Maximizing AI’s Potential: The Key Role of Data Ecosystems

The Future
of Enterprise
Data & AI
100 Data, Analytics, and AI Leaders Reveal
How Businesses are Unleashing the Power
of Data and AI to Improve Performance

Contents
3
17
Foreword, Methodology
and Contributors
Beyond Automation:
Embracing the Promises and
Challenges of Generative AI
11 Enterprises Grapple with Data
Quality, Talent Shortage,
and Challenges in Data
Democratization and Migration
Building the Foundation:
Data Ecosystems Essential
for AI Success
16 Data Integrity, Technology
Partnerships, and Trust in
AI Systems Top the Agenda
Click below to navigate
5
21
Executive Summary
and Key Findings
Generative AI Adoption on
the Rise Despite Talent Gaps,
Security Concerns, and
Reactive Strategies
12 Treading the Tightrope:
Balancing AI’s Potential with
Privacy and Ethical Concerns
7
22 Conclusion
C H A P T E R T W O
D ATA I N S I G H T S
C H A P T E R O N E
C H A P T E R T H R E E
The Future of Enterprise Data & AI
2

Foreword
The Fourth Industrial Revolution,
marked by rapid digital growth,
has closely linked global
businesses to the evolving world
of Artificial Intelligence (AI),
cloud, and advanced analytics.
AI has transitioned from a realm
of speculation to an undeniable
reality, reshaping industries and
altering the very foundation of how
businesses operate. Generative
AI serves as a testament to this
incredible transformation.
This report uncovers the multi-
faceted relationship between data,
analytics, and AI, as seen through
the lens of 100 C-suite leaders and
senior decision-makers in Data,
AI, and Innovation. As you embark
on this exploratory journey, you’ll
uncover key trends, such as the
surge in Generative AI adoption, and
dive deeper into challenges like data
quality and concerns around privacy
and ethics. This comprehensive
study delves into the intricacies of
data ecosystems, highlighting the
critical nature of data quality, the
drive toward data democratization,
the vast potential and ethical
quandaries associated with AI, and
the promises and hurdles inherent in
adopting Generative AI.
Industry stalwarts lend their voices
to this discourse, shedding light
on the real-world challenges and
opportunities at the crossroads
of data and business. Through
their narratives, a common theme
emerges: For AI and analytics to
bear fruit, the data foundations
must be robust, accessible, and
ethically managed.
I firmly believe that integrating AI in
businesses is a journey encompassing
ethical decision-making, robust
security frameworks, and the nurturing
of talent that understands both
the language of machines and the
nuances of human needs.
We have curated insights and
synthesized them into a guide for
any enterprise aiming to sail smoothly
on the digital seas of the future.
Whether you are an industry leader,
a market observer, or a policymaker,
this report promises a deeper
understanding of the shifting sands
of the AI and data landscape.
Akhilesh Ayer
EVP & Global
Business Unit Head,
WNS Triange
3

Methodology
We surveyed 100 C-suite leaders
and senior decision-makers in Data,
AI, and Innovation from industries
including Manufacturing, Retail,
Consumer Packaged Goods, Banking
and Financial Services, Insurance,
and Healthcare. Of these, 40% were
from The United States, 40% were
from Europe, and 20% were from Asia
Pacific. Respondents were selected
from global enterprises with at least
$500 million USD in annual revenues.
Participants in the research hold
senior decision-making roles, such
as Chief Data Officers, Chief Data
and Analytics Officers, and Heads of
Data, Analytics, Innovation, and AI in
major international enterprises.
Respondents answered 16 questions
about how they are overcoming
challenges related to managing
their data to enable the success of
advanced analytics and AI initiatives.
Contributors
Anish Agarwal
Global Head
of Analytics,
Dr. Reddy’s
Laboratories
Ines Ashton
Director of
Advance Analytics,
Mars Petcare
Siddharth Bhatia
EMEA Growth &
Practice Leader –
AI, Analytics, Data and
Research, WNS Triange
Cecilia Dones
Former Head
of Data Sciences,
Moët Hennessy
Zachary Elewitz
Director of
Data Science,
Wex
Serena H. Huang
Chief Data Officer,
ABE.work
Ravindra Salavi
Senior Vice President –
AI, Analytics Data and
Vivek Soneja,
Corporate Vice President –
AI, Analytics Data and
4

