AI Chatbots

AI chatbots can significantly enhance the user experience on e-commerce platforms. With the introduction of Chatgpt by ​OpenAI, numerous ​businesses spanning various industries have employed new generative AI chatbot experiences. As the AI market grows, many companies are expected to adopt AI chatbots. Studies conducted by Juniper indicate that global retail spending on chatbots hit $12 billion in 2023 and will potentially rise by as much as 470% to reach $72 billion within the next five years.

Conversational AI

​Chatbots today often use Natural Language Processing (NLP) technology. This lets them understand human language by breaking it into smaller, manageable pieces. They can interact with and respond to both written and spoken words. An aspect of NLP is Natural Language Understanding (NLU), which improves how machines understand language. NLU uses different methods to find meaning, context, and insights from text or speech. It can also grasp different definitions of the same word depending on the context and correct language mistakes, like spelling errors.

Conversational AI combines NLP and NLU with traditional conversation tools like chatbots, voice assistants, and voice recognition systems. This can happen through speaking or writing. There are mainly three types of conversational AI:

Scripted Chatbots: The chatbots follow the rules and use decision trees to answer customer questions. Their responses are limited and often used to answer specific questions, like FAQs.

NLP Chatbots: NLP chatbots can provide more varied responses to customer questions because they can learn. They analyze the user’s input and determine its meaning to give the best answer or action.

-Contextual chatbots are the most advanced in processing language. They use AI and machine learning to remember past interactions and learn behavior patterns. They use NLU to understand language and detect feelings and intentions. They can also understand the context of a conversation and correct language mistakes, ensuring the conversation continues.

IBM Watson: AI Pioneer.

The History of IBM Watson

Early Achievements and Jeopardy! Victory

IBM Watson was one of the pioneers in the AI market, gaining widespread recognition in 2011 when it competed on the quiz show Jeopardy! and beat two of the most successful contestants, Ken Jennings and Brad Rutter. This event showcased the potential of AI in understanding and processing human language. Watson’s success on Jeopardy! was made possible by a powerful AI system, DeepQA, developed by a team of 20 researchers over three years.

DeepQA was designed to understand and process the complex clues presented in the game and search for the correct answers within Watson’s vast database. The system’s ability to interpret nuanced language and respond accurately demonstrated that machines could indeed comprehend and process human-generated questions. This achievement was a landmark moment in AI development, proving that machines could empower humans and handle tasks previously thought to require human intelligence.

Post-Jeopardy! Expansion and Industry Applications

Following its Jeopardy! Victory, IBM’s valuation soared, and the company set its sights on applying Watson’s capabilities to various industries. Watson’s potential applications spanned law, healthcare, finance, and education. IBM envisioned Watson as a tool to assist professionals in these fields by providing insights, improving decision-making, and automating complex processes.

To build trust with the public and showcase Watson’s capabilities, IBM’s marketing department engaged celebrities like Bob Dylan and Serena Williams in promotional campaigns, where they interacted with Watson. These interactions aimed to humanize the AI and demonstrate its versatility and potential. Watson even appeared on the television show 60 Minutes, where the audience learned about its possible contributions to solving global challenges such as climate change and cancer.

Evolution and Current Status

Over time, Watson became a symbol of artificial intelligence, representing the potential of AI to transform various aspects of society. However, the initial buzz around Watson eventually died down as new AI technologies emerged and the market evolved. Despite this, Watson continued to be developed and applied in various domains, with IBM focusing on integrating its AI capabilities into its broader enterprise solutions.

The advent of generative AI, particularly with the release of ChatGPT by OpenAI, brought renewed public attention to the field of artificial intelligence. ChatGPT’s ability to generate human-like text based on prompts highlighted AI’s evolving capabilities and reignited interest in its potential applications.

Today, IBM Watson remains a significant player in the AI market, evolving and adapting to new challenges and opportunities. Its legacy as a pioneer in AI, combined with ongoing advancements in the field, ensures that Watson will continue to shape the future of artificial intelligence.

IBM currently has over 100 million users of its AI systems, including high-profile companies such as Ernst and Young, SAP, General Motors, GSK, Samsung, Intel, and Moderna. In May this year, IBM launched a revamped platform called

​ is a comprehensive platform with three parts:

  1. A workspace for businesses to train and test AI models. It’s user-friendly, making it easier to build complex solutions using ​machine learning.
  2. ​ This toolkit helps businesses manage their data and AI effectively. It supports the creation of ​AI workflows that are responsible and clear. Companies can use it to handle more significant AI projects using all their available data. It also supports searches, management, and various data formats.
  3. ​Watsonx.governance: The platform allows companies to direct, manage, and monitor their organization’s AI activities, and it employs software automation.


Follow the step-by-step instructions below to start creating your IBM WatsonX assistant. Your bot should include at least three different actions and more than three steps and be deployed on your website.

IBM Discovery

After setting up your bot, follow the tutorial below to set up your Discovery Instance on the IBM Cloud Platform. This will allow us to train the bot using existing URLs and data. After setting up the discovery, add links to your different pages to the discovery, and add a document or Excel file with FAQS.

Please find an additional tutorial explaining how to work with Watsonx