Latest Tech Trends Series 2024 By EY India

1 EY GNEWS, An EY Government Newsletter
Tech Trends
Series: EY India
June 2024
Enter

Tech Trends 2024 Tech Trends 2024
2 3
Tech
Trends
2024 Foreword
01 AI-augmented
software development:
A new era of efficiency and
innovation
02 Sustainable coding
is the need for a greener
tomorrow
03 Digital twins:
Creating intelligent
industries
04 Responsible AI:
Building a sustainable
framework
05 Responsible AI:
Building a sustainable
framework
06 Unleashing next-gen
employee experience
with digital and AI

4 5
As the Nobel laureate in Physics,
Niels Bohr famously noted,
“Prediction is very difficult,
especially if it’s about the future.”
While many technological
innovations may not endure, a
select few evolve into indispensable
tools for specialized enterprise
applications, with only a handful
achieving widespread recognition.
At EY Tech Trends, we dedicate
ourselves to understanding the
potential of emerging technologies
and their future impact on the
business landscape.
Launched in 2023, the EY Tech
Trends series presents a curated
list of breakthrough technologies
poised to revolutionize the
enterprise world. It offers a
comprehensive package of articles,
podcasts, and videos designed
to help business and technology
leaders distinguish transformative
advancements from fleeting
trends, guiding them to harness
technology’s potential for business
innovation.
In an era where generative AI is
rapidly advancing, it is crucial
for organizations to maintain an
integrated business strategy, a
robust technological foundation,
and a creative workforce. Our
research focuses on strategic
technology trends that will
shape business and technology
decisions over the next three years,
emphasizing the importance of
prioritizing investments in the age
of AI. EY encourages you to assess
the impacts and benefits of each
trend, identifying the innovations—
or strategic combinations—that will
drive significant success for your
organization.
In this year’s EY Tech Trends
2.0, we delve into six emerging
technologies: AI-augmented
software development, sustainable
coding, industry cloud, digital twins,
responsible AI, and next-generation
employee tech. While some of these
technologies are rapidly gaining
traction across various industries,
others are still being explored for
their potential to deliver scalable
business value.
As we navigate an era dominated
by generative machines, it is crucial
for organizations to maintain an
integrated business strategy, a
robust technology foundation,
and a creative workforce. By
embracing these advancements,
your organization can unlock real
value and achieve business impact
through technological convergence.
Explore EY’s insights on these
critical topics and discover how
these trends can shape the future
of your enterprise.
Foreword
Mahesh Makhija
Partner and
Technology Consulting
Leader, EY India

