Generative AI vs. Conversational AI: What’s the difference, and which solution does your business actually need? The AI landscape is evolving faster than ever before. In the last couple of years alone, we’ve seen the meteoric rise of LLMs (large language models) and generative AI.
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At the same time, machine learning algorithms, natural language processing solutions, and neural networks are growing and becoming increasingly advanced. In this vast landscape, two flavours of artificial intelligence stand out for business leaders, contact centres, and modern teams: generative AI and conversational AI.
While these two solutions might work together, they have very distinct differences and capabilities. Understanding the key differences is how you ensure you’re investing in the right cutting-edge technology for your business.
So, let’s take a closer look at conversational AI and generative AI.
What is Conversational AI?
Conversational AI is a subset of artificial intelligence that allows bots or computers to simulate human conversation and understand natural input from users. Think of it like a tool that empowers people to interact with a machine just like they were speaking to another person (without the need for code).
If you’ve ever interacted with a chatbot on a website, a voice bot in an IVR system, or a handy self-help solution like the Slackbot, you’ve probably experienced conversational AI.
All conversational AI solutions rely on natural language processing to interpret human input. They also source insights from rich databases full of information to determine how to respond to a user via natural language generation. However, some bots are more advanced than others.
For instance, some tools use sentiment analysis to detect a user’s mood by evaluating their tone of voice or the words they use. Solutions can also draw insights from customer profiles and CRM systems to personalise the user experience.
Though conversational AI tools can simulate human interactions, they can’t create unique responses to questions and queries. Most of these tools are trained on massive datasets and insights into human dialogue, and they draw responses from a pre-defined pool of data.
What Companies Can Do with Conversational AI
Conversational AI significantly impacts the customer experience landscape. It can augment virtually every customer-facing operation, from helping customers to answering questions, troubleshooting product problems, and completing tasks like checking on an order status.
Many contact centres build conversational AI tools into their platforms, which can help:
- Sales teams: Conversational AI tools can help sales professionals gather and qualify leads, analyse market trends, and even deliver personalised product recommendations to customers. They can also empower customers to complete transactions themselves.
- Marketing teams: Offering insights into customer trends, preferences, and journeys, conversational AI tools can help marketing teams enhance the quality of their campaigns. They can also dynamically share marketing content with customers online.
- Customer service teams: In the customer service landscape, conversational AI solutions empower teams to offer 24/7 service to customers in the language of their choice. They can also provide valuable insights into customer needs, preferences, and trends.
Conversational AI impacts virtually every industry. In retail, it can help with 24/7 order processing and customer engagement; in banking, it can streamline transactional tasks; and in healthcare, it can help teams deliver personalised patient experiences.
You may also have seen examples of conversational AI in the world of smart speakers and personal assistants. Apps like Siri, Alexa, and Google Assistant all leverage conversational AI algorithms.
The Benefits and Challenges of Conversational AI
Pros:
- Excellent for enhancing and delivering omnichannel customer service.
- Supports 24/7 customer support strategies.
- Helps teams save money by boosting productivity and efficiency.
- Offers valuable insights to guide business decision-making.
- Enhances team performance with always-on support and guidance.
Cons:
- Limited ability to process complex queries (without regular training).
- Issues with understanding specific linguistic nuances.
- Potential risks with data compliance.
If you’re evaluating the benefits of generative AI vs. conversational AI for your business, it’s worth noting that both options have pros and cons. Conversational AI can empower teams to deliver exceptional customer service 24/7 across any channel.
It also improves operational efficiency by automating routine and recurrent tasks (like summarising and transcribing text). Plus, it can save your team money by boosting agent productivity and efficiency. What’s more, conversational AI tools can give businesses the insights they need to make intelligent decisions and optimise workplace processes.
However, there are potential challenges, too. For instance, most conversational AI solutions can easily handle routine requests but struggle with complex queries. Conversational AI tools need constant training and fine-tuning to deal with more complex requests.
Some solutions can struggle to understand finer linguistic nuances, like satire, humour, or accents, leading to issues with customer experience and regular errors. Plus, like most forms of AI, since conversational tools interact with customer data, there’s always a risk involved in ensuring your company remains compliant with data privacy regulations.
What is Generative AI?
Generative AI is a form of AI that allows users to create new content, such as text, images, and sounds, using deep learning and neural networks. These tools can create content based on the prompts you give, with some multi-modal options responding to text, video, audio, and images.
Here’s where the generative AI vs conversational AI battle gets confusing. Generative AI often seems like an updated version of conversational AI. After all, apps like ChatGPT and Microsoft Copilot still use natural language processing and generation tools to enable interactions between bots and humans.
However, while both generative AI and conversational AI tools use massive databases to respond creatively to queries, generative AI takes things a step further. It can create original content rather than just responding to a question based on what it finds in its database.
