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How to Use A Conversational Assistant Framework for Automation?

 

What is business automation? Is it just usage of Artificial Intelligence, Machine Learning, and the Internet of things? Or it has more to do with autonomous agents. Off late, there has been much automation in the business world. Here, we are going to discuss a unified conversational assistant framework for effective business automation. 

The global digital process automation market is expected to grow up to $12.61 billion by 2023, at a CAGR of 13.3% from 2018 to 2023. As the businesses expand to new markets they are subjected to more and more data. The problem of BigData has led to the rise of business process automation. 

Automation can help get more productivity. It can also help make the user-experience more comprehensive. From IBM to Amazon, every other firm in the fortune 500 is digging for better business process automation solutions. 

So, let’s discover how a conversational assistant can help automate business processes.

What are Conversational Agents?

Natural language is the main communication source in business enterprises. From the written documents to emails and other forms,  understanding and generating natural language can significantly facilitate the integration of various autonomous agents. But, every business vertical does not have the infrastructure to integrate such interpretation models. 

Conversational agents have been at the forefront of speech recognition and Natural Programming Language(NLP). They are used in many applications. They infuse machine learning techniques for better data interpretation in natural communication. 

Many companies are now building chatbots with integrations in RPA or Robotic Process Automation.  There are two approaches to develop chatbots.

  1. Building domain-specific chatbots.
  2. Training deep learning models on data samples.

Domain-Specific Chatbots:

These are autonomous agents built on the platform of machine learning and AI through app-like interface. It uses technologies like speech recognition and  NLP. These are chatbots that mimic human-to-human conversations. They use the data achieved from conversation to provide solutions to the users. They are used for specific purposes and are not general-purpose.

Deep Learning Models For Chatbots:

Deep learning uses abstraction layers for algorithms to analyze the data. The backpropagation techniques of deep learning can help data synthesis and the creation of autonomous conversational agents. 

The first adaptation of conversational agents into the enterprises was for customer support. Soon, we saw the chatbots being used for food delivery and smart cars. Now, industries are using bots for process flow executions and automating business processes.

A Unified Conversational Framework:

A unified conversational framework can be used to automate business processes. There are three major components of the framework.

1. Skills:

Skills are an important part of the framework. They are performed through predefined tasks. They require a set of instructions. The inputs are also quantified for relevant outcomes. These skills depend upon the agents for the delivery of instructions. 

2. Agents:

Agents are formed of skills. These skills are classified into three major types. 

  • Understand skills.
  • Act skills.
  • The Respond skills. 

There will be an execution pattern. The pattern is designed for skills. It provides instructions to the skills for execution. It can be a script or a graph with all the details.

3. The Orchestrator:

The orchestrator coordinates between agents. It determines which agents must execute a request and successfully respond to a user. Whenever a user input requests through the natural language sentence, it is forwarded to all the agents in the assistant by the orchestrator.

Agents pre-process the input. Then the orchestrator requests a preview. All the agents provide a preview of the response. The orchestrator assesses the response and provides a score. The score depicts how confident an agent is in its ability to respond to the user. Depending on the adopted orchestration pattern, the orchestrator selects one or more agents to execute and return their responses to the user. 

Now that we know the framework, let’s understand some of the conversational agents that can help process automation! 

Types Of Conversational Agents:

Using individual agents may not work for automation effectively. So, we define contracts for the skills. It will facilitate their orchestration by a single unified assistant. So, the time that is wasted in the preview of different agent’s responses can be reduced. The single assistant will orchestrate the responses from a pipeline of agents. The re usability of skills and execution of patterns can be done through different types of agents in the pipeline.

1. Dialog Agents:

Often users tend to ask for queries pertaining to certain information. Here, there is no need for action. So, a dialog agent is ideal. It is composed of skills to understand the intent and entity recognition of a user query. 

Here, the response is all about answering with relevant information. For example, you can use agents that answer FAQs or the ones you find in the “Help” section. They can be implemented into a tree type of service, depending on the sequence of queries and answers.

2.Information Retrieval Agents:

These are different from the dialog agents as they need to connect with an external protocol for data retrieval. Here, companies can use an API(Application Programming Interface). It is a protocol for data exchange between heterogeneous systems. Businesses can get any app development company to integrate APIs with the conversational agent. There are three types of skills needed for this agent.

  • Understand Skill
  • Act Skill
  • Response Skill

3. Task Executing Agents:

These agents execute the set of instructions provided by the business users. So, it needs a preview and executes mode. It helps automate key business processes. Such agents execute the patterns based on empirical data. They are often paired with algorithms to analyze empirical data. Agents automate key decision points for forwarding the business process with the submission of applications. 

4. Alert Agents:

These agents are purely preventive. They alert the businesses on key processes. Agents use notifications, emails, SMS, and other modes of communication to convey alerts on key process issues. It can help automate preventive measures to reduce production losses. For manufacturing sectors, such agents can be more than just notifiers. They can be coupled with key processes to stop the production as and when required rather than alerting the operators.

Conclusion:

We know that there are automated machines that stop production in case of errors in the process. But, a synchronized multi-agent framework can help you integrate the human workforce with automated machines. It can help create an ecosystem, where prevention, detection, and rectification of process issues are automated.

So, businesses can focus on product development rather than too much emphasis on production. It helps cater to the better quality products that can create more business opportunities. Even the production costs can be reduced. Such automation of business processes will make decision making more efficient and rapid. Don’t let the opportunity of growing your business slip away! Invest in a unified conversational agent framework and see the difference!

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