n the fast-changing digital age,
artificial intelligence (AI) has
become a key component of
global enterprise strategy, capturing
the attention of business leaders and
the public. This shift became more
pronounced with the introduction of
ChatGPT, the first widely adopted
large language model, in late 2022.
Our extensive research,
conducted among 100 C-suite
leaders and senior decision-makers
in Data, AI, and Innovation from the
US, Europe, and APAC, highlights
significant changes, challenges, and
opportunities in data management
and AI integration within large
global enterprises.
The findings show a marked
interest in generative AI, with an
impressive 76% of respondents either
preparing for or actively involved
in generative AI projects. However,
there is also a notable concern
about security and data privacy.
Almost half (47%) of the participants
reported significant challenges in
implementing generative AI, mainly
due to security concerns.
The ethical foundation of AI
adoption is also a major concern
for industry leaders. A significant
72% of respondents expressed
deep concerns about the ethical
implications of AI decision-
making within their organizations.
Moreover, finding skilled AI
professionals is another hurdle.
Two-thirds (66%) of our participants
found it difficult to attract
professionals skilled in AI-specific
tools or programming languages.
The report is organized to
provide a comprehensive view
of this changing landscape. First,
we’ll examine the data ecosystems
within enterprises, highlighting
the importance of modern data
management and how it underpins
the transformative potential of AI
and analytics projects. Then, we’ll
look at how data and AI leaders
are creating value from their
AI initiatives, as well as the key
challenges our research revealed.
The final section focuses
on generative AI, discussing
its fundamental principles, its
potential to transform industries,
and the associated challenges,
especially regarding talent
acquisition and retention.
As global enterprises prepare
for an AI-driven transformation,
this report offers a comprehensive
perspective on the many complexities
and opportunities that lie ahead. By
delving deeper into the subsequent
sections, enterprise leaders, data
professionals, and policymakers can
better equip themselves to navigate
and lead a future where data and AI
converge to drive unprecedented
business performance.
I
Executive Summary
5

Key Takeaways
Source: Corinium Intelligence, 2023
of respondents rate their
data democratization
efforts as only
moderately successful
47%
of respondents are
extremely concerned about
the ethical implications
of AI decision-making in
their organizations
72%
of respondents say that the
integrity and quality of the
data to be used in AI and
analytics initiatives is the
most crucial aspect
60%
are either planning or
are currently involved in
generative AI projects
76%
of respondents are
implementing phased
integration to bridge the gap
between legacy systems and
intelligent cloud and data
systems
54%
of respondents say that
security and privacy
concerns top the list of
challenges in hosting or
implementing generative AI
47%
6

ata, analytics, and AI are essential enablers in
today’s business landscape. They are key to
promoting innovation, increasing efficiency,
and managing risks.
To wield these tools effectively, modern businesses
rely on interconnected networks of data, tools, and
processes that enable the management and analysis
of vast amounts of data. These data ecosystems
include various data sources, both internal and
external, as well as data storage, data processing
tools, and analytics applications.
A well-organized data ecosystem is crucial for large
organizations as it ensures data accuracy, consistency,
and availability, which are key for informed decision-
making and gaining a competitive edge. Moreover,
it also addresses challenges related to data security,
compliance, and integration, thus ensuring smooth
functioning and maximizing the value derived from
the data.
“Enterprise data ecosystems involve making data
available for business use, not only for insights and
analytics but also for dashboarding, reporting, and
critical decision-making at the executive level,”
says Anish Agarwal, Global Head of Analytics at
Dr. Reddy’s Laboratories, an Indian multinational
pharmaceutical company.
D
Building the Foundation:
Data Ecosystems Essential
for Advanced Analytics
and AI Success
Optimizing data ecosystems is critical for leveraging AI
and advanced analytics in business decision-making amid
challenges of data quality, availability, and integration
K E Y F I N D I N G
C H A P T E R O N E
7

Cecilia Dones
Former Head of Data Sciences, Moët Hennessy
Yet, the implementation of innovative
data, analytics, and AI initiatives is often
more difficult than anticipated. Over
time, many businesses accumulate
disparate systems, fragmented data,
and outdated technology, all of which
can hinder or even halt efforts to
innovate and modernize.
Our survey of 100 C-suite leaders
and senior decision-makers in Data,
AI, and Innovation conducted in
partnership with AI, analytics, data
and research firm, WNS Triange,
has identified a host of challenges
for enterprises on the road to
modernizing their data ecosystems.
The Importance of Modernized
Data Management and Its
Key Challenges
The single biggest enterprise data
challenge revealed in our research
is ‘data quality,’ named by 57%
of respondents.
“From a data perspective, the
primary challenge is data quality,
which includes several dimensions:
completeness, conformity, accuracy,
integrity, and currency,” Agarwal
explains. “It is vital to establish a
method to measure, monitor, and
improve data quality.”
Additionally, nearly half of
respondents cited data availability,
accessibility, useability, and
data governance (48% and
It’s crucial to have the right data, in
the right format, available when it’s
needed,” adds Ravindra Salavi, Senior
Vice President – AI, Analytics Data
and Research at WNS Triange.
He continues: “Many analytical
programs haven’t delivered the ROI
businesses expected because data
scientists often haven’t received the
correct data. Additionally, the data
might not be updated frequently
enough, or it might not meet the
quality and conformity standards
the business needs. The depth and
history of data are also essential for
analytical models.”
Salavi’s insight underscores the
ripple effect of this challenge, affecting
not only the ROI of analytical programs
but also the overall decision-making
process of the business.
What’s more, aspects of
modernized data management, like
having an efficient data catalog,
are at the heart of solving data
accessibility issues and are essential
to implementing advanced analytics
and AI initiatives.
47%, respectively) as significant
challenges when creating better
data ecosystems.
“Many organizations view data
governance as a significant obstacle,
regardless of their level of maturity,”
notes Cecilia Dones, Former Head of
Data Sciences at Moët Hennessy. “This
is because governance often reveals
accumulated technical debt from past
decisions, such as failing to normalize
data or maintaining records exclusively
in Excel. These short-term choices can
hinder long-term capabilities.”
“One of the key challenges in data
management today is data availability.
“Creating value comes from resolving these
friction points. Leaders should resist the urge
to deploy AI indiscriminately. The goal should
always be value creation—finding ways to
reduce friction within the ecosystem”
The Future of Enterprise Data AI
8

