solutions do you need, and do you need to choose between one or the other? Here’s your guide to conversational AI and generative AI in the contact center.
What is Conversational AI?
Conversational AI is a type of artificial intelligence that allows computer programs (bots) to simulate human conversations. It combines various AI techniques to ensure people can interact with computer systems just like talking to another human being.
Examples of conversational AI are everywhere. Smart assistants like Alexa and Siri use conversational AI to interact with users. Many of the chatbots installed on company websites leverage the same technology.
So, how does it all work?
While the nature of each conversational AI solution can vary depending on your chosen vendor, most tools feature the same central components:
- Natural language processing: The core technology that allows a system to interpret and understand human language by breaking speech or text into comprehensible structures. It uses syntax analysis to understand grammar, semantic analysis for meaning, and context analysis to grasp user intent.
- Machine learning: Machine learning algorithms allow conversational AI tools to learn from each interaction and improve over time. The system can recognize conversation patterns, adapt to user preferences, and refine its responses with supervised and unsupervised learning.
- Data and contextual awareness: Conversational AI needs access to relevant data (such as contact center recordings) and understand the context of conversations. Often, it needs to be integrated with your databases, external systems, and CRM platforms.
After processing input, conversational AI tools can generate responses based on their data. Some more advanced solutions can even enhance their responses by using additional forms of analysis, such as sentiment analysis.
Examples of Conversational AI in Customer Service
Conversational AI has become the backbone of many advances in the customer experience and contact center landscapes. It forms part of the tech behind conversational intelligence tools, such as those offered by CallMiner, Calabrio, and Talkdesk.
It’s also a common component in the chatbots and virtual assistants customers interact with through text and speech, for self-service interactions.
The most common examples of conversational AI in customer service include:
Chatbots
Older chatbots were primarily rule-based solutions that used scripts to answer customer questions. Advanced chatbots, powered by conversational AI, use natural language processing to recognize speech, imitate human interaction, and respond to more complex inputs.
They can also operate across multiple channels, accompanying your contact center IVR system, chat apps, social media service strategies, and more. Plus, they can learn from interactions over time, becoming more effective and advanced.
IVR Systems
Modern IVR systems also leverage conversational AI. Instead of giving customers a list of limited options to choose from, they can listen to what customers say, recognize their intent, and route them to the best agent or department.
With NLP, IVR systems can provide more accurate responses and even draw insights from company databases and CRMs to personalize interactions. They can also be configured to route conversations based on various factors, such as customer sentiment or agent skill level.
Conversational Intelligence
As mentioned above, conversational AI tools are a common component of conversational intelligence. Because they can process language and analyze interactions, they can offer companies insight into customer sentiment, track customer service trends, and highlight growth opportunities.
Some solutions can also automatically transcribe and translate calls, which can be ideal for enhancing compliance, as well as training initiatives.
The Pros and Cons of Conversational AI
When analyzing conversational AI vs. generative AI, it’s worth noting that both solutions have strengths and limitations. Conversational AI, for instance, can empower teams to deliver fantastic service across multiple channels 24/7. It can also help personalize interactions.
By analyzing previous discussions and real-time sentiment or intent, conversational AI can help ensure every customer gets a bespoke experience with your contact center.
Beyond that, conversational AI can:
- Enhance operational efficiency by automating tasks like transcription or translation.
- Reduce operational costs by boosting agent productivity and reducing workloads.
- Optimize business insights to help with strategic decision-making.
- Scale endlessly to handle various conversations across numerous channels.
However, conversational AI also has limitations. Although conversational AI tools are more advanced than traditional chatbots, they can still struggle with complex linguistic nuances and requests. They don’t always understand customer accents or things like humor or sarcasm.
Plus, since they’re reliant on collecting and processing customer data, there’s always a risk to the privacy and security of your contact center. Business leaders need to ensure they have the right security strategies in place to protect sensitive data.
What is Generative AI?
Generative AI is a form of artificial intelligence that can generate new, original content, such as text and images, based on basic prompts. It uses deep learning and neural networks to produce highly creative answers to queries and requests.
Like conversational AI, generative AI is becoming a more common component of the contact center. CCaaS vendors offer companies access to generative AI-powered bots that can provide real-time coaching and assistance to agents or enhance the customer service experience.
Most of these solutions build on the foundations of conversational AI, enhancing bot performance with access to large language models (LLMs).
Alongside leveraging NLP technologies, most generative AI solutions rely on:
- Data training: Generative AI systems are trained on massive datasets, which include images, sounds, videos, and text. This allows them to respond to various input types, in the case of multi-modal models.
