Generative AI in the
manufacturing Industry

Introduction
Generative AI helps organizations create competitive advantage throughunique capabilities.
The manufacturing industry is continuously evolving—striving to improve efficiency while reducing costand improving the customer experience (CX). All this is set against a backdrop of growing customer
expectations, rapid market fluctuations, and the rising need for sustainable practices.
Generative AI (gen AI) offers promising solutions to these challenges. By leveraging the power of gen
AI, manufacturers can analyze large volumes of diverse data in real time to gain contextual insights.
They can also evaluate designs quickly, thus improving efficiency and productivity, and drive down
costs in the supply chain. It can also serve as a sort of co-pilot for operations and customer service
organizations—again, boosting productivity while reducing the cost of errors.

What is gen AI?
Gen AI represents the forefront of artificial intelligence, focusing on creating contextually appropriate
content. In general, the term refers to a type of neural network that can create updated content basedon human instructions. The large language models that underpin gen AI have been trained on vast
amounts (typically more than a billion parameters) of text data, enabling them to comprehend andgenerate human-like responses in natural language.
Instead of relying solely on historical data, gen AI focuses on generating new instances of data that
resemble the training dataset. Gen AI algorithms, such as generative adversarial networks (GANs) orvariational autoencoders (VAEs), learn underlying patterns and characteristics of the data and use thisknowledge to generate new data points.

Fundamentally, gen AI tools perform four key things well:
Generating new content with little or no guidance.
Summarizing existing content in a manner that can be understood by the target audience.
Holding intelligent conversations with a human-like understanding of various domains.
Performing chain-of-thought (CoT) activities.

Gen AI potential for manufacturing industry
Gen AI offers immense potential to drive productivity, improve user experience, across key areas ofthe manufacturing industry such as after-sales operation, research and product development andmarketing and sales. Multiple analysts—McKinsey, IDC and Gartner—are expecting similar areas tobenefit from gen AI.
McKinsey has identified customer operations, marketing and sales, software engineering, andresearch and development as four key areas that could account for approximately 75% of the total
annual value from gen AI use cases in a recent report on gen AI.

Impact as a percentage of functional spend, %40
Customer operations
Software engineering(for product development)

30

Softwareengineering(forcorporate IT)

20

Procurement management
Legal

10

Pricing

Talent andorganization(incl HR)

Strategy

Represent ~75%of total annual
impact of gen AI

Risk and compliance
Product R&D1
Marketing
Finance

Manufacturing

Supply chain

Corporate IT1

Sales

0
0

100

200

300

Impact, $ billion

400

500

IDC published its findings from the manufacturing industry survey, which found out that 27% of thecompanies are actively investing in gen AI technology. The industry overall is expected to realize
greater impact from gen AI, considering the excessive costs of operating and maintaining industrial/
production equipment.

What is your organization’s current approach to gen AI?
34.6%

27.1%

We are ivnvesting in gen AI
technologies in 2023
We are doing some initial exploration ofpotential use cases
We are not doing anything yet

38.2%
Gartner predicts gen AI’s initiatives would be mostly focused on improving customer experience andretention, followed by revenue growth by building applications or processes to drive new revenue
streams.
While there are differing views on the use of gen AI for manufacturing, there is overwhelming
agreement that it will offer a disproportionate advantage to companies and will be extremely
beneficial to their workforces, and to the organizations themselves.

Primary focus of gen AI initiatives
38%

Customer experience/retention

26%

Revenue growth17%

Cost optimization

7%

Business continuity

12%

None of the above or not applicable (e.g. vendor or investor)

gartner.com

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Generative AI in the manufacturing industry

We at Cognizant, through interactions with our customers, and prospective discussions, haveidentified a set of use cases where gen AI can drive competitive advantage for the manufacturingindustry. While these use cases are not exhaustive, gen AI use cases are broadly classified into three
categories:

Conversational

Referential

Creative

Human-like conversations
with customers, suppliers,
and more.

Refer, summarize, andinterpret information suchas contractual documents
and more.

Create new content such as
documentation, product design
enhancements, software codeand more.

As gen AI technology evolves, some of the generative models have gained traction in the
manufacturing space for their wide-ranging applicability across manufacturing value chain. Below
are some of the models, and their relevance for the manufacturing industry.

