Unlock Value Using
Generative AI in theUtilities Industry
Introduction
Shifting load growth in utilities across the world,
along with the headwinds of soaring energy
prices, rising inflation and unpredictable weather
conditions, mean grid reliability and resiliency is
becoming an ever-present concern.
In the meantime, most utilities have bold
aspirations to reduce their carbon footprint to
net zero in coming decades—and, in doing so,face capital, labor and materials challenges.
Moreover, the grid is only getting more complex

to operate as distributed energy resources
introduce new information and variables
into the system.
The emergence of generative AI is set against
this turbulent backdrop. Here we’ll explore someof the possibilities gen AI presents to the sector.
We’ll look at ways energy suppliers can leverage
the technology’s benefits to improve operations,and we’ll explore the transformational potentialof gen AI to help create a highly interconnectedenergy system and improve customer
experience (CX).

What Is Generative AI?
Generative 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 newcontent based on human instructions. The large
language models that underpin gen AI havebeen trained on vast amounts (typically morethan a billion parameters) of text data, enablingthem to comprehend and generate human-like
responses in natural language.

Fundamentally, gen AI tools perform
four key things well:
1. Generating new content with little or noguidance.
2. Summarizing existing content in a mannerthat can be understood by the target
audience.
3. Holding intelligent conversations with analmost human-like understanding of various
domains.
4. Performing Chain of Thought activities.

Market Opportunity
The tsunami of data generated in utilities frommyriad sources (sensors, smart meters, grid
infrastructure, customer interactions and more)mean gen AI has enormous potential in thesector. This is reflected in the following figures.
The gen AI market in utilities is expected tohit $8,676 million in less than a decade.

Operating segments due to see immediate
investment include transmission and distribution;
renewable energy integration; and energy
efficiency. Focused applications like personalized
CX, improved asset management, new energy
business, and safety are gaining the mostrapid traction.

Impending Use Cases for Utilities
There are several business functions in Energy &Utilities that can improve or acquire differentiablecapabilities. Our approach is based on SIG:
Search + Interactive + Generate. Some of thesedifferentiable capabilities are listed below:
Idea Evaluation and Refinement
Gen AI tools can aggregate the raw data(from process gaps, logs, defects, historical
data, procedures, specifications, and regulatorydocumentation) and combine or merge large
numbers of ideas to produce a best idea fora specific problem. For example, gen AI canconsume large ideas fed into training model andproduce a best sequence for a specific businessprocess, optimizing both cost and time.
Customer Engagement
Gen AI can generate a load profile for service
technicians and provide relevant information
to customers based on historical data, energy
usage, weather patterns and family size.
Facilitating open dialogue between customers
and the data which supports their utility needs
will build a stronger relationship between
company and consumer.
Employee Engagement and Asset Optimization
Gen AI can provide live asset information andengineering knowledge to help technicians better
understand asset history, engineering manuals,
and manufacturing specifications. This canact as a chatbot for technicians to access andunderstand T&D assets more effectively.
Intelligent chatbots for customer service
language models can power intelligent
chatbots that offer round-the-clock support tocustomers addressing their queries, schedulingappointments, and providing relevant
information on account-specific, outage info,
usage predictions, EV rates, and solar panel
installations. These chatbots enhance customer
service while interacting with the customers togenerate responses from in-house data. AI-driven
chatbots are well-versed in human speech andprovide more holistic and empathetic responses
than a traditional chatbot.

Maintenance Operations
Utilities can leverage gen AI to optimize
maintenance processes. These models
can generate and follow rules and inputs,
including work order statuses, crew schedules,
weather patterns, asset inspection histories,
circuit diagnostics, public safety needs,and manufacturer recalls. Maintenance
operators can use this information to create aninspection schedule for efficient routines andemergency maintenance.
Drone Data Processing
Along with the growing interest in gen AI, utilities
are also increasingly using drones to streamline
field processes and improve data capture.
Blending these two new technologies can give
utilities a better way to manage assets in thefield, remotely perform inspections, collect datafor restoration, and mitigate weather risks.
Specifically, gen AI can be used in conjunction
with a post-processing engine to delineate theinformation drones are capturing in the field.
Summarizing the results of the data a drone(or many drones) is capturing, then creating aplan of action for support, is a prime use case forgenerative AI.
Improving Failure Mode Analysis
Gen AI will help utilities mine unstructured
information captured from field visits, picturesand historical data to identify critical conditions
that signal potential field-equipment failure(such as damaged conductors or dampers,
loose cotter pins, vegetation encroachment,etc.), reducing maintenance costs whilemaintaining reliability.
Additionally, gen AI-powered chatbots will act
as intelligent personal assistants that help fieldengineers get things done, answer questions,provide advice, send messages, place calls, andcheck location information.

Generative AI in the utilities industry| 3

Automated Energy Usage Advisor
Gen AI excels at analyzing claims or priorauthorizations, swiftly extracting crucial
information, and identifying areas in whichhumans need to focus more. By reducing
the decision-making load on electricity users
around electricity use and providing information
on batteries, EV usage and solar household
consumption, gen AI can generate high-quality
recommendations to assist consumers in
reducing electricity consumption—and relieving
them of sticker shock.
The models can help consumers understandhome energy consumption, providing answers tosuch questions as:••••

How do I consume?
How should I consume?
When should I consume?
What is my consumption lifecycle?

