Generative AI and the future of AI Chatbots

Chatbot background and market

Chatbots have been around for decades. While they were a disruptive innovation at the time, today the tool is controversial and struggling to get off the ground.

Our uses and expectations have largely evolved. The results obtained with traditional chatbots are no longer up to scratch, and the experience is often disappointing.

But the development of natural language processing techniques and technological advances in machine learning - in recent years - have reawakened interest in chatbots.

The advent of Large Language Models andGenerative AI is an inflection point that is totally revitalizing the market. The subject has become a priority for many companies and industries, as the opportunities are enormous.

ChatGPT, released on November 30, 2022 by Open AI, has triggered an avalanche of new AI-based projects - particularly since they made their model accessible via API.

All the giants are getting in on the act: Microsoft has integrated ChatGPT into Edge and Bing, Salesforce into Einstein Bot Builder, and IBM has integrated its NeuralSee model into Watson Assistant.

Google was also quick to release its own model and chatbot Bard in May 2023.

From finance to retail, healthcare and many other industries, chatbots are set to become indispensable for both businesses and users alike. Their ability to generate human-like responses, speak different languages, and understand context and intent makes this new generation of chatbots a must-have in the years to come. It's real assistance, available 24/7.

In fact, the chatbot market - estimated at $526 million in 2021 - is on the rise again, reaching $3.62 billion by 2030. This represents a compound annual growth rate of 23.9%.

What is a chatbot?

To get off to a good start for the rest of this article, let's review the definition of a chatbot.

A chatbot is a computer program designed to simulate a real-time human conversation with users, usually via a messaging or chat interface.

Technology providers will typically connect to various communication services such as Messenger, Instagram, Slack or WhatsApp to enable their bots to be deployed across as many touchpoints as possible, thereby enhancing the overall experience.

But as with any computer program, there are some very basic chatbots and some much more advanced ones. That's what we're going to look at next. 

From traditional chatbots to AI chatbots

Chatbots have been a wonderful invention for answering users' questions. But faced with ever-higher expectations on their part, these tools have had to evolve with the times.

Traditional chatbot

This is the simplest type of chatbot, as it is rules-based and has no embedded AI. Initially, the user is presented with several choices. Then, depending on the choice, pre-written answers are sent to the user. HubSpot CRM and other platforms have made a major contribution to the growth of this technology.

An improved version of these bots integrates an NLP (Natural Language Processing) tool to identify certain keywords in the question, and then fetches the most relevant, pre-designed answer from a database.

This can be text or a suggestion of articles already present in a knowledge base.

While these early versions of chatbots were very useful (and sometimes still are), they still show many disadvantages - namely the lack of AI and that very robotic experience.

Conversational agent

From 2010 onwards, we began to see the emergence of true conversational agents - capable of understanding complex human conversations, processing audio formats and taking into account user parameters and data. Advances in NLP and machine learning (ML) have made it possible to respond to a human even when the question differs from a pre-programmed script.

This is the first time we've really talked about virtual assistants or conversational robots.

These agents can also perform tasks. Examples include Siri, Google Home and Amazon's Alexa.

Generative AI Chatbot

Until very recently, developing a bespoke chatbot required the mobilization of considerable human and financial resources, as well as a large corpus of data. Training time was also very long (several months).

But the advent of Large Language Models (LLMs) has changed all that. These models enable virtual assistants based on generative AI to predict users' intentions and respond in a highly specific, personalized way. These models learn from each interaction to always be as relevant as possible. Bard, Jasper AI and Bard are good examples.

How do you build a custom chatbot?

There are many different ways to build a chatbot. It's important to be aware of them, so you can choose the method that best suits your needs. This will depend on a number of criteria.

Developing your own chatbot(from scratch)

Unsurprisingly, developing your own bot is the most expensive method, as it requires the mobilization of many resources, including software engineers, data scientists, designers and others specialized in artificial intelligence (AI) and product development. What's more, the model needs to be tested and refined over several months.

These projects can cost several hundred thousand euros.

ChatGPT, Bard or other well-established models

Another way is to connect to the APIs of well-established models like GPT or Bard, to benefit from artificial intelligence models already trained on huge public and open source datasets like WikiQA Corpus or Ubuntu Dialogue Corpus or CommonCrawl.

For many projects, developing bots based on these models is preferable, as the value perceived by the user can be very fast - and at low cost: no need to inject data, and no need for in-house human resources.

Be careful, however, to analyze the token consumption for each operation carried out, and consider whether you need to adapt your pricing model accordingly. Either you decide to add value for the user in order to be more competitive. Or you decide to create an additional offer to charge the end customer for operations.

For other companies or projects, these models are not sufficiently accurate, as they are trained on huge amounts of public data (CommonCrawl in the case of ChatGPT). They are therefore very good at answering general questions, but much less so for very specific ones.

Another important point to emphasize is that these solutions are often associated with huge groups such as Google, Microsoft or Facebook. Not everything is transparent, so be sure to consult your legal team to be sure of your strategic choices.

Use existing ML models on a proprietary or highly specific basis

The final method - increasingly popular - is a perfect in-between.

You can use pre-built machine learning (ML) models into which you can inject specific data based on your industry, for example, as well as proprietary data such as :

1. Data from your website
2. Data from your knowledge base(help desk)
3. Historical data based on exchanges with your users.

This solution allows you to obtain much finer, more precise answers, but on the other hand, it takes a certain amount of practice before you get satisfactory results.

It's also important to be vigilant and to set up control and approval processes throughout the training phase.

A good way is to first use these templates as an assistant for your "Customer Success" team, for example - so as to have control but still improve their productivity.