Gen AI model
Variational
autoencoder (VAE)

Description• Often used in image andvideo processing• Works by taking in input
image and encoding it intoa lower-dimentional
representation, which isthen decoded to produce
an output image

Generative
adversarial
networks (GANs)• Used to generate new data
samples that are similar to
the training data (generator)• Accurately classify the data
as real or fake (discriminator)

Recurrent neuralnetworks (RNNs)• Used for sequential dataprocessing, such as natural
language processing andtime-series analysis• Output of each step is usedas the input for next stepLong short-term
memory (LSTM)
networks• Type of RNN that is designed to handle long sequences of data which
can be complex anddifficult to analyze• LSTMs can learn to recognize
patterns in data that occur
over a period of time

5|

Generative AI in the manufacturing industry

Application in
manufacturing• Intelligent quality control• Product servicing• Smart operational technology
systems management (OTSM)• Document generation, searchand synthesis• Inbound/outbound marketing• Product R&D• Better CRM, CX and Customer
seft-service• Product and processdevelopment• Informed procurement
decisions• Smart supplier management
contract negotiation andoptimization• Digital twin powered by gen AI

Early interest use cases
While the gen AI technology is still evolving, theindustry is investing in a few use cases in parallel,
to leverage the technology’s ability to process
large volumes of data and offer contextual
intelligence. Here are three of those areas anduse cases where we see early investments.
Through these experiments, organizations
are testing the waters, with a commitment toexpand the usage of gen AI to other parts of
their manufacturing supply chain.

• Cognizant is working with a top elevator
manufacturer, leveraging the gen AI
technology to develop a maintenanceadvisory solution that can ingest thousandsof pages of product specs, log data and FAQs
into an application that can respond withnatural-language chat or a speech interface.
This will enable technicians to diagnose andresolve problems much faster, without having
to consult other systems or experts.

Maintenance/service advisory

Smart operational technology
support and services

Product servicing• The manufacturing industry relies on a large
workforce to support the after-sales service
needs of their customers. This workforce
needs extensive training, and years ofexperience to gain the expertise required forfaster issue diagnosis and resolution. Gen
AI offers an opportunity to simplify this withthe ability to process large volumes of datafrom the user manuals, service manuals, andanalysis of past diagnosis and resolution
information to diagnose service issues quickly
and suggest resolution for maintenanceservice personnel with greater accuracy.
For manufacturing organizations and theircustomers, this reduces the overall equipment
downtime, and helps build product trust withcustomers..
• Companies like Embassy of Things andCognite have ventured into gen AI to train andvalidate their predictive maintenance models(with Twin Talk GPT) and enhance asset
performance management (with Cognite
Data Fusion), respectively.

6|

Generative AI in the manufacturing industry• Gen AI enables IT support professionals toeasily troubleshoot and resolve operational
technology (OT) issues without extensive priorknowledge or training of specific technologies.
• OT support requires extremely specific skill
sets and relies heavily on a limited numberof workers. Smart OT Support is an effective
way to reduce the mean time to repair, sothat support workers can be more productive.
Other benefits include less uncertainty
surrounding issue resolution, better issue
prioritization, query handling, and offline
analysis..
• By capturing historical data and making it
digestible, gen AI helpdesks can assist users
in sifting through information. It can enable
users to resolve issues with easy-to-follow
instructions and clear process descriptions.

Intelligent quality control

Gen AI-powered digital twins• Manufacturers are constantly implementing
and finding new avenues to minimize
errors, and defects in products during the
manufacturing process. Gen AI offers thepotential to predict potential issues byanalyzing vast sets of test cases and quickly
spotting trends, recurring issues, and potential
points of failure in new or modified products.
This allows manufacturers to realize improved
process accuracy and efficiency, scalability,
broader coverage on risk assessment andimproved product quality.

• The synergy of gen AI and digital twins offers
an opportunity to create virtual worlds, using
real-time information, that can mirror reality
with detailed simulations to support predictive
maintenance, minimize costly repairs andidentify potential bottlenecks ahead of time.

• Aerospace company Airbus is using gen AI to
power AR (augmented reality) glasses for more
efficient and accurate quality control. These
glasses transmit data captured from drones to
the AR glasses and tablets, enabling the user
to easily digest the data.

7|

Generative AI in the manufacturing industry• As gen AI technology progresses, we will seeorganizations progress from an early stageof modeling to a more connected digital twin
and then a digital twin that uses real data
(predictive twin/live data twin), updating in realtime and then eventually helping businesses
forecast the future (of your shop floor, forexample).

Contract advisory

Data insights

Smart supplier contract management

Informed procurement decisions
with market data

• Gen AI has the potential to revolutionize
contract management by tracking, analyzing
and automating compliance processes,
generating standardized and customized
templates that can reduce the overall costs
and time involved. It allows manufacturers to
get ahead of compliance issues and expedite
remediation.
• Such systems can set alerts for contractual
commitments, clause deviations, data privacy
commitments, contractual obligations
regarding data breaches and much more,thus improving overall compliance.
• Companies like Ironclad, Evisort and Spotdraft
have already integrated gen AI with contract
management systems to speed up negotiationand contract generation, and review process.
gen AI can identify and categorize types ofcontracts from your database into groups
based on type and function, using patternmatching algorithms.

