The rapid expansion of artificial intelligence in the world of business means it’s now starting to become a mainstream activity. According to IBM, 42% of IT professionals in large organizations report to have deployed AI within their operations, while another 40% are actively exploring their own opportunities to do so.
But amid the gold rush to get on board with AI technology, it’s important to understand the different types of AI tools out there, what they do, and the key differences between them. This blog explores the distinctions between two of the most popular forms around: Conversational AI and Generative AI, and how to work out where you should apply them to your business activities.
What’s the difference between Conversational AI and Generative AI?
Conversational AI refers to technology that can understand, process and reply to human language, in forms that mimic the natural ways in which we all talk, listen, read and write. Generative AI, on the other hand, is the technology that can create content based on user prompts, such as written text, audio, still images and videos.
Aside from the functionality that they offer, there are several key differences between the two. For example, Conversational AI relies on language-based data and user interactions, whereas Generative AI can use these datasets and many others when creating content. However, there is some scope for overlap between the two, such as when text-based Generative AI is used to enhance Conversational AI services.
There’s also plenty of variation between the main suppliers of each technology, and the costs involved. Conversational AI features many of the big tech players through Virtual Assistants: think Google Assistant, Amazon’s Alexa and IBM Watson; however, a number of smaller players like Kore.ai are making waves, too. As for Generative AI, many new businesses have made real headway in gaining market share, such as OpenAI with its Artificial Intelligence application ChatGPT. But even Generative AI is becoming increasingly centred around Big Tech, particularly when it comes to infrastructure models.
Where is Conversational AI best used?
There is a wide range of industries that are already benefiting from Conversational AI implementation, including (but not limited to):
Data collection:
Conversational AI can help gather important data from several sources and collate it for driving meaningful and digestible insights to guide data-driven AI decision-making.
Customer support:
Responses to the most common queries and issues can be automated by chatbots, freeing up service agent time to deal with more complex cases.
E-commerce:
Feeding personalized recommendations to customers to encourage them to purchase, as well as supporting order management when customers look for information.
Healthcare:
Preliminary diagnoses for common ailments can be taken care of by virtual healthcare platforms, which can also support the management of appointment scheduling.
Banking:
The process of conducting financial transactions and dispensing financial advice can be eased through Conversational AI.
Human resources:
Many of the important but relatively straightforward HR functions can be covered by Conversational AI, such as onboarding processes, recruitment procedures and employee support.
Where is Generative AI best used?
The use cases for Generative AI tend to be very different to its conversational counterpart, but they’re no less valuable, such as:
Business process automation:
Repetitive tasks and processes can be intelligently automated, as Generative AI can extract the key data required and complete the process independently.
Content creation:
Every type of organization can benefit from creating marketing copy or writing blog articles with some assistance from Generative AI.
Media:
Similarly, Generative AI can be used to create images, logos, videos and other visual promotional content.
Software development:
Snippets of code can be generated to expedite development processes, while Generative AI can also assist in software debugging.
Education:
Personalized learning experiences can be supported through the generation of educational materials.
Finance:
Generative AI can also understand patterns of human activity, helping finance firms with fraud detection, especially when combining Generative AI with existing Machine Learning classification problems to boost the performance of both technologies.
R&D:
The ability to analyze and process data at scale to create hypotheses can be helpful in assisting scientific research.
In Summary: Choosing the Right AI Strategy
The business AI solutions landscape is complex, and it’s evolving at a rapid rate. Not only that, but the global AI marketplace is saturated, meaning that it can be hard to know how to get started with what is a very important investment for your organization.
The key is to establish a comprehensive, agile strategy for AI, and that begins by understanding where you can apply Conversational AI vs Generative AI. The following five steps are a good place to start:
- Align AI decision-making with business goals and objectives to ensure you get the most out of the technology.
- Structure AI implementation in a modular way to encompass all the different variants of AI.
- Ensure you’re well versed in ethical AI use and create appropriate intellectual property strategies and priorities to avoid getting caught out by existing and emerging regulations.
- Invest in upskilling your employees on both the technology and business sides of AI to ensure AI strategy filters through the entire organization.
- Monitor emerging trends and industry practices like multi-bot experiences, omni-channel experiences, and voice assistants for Conversational AI, and multi-modal education, Artificial Intelligence applications and services for Generative AI.
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