6 7
AI-augmented
software
development:
A new era of
efficiency and
innovation
01
“
Generative AI-assisted tools
are enhancing productivity and
innovation in software development.
In brief
GenAI tools analyze extensive data,
including customer requests, market
trends, and user feedback for software
requirement planning.
GenAI tools like GitHub’s Copilot and
Jasper can significantly boost developer
productivity by swiftly generating code.
AI is automating various aspects of the
software development and delivery
processes in DevOps.
Developers can also optimize their
workload in cloud resources using GenAI.
n May 2017, at NVIDIA’s GPU Technology
Conference (GTC), its CEO Jensen Huang
made a bold prediction: “Software is eating
the world, but AI is eating software.” This statement,
inspired by Marc Andreessen’s seminal essay “Why
Software Is Eating the World,” captured the essence of
the transformative power of artificial intelligence (AI)
in software development.
At the time, Huang’s prediction may have seemed
ambitious, as the transformative potential of
transformer models, now integral to contemporary
GenAI models, was undiscovered. Fast forward to
2023 and software development has witnessed a
significant change, with generative tools playing an
important role in addressing software quality issues,
offering real-time code suggestions, automating
various steps in the software development life cycle,
thus validating Huang’s prediction.
AI has long been streamlining routine software
development tasks, from code review and bug
detection to software testing and project optimization.
These tools, often acting as spell- and grammar-
checkers for code, have undoubtedly reduced the
time and effort required for software development by
minimizing keystrokes. At present, with the advent
of GenAI, AI-augmented software development has
ascended to new heights, creating more efficient
and reliable software solutions that align with the
contemporary requirements. GenAI tools, such as
GitHub’s Copilot, Microsoft’s Intellicode, and Jasper,
are fundamentally changing the way developers
approach software creation. These tools treat
computer languages as natural languages, opening
up new possibilities for software engineering. Over
the next few years, GenAI is set to dominate software
development, extending its influence, and reshaping
of companies’ digital transformation. Gartner predicts
that by 2028, 75% of enterprise software engineers
will use AI coding assistants, up from less than 10% in
early 2023.
The impact of AI on software development will mainly
reshape four key areas: requirement planning,
I
enhancing developer productivity, DevOps and
deployment, and workload optimization.
Requirement planning: GenAI tools,
exemplified by various applications, exhibit
the capacity to analyze copious amounts
of data, including customer requests,
market trends, and user feedback. These
tools can generate user stories based on
requirements, propose ideas for prototype/
application design, and outline high-level
architecture diagrams. Additionally, they
can recommend suitable technologies
based on specified constraints, such as
performance, scalability, security, and best
practices.
Developer productivity: Code
development marks the arena where
GenAI is taking remarkable strides. There
are platforms that are transforming
the software creation process, treating
computer languages as just another form
of language. These tools draft code based
on contextual cues from input code or
natural language, enabling faster and
smoother coding with reduced friction.
Code generators are adept at swiftly
producing code for routine tasks, saving
developers a considerable amount of
time, and allowing them to focus on more
intricate tasks. By 2025, according to
Gartner estimates, 80% of the software
development life cycle will involve GenAI
code generation, enhancing developer
productivity up to 75% in various use cases.
DevOps: From automation of testing and
deployment to resource management and
security enhancement, AI is reshaping the
current process. Leveraging historical code
changes, GenAI identifies patterns, detects
potential issues, and offers intelligent
recommendations for automated testing
and deployment, thereby streamlining the
AI-augmented
software development
Click here to watch
Digital
twins
Responsible
AI
Empowering
industries
Sustainable
coding
Unleashing next-gen
employee experience
Radhika Saigal
Partner, Technology
Consulting, EY India

8 9
development pipeline. AI-integrated next-
generation ChatOps (interaction systems
to communicate with bots and perform
instructions for deployment, monitoring,
and incident response) will not only detect
anomalies but also generate optimal
solutions based on historical data and real-
time insights.
Enterprise-grade machine learning
applications, which took 6 to 12 months
to deploy, can now be operational in a
matter of weeks, significantly reducing
development costs. GenAI tools that
can also generate deployment scripts
- currently in the pilot phase - will
significantly reduce development time
and costs. GenAI can also generate
infrastructure as code scripts based on
natural language queries for high-level
infrastructure requirements and can
generate workflow configuration files that
can specify the settings and parameters of
various applications.
Workload optimization: GenAI can excel
in cloud resources workload optimization.
By analyzing historical data and predicting
resource needs, it generates actionable
recommendations that optimize resource
allocation, enhancing performance
resources. The tools also recommend
cost-cutting strategies like downsizing
instances, adjusting auto-scaling, and
utilizing reserved instances for optimal
spending. Predictive AI allows the team to
address potential issues before they impact
the users, enhancing overall reliability.
Cloud Service Providers (CSPs) are now
integrating GenAI capabilities to their
existing set of services where operations
can query large data sets or logs using
natural language.
While GenAI and software development form a
synergistic partnership, it is essential to recognize
that AI cannot function autonomously. At present, AI
draws its power from the data it processes, lacking the
touch of human intelligence. Moreover, issues such as
hallucinated responses and biased outputs underscore
the need to address data privacy concerns. As
regulations in this sector evolve, these challenges are
expected to be mitigated.
Despite these challenges, the potential benefits of
GenAI are undeniable. Through accelerated coding,
automation, and performance optimization, AI can
transform the software development industry, pushing
the boundaries of innovation and efficiency.
As many tools have displayed, GenAI can transform
key areas of software development and, the
challenges notwithstanding, undeniably offer benefits
such as higher efficiency and innovation. Enterprise
software engineers are expected to increasingly
adopt AI. The synergy between GenAI and software
development is reshaping the industry, pushing
boundaries, and driving digital transformation, even as
the regulatory framework evolves.
Summary
Generative AI (GenAI) tools are helping
software development in several crucial
areas. They enhance resource planning,
boost developer productivity with fast
code generation, automate DevOps,
speed up deploying machine learning
apps, and excel in workload optimization
of cloud resources. AI-embedded tools
let engineers spend less time coding,
enabling them to focus more on
higher-level tasks. Despite challenges
like AI’s reliance on data, which may
have bias and privacy concerns,
GenAI is reshaping the industry,
pushing boundaries, and driving digital
transformation.