Then, once you're sure of the expected results, you can deploy the solution.

What advantages do these AI chatbots offer?

Benefits for companies

• 24/7 customer support: Chatbots are available at all times, enabling companies to provide uninterrupted 24/7 customer support.

• Cost reduction: Whether through service availability or the ability to handle a higher volume of requests simultaneously, the use of chatbots can significantly reduce the labor costs associated with customer service.
• Improved employee efficiency: chatbots are able to provide fast, accurate answers to common questions, improving customer service efficiency. Employees responsible for customer service can then concentrate on higher value-added activities.
• Data collection: Customer interactions with chatbots can provide valuable data that can be used to improve products and services, as well as to personalize future interactions.
• Customer loyalty: Generative AI makes it possible to generate answers at any time of day in a personalized way that's specific to each user's needs and profile - boosting satisfaction and therefore retention.

Benefits for users

For the most part, corporate benefits are user benefits (or so we hope! 😉 ).

• Availability: Chatbots are available at all times, enabling users to receive help at any time, day or night.
• Speed of response: Chatbots are able to provide instant responses, which can improve user satisfaction.
• Ease of use: In many cases, chatbots can be easier to use than traditional alternatives. For example, a user can simply ask a chatbot a question instead of searching for an answer on a website.
• Personalized experience: Some chatbots are able to learn from previous interactions and personalize their responses accordingly, which can improve the user experience.

It's important to note that while generative AI chatbots have many advantages, they don't completely replace human intervention. For example, they may struggle to understand complex or ambiguous questions and handle emotionally sensitive situations.

Use cases

We could write an entire article on this subject, given the wide range of functions that AI bots offer, and the fact that they can meet the needs of every team in a company. So we'll try to summarize here.

Customer service

This is the first use case we think of when we talk about chatbots. We've all interacted with a bot connected to a website or social network.

We've also all experienced a lot of frustration over impossible-to-find information, or the feeling that we're being thrown long-winded FAQ articles.

But chatbots based on generative AI are different. They can be trained on specific data and proprietary data such as FAQs and customer data - and so generate a concise, accurate response. And therefore satisfying.

Imagine a Ryanair or SFR bot that responds perfectly to your requests from chat. A dream come true? 😉

Human Resources (HR)

Just ask your HR team and you'll see: the number of recurring questions already documented in the internal knowledge base is enormous. Questions relating to wages and leave, for example.

Providing an internal chatbot that is trained on these questions/answers as well as internal documentation would save everyone time and improve overall employee focus.

Another very important point to emphasize. Chatbots can create an environment of trust. In some cases, employees may prefer to turn to a tool rather than having to ask their superiors for information again.

Marketing & Lead Generation

By integrating an AI advisor into your website, such as a personalized ChatGPT chatbot, you can efficiently guide your customers through your content, providing them with precise information based on their needs.

Beyond simply distributing information, this virtual assistant can also generate leads, for example by suggesting an appointment to take the process further. This offers a personalized experience while capturing potential new customers for your company.

Here's an overview of what's possible. Of course, we could go on and on about many other use cases.

Which industries are most affected?

Retail

Most questions in retail relate to refund policies, product returns or order information.

The chatbot would enable customer service to save an enormous amount of time and assist consumers 24/7.

Health

AI chatbots can be used to provide information about symptoms, help book appointments, or give reminders to take medication. They could also provide mental health support, for example by helping users manage stress or anxiety.

Banking, insurance and financial services (BFSI)

Chatbots can help automate many common tasks, such as checking balances, transferring funds, or answering questions about specific financial products. They can also help detect and prevent fraud by monitoring suspicious behavior.

Tourism

Chatbots can facilitate the booking process by enabling customers to search for and book flights, hotels and tourism experiences in real time. Customers can also use chatbots to obtain up-to-date information on travel conditions, which is particularly useful in times of disruption.

Challenges and risks

Every new opportunity comes with its own challenges and risks.

Generally speaking, generative AI is causing various regulatory institutions to move to better control it, and is demanding greater transparency from certain players.

Among these risks are problems relating to ethics, personal data protection and influence through misinformation. So it's important to choose the right tools and apply good security and ethical practices.

AI chatbots are no exception to the rule. It's important to ensure that the product is calibrated to avoid any leakage of personal data between different users.

Trained on historical data such as past conversations, chatbots can learn a lot to complement a well-stocked FAQ. But they also need to be able to identify all the personal data specific to each user.

That's why a chatbot can't be turnkey if you want to inject these exchanges and interactions from users.

It's a good idea to enlist the help of a partner to map and manage the risks inherent in the generative AI embedded in these chatbots.

Another risk concerns the reliability and accuracy of the answers generated. Chatbots based on generative artificial intelligence can "hallucinate", certifying certain statements that are patently false. AI can quickly take shortcuts and claim certain things without it being true.

Conclusion

The chatbot landscape has undergone a radical evolution in recent years, moving from basic tools with fixed rules to generative AI models - offering near-human conversational capabilities. This metamorphosis, supported by the rise of natural language processing and machine learning technologies, has redefined the value of chatbots, propelling their market to new heights.

It should be stressed, however, that there is no one-size-fits-all solution. The appropriate approach to developing a chatbot depends very much on the specific situation and requirements of each company. Whether it's developing a bespoke chatbot with its own training rules and data, or a GPT-based chatbot, each strategy has its own advantages and disadvantages.

It's an exciting time for AI chatbots. With their ability to make interactions with machines more natural and efficient, they're poised to reshape the way we communicate with technology.

https://www.techtarget.com/searchcustomerexperience/infographic/The-evolution-of-chatbots-and-generative-AI

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