“When building a model, it’s crucial
to determine if a variable is well-
populated, as this impacts model
accuracy,” says Ines Ashton, Director
of Advance Analytics at Mars Petcare.
“With an efficient data catalog, one
can quickly identify and prioritize the
most reliable variables for modeling.
This speeds up the decision-making
process and reduces the chance of
investing in ineffective models.”
Efforts to Make Data
Accessible Yield Mixed Results
Making data accessible to everyone
in an organization, regardless of
technical expertise or rank, is the goal
of data democratization. This is crucial
as it enables all employees to make
informed decisions, encourages
innovation, and helps to break down
data silos.
Yet, our research shows that for
many organizations, the journey
toward data accessibility has seen
varied success.
Nearly half (47%) of the respondents
in our research feel their efforts in
making data accessible are only
have access to this data. You can’t
put that genie back in the bottle. So,
getting that governance model correct
is important,” says Serena H. Huang,
Chief Data Officer at ABE.work.
“From there you can create personas
for your different kinds of users to
manage the data they can see.”
Implementing a robust governance
model and managing data access
through personas, as recommended
by Huang, along with treating data
as a product and establishing
an internal marketplace for data
products, as advised by Salavi,
are crucial strategies that can help
organizations more successfully
make their data accessible.
Data Integration Tools and the
Role They Play in Integrating
Siloed and Fragmented Data
In a world increasingly powered
by data, enterprises face the
overwhelming task of managing
scattered and compartmentalized
data due to a variety of factors,
including the use of diverse systems
and technologies, and the impact
of legacy technology on data and
analytics initiatives.
Over half of companies (57%) use
data integration tools to dismantle
silos, making it the most adopted
strategy by companies to address
fragmented data. Meanwhile,
54% of companies are executing
phased integration of systems to
minimize disruption, which is the
leading strategy for bridging the gap
between legacy and cloud systems.
However, data migration to the
cloud remains a significant obstacle
for two-thirds of respondents, 67%
of whom find it difficult to navigate
the complexities and challenges
associated with this task.
‘moderately effective.’ This suggests
that while there is some access
to democratized data, there is a
significant scope for improvement.
“The core [of data democratization]
remains that every stakeholder should
have access to the data they need,”
elucidates Salavi. “A key example
from my experience involves treating
data as a product. Whether it’s a
verified dataset, an analytical model, a
visual dashboard, or a master record
from a master data management
initiative – each of these is a ‘data
product.’ We then created an internal
marketplace for these products,
enabling colleagues to ‘publish’ or
‘subscribe’ to these resources. By
doing so, we eliminated redundancies
and streamlined data usage.”
However, making data accessible
comes with its challenges. Not least,
privacy and security of sensitive data.
As a result, proper governance of the
data is essential to the success of
democratization initiatives.
“The first step when making data
accessible is to connect with your
legal team to work through who can
Data Quality Edges Out Talent as Top Enterprise Concern
Data quality
Data governance
Talent
Data infrastructure and architecture
Data availability, accessibility,
and usability
None of the above
Which of the following enterprise data challenges are the most significant for
your organization today?
57%
55%
48%
47%
38%
1%
9

Serena H. Huang
Chief Data Officer, ABE.work
“Data integration platforms offer
significant benefits,” says Agarwal.
“First, they provide a comprehensive
view of underlying metadata, allowing
insights into additional attributes
essential for data cataloging. Second,
these platforms can measure and, to
a large extent, remediate data quality.
Plus, they integrate with third-party
databases, ensuring data consistency.”
The need for data integration tools
is accentuated by the complexity
of aggregating data from multiple
sources while ensuring data integrity.
Indeed, our research indicates that
more work needs to be done to
prepare data for use in AI initiatives.
“Aggregating data from multiple
sources is a complex problem,”
Agarwal emphasizes. “Data integrity
is crucial. Usability is not just about
availability; it’s also about creating
a comprehensive data catalog that
allows data scientists, analysts, and
businesses to understand and utilize
the data effectively.”
The impact of legacy technology
is significant. Cecilia Dones, Former
Head of Data Sciences at Moët
Hennessy, notes: “For legacy
organizations with established data
architectures, transitioning everything
to the cloud can be risky due to
fragile components in their system.
For most large organizations with
legacy systems, a hybrid solution
might be necessary.”
transitioning to a modernization
agenda within their organization.
The primary concern is falling
behind technologically due to the
weight of their legacy systems
and the vast amount of potentially
irrelevant data they’ve amassed,”
he concludes.
The transformational possibilities
of well-managed data, free from
fragmentation and silos, are
immense and crucial for the future
success of any enterprise. Data
leaders who successfully navigate
these complexities can unlock
a wealth of insights, enabling
smarter decision-making, enhanced
customer experiences, and
streamlined operations.
Moreover, by dismantling silos
and integrating data across the
enterprise, organizations can
cultivate a culture of collaboration
and innovation, ultimately driving
competitive advantage in an
increasingly data-driven world. As
technology continues to evolve,
so too will the tools and strategies
available to data leaders, opening
up new opportunities for leveraging
data as a strategic asset and
catalyzing positive change across
the organization.
The challenge of modernization
is further complicated by the
makeshift solutions many
organizations use to supplement
their older systems.
“Many organizations are trying to
compete with the surge of tech-
enabled companies by applying
makeshift solutions. They’re trying to
supplement their older systems with
solutions like intelligent process
automation, robotics process,
or data workflows. However, no
matter how much you modify an old
system, it won’t become a cutting-
edge solution,” Siddharth Bhatia,
EMEA Growth Practice Leader -
AI, Analytics, Data and Research at
WNS Triange elaborates.
“The biggest challenge for
many CIOs, CDOs, and CTOs is
“The first step when making data accessible is to
connect with your legal team to work through
who can have access to this data. You can’t put
that genie back in the bottle. So, getting that
governance model correct is important”
10

