AI is more popular now than ever before. Each tech superpower is betting on it and every company seems to be trying to find a way to squeeze it into its products - whether it fits or not.
But AI is not one thing. In fact, it’s hard to say what it is at all.
Most discussion about AI currently is focused around Large Language Models, but they represent only a portion of the market for machine learning. Is some machine learning AI and other bits not? At what point does this all just become “software”?
I appreciate that if you’re reading this in 2026 while on the run from Skynet, the concern for distinctions might seem misplaced - but it’s good to be clear what we’re talking about.
In this Telerivet article, we’ll cover:
- What do we mean when we talk about AI?
- What is generative AI?
- What is conversational AI?
- How are conversational AI and generative AI different?
- How can I use conversational AI in my business?
- How can I use generative AI in my business?
What do we mean when we talk about AI?
AI is all the rage right now, whether it’s in forms like Conversational AI, Generative AI, or other conceptions of AI that, at present, may be harder to explain and conceptualize. AI seems to be everywhere and doing everything—or at least that’s how it feels. Yet, in some ways, it isn’t.
AI currently has the capability to perform an incredible range of tasks and is applicable to a wide array of use cases. However, there are still questions about how effectively AI is completing many of the tasks it’s capable of. There are also ongoing debates about the actual impact AI will have on the economy and how much of its current usage is genuinely productive, as opposed to being limited to novel proof-of-concept applications.
For example, while AI has an obvious use case in generating content, Google has cracked down on sites that use AI to churn out large volumes of content quickly, penalizing them in search rankings.
And despite all the investment being poured into AI, current figures indicate that, as a sector, AI is not yet accounting for a proportional return on the massive spending it requires.
The analyst Benedict Evans has an interesting take on what we mean when we talk about AI. He describes it as "software that doesn’t quite work yet." His argument, in essence, is that previous iterations of machine learning technologies were also branded as AI. But once those technologies matured, became consistent, and worked well, they were reclassified as another branch of software—stripped of the "AI" label and the accompanying hype.
To some extent, this dynamic is at play with advances in large language models. On one hand, these advances could be viewed as pushing the field of Natural Language Processing (NLP) further. On the other hand, some argue that the level of reasoning involved suggests a paradigm shift in the underlying technology.
What is generative AI?
Generative AI is a broad term that typically refers to the ability of large language models to generate useful outputs.
These models ingest massive amounts of data. By reading much of the world’s online content, watching much of the world’s online video, and listening to much of the world’s online audio, large language models become remarkably good at predicting what is likely to come next in a given sequence—whether it’s words, actions, images, or sounds.
Over time, these models have become increasingly complex, to the point where they can now achieve impressive reasoning and mimic a wide range of forms of communication.
Generative AI can create images with ease, produce long-form text, write code, and even generate human-like podcasts. For example, Google’s Notebook LM can produce podcasts that sound strikingly human for the first five minutes—until you begin to notice the patterns, trends, and subtle "tells" that reveal the mimicry behind the process.
Generative AI has a range of immediate use cases, especially in generating text. Beyond that, it is incredibly useful for editing, modifying, or refining existing text.
For instance, I’ll use Generative AI in the creation of this article—not to spin up entirely new content, but to clean up and reformulate audio transcripts and notes for specific sections. Toward the end of the process, I may also use it as a second pair of eyes to proofread, ask for feedback, and occasionally incorporate its suggestions.
Generative AI feels like a creative partner that helps you. It makes new things—things that don’t always resemble the old.
What is conversational AI?
Conversational AI may or may not incorporate what we call Generative AI. These terms are fairly loosely applied and overlap in many contexts.
When it comes to Conversational AI, the purpose is slightly different. The goal is not just to generate content, but to maintain a meaningful conversation and, ideally, to do something useful with that conversation.
For example, I created a chatbot using OpenAI’s GPT-4 API to test how well their Generative AI could work for a range of conversational use cases. I gave it a long, structured prompt that provided a lot of prior context and knowledge. I explained its purpose, outlined its tone of voice, and then set it loose in the real world.
One of the key advantages of deploying Generative AI models for conversational purposes is that they can feel incredibly natural—almost like having a real conversation with a person. This is especially true when the model’s style is adjusted to be a bit more playful or engaging.
Some of the newest updates have also introduced memory capabilities, allowing the model to recall previous entries in the conversation. This creates a much greater sense of consistency compared to when these models were first released. Through this kind of back-and-forth conversation, users can spend more time engaging with the model, sharing information, and finding answers.
However, using Generative AI models for Conversational AI comes with its own set of challenges.
In Conversational AI, the goal isn’t just to mimic the ability to hold a conversation. It’s also about understanding the answers provided by the user, leveraging those answers to shape follow-up questions, and ultimately using that information to impact the outcomes or improve the service being offered.
Many of these goals—such as making conversations useful and extracting value from them—were being addressed even before the rise of large language models, using technologies like Natural Language Processing (NLP) and Natural Language Understanding (NLU). These earlier approaches were already enabling AI to engage in conversations that were both functional and informative.