10 11
Sustainable
coding is
the need for
a greener
tomorrow
Sustainable
coding
n May 2021, industry giants Microsoft,
Thoughtworks, Accenture, and GitHub
teamed up with the Joint Development
Foundation Projects and The Linux Foundation to
launch the Green Software Foundation. This non-profit
is laser-focused on building a community for eco-
friendly software development. The driving forces?
A growing corporate awareness of the energy toll
exacted by software development and operation— a
pressing concern in our digital space. Until recently,
sustainability in software and architectures took
a back seat, with many companies mistakenly
assuming that, unlike hardware, software did not pose
environmental challenges. However, this perception
shifted as it became clear that while software does not
directly consume energy, poor development practices
and its influence on computer hardware significantly
impact overall energy consumption and carbon
emissions.
As computationally inefficient software drives
increased energy consumption, the imperative for
green software grows alongside the booming global
software market, projected to hit a trillion dollars
by 2024 from $825 billion in 2022. IDC states the
AI-centric software sector is set to surge with a 30%+
CAGR, reaching $251 billion by 2027. Currently, the
Information and Communications Technology (ICT)
industry contributes 3.9% to global greenhouse gas
emissions, up from 1.6% in 2007, with a predicted
14% share by 2040. Notably, training a single neural
network model today emits as much carbon as five
cars throughout their lifetimes.
To mitigate emissions, the software industry must
transition to sustainable practices, starting with green
coding. Sustainable or green coding development is
an approach to software engineering that prioritizes
energy-efficient patterns and processes throughout
the software delivery lifecycle. It involves optimizing
code to minimize energy consumption and resource
usage, promoting sustainable development practices,
and utilizing low-power hardware or energy-efficient
infrastructure.
I
Sustainable software integrates energy-efficient
algorithms that execute computing operations
more swiftly and effectively than standard software
implementing green coding principles reducing
application footprint, memory, CPU, network, and data
footprint. The benefits of sustainable software extend
beyond environmental considerations, including a less
complicated architecture, faster computing speeds,
and cost savings.
Employing green coding practices
A significant challenge in sustainable coding lies in
poor coding standards and a lack of knowledge in
green software development. First and foremost,
companies should articulate a green strategy during
software development that guides trade-offs and
allows for flexibility. This strategy should also include
creating new software standards that extend devices’
lifecycles, thereby reducing total e-waste.
Developers should adopt application development
practices, such as optimizing code, to minimize
energy consumption and computation requirements.
A thoughtful code base that applies pure functions
and limits abstraction layers can reduce overall
computation effort. Organizations can implement logic
to clean, validate, and aggregate incoming data within
the code base to avoid redundant tasks.
Choosing algorithms, programming languages, APIs,
and libraries should consider their carbon emissions.
Efficient algorithms with linear time complexity and
compiled languages like C and C++ are preferable
to energy-intensive interpretive languages like
Python. For instance, Python takes up as much as
76 times more energy than C. Making AI greener
involves developing and using less power-consuming
ML models, creating reproducible code, and using
specialized hardware optimized for AI workloads.
Monitoring real-time power consumption through
dynamic code analysis is crucial for understanding
the gaps between design choices and actual energy
profiles. From a design perspective, the libraries
“
Software industry must adopt green
coding and efficient algorithms to
curb rising carbon emissions.
In brief
Sustainable software development involves
optimizing code for energy efficiency,
utilizing energy-efficient algorithms, and
integrating low-power hardware.
A key challenge in sustainable coding involves
addressing poor coding standards and a lack
of knowledge in green software development.
Efficient architecture, including optimized
cache policies, minimized data exchange,
and careful data lifecycle management,
plays a crucial role in reducing energy
consumption in software design.
Green coding principles extend to intelligent
workload orchestration, addressing the
threat of embedded carbon by efficiently
managing workloads and reducing the need
for new hardware.
02
Digital
twins
Responsible
AI
Empowering
industries
Unleashing next-gen
employee experience
AI-augmented
Alexy Thomas
Partner, Technology