Enterprises Grapple with Data
Quality, Talent Shortages,
and Challenges in Data
Democratization and Migration
Phased Integration Tops List:
Companies Cautious in Modernizing Legacy Systems
Migration Woes
What strategies has your organization utilized to bridge the gap
between legacy systems and intelligent cloud/data systems?
What specific challenges has your
organization encountered when
implementing cloud solutions and
consolidating organizational data?
Complexities and challenges
related to data migration
67%
Compatibility issues between
cloud solutions and existing
infrastructure
47%
Concerns over data security
and privacy in the cloud
42%
Data is fully
democratized and
easily accessible to
all relevant staff
Deployed data integration
tools or platforms
Implementing phased integration of
systems to minimize disruption
Established a unified
data governance policy
Hiring new talent familiar with both systems
Developed cross-department collaboration
and communication strategies
Engaging expert third-party consultants
for a smoother transition
Implemented enterprise
data platforms
Conducting training programs
for staff to operate both systems
Hired data management specialists or
engaged third-party service providers
Using middleware or APIs to connect
legacy and modern systems
We have not yet addressed this issue
Planning for a complete overhaul,
currently in a hybrid infrastructure setup
Establishing a detailed modernization
strategy and roadmap
Most data is
democratized
and somewhat
accessible
Some data is
democratized
and occasionally
accessible
Only a limited
amount of data is
democratized and
rarely accessible
Data is neither
democratized
nor accessible
Extremely
effective
Very
effective
Moderately
effective
Slightly
effective
Not at all
effective
Nearly Half Admit that Data Democratization is Only ‘Moderately Effective’
Over Half of Companies Turn to Data
Integration Tools to Break Down Silos
How would you evaluate the effectiveness of your
organization’s efforts to implement data democratization?
What strategies has your organization implemented to address
issues related to siloed and fragmented data?
10%
10% 26% 47% 16%
1%
57%
54%
50%
41%
49%
37%
41%
34%
33%
32%
2%
30%
30%
11

nterprise data and AI are crucial
for modern businesses seeking
to create value. But which use
cases are prime for exploitation?
And where might AI be less than
commercially viable?
Data leaders are faced with the
challenge of selecting the right AI
initiatives that not only resolve friction
points in the business but also lead to
demonstrable value creation.
“If I were to advise executive
leaders on AI strategy, I’d suggest
focusing on identifying friction points
within the business processes. Then,
bring in experts to recommend
whether AI or other technologies
can address these issues,” says
Cecilia Dones, Former Head of
E
Treading the Tightrope:
Balancing AI’s Potential with
Privacy and Ethical Concerns
Data, analytics, and AI
leaders are navigating the
promise of AI and advanced
analytics while weighing
data quality issues as well
as privacy and ethical
considerations
K E Y F I N D I N G
C H A P T E R T W O
Data Sciences at Moët Hennessy.
“Creating value comes from
resolving these friction points.
Leaders should resist the urge to
deploy AI indiscriminately. The goal
should always be value creation—
finding ways to reduce friction within
the ecosystem.”
“The business side understands
the data and sometimes the potential
of AI and machine learning, but they
might not know where or how to
apply it,” adds Ines Ashton, Director
of Advance Analytics at Mars Petcare.
“Balancing feasibility with value is
crucial. AI and machine learning can
be exciting for business, but they
must be applied where the value
generated outweighs the cost.”
12