The challenge with Conversational AI has always been about utility: how to go beyond just talking to create meaningful outcomes and real value from the interaction.
How are conversational AI and generative AI different?
As we’ve mentioned, Conversational AI and Generative AI are loosely defined categories, and there are certain approaches that overlap between the two.
At this stage, the primary distinction between the two lies in their goals and functionality. Conversational AI requires a high level of utility, presence, and adaptability. Its usability in the moment is critical. Generative AI, on the other hand, doesn’t necessarily require the same kind of immediate adaptability. When it does, it achieves it differently.
For example, a Generative AI system might generate ten images and allow the user to select the one they like best. This iterative approach works well for Generative AI. However, Conversational AI cannot rely on iteration—it needs to deliver the right answer at the right time, along with an appropriate and contextually relevant response.
This is why many of the best Conversational AI approaches are built on Natural Language Processing (NLP) and Natural Language Understanding (NLU). These technologies prioritize utility first and mimicry second.
In the chatbot I built using GPT-4, for instance, we found that while it could perform admirably for simple information retrieval, search functions, or as an FAQ interface, it had limited use when it came to actual conversations.
Conversational AI tools like Zendesk or Zembla illustrate this distinction. These tools offer AI assistants that can schedule meetings, take meeting notes, create and assign tasks, and more. While they may use elements of Generative AI in their processes, they are primarily built on NLP foundations.
These foundations enable them to effectively communicate with other systems and services to create and manage actions. These tools may feel less human in their interactions, but they are far more functional as software. In this way, they’re less about AI as we imagine it and more about software that gets things done—but that still makes them incredibly useful.
How can I use conversational AI in my business?
There are several ways you can use Conversational AI to create value for your business today.
The first is in your Level 1 customer support, implemented through a chatbot on your website. You can train the AI on resources like your help articles, videos, blog posts, pricing details, past customer success stories, support tickets, and more. With this training, the AI can address a wide array of customer queries and concerns.
In cases where the AI cannot resolve an issue, you can provide an option to escalate the conversation to a human agent. Anyone who has worked in customer support knows how valuable this can be—many customer questions can be resolved using the existing material on your help site. This frees up human agents to focus on more complex issues, increasing efficiency across the board.
There are various tools available that offer different approaches to managing this. Some rely more heavily on Natural Language Processing (NLP), while others are built around Generative AI. Generally, Generative AI may provide a more human-like conversational experience, while NLP-based systems tend to deliver a more complex and functional framework.
The second way Conversational AI can be used is to communicate with people at scale across different devices and platforms. This is an area where Telerivet provides useful support.
With Telerivet’s Natural Language Understanding (NLU) capabilities, you can send out messages via text, WhatsApp, or Interactive Voice Response (IVR) systems. The platform allows you to program a series of questions, enabling the system to understand customer responses and provide the correct replies in return.
While such systems might not have the same human-like feel as Generative AI approaches, they are highly functional and tend to perform more consistently, making them reliable for business communications.
On a more experimental level, businesses can also create internal tools utilizing Conversational AI to handle basic queries. For instance, Conversational AI could assist with onboarding or help employees address day-to-day work questions. By handling routine tasks, these tools reduce interruptions for managers, freeing them up to focus on higher-value work while enabling employees to get faster answers to their questions. This could even include creating tailored solutions for individual countries or departments within the organization.
How can I use generative AI in my business?
When it comes to Generative AI use cases, there’s some overlap with Conversational AI, as the two categories aren’t completely distinct. That said, Generative AI offers almost endless possibilities for your business. Here are three specific ways you might leverage it:
1. Creating client-facing content
Generative AI can be used to draft client-facing content from scratch. For example, if your AI is trained on your help center and product information, you could ask it to generate a 300-word article explaining how to use your product for a specific use case.
The AI can draft the help center article and even incorporate edits or relevant images to make it both accurate and useful. By doing this, you may save yourself half the time it would normally take to create such content manually.
For factual, straightforward descriptions like these, Generative AI can be surprisingly accurate—especially if it has access to dense resources like video walkthroughs or demo videos as reference material.
2. Drafting documents from notes
Another way to use Generative AI in your business is to provide it with notes and have it spin up a well-structured document for you.
For instance, say you’re working on a new strategy. You can outline your goals, how you plan to achieve them, and any key points as bullet notes. Feed these into your AI model, ensuring you’ve given it clear instructions to be as specific as possible.
The output will often be a polished, well-organized document that simply needs a quick proofread and a few minor edits. In no time, you’ll have a draft ready to share with your team or stakeholders.
3. AI for coding tasks
Generative AI can also assist with coding. Programming copilots, such as AI-powered code assistants, are a popular use case. These tools can analyze your codebase and provide suggestions that you can accept or reject as needed.
While these copilots aren’t always perfectly useful, one area where they tend to excel is in generating tests, such as unit tests. They can quickly spin up these tests, saving developers time and effort. Many developers appreciate this, as it streamlines an otherwise tedious part of the coding process.
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