There are many different definitions of artificial intelligence (AI) floating around, but the consensus is that it’s a field of computer science that aims to make machines think and act like humans. In recent years, AI has advanced rapidly in various fields, especially as far as speech recognition, natural language processing, and machine learning go. As a result, we’re starting to see AI integrated into everything from our operating systems to our cars. The implications of this new technology on the way we live and work will be profound. Because AI can analyze vast amounts of unstructured data in real-time and take action based on its findings in seconds or less, it can add great value across many fields - including communications software. That’s why so many developers have begun integrating AI into their solutions.
What is Real-Time Communication?
Real-time communication is a type of communication in which no time delay is imposed. This can occur in both synchronous and asynchronous modes. During real-time communication, both or all parties are engaged in the conversation at the same time. Real-time communication is interactive. Compared with non-real-time communication, where a message is sent, then there is a delay until the receiver decodes and responds to that message, during which time both parties remain passive, real-time communication is more like a game of ping pong. During real-time communication, both parties are actively participating in the exchange, with neither party being passive. Synchronous real-time communication happens “at the same time”, as in person-to-person conversation, or “as in same time”, as in using audio and video to communicate “at the same time”.
Why Real-Time AI Is Important in Communication Software
As we’ve discussed, AI is good at analyzing vast amounts of unstructured data quickly. Now consider, for example, a customer support rep who is trying to help a customer solve a problem with their WiFi router. The rep will need to go through multiple steps: They’ll have to ask the customer what the problem is, they’ll have to ask them to check their network settings, they might need to ask them to perform basic troubleshooting, they might have to ask them to check their device settings, etc. This can take a long time, and the rep might not be able to get through this process before the customer gets frustrated and hangs up. With AI, the rep could ask the customer to describe the problem in natural language, allowing the rep to analyze the problem in real-time and determine the best course of action to help solve the problem. AI can greatly reduce the average amount of time it takes for a rep to solve a problem, and it can also increase customer satisfaction as well.
How AI Works in Real-Time Communications
In this section, we’ll look at a real-time AI workflow to see how a company uses AI to create a real-time communication solution. First, the company will collect structured and unstructured data from various sources (e.g. website, social media, email, etc.) and store it in the data lake. The data lake is a large repository for storing data that has not yet been organized or processed. The company will then process this data using a data processing engine. The data processing engine will create a data model that holds the data and creates a virtual data warehouse. The virtual data warehouse will provide an interface for the company to query and analyze data. The company will then use the data warehouse to create rules and generate automated messages to customers. These rules and messages will be used to create a transactional message service (TM service). TM services are used to create real-time communication solutions.
Why Use Artificial Intelligence in Real-Time Comm?
As we’ve discussed, AI is good at analyzing vast amounts of unstructured data quickly and efficiently. Now consider a telecom company offering a VoIP service. Let’s say they have 2 million customers who are all using the same phone number. A telecom company might have a database of 2 million phone numbers that they share between their multiple departments: marketing, sales, finance, customer service, etc. If someone from marketing wants to call these customers and find out if they are interested in an upcoming promotional offer, they might be able to find the phone numbers, but they wouldn’t be able to call them unless they had a way to identify which customers are in the database. If the customers are in customer service and they want to call those customers to troubleshoot an issue, they might be able to find the customers, but they wouldn’t be able to call them because they would need to know which department they belong to. With an AI-powered solution, the telecom company can put all 2 million phone numbers in the database and then use AI to analyze the data and keep all departments up to date with the latest information. They can find all 2 million phone numbers, they can call all 2 million phone numbers, and they can find the departments responsible for each customer. In short, AI can give all departments access to the same data and a central way to communicate with all customers.
Pros and Cons of Using AI in Communication Software
As we’ve discussed, AI is good at analyzing vast amounts of unstructured data quickly. Now consider a communications solution that uses AI. This solution will collect data from various sources and then use AI to analyze the data and keep all departments up to date with the latest information. This solution will allow all departments to access the same data and use a central way to communicate with all customers. Having a solution like this would be a huge benefit because it would allow all departments to share data and communicate with one another. This would help improve collaboration across the organization and, in turn, boost productivity and create a better customer experience. The only major downside to this would be if one department collected data that is inaccurate or in need of updates. In that case, the other departments would be working with outdated data, which could create inaccurate or incomplete analysis.
Using AI in real-time communications apps
Now that we’ve discussed what real-time communication apps are and why they’re important, let’s look at some of the most important features to consider when building a real-time communication app with AI.
- Data source - Where will you collect the data you need to power your app? Will you collect it from your website, or will you pull it from your partners’ websites? How will you collect it? What formats does it need to be in?
- Data store - Where will you store your data? Will you store it in a database, a data lake, or a data warehouse? What kind of database do you need?
- Data model - What data model do you need to create? What data are you trying to collect? What fields do you need for each piece of data? How will you organize it?
- Rules engine - What rules engine do you need? What rules do you need to create? What actions do those rules trigger?
- Transactional message service - What TM service do you need? What type of data do you need to create messages from? What type of data do you need to send to customers?
Artificial intelligence is a rapidly evolving field, and it has already started to change the way we live and work. Real-time communication apps are a new type of software that relies heavily on AI to function. These apps aim to make it easy for users to communicate with each other without needing to configure complicated settings or set up the software.