Additionally, technological
partnerships play a pivotal role in
the success of AI initiatives. Knowing
when to employ external assistance
might be a far more cost-effective
solution than attempting to develop
one in-house.
Our research shows that ‘leveraging
technology partnerships’ is a top
priority for 65% of organizations when
it comes to AI investments.
Zachary Elewitz, Director of Data
Science at Wex, emphasizes this point:
“Just because AI can be used doesn’t
mean we should always develop it
in-house. There are countless tools
available, and sometimes external
solutions can offer value more rapidly
and cost-effectively.”
Empowering AI Initiatives
with Quality Data
In a world where businesses
increasingly rely on analytics and AI
for decision-making and innovation,
the quality of data fed into these
systems is crucial. Data and analytics
leaders must consider several key
aspects when preparing data for AI
and analytics initiatives.
Our research with 100 C-suite
leaders and senior decision-makers
in Data, AI, and Innovation shows that
the most important data preparation
aspects to consider are: ‘maintaining
data integrity and quality’ (60%),
‘categorizing or classifying data for
easier analysis’ (48%), and ‘cleaning
data to remove noise and errors’ (46%).
Vivek Soneja, Corporate Vice
President - AI, Analytics Data and
Research at WNS Triange, stresses
the need for a strategic approach to
AI implementation and the role of
data quality in that strategy.
“AI is already present in many
organizations. But for AI to be a
game-changer, businesses need a
strategic approach. Implementation
often faces issues due to data quality,
which needs addressing,” Soneja
notes. “Feeding poor data into an
AI system results in equally poor
output. Enterprises need advanced
data engineering and governance to
maintain clean data, especially if AI
will increasingly complement human
decision making in future.”
“Poor data quality can lead to
significantly flawed outcomes,”
Agarwal adds. “Inadequate data
quality can negatively affect AI
outcomes, resulting in inaccurate
predictions or ‘hallucinations’
in large language models.
Additionally, there could be
regulatory repercussions if reports
based on poor-quality data are
found to be inaccurate.”
“Just because AI can be used doesn’t mean
we should always develop it in-house.
There are countless tools available, and
sometimes external solutions can offer
value more rapidly and cost-effectively”
Zachary Elewitz
Director of Data Science, Wex
Integrity is Key: 60% Prioritize Data
Quality for AI and Machine Learning
Maintaining data integrity and quality
Ensuring data privacy and security
Categorizing or classifying
data for easier analysis
Rescaling or normalizing data
for better interpretation
Cleaning data to remove
noise and errors
Ensuring data diversity
and representativeness
Reducing data to a manageable size
What are the most crucial aspects to consider for data
that is being prepared for use in AI/ML and analytics?
60%
48%
46%
45%
41%
24%
21%
13

Challenges When Scaling AI Initiatives
Scaling AI initiatives in enterprises comes with its own
set of challenges, which are underscored by the findings
of our research.
These challenges include ‘building trust in AI systems
among stakeholders’ at 55%, ‘scarcity or lack of
quality data for AI training’ at 47%, and ‘high cost of AI
implementation and maintenance’ at 42%.
Ravindra Salavi, Senior Vice President – AI, Analytics
Data and Research at WNS Triange emphasized these
challenges and others: “Many of these challenges tie
back to the aspects of starting correctly and ensuring
continuous improvement. Initial challenges arise if there’s
insufficient data, lack of skillset, or no clear use case.”
He continues: “As companies scale AI, they need
wider and deeper data – spanning multiple domains and
historical data. Another challenge is the need for skilled
resources and increased involvement from stakeholders.
An AI initiative won’t succeed if it’s limited to just the
engineering and data science teams.”
Salavi’s insights, combined with our research findings,
underline the importance of building trust among
stakeholders, having sufficient and wide-ranging data,
and skilled resources from the outset.
Organizations need to prioritize maintaining data
integrity and quality, categorizing, or classifying data for
easier analysis, and cleaning data to remove noise and
errors. This will ensure that the AI systems are fed with
clean and accurate data, leading to more accurate and
reliable outcomes.
“As companies scale AI, they
need wider and deeper data
– spanning multiple domains
and historical data”
Ravindra Salavi
Senior Vice President – AI, Analytics
Data and Research at WNS Triange
14

“Algorithmic objectivity can be compromised
if algorithms unintentionally reflect
human biases. Addressing these challenges
is crucial for ethical AI deployment, and
defining accountability is essential”
Anish Agarwal
Global Head of Analytics, Dr. Reddy’s Laboratories
Avoiding an ‘Accountability
Crisis’ in AI Decision-Making
The swift expansion and utilization
of AI in various enterprise sectors,
especially in heavily regulated
industries such as financial services,
banking, and healthcare, has brought
concerns about data security and
privacy into sharp focus.
Our research highlights key
concerns among data, analytics, and
AI leaders when it comes to data
privacy and security as it relates to
AI use. Organizations face several
challenges, primarily ‘risks of model
poisoning or adversarial attacks’ at
66%, ‘ensuring data privacy during AI
processing’ at 52%, and ‘compliance
with data protection regulations in AI
use’ at 47%.
“We need clear strategies and
policies to ensure we’re not
infringing on privacy or ethics. With
the vast amount of data available,
it’s possible to know intricate details
about a person’s life. Organizations
must establish boundaries,” says
Vivek Soneja, Corporate Vice
President - AI, Analytics Data and
Research at WNS Triange.
Indeed, this concern about AI
decision-making was echoed in our
research findings. A remarkable
72% of respondents are extremely
concerned about ‘AI decision-making
accountability,’ making it the most
significant ethical concern related
to AI use. This ‘accountability crisis’
underscores the importance of
understanding and controlling AI
actions to prevent uncontrollable or
dangerous situations.
“Algorithmic objectivity can
be compromised if algorithms
unintentionally reflect human biases.
Addressing these challenges is
crucial for ethical AI deployment, and
defining accountability is essential,”
says Anish Agarwal, Global Head of
Analytics at Dr. Reddy’s Laboratories.
“Some organizations now have a
Chief Ethics Officer responsible for
ethical standards and accountability,”
he continues. “In the context of data,
informed consent is vital, ensuring
individuals are aware of how their
data is used.”
As enterprises continue to
undertake AI initiatives, it is crucial
to proactively address data privacy
and security concerns. Organizations
must create clear strategies and
policies and understand and control
AI actions without breaking social
contracts. By addressing these
concerns, organizations can harness
the power of AI while maintaining
trust and ethical integrity.
Bias
and fairness
AI
decision-
making
accountability
Data
privacy and
confidentiality
Transparency
and
explainability
72% Extremely Concerned About Ethical AI Decision-Making
How concerned are you about the following ethical
implications of AI use within your organization?
Extremely concerned Moderately concerned Somewhat concerned
2
5
%
72%
48%
47%
45%
43%
55%
36%
12%
9%
5%
3%
15

