Generative AI in communications,media and technology
Introduction
Science fiction writer Arthur C. Clarke famously wrote, “Any sufficiently advanced technology is
indistinguishable from magic.” This wisdom seemed especially true when OpenAI unveiled its large
language model-based chatbot, ChatGPT, to the public. Seemingly all at once, tasks and talents
thought to be exclusive to humans were shared with machines. Generative AI (Gen AI) presents a stepchange in humanity’s ability to apply specialized knowledge at unprecedented speed, breadth andscale. Perhaps sooner than expected, Communications, Media & Technology (CMT) is poised to be abeneficiary of use cases that are likely to impact billions of people daily, as well as to be the staging
ground from which gen AI becomes ubiquitous in every industry.

An introduction to generative AI and the importance of mitigating risks
Gen AI refers to the branch of artificial intelligence that focuses on creating or generating newcontent, such as text, images, or music, that is indistinguishable from content created by humans.
At its best, it can rapidly accelerate knowledge and decision-making, and even emulate humaninsights and creativity on a vast scale.
And yet—as much as this powerful technology may seem like magic, it is not. A lot of work, byhumans, must go into organizing and curating underlying data, and in training algorithms forcontext and decision-making cues. This comes with risks. To mitigate them, enterprises will needto consider how to build trustworthy and responsible gen AI that is unbiased, secure, transparent,
explainable and which complies with privacy and copyright laws.

Harnessing the power of language models
Large language models (LLMs), like ChatGPT and Google’s Bard, are examples of gen AI systems
that are trained on vast amounts of text data from the Internet. Built on massively scaled neuralnetworks, these models utilize deep learning techniques to learn the statistical patterns, semantic
relationships and syntactical structures present in data. By training on such extensive datasets,
these models can generate human-like responses to text prompts, engage in conversations,answer questions and even assist in content creation.
Several trends spanning industries have made generative AI increasingly relevant. The explosionof big data and the availability of powerful computing resources, advances in machine learningalgorithms and techniques, as well as improved natural language processing capabilities are
fueling the development of more sophisticated generative models that can significantly improvebusiness functions across multiple industries.
In many instances, a private language model is the best fit to address a use case. Thesemodels may be fed by any number of data sources, including proprietary ones. A model may
aggregate outputs of multiple subsidiary language models. Additionally, insights gained throughcomplementary AI or machine learning (ML) models may also be used as inputs for algorithms thatgenerate prompt responses. For example, gen AI may be trained to produce an evidence-based
argument for how to best optimize a process.
Enterprises will need to face important questions about the degree to which they might sacrificeauthenticity and neutrality to produce a business outcome. For example, in response to a productrecommendation, an organization may be understandably reluctant to recommend a competitor;

Generative AI in communications, media and technology

2yet how a company approaches the situation
may affect trust. Decisions like this require
mindful planning, monitoring, governance,measurement—and direct user feedback.
Overall, the advancements in gen AI and large
language models offer enterprises powerfultools to automate customer interactions,enhance content creation, extract meaningful
insights from data, and more. By leveraging
these technologies, businesses can improve
operational efficiency, deliver personalized
experiences and gain a competitive edge inthe fast-paced digital landscape.

Implications for CMT
CMT lies at the crossroads of gen AI
foundational technologies and many of
its highest potential use cases, making it abreeding ground and a nexus point to improve
the lives of billions of people every day.
For communications service providers,
gen AI promises to inform how services
are sold, delivered, tailored and supported.
For media companies, it promises to be agame changer for how content is produced,
discovered, personalized and monetized. And
for technology companies, it promises to lay
the foundation from which gen AI will scale
into myriad applications, while simultaneously
revolutionizing search and intra-language
communications.

Generative AI in communications, media and technology

In each of theseindustries, we’re likely tosee significant tractionin the near future fromfoundational use casesthat drive improved
outcomes.
Communications
Contact centers for customer support
While AI has been used in contact centersby communication service providers (CSPs)
for years, generative AI will provide improved
capabilities and greater impact. Language
models can power intelligent chatbots thatoffer round-the-clock support to customers,
addressing their queries and providing relevant
responses, as well as letting customers makepayments and execute similar transactions.
CSPs will be able to more accurately gauge
customer call summaries to understandcustomer sentiment and identify cross-sell andup-sell opportunities. This iterative process will
result in CSPs deploying increasingly informed
virtual agents that can engage in moresophisticated and personalized customer
interactions.

3Network optimization
Generative AI connects multiple complex AI/
ML models used across network planningand operations with LLMs that are able tocomprehend network behaviors and develop
initiatives for network capacity planning andperformance. It can analyze the network
traffic, identify patterns, and provide feedback
on how to optimize the network’s performancebased on those patterns. The models will be
trained on curated internal customer data,
making the predictions more factual andrelevant, and quelling privacy concerns.