• Through its ability to process large volumes ofexternal data to identify comparable products
and competing suppliers, gen AI offers aunique opportunity to benchmark product,
quality and cost information, and drive
recommendations for minimizing the overall
cost of purchase• Sourcing and procurement functions canbenefit using gen AI across areas from spend
capitalization to guided buying/purchasing
processes, thus enabling companies tomake well-informed decisions with high value
realization.
• An autonomous sourcing start-up Globality Inc.
has launched a gen AI based conversational
chatbot called “Glo” in its procurement
platform to gain insights on the supplier andproduct data that is already implemented bymany of its clients to make informed decisions
around purchasing products and identifying
suppliers.
• Gen AI-powered methodologies can uncoverpotential opportunities, provide benchmarks,
and indicate supplier risk positions by utilizing
immense amounts of external data such as
market indexes, company credit ratings andpublicly available information about suppliers.

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Generative AI in the manufacturing industry

Potential use cases
Gen AI is in the initial stages of maturity, and as society is starting to realize its benefits,
Cognizant has identified additional use cases where gen AI would be relevant across the
manufacturing value chain.

Research and design
Transformation of product R&D• Research and design are the backbone of any successful
product development effort in the manufacturing sector.
Gen AI will enable manufacturers to iterate very quickly
through a broad variety of design ideas, choosing whichmodel best fits their needs. What was once a time/costconsuming process will now be streamlined with gen AI.
• Early research analysis: Researchers use gen AI
to enhance market reporting, ideation, and product orsolution drafting.
• Virtual design and simulations: Researchers use gen AI
to generate prompt-based drafts and virtual designs,
allowing them to iterate quickly with more design
options.

Toyota research
institute researchers
have developed a processthat uses gen AI to
implement constraints
to the design process,producing specific,
innovative design
solutions.

Sales and marketing: Revolutionize content generation
Inbound and outbound marketing communication workflows• Gen AI has tremendous potential to improvise customer targeting by leveraging machinelearning algorithms to identify patterns and trends in customer buying behavior and hence tailor
sales and marketing strategies to specific customer segments. This can also optimize productrecommendations to customers.
• Lead generation, follow-ups and conversion become more effective using gen AI, as the sales team
can tailor its pitch to specific customers based on the latest market information.

After sales services: Transforming customer operations• Customer service is another area that can benefit fromgen AI, to offer real-time responses, multilingual support,better service availability, and autonomous, impactful
insights to customers.
• Customer self-service interactions: Customers caninteract with a human-like chatbot that delivers
immediate, accurate and personalized responses
sourced from voluminous documents such as user
manuals, product installation guides, etc., to answer
complex queries and ensuring brand voice regardlessof customer language and location.

9|

Generative AI in the manufacturing industry

A new tool called Air
can do 5 to 40-minutecustomer service calls andit sounds like a human
responding. It can work
autonomously and canperform actions acrossdifferent apps.

Document creation, search, and synthesis
Interacting with and auto-generating logistics documents• The time-consuming and meticulous process of generating and searching through and drafting
logistics documentation (such as BOL, POD, rate confirmations, Invoices, etc.) can be streamlined
and standardized with gen AI. This can reduce errors, decrease resolution cycle time.
• Enabling chat with the documents can help gain insights and information even from different
document types which contain images, scanned PDFs, digitized PDFs, etc., making reading of
documents faster.
• Microsoft’s Document Generative AI, that combines technologies like Azure AI, document
intelligence and Azure OpenAI Service with cognitive search as its knowledge base can improve
workflows like invoice processing (to automatically extract important information and generate
payments), report generation (to automatically generate a variety of graphs, tables and summaries
for stakeholders) and document classification (to automatically classify documents for easy retrieval
and management).

Key considerations
Generative AI is an evolving technology that promises greater
benefits to businesses. However, organizations including
manufacturers will need to evaluate data privacy, bias, ethics
and more in their approach towards gen AI. Here are some ofthe early considerations for manufacturers seeking to handle
the power of this technology:• Include only depersonalized and non-sensitive data.
• Stay current with gen AI news and trends.
• Develop a gen AI usage and ethics policy.
• Consider workforce implications. gen AI adoption will create
new jobs that require skilled workers.
• Offer career training to all employees; this will help them
use gen AI appropriately in their work, while also building
their skills.
• It remains vitally important for humans to use their best
judgement when deciding how involved Gen AI will bein their company. Ensure that contextual knowledge anddomain expertise are utilized before allowing anything to bepermanently altered by gen AI.
• Be wary of overreliance on gen AI. Complete relianceon the technology may lead to critical errors or missed
opportunities—which can be avoided by mandating
human reviews where applicable.