Building trust in AI systems
among stakeholders
Enhanced customer service quality
55% 46%
Scarcity or lack of quality
data for AI training
Increased competitive advantage
47% 42%
High cost of AI implementation
and maintenance
Improved the speed of business operations
42% 40%
Dealing with biased data
or bias in AI outputs
Improved data quality
and reduced human error
39% 37%
Concerns over data privacy
and security related to AI use
Streamlined decision-making processes
37% 35%
Limited technical knowledge or
understanding of AI within the organization
Provided efficiency and productivity gains
27% 28%
Technical challenges in integrating
AI with existing systems
Optimized talent management processes
20% 22%
Shortage of skilled talent to
build and maintain AI solutions
Has not led to any significant improvements
18% 7%
Not applicable (We are not
ready to scale AI solutions) 2%
Technology Partnerships: The Top AI Investment for 65% of Organizations
Trust Issues: Over Half Wary of AI
System Reliability in the Enterprise
AI Boosts Customer Service
Quality for 46% of Businesses
What aspects of AI investment are currently your top priority?
What are your organization’s primary
challenges when scaling AI initiatives?
In what ways has the use of AI successfully
created value for your business?
Leveraging
technology
partnerships
Upskilling
existing
resources
Recruiting
the right
resources
Focusing on
developing
new products
Partnering
with third-party
service providers
65% 53%
55% 46% 28%
What are your organization’s
primary data privacy and security
concerns when using AI solutions?
Beware the Hackers!
Risks of model poisoning
or adversarial attacks
66%
Ensuring data privacy
during AI processing
52%
Compliance with data protection
regulations in AI use
47%
Data Integrity, Technology
Partnerships, and Trust in
AI Systems Are Top of Mind
16

enerative AI represents a
revolutionary leap in the
field of artificial intelligence,
enabling machines to generate new
data instances that are similar to a
given set of examples.
As we dive into this transformative
technology, it is essential to
understand its impact on enterprises
and the associated challenges in
hosting or implementing it.
The advent of generative AI has
profoundly affected businesses
by unlocking new opportunities
for innovation and growth. Most
respondents (76%) to our research
among 100 C-suite leaders and
senior decision-makers in Data,
AI, and Innovation indicate that
they are planning or are currently
involved in Generative AI projects
Beyond Automation:
Embracing the Promises and
Challenges of Generative AI
Generative AI offers
vast opportunities for
innovation and growth
but requires a strategic
approach, addressing
talent gaps, security
concerns, and ethical
implications, to harness
its full potential
in their organizations. This reflects
the increasing recognition of the
technology’s potential to drive
competitive advantage, but also,
perhaps, a level of hype associated
with the technology.
“With large language models
and generative AI, there are vast
possibilities for enterprises to
outpace competitors, not only
in decision-making but also in
identifying new organizational
opportunities,” says Anish Agarwal,
Global Head of Analytics, Dr.
Reddy’s Laboratories.
G
K E Y F I N D I N G
C H A P T E R T H R E E
Anish Agarwal
Global Head of Analytics,
Dr. Reddy’s Laboratories
“With large
language models
and generative
AI, there are
vast possibilities
for enterprises
to outpace
competitors”
17

“It’s essential first to understand
the core of generative AI.
Unfortunately, many jump on
the bandwagon after reading a
trending article. Generative AI is
not about automation; it’s about
decision-making and forecasting,”
Agarwal adds. “It’s vital to prioritize
use cases by evaluating their
complexity, potential impact, and
whether third-party expertise is
required. The benefits of generative
AI are undeniable, but they must be
employed within the framework of
data privacy and ethics.”
Implementing generative AI is
not without its challenges. The
most significant challenges faced
by organizations are ‘security and
privacy concerns’ at 47% and ‘talent
acquisition and skill gaps’ at 43%.
Furthermore, the management
of generative AI is critical, as it
could adversely affect customer
experiences and, consequently, the
organization’s reputation.
“Generative AI if not managed
well, could degrade and impact
customers adversely, affecting
reputation,” says Cecilia Dones,
Former Head of Data Sciences,
Moët Hennessy. “Organizations
must weigh the risks, but avoiding
AI entirely also means missing out
on its potential benefits. The key is
to find a balance and remain open
to learning and adaptation.”
It is also important to understand
the practical implications of
generative AI, as its broader
business applications remain
somewhat nebulous for many.
“While GPT has garnered
significant attention, many are still
trying to determine its practical
implications as the broader business
applications remain somewhat
nebulous. It’s essential to understand
GPT’s capabilities first to determine
its business value,” says Vivek
Soneja, Corporate Vice President - AI,
Analytics Data and Research at WNS
Triange. “It goes beyond just writing
code; it’s about processing and
converting data. Helping business
executives realize its potential and
applications remains crucial.”
Building a Talent Pipeline for
Generative AI Initiatives
The global market for AI, exacerbated
by the meteoric rise to prominence of
generative AI, has led to a surge
in demand for specialized skills in
the workforce.
“While GPT has garnered significant attention,
many are still trying to determine its
practical implications as the broader business
applications remain somewhat nebulous.
It’s essential to understand GPT’s capabilities
first to determine its business value”
Vivek Soneja
Corporate Vice President, AI, Analytics Data and Research, WNS Triange
18