Network troubleshooting anddiagnostics
CSPs will benefit from the cost savings
stemming from optimized field service
devices which will have improved diagnostic
and analytical capabilities. Generative
AI can facilitate installation, parts andtroubleshooting, while also analyzing
equipment data to identify potential problems
and schedule preventative maintenance. This
will reduce the number of impromptu service
calls that companies will need to respond toand could ultimately prevent outages andimprove reliability.

Media
Content creation, production andmanagement
Generative AI has democratized multiple
dimensions of content creation, ranging

Generative AI in communications, media and technology

from text to audio to video. This will havesignificant impacts on all kinds of content
creation, including synthesizing research forjournalists, automating post-production editingfor videographers, and consolidating music
libraries for music producers. Generative AI
will enable creatives to spend less time on themundane, repetitive aspects of their work,
freeing them to focus on the highly creative
elements.

Personalized audience experiences
Low switching costs have put pressure
on media companies to provide fluid andengaging audience experiences that will
reduce subscriber churn. Generative AI
can help in this effort through its optimized
search and recommendation capabilities,
matching viewers with personalized content
with increasing accuracy. Models are currently
being trained to use sentiment analysis toprovide content recommendations for viewers
based on their mood and similar behavioral
data. Personalized content will become
increasingly sophisticated as generative
AI plays a larger role in content curation,assembly and creation.

Advertising and subscription
monetization
Maintaining and improving monetization
has been a challenge for media companies
as they navigate the ever-expanding digitaloffering landscape hosted on various
platforms and devices. Conventional
advertising and subscription models are not44

providing the necessary returns that they once did; however, generative AI can mitigate someof these losses. Enhanced personalization can help to reduce churn while also providing moretargeted and contextualized advertisements.

Technology
Search engine accuracy
Search engines that utilize LLM are built with a deep learning algorithm that better understands
language, resulting in increased accuracy of results . As consumers gravitate toward more
personalized questions, LLM allows search engines to provide increasingly more nuanced andsynthesized answers. This process improvement can allow for more creative uses of the traditional
search bar such as conversational replies, personalized recommendations and predictive inquiry
response.

Data extraction
In a market where data propels competitive advantages, the ability to extract information from
unstructured data sets can set a company apart. With LLM, sources such as social media,
customer reviews and other unstructured online content are available for inclusion in data sets. This
expansion of data access directly improves a company’s ability to classify customer preferences
and provide more personalized service.

Language translation
Global businesses rely heavily on language translation to meet their business objectives. Language
models enable faster and more precise language translation for both internal and external
parties. LLMs are also able to navigate language nuances and cultural differences, improving
communication efficiency and accuracy across the business.

Enterprise-wide applications
In addition to vertical use cases, gen AI is likely to also have a major impact on CMT from ahorizontal standpoint. Language models have many enterprise-wide applications, especially as
they relate to IT operations, including:

DevSecOps
Language models improve IT worker productivity by helping generate more accurate code. In
addition to code generation, language models can also analyze code bases, identify security
vulnerabilities and assist in building secure software development practices. By integrating
language models into DevSecOps workflows, organizations can enhance their code review
processes and mitigate cybersecurity risks.

Generative AI in communications, media and technology

5Data governance and compliance
How organizations handle customer data has become an increasing concern in recent years.
Customers are demanding heightened data security measures, necessitating robust data
governance and compliance practices. Language models can aid in data management andgovernance tasks, ensuring compliance with privacy regulations, facilitating data anonymizationand improving data quality.

Knowledge management
Language models can automatically extract key concepts, relationships, and insights from textual
data such as internal policy documents, market research, customer data and financial statements.
When a user enters a search query, the language model processes the query and maps it torelevant documents based on semantic similarity, context and topic modeling. This enables moreaccurate and contextually relevant knowledge retrieval, while ensuring necessary security measures
are followed.

Data privacy, security and AI safety
As language models are increasingly deployed in companies, organizations must take proactive
steps to ensure data security, privacy and AI safety. Additionally, there are three levels of safeguards
that an enterprise needs to put together to use language models safely.
At the first level, data deidentification and tokenization are essential strategies to ensure that anorganization’s interaction with the language model remains secure and that the data remains private.
The second measure takes place at the operating model level where the operating model, by
design, prevents any detokenized data leakage. Similarly, the operating model also needs to ensurethat any incorrect outputs from the language model are prevented from contaminating the current
business processes. Both human- and AI-based feedback loops must be utilized.

