How to Implement GenAI Solutions in Manufacturing

Introduction

GenAI technology is profoundly impacting the global manufacturing scene, whether for after-sales operations or product development. Industry analysts like McKinsey, Gartner, and IDC are “positive” about GenAI’s impact in this sector.

According to McKinsey, manufacturers can generate 75% of their annual value from GenAI just off the following 4 use cases:

• Product R&D
• Customer operations
• Sales and marketing
• Software engineering

As a recent case study, a company with over 5,000 customer service agents applied GenAI to:

• Improve issue resolution by 14% every hour.
• Reduce time taken to address customer issues by 9%.

Similarly, IDC found that 27% of manufacturing companies invest in GenAI technology. 38.2% of manufacturers are at the stage of “initial exploration” of potential use cases of GenAI.

According to this Gartner prediction, manufacturers are most likely to use GenAI to improve their customer experience and retention (38%) – followed by revenue growth (26%).

Here are 4 potential GenAI use cases to look out for in the global manufacturing industry

1. Product research and design

Effective product research and design innovation are the cornerstones of any successful manufacturing company. Generative AI enables product designers to leverage their text-to-image capabilities to convert “ideas and concepts” into production-ready designs.

GenAI can aid product designers to create multiple designs based on predefined parameters like:

• Production costs
• Sustainability goals
• Product Criteria

Product R&D teams can now sift through various product-related design ideas and choose the one most suited to their needs. This can potentially save both research time and costs. Similarly, research teams are leveraging GenAI to generate prompt-based virtual designs to iterate design choices quickly.

Here’s an example of how Toyota Research Institute used GenAI techniques to design their vehicles.

2. Sales and marketing

With GenAI, B2C manufacturers can now leverage their machine learning algorithms to identify customer buying patterns and behaviour. This helps sales executives personalize their text-based communication and interaction with potential customers.

Among the significant use cases, GenAI can reduce the time and effort spent on content creation – and ensure a consistent tone in the manufacturing brand voice and writing style.

In the marketing sphere, GenAI can overcome the limitation of disconnected data by accurately interpreting information from diverse data sources including text and images. Besides, GenAI is enabling SEO optimization by:

• Analyzing technical components including page titles, image tags, and URLs.
• Synthesizing crucial SEO tokens.
• Helping SEO specialists in creating digital content.
• Distributing customer-specific content.

3. Customer support

In the manufacturing domain, after-sales customer support is another focus area for implementing GenAI solutions. Among the basic use cases, GenAI-powered chatbots can facilitate faster customer interactions and issue resolution.

With the advancement of large language models (LLMs), GenAI tools can engage with customers in more human-like conversations. Besides quicker responses, GenAI can transform customer operations with the following capabilities:

• Multilingual support can help customers ask their queries in their preferred language.
• 24/7 service availability provides round-the-clock customer support – outside business hours.
• Technical responses to customer queries – by accessing product manuals, user guides, and installation guides.

Launched in 2022, Air – powered by GenAI – is transforming sales and customer service by performing human-like calls – that last up to 40 minutes.

4. Logistics documentation

Here are some revealing documentation-related statistics:

• 97% of organizations have no (or minimum) processes for creating digital documents.
• Human errors in data entry cost businesses over $600 billion each year.
• 83% of employees are involved in recreating missing documents.

Manufacturing executives spend a lot of time generating necessary logistics documents such as invoices, bills of lading, and proof of delivery. With GenAI tools, manufacturers can automatically generate logistics documents, thus saving time and reducing human errors.

For example, using Document AI, manufacturers can combine AI capabilities with document intelligence in areas like:

• Invoice processing – by automatically extracting customer information and generating payments.
• Business report generation – by automatically generating visual graphs and summaries for decision-makers and stakeholders.
• Document classification – by automatically classifying documents (for easier search and retrieval).

How manufacturers can adopt GenAI technology

1. Exploration

In this phase, manufacturers can identify the benefits and limitations of GenAI technology and identify potential business functions where they can deploy this technology. This phase also involves identifying the business use cases of GenAI with the maximum ROI.

2. Planning

In the planning phase, manufacturers can assess data quality and readiness – along with the required skills to make this transition. In some cases, manufacturers have to deal with inconsistent data arising from legacy systems and diverse data warehouses.

Effective planning also includes analyzing the cost benefits of GenAI and the business value from generating each use case.

3. Pilot

According to Gartner, 30% of companies face hurdles in implementing GenAI after the pilot phase. The pilot phase of GenAI implementation is crucial for manufacturers to:

• Designing and developing the right solution architecture.
• Preparing high-quality relevant data for the selected use case.
• Implementing each use case as a pilot project.
• Developing a detailed roadmap for GenAI implementation.

4. Scaling

The scaling phase in GenAI implementation is all about continuous monitoring (or oversight) of each use case. Some questions that manufacturers must address include:

• What is the level of human intervention required for the GenAI application?
• What is the business cost of a GenAI “error?”

Scaling also involves how to extend GenAI capabilities to other business areas or applications.

5. Innovation

This phase involves developing an “innovative” mindset or culture within the organization. Manufacturers must perform a periodic assessment of the following:

• Implemented use cases
• Underlying technologies and tools
• User’s feedback and suggestion

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