However, enterprises are finding
it increasingly challenging to build
a robust talent pipeline to support
their generative AI initiatives.
Our research shows that ‘talent
acquisition and skill gaps’ (43%)
emerge as one of the most
pronounced challenges when it
comes to implementing generative
AI. Furthermore, 64% of respondents
primarily focus on attracting new
talent to their organizations instead
of focusing internally.
The scarcity of applicants with
expertise in large language
models is another challenge
that enterprises face. This is
exacerbated by the relative
recency of the technology.
“While many applicants are
adept data scientists, few have
the specific large language model
expertise we’re seeking. Those
who do often command a hefty
price. I’m focusing on candidates
with some experience in fine-tuning
large language models,” Elewitz
says. “But, if they can communicate
effectively, have a product-centric
approach, know how to lead a
project, and can engage with non-
technical team members, they’re a
good fit for us.”
A Twist on the ‘Offensive
vs. Defensive’ Paradigm
Data and AI leaders are finding
themselves at a crossroads,
grappling with the question of
whether to adopt generative AI,
and if so, how to do it strategically.
This dilemma puts a twist on the
traditional ‘offensive vs. defensive’
paradigm commonly associated
with data use. It requires leaders
to critically evaluate the use
cases where generative AI can
be harnessed to generate value,
weighing the potential benefits
and risks.
“Whether you take a proactive or
a reactive approach truly depends
on the nature of your organization.
Companies have specific objectives,
and if the benefits of generative
AI exceed the value of alternative
approaches, it’s worth the
investment,” says Zachary Elewitz,
Director of Data Science at Wex.
He continues: “Some organizations
might need to be proactive and
develop in-house skills, while others
will be better off taking a reactive
approach and adopting third-party
tools as they become available. It’s
essential to stay goal-oriented rather
than being solely technology-driven.”
“Our team is using generative AI and
natural language processing for
product development. This process
used to take three to four days; it
now only takes about 10 minutes.
The cost efficiency is remarkable”
Siddharth Bhatia
EMEA Growth Practice Leader, AI,
Analytics, Data and Research, WNS Triange
19

Elewitz’s perspective highlights the
need for a tailored approach when
it comes to generative AI adoption.
A one-size-fits-all strategy is unlikely
to be effective, given the unique
objectives and challenges faced by
different organizations. The key to
success lies in aligning the adoption
of generative AI with the broader
strategic goals of the organization.
Our research reveals that only 8%
of respondents are currently taking
a ‘proactive’ approach, actively
seeking opportunities, and investing
in generative AI projects to drive
innovation and gain a competitive
edge. 35% have a ‘reactive’
approach, initiating generative AI
projects primarily in response to
specific business needs or external
demands. Finally, 34% have adopted
a ‘balanced’ approach, combining
proactive initiatives with reactive
responses as needed.
This data suggests that a
significant majority of organizations
are adopting a reactive or
balanced approach to generative
AI, rather than proactively seeking
out opportunities. However, this
may not be a complete surprise
to some, as many businesses,
especially those in ‘high-risk’
industries, tend to be conservative
when it comes to the application
of new technologies.
“In the data realm, we often
discuss ‘offensive’ versus ‘defensive’
strategies. Typically, in high-risk or
conservative environments, there’s
an inclination toward defensive
strategies,” explains Cecilia Dones,
Former Head of Data Sciences at
Moët Hennessy. “I would expect
organizations to initially focus
internally, enhancing efficiencies
or mitigating risk. As they mature,
they might look externally for
revenue-generation opportunities
using AI technologies, such as
generative AI.”
Dones’s insight underscores
the potential of generative AI to
revolutionize both offensive and
defensive strategies. By enhancing
efficiencies and mitigating risks,
organizations can free up resources
to focus on more strategic
initiatives, ultimately driving value
and gaining a competitive edge. As
organizations mature in their use of
AI technologies, they may also find
opportunities to leverage generative
AI for revenue generation.
The decision to adopt generative
AI should be carefully considered
and aligned with the organization’s
strategic goals. Whether taking a
proactive, reactive, or balanced
approach, it is crucial to stay goal-
oriented and assess the potential
value and risks associated with
generative AI.
“When it comes to market speed
and delivery cost, AI can make a
significant difference. For instance,
our team is using generative AI
and natural language processing
for product development. This
process used to take three to four
days; it now only takes about 10
minutes. The cost efficiency is
remarkable,” says Siddharth Bhatia,
EMEA Growth Practice Leader, AI,
Analytics, Data and Research
at WNS Triange.
As enterprises navigate the
complexities of implementing
generative AI initiatives,
addressing the talent acquisition
and skill gaps, while considering
external support, might be the
most pragmatic approach to
overcome these challenges.
React vs. Act: 69% Take a Reactive or Balanced Approach
How would you describe the primary approach of your
organization toward generative AI projects?
We actively seek
opportunities and invest in
Generative AI projects to
drive innovation and gain
a competitive edge
Our approach to Generative AI
projects combines proactive
initiatives with reactive
responses as needed
We primarily initiate Generative
AI projects in response to
specific business needs or
external demands
8% 35% 34% 23%
Proactive
Reactive
Balanced
Not applicable
20