Generative AI in communications, media and technology

6

The third measure occurs at the business level; it ensures that consumers of generative AI content
understand that the content has been created by AI. The organization will also need to provide
delineation of responsibilities based on the language models being used and their contract withthe language model provider.
The hyperscalers and model vendors do play a key role in determining the security of the operating
model, but with the right set of safeguards in place, generative AI can be extremely secure andreliable, meeting an organization’s data privacy and security needs.
The EU has proposed the AI Act, a sweeping set of consumer protections from potentially
dangerous applications of AI in general. In the US, legal frameworks around AI-generated content
are still being discussed, though it is expected that this area will quickly evolve in the near future.
The companies that are able to stay one step ahead of these policy decisions will find that they
have a unique competitive advantage in the years ahead.
Lastly, it’s important to remember that language models have been trained on sets of data that
may contain inherent biases. These biases are manifestations of years of decision-making byhuman agents. To ensure organizational equity, companies must track the AI-driven decisionmaking processes over an extended time horizon in order to detect potential biases and correct
them as needed.

Prioritizing areas of investment
on generative AI
The utilization of language models holds immense promise for the communications, media andtechnology industries, enabling organizations to unlock new capabilities and improve customer
care. However, selecting the right use case for language model deployment requires carefulconsideration of the risks and rewards involved. By balancing the potential risks, rewards andcosts of integrating the language model output within their workflow, organizations can select the
optimal generative AI investment areas.

Stakeholder benefits
A successful language model deployment should bring tangible benefits to multiple stakeholders
within the organization’s ecosystem. Organizations can determine the value of a particular area ofinvestment based on the differentiated capability generative AI brings in or the size of the problem
it can potentially solve.

Generative AI in communications, media and technology

7

Risk assessment
For every generative AI use case, organizations need to consider the risks associated witheach specific implementation, which vary considerably based on the use case. For example, arecommendation for network coverage and scheduled maintenance based on AI-analyzed traffic
patterns carries far higher risk for a communications provider than producing a digital marketing
campaign based on AI-generated content.

System integrations
Language models may generate valuable insights on their own, but integrating them into anorganization’s existing operating systems and workflows is essential to realize their full potential.
These integrations will enable the models to develop the domain-specific knowledge they need toperform optimally for that organization. Adequate resources, technical expertise and collaboration
with IT teams is necessary to ensure seamless integration and implementation.

To strike the right balance between risk and reward in language
model deployment, organizations can follow these guiding
principles:
Iterative deployment
Adopt an iterative approach, continuously evaluating the performance, impact and risks
associated with language models. Implement feedback loops and mechanisms to address issues,refine processes and improve outcomes over time.

Collaborative approach
Generative AI needs a truly diverse set of skills to implement it in a safe and responsible manner.
Organizations should collaborate with stakeholders such as managers, data scientists, legal experts
and customer representatives—in addition to the traditional IT organization—throughout thedecision-making process. An environment that fosters communication and collaboration is essential
to ensure the most comprehensive assessment of risks and rewards.

Start with low stakes use cases
Begin with use cases that have relatively low impact and risk to understand the capabilities andlimitations of the language model within a particular business context. Gradually expand to more
complex scenarios as confidence and experience grow. While many of the above-mentioned use
cases would serve as great starting points for a company’s generative AI journey, we recommend
starting with use cases that deal with improving internal processes, since it is relatively inexpensive
to create a framework to supervise the output of the model in such scenarios.

Select the right partner
It is critical that an organization selects the right partner as it begins their generative AI journey.
Keeping in mind how critical generative AI will be to the CMT industries, Cognizant has invested
heavily in offerings that bring together the IT services, automation and generative AI capabilities
required by the modern enterprise. Cognizant also has forged best-in-class partnerships with the
major hyperscalers such as Google, Microsoft and Amazon Web Services, and each partner brings
its own unique perspective on developing optimal generative AI solutions. Cognizant has deep
knowledge of open-source language models that can be deployed for specific use cases. We are
currently working with several CMT clients to plan their generative AI journeys in collaboration with
our hyperscaler partners and we hope to partner with you as we look ahead.

Generative AI in communications, media and technology

8Conclusion:

Move forward while balancing risk and reward
Generative AI and large language models have the potential to revolutionize thecommunications, media and technology industries. By leveraging gen AI capabilities,
organizations can streamline operations, enhance decision-making and improve customer
experiences.
However, this transformation must be approached with a strong commitment to strategy,
data security, privacy, and AI safety. To learn more about generative AI and its potential foryour organization contact us.

To learn more about how Cognizant can help you with your digital transformation initiatives,
visit Cognizant.com/consulting or contact the authors:
Laurie Hutto-Hill
VP Consulting, Communications, Media & Technology, Cognizant
Laurie.Hutto-Hill@cognizant.com
Chad Andrews
AVP Consulting, Communications, Media & Technology, Cognizant
Chad.Andrews@cognizant.com

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

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