In this way, gen AI will benefit consumers,
promote green energy use, and generally reduce
the world’s carbon footprint.
Risk-Based Replacement Decisions
Gen AI models can serve as intelligent
companions, providing operators withcomprehensive information to easedecision-making chores as utilities consider theprioritization of network replacement projects
based on customer risk, environment, health,safety impacts, and more.
Through integration with field information,
electrical equipment status and spatial data
overlay, gen AI can offer real-time insights
boosting the accuracy of:• Location-based network risk visualization.
• The risk estimate methodology of multiple
massive datasets.
Utility team leaders, network designers, capitalworks program leaders, and others will enjoy
reduced stress and better decision-making.

Smart Back-Office Intelligence
Gen AI can automatically extract key concepts,
relationships, and insights from textual datasuch as internal policy documents, procurement
contracts, HR policies, financial documents,
training content, electricity literature, salaries,benefits, and pensions.
When a user enters a search query, the software
processes the query and maps it to relevant
documents based on semantic similarity, context,and topic modeling. This will help in reducing
back-office mistakes and will produce a fasterprocurement process and a better employee
experience in the following ways:• Experience usage through automation forback-office issue resolution like held tickets,
stuck invoices, regulatory filing, etc.
• Elimination of technical debt created throughmassive applications and multiple process
documentation with removal of human labor.
• Provision of a smart interface to assist
in document flow, approvals, anderror correction.
Procurement leads, the finance operations
team, regulatory and corporate lawyers,
HR leaders, and training managers will all
benefit significantly.

Generative AI in the utilities industry| 4Immediate tangible business benefits
Decrease Unplanned
Downtime

Improve AssetOptimization

10%

15%

Increase first-time
fix rate and improve
technician productivity

20%

Decrease number ofsafety incidents

20%

Developing a strategy
While more utilities are starting to explore the industrialization of gen AI, the rate of adoption must keep
pace with innovation. There exists enormous opportunity in several areas, as shown in this verticallyTransmission
integrated Generation
value chain for electric andgas utilities.
RetailTrading

Categories

Stagesand istribution

Transmissionand Distribution

Generation

RetailTrading

Asset management

Asset management

Customer relationshipmanagement

Contract management

Work management

Work management

Customenr information
system (CIS)/billingPricing

GIS

Outage management

Customer self service

Trade operations

Fuel management

Field force management

Customer communication

Balancing and settlements

EHS and regulatorycompliance

GIS

BPO/call centre management

Risk management

Workforce management

Regulatory reporting

Energy efficiency/DSM
programs management

Supply chain andprocurement

PHEV Management

Outage/complaintmanagement

Plant operations

Advanced meteringinfrastructureHigh applicability

Medium applicability

Low applicability

Gen AI needs orchestration tobalance risk and rewards
Look for ways to foster awareness programs:
It is crucial to educate employees about gen AI
to dispel fears of job replacement and promote
understanding of its capabilities. This can beachieved through awareness campaigns,workshops, and educational programs.

Work with regulators and government agencies:
Considering the importance of data quality inbuilding robust gen AI models, there is a major
need for electricity, gas, and water regulatory
frameworks to govern data-sharing policies
in both regulated and deregulated markets.
This can help protect privacy, ensure ethicalpractices, and maintain transparency inAI development.

Generative AI in the utilities industry| 5

Emphasize safety in workplace procedures: As
gen AI continues to advance, prioritizing safetycontrols is a must. As critical infrastructure,utilities tend to be high-risk environments thatare incident-prone. There ought to be active
collaboration with governments, forestry
agencies, defense establishments, and customer
privacy and transportation authorities toestablish safeguards that prevent malicious use
or unintended consequences.

Implement continuous quality control:
Combining countermeasures like probing“hallucinations” will help monitor and control thetypes of inputs allowed in a gen AI model. These
countermeasures include using varied examples,
asking irrelevant questions, and checking privacy
data; all will improve gen AI’s ability to respond
appropriately. It’s vital to audit all generated
outputs in continuous cycles.

Adoption and acceleration
Develop a strategy for gen AI that integrates
it with existing AI: Providing access to curated
enterprise data is crucial in enabling AI
governance. Utilities must incorporate processtransformation to leverage a cognitive approach.
Due to the rapid growth and adoption of gen AI,
whether a utility is modernizing existing models
or building them from scratch, it is important to
use third-party partners.
Use high-quality training data: High-quality input
data is crucial; garbage in, garbage out. Gen AI
must be trained on a varied and representational
dataset that covers a wide range of systems andapplications. Using sufficient, unbiased data iskey to training an accurate, resilient model.
Incorporate human feedback: The training
process must incorporate feedback fromhumans. This will ensure that an understandingof the world that only a human can have isincorporated into the gen AI.

In Conclusion Cognizant’s Point of View
As gen AI keeps changing our way of life, its rapid
development is likely to multiply the impact of AI
overall, generating trillions of dollars of additional
value each year and transforming the natureof work. Stakeholders must act quickly to adopt
gen AI, and must prepare to address both theopportunities and the risks.
At Cognizant, we are looking to gen AI to bringvalue to our clients’ business. We are mobilizing
speed to scale our own capabilities; focusing
on value; and helping our utility clients define
their strategy. We are helping utilities develop
the full solution architecture and comprehensive
transformation approach encompassing people,
an operating model, technology and tooling,
data, adoption and change management, andassociated risk management.

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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