AI leaders focus on acquiring new AI talent
How does your organization identify the skilled individuals
you need to fill your data, analytics, and AI talent pipeline?
Talent Hunt Gets Tricky: 66% Can’t Find the AI Pros They Need
What challenges does your organization face in recruiting and retaining
talent with the skills needed for building and scaling AI systems?
are onboard or planning
to be involved in
generative AI projects
in their organizations
Generative AI Wave:
Recruitment to attract new talent
with the required skills
Scarcity of professionals proficient in specific
AI-related programming languages or tools
Collaboration with specialist recruitment firms
Retaining existing talent within the
organization due to competition and
high attrition rates in AI-related roles
Engaging consultants with industry
and data analytics expertise
Training and upskilling existing staff
to work on AI initiatives
Internal training programs
High cost of hiring new talent with AI skills
due to high demand and salary expectations
Partnerships with universities or training
institutions to nurture future talent
Onboarding candidates who possess the
necessary combination of technical AI
skills and business understanding
Internal recruitment programs
Recruiting candidates who can work
effectively in remote or hybrid settings
Finding candidates with the necessary
soft skills, such as problem-solving and
creativity, for AI roles
64%
66%
55%
57%
53%
39%
50%
38%
34%
34%
34%
21%
16%
76%
What challenges have you faced
in hosting or implementing
Generative AI in your organization?
Security Fears and
Talent Gaps Are Blockers
For Generative AI
Implementation
Security and
privacy concerns
47%
Talent acquisition
and skill gaps
43%
Data quality
and availability
35%
Generative AI Adoption on
the Rise Despite Talent Gaps,
Security Concerns,
and Reactive Strategies
21

he digital age is rapidly
evolving, with artificial
intelligence at the forefront of
global enterprise strategy.
The introduction of large language
models like ChatGPT has fueled a
surge in interest in generative AI, as
evidenced by the 76% of data and
analytics leaders either preparing for
or actively involved in generative AI
projects. However, this enthusiasm
is tempered by significant concerns
about security, data privacy,
and the ethical implications of
AI decision-making, with 47% of
participants reporting challenges
in implementing generative AI
due to security concerns and 72%
expressing deep concerns about
the ethical implications.
Additionally, attracting
professionals skilled in AI-specific
tools or programming languages
remains a significant challenge for
two-thirds (66%) of the participants.
This report has demonstrated
the importance of modern data
management as the foundation
for the transformative potential of
AI and analytics projects. We’ve
also highlighted the fundamental
principles of generative AI, its
potential to transform industries,
and the associated challenges,
particularly in talent acquisition
and retention.
These innovative initiatives in
data, analytics, and AI are already
creating value for their organizations
despite the challenges identified in
our research.
As enterprises globally prepare
for an AI-driven transformation,
enterprise leaders, data
professionals, and policymakers
must equip themselves to navigate
and lead in a future where data and
AI converge to drive unprecedented
business performance.
By understanding the complexities
and opportunities detailed in this
report, stakeholders can better
position themselves to harness the
power of data and AI, ultimately
driving their businesses forward in
this ever-evolving landscape.
T
Conclusion
22

About WNS Triange
WNS Triange powers business
growth and innovation for 200+
global companies with Artificial
Intelligence (AI), Analytics, Data and
Research. Driven by a specialized
team of over 6,000+ AI, Analytics,
Data and Domain experts, WNS
Triange helps translate data into
actionable insights for impactful
decision-making.
Built on the pillars of consulting
(Triange Consult), future-ready
platforms (Triange NxT), and domain
and technology (Triange CoE), WNS
Triange seamlessly blends strategy,
industry-specific nuances, AI and
Machine Learning (ML) operations,
and intelligent cloud platforms.
Driving a futuristic edge are WNS
Triange’s modular cloud-based
platforms and solutions leveraging
advanced AI and ML to provide
end-to-end integration and
processing of data to actionable
insights. WNS Triange leverages the
combined strength of WNS’ domain
expertise, co-creation labs, strategic
partnerships and outcome-based
engagement models.
Contact us today, here
About the Editor
Gareth Becker is an experienced editor and
content marketer and produces B2B stories
that focus on emergent trends in data and
analytics, cloud computing, information
security and more.
He works with world-leading brands to shine a
light on fresh ideas and innovative products
using a range of multimedia content.
To share your story or enquire about
appearing in a Corinium report, blog post
or digital event, contact him directly at
gareth.becker@coriniumgroup.com
Gareth Becker
Content Strategist,
Corinium Global Intelligence

24
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than 250,000 data, analytics, customer experience and digital
transformation leaders.
We’re excited by the incredible pace of innovation and
disruption in today’s digital landscape. That’s why we produce
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