- Deep learning and neural networks: Generative AI solutions utilize deep learning algorithms and neural network architectures, like generative adversarial networks, to analyze and process complex data patterns.
- Generative models: Using neural networks, the AI system develops generative models. For instance, in the case of “GAN,” there’s the generator that creates content and the discriminator, which evaluates its accuracy against existing data.
- Refinement and learning: Like conversational AI, generative AI models use machine learning to refine and improve their performance over time. They can adjust their models consistently to boost the accuracy of their output.
Examples of Conversational AI in Customer Service
Since generative AI tools share many of the same features as conversational AI solutions, they can also address many of the same use cases. We’re already seeing an increase in companies using generative AI to create intuitive chatbots and virtual assistants.
However, there are also additional opportunities for generative AI in the contact center, such as:
The Creation Of More Robust Knowledge Centers
Generative AI excels at producing original content. It can help contact centers create knowledge bases, drawing on existing data in their ecosystem to design comprehensive guides. Generative AI bots can then surface this information to contact center agents in real-time and offer recommendations to guide them through a conversation.
They can even help organizations create more comprehensive training resources and onboarding tools for new contact center agents, boosting team performance.
Enhancing Customer Interactions
Like conversational AI, generative AI tools can have a huge impact on customer service. They can understand the input shared by customers in real time and use their knowledge and data to help agents deliver more personalized, intuitive experiences.
Generative AI solutions can automatically create responses to questions on behalf of an agent and recognize keywords spoken in a conversation to surface relevant information. It can even draw insights from multiple different environments to help answer more complex queries.
Repetitive Task Automation
One major use case for generative AI in the contact center is the ability to automate repetitive tasks, improving workplace efficiency. Generative AI bots can transcribe and translate conversations like their conversational alternatives and even summarize discussions.
They can pinpoint key action items and discussion trends, automatically classify and triage customer service tickets, and improve the routing process.
The Pros and Cons of Generative AI
Like conversational AI, generative AI has both it’s pros and cons to consider. It can significantly enhance team productivity and creativity and guide agents through the process of delivering exceptional customer service. It can also help improve team efficiency by automating repetitive tasks like call summarization.
Plus, generative AI solutions can:
- Simplify the creation of content for training purposes.
- Generate automatic responses to customer queries.
- Transform customer interactions with personalized insights.
- Respond to a range of types of input, such as images and text.
However, there are risks to generative AI, too. Like most forms of AI, generative AI relies on access to large volumes of data, which needs to be protected for compliance purposes. It can cause issues with data governance, particularly when teams have limited transparency into how an LLM works.
Plus, since generative AI creates unique “original” content, it’s subject to AI hallucinations, which means not all of the answers it gives will be correct.
Conversational AI vs Generative AI: At a Glance
Conversational AI and generative AI have a lot of overlapping capabilities and features. They both make it easier for human beings to interact intuitively with machines, and they can both understand “natural input”. However, there are some major differences:
| Conversational AI | Generative AI | |
| Primary function | Enabling interactions between bots and human users. | Creating original, new content based on prompts. |
| Core technologies | Natural language processing, machine learning, and data analysis. | Deep learning, neural networks, and large language models. |
| Data utilization | Processes and interprets human language. | Learns patterns from large datasets to create content and respond to prompts. |
| Training focus | To understand and generate human-like responses. | To recognize patterns and generate new, unique content. |
| Use cases | Chatbots, virtual assistants, and conversational intelligence. | Chatbots, content creation, training, coaching, and customer support. |
| Examples | Amazon Lex, Google Dialogflow, IBM Watson Assistant | Google Gemini, ChatGPT, Microsoft Copilot |
Conversational AI vs Generative AI: Why Not Both?
So, conversational AI vs generative AI: which do you actually need?
Though conversational AI and generative AI have different strengths, they can both work in tandem to improve customer experience. Tools like Microsoft Copilot for Sales are considered generative AI models, but they actually use conversational AI, too.
There are various ways contact centers can connect generative AI and conversational AI. For instance, conversational AI bots can generate better answers to customer questions by calling on the insights of back-end generative models.
Smart conversational assistants can analyze inbound ticket information and assign issues to specialized generative models to help with customer service. Conversational bots can even draw insights from FAQs and knowledge bases created by generative AI during discussions.
Ultimately, weaving conversational and generative AI together amplifies the strengths of both solutions. While conversational AI bots can handle high-volume routine interactions in contact centers, solutions powered with generative algorithms can address more complex queries and offer additional support to agents.
The chances are, as both of these technologies continue to mature, we’ll see CCaaS and contact center leaders introducing more tools that allow users to design their own systems that use the best of both models, such as Five9’s generative AI studio.
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