Conversational AI for your enterprise

Artificial intelligence is starting to transform our lives. Generative AI has shown jaw-dropping potential for writing poetry, stories, music and pictures. Predictive analytics gives us answers before we’ve thought of the questions.

AI also has the potential to improve our everyday work environment. Algorithms are finally reaching a tipping point of being able to solve difficult problems we once thought only humans could solve.

How does conversational AI work?

All conversation relies on understanding what was said, working out the best response, and constructing a reply.

Natural language processing helps computers understand plain text. The source might be a text message or an email, extracted from a document, or captured from speech recognition. It might even be translated from a foreign language. Generative AI can create replies that engage people. The tone of voice can be controlled to meet the user's needs.

Conversational AI can create better conversations with more effective outcomes. They can improve customer experience and deliver better services for people.

Chatbots for business

People often find it easier to find information by asking questions than by trawling through endless possible locations. Search engines have raised people’s expectations of how easy it should be to find what they’re looking for. Google appears to have an almost intuitive understanding of what people are looking for. Searches are often augmented with additional information beyond the plain search text. Recent searches, location, and even previous behaviours can all affect search results.

Enterprise chatbots can offer a similar experience for customers and employees.

Chatbots can be trained (or “grounded”) with company documents and information. They can offer free-form styles of conversation or guide their users with suggested questions. These options can make chatbots appear intelligent, or at least helpful.

Business chatbots can be embedded into public websites, internal web pages or even added to existing business chat services such as Microsoft Teams and Slack.

Content curation, document discovery and presentation

Many organisations are overwhelmed by information. Excellent documents are written, only to languish unloved, buried somewhere on any one of many SharePoint sites. Many useful documents go undiscovered by people who could benefit from the content.

AI can put your important documents to better use.

Existing documents can be indexed and processed to make them discoverable. AI can use natural language processing techniques to read, understand and derive meaning from documents. This allows long documents to be condensed into short abstracts. Key points from documents can be extracted and presented to potential readers in a format that is much easier for people to understand.

Automated question and answer generation

Frequently asked questions are a great approach to providing content in a form that mirrors how people search.

Modern AI technology can parse documents and extract meaning from text. AI tool can automatically create lists of questions and answers (and keep them updated). The FAQs don't need to come from a single document. Multiple documents from different sources can combine to create an effective FAQ.

Q and As can be published on internal or public websites, or used as the basis for interactive chatbots. They can also feed interactive voice responders (IVRs). Innovative tools allow speech recognition and automated text-to-speech conversion.

Question and answer formats can be useful for a range of situations, including customer support, internal support, employee onboarding, training, etc.

• Improve customer experience

  AI allows users to control how they access the information they need. They can discover your content in a way that is natural to them. This improves their experience and improves the effectiveness of your content.

  Content delivery is more personalised and more relevant. Your end users can access the information when they need it and in the ways they choose.

• Reduce costs

  Delivering real-time interactive conversations with customers and employees is expensive. Human agents are effective but difficult to scale. Chatbots and virtual assistances can deliver consistent, excellent experiences.

  Conversational ai platforms have the potential to reduce errors and increase consistency. Automated voice assistants and web-based chat can respond to inquiries in ways that exceed customer expectations. When implemented well, they can integrate successfully with human agents to reduce costs.

• Gain insights from chat data

  User behaviour is some of the most valuable data available. When chatbots interact with people, the success of the conversation can be tracked. A wide range of metrics can provide useful insight for improving the conversations and informing the use of AI in other parts of the business.

  Understanding user pain points and common difficulties can be used to improve question flow. For example, some conversations can incorrectly lead customers in circles, or force them to repeat themselves.

  Customer feedback can also be useful. Asking the user whether their query has been answered can highlight areas where the conversation flows or supporting information could be improved.

Step 1 – Machine Learning

All intelligence starts somewhere. Conversational intelligence starts with machine learning. Source documents are analysed to understand the material. The more documents, the better the opportunity the machine learning tools have for spotting common patterns and connections. In this context, documents could be free-form text, structured forms, user input or structured data sources from business databases.

Step 2 – Natural Language Parsing

People ask questions in as many different ways as there are people. Technology allows software to understand these questions in a structured format.

Parsing and understanding single sentences is complex but has been possible for some time. Modern AI has recently advanced to allow conversations that respond to questions based on previous questions and answers. These new developments have transformed the user experience. Conversations can flow and evolve much more naturally. The experience feels more like chatting with a helpful assistant than a smart robot.

Step 3 – Enterprise Scale Search

Tools are now available that solve the challenges of indexing large numbers of long-form documents and searching for their contents. The key challenge has been how to deliver reliable document indexing when scaled up to a large number of documents. Deep learning models have advanced to a point where the complexities of search can be largely hidden, while still allowing flexible search strategies.

Step 4 – Response generation

Conversation relies on both parties being able to form coherent and relevant responses. Generative artificial intelligence can be used to create responses that make sense to the end user. Sometimes they might provide a summary of the content the AI has found. Sometimes the response might need to offer some alternative options, or perhaps a request for more information.

Generative AI allows the responses to be styled and phrased in various tones. Some organisations find a less formal tone works most effectively for their users. Others prefer a more formal style to build confidence. Conversational AI can generate responses that match an organisation’s brand language and style.

Step 5 - Repeat

Effective conversations build on previous conversations. AI is capable of learning from previous conversations as a human might.

Conversational AI can be guided by whether the end users found the responses helpful. The most straightforward approach is by gathering a yes/no response from the user if their question was answered. More sophisticated evolution is possible by analysing the user input itself.

Continuous improvement of the AI allows organisations to refine the learning models and focus the AI on delivering the most relevant information for the users.