Generative AI tools use neural networks to identify patterns and other structures in their training data and generate new content based on those patterns. For instance, if you ask Microsoft Copilot to suggest a list of dates for your next team meeting, it will scan through data about your meeting habits, schedules, and shared calendars to generate a response.
What Companies Can Do with Gen AI
Since generative AI tools share many of the benefits of conversational AI solutions, they can address many of the same use cases. Sales teams can use generative AI tools to analyse market trends, create customer segments, and even design product pitches.
Customer service teams can embed intelligent bots into their websites and contact centres to offer customers a higher level of personalised 24/7 service. Even marketing teams can use generative AI apps to create content, optimise it for search engines, design videos, and generate images.
However, generative AI is much more versatile than conversational AI. It’s the only version of AI that can create original content.
For instance, conversational AI tools might give your marketing teams the insights they need to create a fantastic campaign. However, they’ll still have to make that content themselves. Generative AI can draft the content and even create a promotional plan for your team.
It can also act as an incredible virtual assistant for your team members, automating tasks like meeting summarisation, offering real-time coaching and advice to staff, and enhancing collaboration.
The Benefits and Challenges of Generative AI
Pros:
- Improves creativity and productivity, empowering employees to accomplish more.
- Enhances collaboration and strengthens connections between teams.
- Provides actionable insights into opportunities for growth.
- Transforms customer experience with personalised 24/7 service.
- Enables the creation of unique, original content in a range of forms.
Cons:
- Issues with ethics and transparency (how data is collected and used).
- Potential risks for copyright and IP infringement.
- AI hallucinations lead to incorrect or biased responses.
Just like conversational AI, generative AI has pros and cons. For most professionals, the biggest benefit of this type of intelligence is its ability to enhance creativity and productivity. These tools can generate novel ideas and original content that inspire and boost team performance.
Generative AI can also enhance collaboration, summarising meetings in seconds with action items for each team member, helping to create meeting agendas, and even translating content in real time. Microsoft Copilot in Outlook can even automate the process of following up with colleagues after an event or conversation and suggest the best times to arrange a call.
Like conversational AI, generative AI can also boost customer experiences, deliver personalised and unique responses to questions, and pinpoint trends. It can even help increase your company’s revenue by opening the door for proactive product recommendations, identifying opportunities for product optimisation, and centralising market research.
However, there are challenges, too. Like conversational AI, generative AI relies on access to data, and how that data is processed and used by your bot will influence your ability to remain compliant with industry regulations.
Additionally, these bots are more likely to suffer from “AI hallucinations” than other forms of AI because they’re making assumptions about how to respond based on massive databases. There’s also the risk that AI tools connected to the web will expose you to copyright infringement issues.
Generative AI vs Conversational AI: The Main Differences
Both conversational AI and generative AI are tools that help human beings interact with machines in a more intuitive, valuable way. However, both of these solutions have different goals, applications, and use cases. Let’s take a look at the key differences side by side:
| Feature | Generative AI | Conversational AI |
| Primary purpose | To enhance and simplify the creation of unique and original content, such as text, videos, and images. | To simulate human conversation, understand user input, and respond to queries naturally and engagingly. |
| Training method | Generative AI models are trained using large language models and, feature neural networks, and deep learning capabilities. | Conversational AI models are trained on large conversational datasets that include real-life interactions and dialogues. |
| Core use case | The creation of unique and original content. | Supporting customer service interactions. |
| Applications | Content creation, research, content, image development, and task automation. | Customer service, virtual assistants, chatbots, and data analysis. |
| Response to input | Generative AI combines a user’s input with a vast database to create new content using learned patterns. | Conversational AI uses input data to create responses based on an existing database of information. |
| Examples | ChatGPT, Google Gemini, Microsoft Copilot. | Amazon Lex, IBM Watson Assistant, Google Dialogflow |
Which Type of AI Do You Need?
Ultimately, conversational AI is the tool companies typically use to enhance customer service interactions, creating chatbots and assistants to support 24/7 service. It can also assist employees in the form of simple AI tech support apps.
Generative AI, on the other hand, can also enhance employee and customer experiences, but its core purpose is to support the generation of original content. If you want to boost your team’s creativity, improve marketing campaigns, and streamline collaboration, generative AI is the tool for you.
However, while each technology has its own purpose and function, they’re not mutually exclusive. The battle of “generative AI vs conversational AI” is increasingly disappearing, as many tools can offer companies the best of both worlds.
Look at Microsoft Copilot, for instance. It’s both a generative AI tool and a conversational AI bot capable of responding to natural human input.
Plus, as companies create more generative AI bot-building solutions, like Copilot Studio, business leaders will have more freedom to design their own AI innovations. You’ll be able to combine the elements of conversational and generative AI into a unique solution for your specific use cases.
These days, generative AI is emerging as a valuable way for companies to enhance conversational AI experiences and access support with a broader range of tasks.
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