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Generative AI in the manufacturing industry

Getting started with gen AI
Getting started with Gen AI solutions require a deep understanding of the technology’s capabilities,
and involves several key activities such as problem definition, gen AI applicability assessment, data
preprocessing, algorithm selection, building a prototype model, fine tuning the model and then scaling
to business functions.
Cognizant recommends a five-step approach for organizations to undertake gen AI adoption
strategically, as depicted in the figure below.
• Assess readinessof data, skills andpeople01• Extended system
integrations• Continous
monitoring• Analyze
cost benefits

03• Select businessvalue generating
use cases for pilot

Explore• Introduce gen
AI Concepts• Identify pros
and cons• Extend to otherbusiness areas

Pilot

Plan

02

• Assess potential
across business
functions• Identify use cases• Define solutionarchitecture• Prepare data
• Implement pilot use
cases• Assess value realized• Prepare
implementation
roadmap

05
Innovate

Scale

04

• Innovation
by culture• Periodic review
of use cases,
prioritization,technology,
adoption and user
feedback

Foundational systems and data assessment, and readiness
Assess organizational data, architecture, infra requirements and build a strong foundation (including
operating model and governance)
Organization readiness and change management
Help employees understand and adopt gen AI to drive company’s business, value and strategy
Cross-disciplinary governance approach
Engage cross-disciplinary teams to define AI strategy as well as track and report progress over time to provide
openness on adoption and success
Security and compliance
Evaluate potential risks, regulatory requirements & compliance and proactively manage these through policy,
control, enablers & training

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Generative AI in the manufacturing industry

Organizations need to invest in gaining an understanding of the technology’s capabilities andevaluate the benefits it can offer to their businesses. This may require identifying specific functions
and use cases that could benefit from gen AI. When use cases are identified, organizations may wantto prioritize the use cases to pilot before considering expansion of gen AI to multiple use cases acrossbusiness functions. Based on the key success metrics, if the results enhance the current customer oruser experience or reduce costs and efforts, a decision can be made to scale and expand the useof gen AI. As with other technologies, the use of gen AI should also be continuously expanded anditerated through an ongoing innovation process.
While the technology offers benefits, organizations would need to make multiple decisions dependingon their use case, starting with the hyperscaler(s) to use (Microsoft Open AI, Google, etc.), perform
data readiness assessment, skills and expertise required to implement, and many more. In addition,organizations also need to consider the impact of the technology on humans, and plan for upskillingand cross-skilling its workforce, while also focusing on governance, security, and compliance.

Selecting the right partner
As organizations embark on their gen AI journey, it is critical and worthwhile to invest and collaborate
with the right IT partner who could bring in and integrate all the required stakeholders (for cloudservices, gen AI model , APIs etc.) and prepare a roadmap for Gen AI adoption. It is important forpartners to be able to bring knowledge of your business, be it about suppliers, dealers, operations,manufacturing providers or customer point of sales.
Keeping in mind how critical gen AI will be in the manufacturing industry, Cognizant has invested
in Neuro, an AI platform that brings together multiple features spanning areas such as IT services,automation, and gen AI. Cognizant has also forged best-in-class partnerships with the major hyperscalers, such as Google, Microsoft, and Amazon Web Services. Each of our hyper-scale partners
have invested and developed their own gen AI solutions, and capabilities. Cognizant also has a deep
knowledge of open-source language models that can be deployed for specific use cases. We are
currently working with several manufacturing clients to plan their gen AI journey in collaboration with
our hyper-scaler partners and can bring in the support and guidance for organizations to begin or
expand their gen AI journey.

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Generative AI in the manufacturing industry

Conclusion
Gen AI has the potential to revolutionize the manufacturing industry—enhancing CX, improving
productivity, reducing cost, and re-imagining every process. Gen AI can add value to company
operations, fuel economic growth, and dramatically transform how companies work and their overallpurpose in society. While technology is still evolving, the potential value it offers is a compelling
proposition for organizations to explore and get started with their respective gen AI initiatives. The timeto act is now!

Cognizant (Nasdaq-100: CTSH) engineers modern businesses. We help our clients modernize technology, reimagine processes and transform experiences so they can stayahead in our fast-changing world. Together, we’re improving everyday life. See how at www.cognizant.com or @Cognizant.

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