conversational AI

Conversational AI in Manufacturing: Top 5 Use Cases

Conversational AI in manufacturing enables precise operations for smarter engineering and efficient asset care. It helps achieve daily, measurable results.

Conversational AI in manufacturing empowers precise operations for more competent engineering and efficient asset care. Integrated into your manufacturing process, conversational AI drives measurable results every day.

What's Conversational AI?

Conversational AI is a set of digital and telecommunications technologies that create an intelligent, programmatic way of offering a conversational experience using computers. Manufacturers that use conversational AI can reduce overhead and materials costs by process optimization. They can avoid costly repairs and shutdowns through predictive asset management. They can achieve sustainable, safe operations, capturing knowledge without writing a single line of code.

Five Examples of Conversational AI in Manufacturing

What can conversational AI do for manufacturers? Let's consider five examples.

AI virtual assistants are perhaps the best-known example of conversational AI. Like a human virtual assistant, an AI virtual assistant can respond to natural language to answer queries and perform tasks. Unlike a human virtual assistant, an AI virtual assistant never needs a day off. An AI virtual assistant can be connected to the Internet of Things and control devices in the production process.

User experience with AI assistants is in many ways similar to user experience with traditional chatbots, but AI assistants have many capabilities that chatbots do not. Conventional chatbots generate responses to queries and commands based on Markov chains and similar analytical tools. Chatbots generate static responses while conversational AI assistants utilize advanced integrations, machine learning models, and Natural Language Processing technology with data science to generate dynamic responses to user input.

Conversational AI can also provide major support to customer service teams. It is no longer necessary for a customer to be in contact with a manager or an engineer about every production question. Conversational AI can take care of many kinds of queries that previously required a human being to answer the phone and can be on the job 24/7.

Conversational AI does not eliminate the need for customer service agents. But AI can file away records of customer interactions with associated metadata without any need for a customer service agent to take perfect notes. Conversational AI can remember not just a customer's details but also their preferences, enabling human customer service agents to offer personalized service.

In the oil and gas industry, conversational AI empowers early detection of anomalies in the processes that provide alerts for potential failures. It drives production forecasting, so engineers can match inputs to contract quotas. It offers predictive reliability for calculating the remaining useful life of oil and gas fields and matches them to refinery capacity.

In the manufacturing industry, conversational AI puts power into the hands of industrial engineers to drive energy efficiencies, eliminate production downtime, reduce scrap, control overhead costs, and curate information gained through experience.

Conversational AI enables industries of all kinds to reduce the scope of their oversight of the production floor, making manufacturing safer and more predictable. Conversational AI even helps HR to develop training programs based on company experience to educate new employees for success on the job.

But what is it about conversational AI that makes it conversational? 

Conversational AI Integrates Eight Technological Components

Every application of conversational AI integrates multiple technologies. In a manufacturing setting, conversational AI relies on:

Natural Language Processing, also known as NLP

Conversational AI has the ability to parse or "read" human language and understand natural sentence structures. This distinguishes it from chatbots that respond to "trigger words" regardless of context.

Entity Recognition 

Conversational AI recognizes abstract concepts through training and integrations with business systems, so it actually learns and speaks the company's language. Conversational AI is capable of learning to distinguish gauge, sealed, differential, and absolute pressure. Entity recognition enables AI to sort through nouns to ascertain the subject of a command or inquiry.

Intent Recognition

Conversational AI can recognize the user's intent even when it is phrased differently. Conversational AI systems use machine learning to sort through the verbs in user speech. They then request input in slots they can use to match the query to information in their database.

Conversational AI does not require an explicit statement of the verbs that describe the user's intent. For example, conversational AI would realize that "I have been locked out of my account" is equivalent to "I want to change my password."


Conversational AI can pull data from previous conversations and their associated metadata. It can integrate data from the Internet of Things, company records, application programming interfaces (APIs), and the Internet. It delivers actionable information in milliseconds through its dialog manager.

Voice Optimized Responses

Conversational AI is able to participate in conversations in a human-like manner. It can emulate appropriate emotions and inflections of human speech. It can also use spoken words to search for information on the Internet relevant to the user's intent.

Dynamic Text to Speech and Speech to Text

Conversational AI generates synthetic speech with pitch, phrasing, and pronunciation appropriate to the human language chosen for communication. It can understand different voices and different accents. It generates written responses with upper case and lowercase letters as needed, with the appropriate spelling of homonyms.

Contextual Awareness

Conversational AI learns from each interaction with its users. It also learns from the Internet of Things, connecting it to gauges, sensors, and controllers on the factory floor. It refines its contextual awareness through the use of its intent recognition routines, refining user queries and commands and relating them to results.

Machine Learning

Conversational AI is constantly learning through its interactions with its users. Machine learning is fundamental to the operation of any kind of conversational AI.

Conversational AI incorporates features to minimize security risks that circumvent hacking and takeovers of control systems. Conversational AI provides reports and analytics showing how well it has adapted to the full manufacturing and sales cycle demands. Algorithms are organically trained with every interaction.

What Are the Metrics of Success for Conversational AI in Manufacturing?

The right conversational AI system is easy to integrate into existing management, reporting, and control systems. Its payback period should be measured in months, not years, impacting the bottom line in multiple ways:

  • Conversational AI can learn to perform routine tasks, leaving employees free to do high-value work.
  • Conversational AI can learn to keep customers better informed at every stage of the manufacturing process, increasing customer satisfaction and reducing customer churn.
  • Conversational AI makes it easier to scale up and scale down production processes in response to fluctuations in the supply chain.
  • Conversational AI can increase revenue per job and revenue per customer, learning from mistakes to refine processes, promotions, and responses to customers.

Where Is Your Company with Conversational AI?

Maybe your company knows you need conversational AI, but you don't know where to start. You need a strategy for getting started and testing the technology.

Or maybe your company has selected a conversational AI product. Still, you need to validate it with users to prove your assumptions and to set a baseline for your expectations for the technology. Then you need to integrate conversational AI across your company.

Or your company may have experience with conversational AI, but you need to make it more scalable and sustainable. You may need to establish a framework for continuous improvement.

Wherever your company is on the road with conversational AI, we are here to help.

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