12 13
chosen significantly influence energy efficiency.
Organizations should challenge assumptions about
end-user expectations and reduce file sizes of text,
images, and videos during design.
While many aspects may be beyond the control of
individual developers, organizations should embed
these principles into their frameworks.
Sustainable architecture and workload
management
Properly architecting applications’ energy
consumption requires reducing the application
footprint and designing with the right architecture.
Efficient cache policies, minimized data exchange, and
managing the lifecycle of stored data contribute to
reducing energy consumption. Running applications
in more efficient data centers, powered by recycled
or renewable energy, further minimizes the overall
carbon footprint.
Green coding principles also involve the intelligent
orchestration of workloads. Embedded carbon poses a
significant threat, and efficiently managing workloads
reduces the need for new hardware. Developers
can contribute by implementing instrumentation,
measuring the carbon footprint during both application
development and deployment, and monitoring real-
time energy consumption to identify modules that can
be optimized.
Writing energy-efficient software is challenging.
It requires a shift in mindset for developers and
designers. Achieving progress in sustainability needs
action at multiple levels. While developers can reduce
carbon emissions by implementing some of the best
practises and being aware of the environmental
impact of their choices, organizations can make
environmental sustainability by having a green coding
framework and evaluating its performance based on
energy efficiency, alongside traditional parameters.
Embracing green software development practices
allows developers to make a significant contribution
to environmental sustainability, reducing the carbon
footprint of software solutions through optimized
energy efficiency and resource management,
sustainable development practices, and user
education. Every step counts in this collective effort.
Even single optimization can make a significant impact
on the environment.
Summary
As technology adoption continues to accelerate worldwide, the software industry’s contribution to global
carbon emissions is increasing. To mitigate its environmental impact, the industry needs to embrace
sustainable practices, including green coding, the use of energy-efficient algorithms, and low-power
hardware. It must address subpar coding standards, and implement a green strategy, guiding trade-offs
and setting standards to prolong device lifecycles. Developers play a crucial role in this shift, employing
strategies like code optimization and algorithm selection to minimize carbon emissions. With an emphasis
on efficient architecture and intelligent workload orchestration, the industry’s significance in promoting
environmental sustainability becomes apparent.
How to measure the carbon intensity of software
According to Green Software Foundation, to calculate the operational emissions associate with software,
multiply the electricity consumption of the hardware the software is running on by the regional, granular
marginal emissions rate. The marginal emissions rate reflects the change in emissions associated with a
change in demand.
Putting principles into practice
What you can do
Architect: Engineers and architects have
to work more closely together to produce
the most sustainable code. Architect should
choose the best possible framework.
Developer: They can control code reuse,
select patterns, choose language and how
to build CD/CI release trains. Developers
can also utilize IDE plugins and other tools
to monitor electricity use in real time.
Tester: Testing and measuring application
software’s carbon intensity at various
release and deployment cycles.
UX designer: Reimagine every step of the
user journey and design process infused
with sustainability. User journeys should be
under constant review and improvement.
Infra architect: Adopt shared and
managed services model to reduce amount
of infra needed.
DevOps engineer: Should have clear test
goals. Deploy DevOps processes that will
support environmental testing in CD/CI
cycles, utilizing standard industry.
E = Energy Consumption (kilowatt hours) for different
components of the software boundary over a given time
period
I = Emissions Factors – available from GHG Protocol, but
should be tracked down to the regional level if possible
M = Embodied emissions data for servers, laptops and
other devices used in the relevant area.
R = Functional Unit being used (e.g., CO2e; days; etc. )
Where:
Testing formula for Sustainability SCI = (E * I) + M per R

14 15
Empowering
industries:
The rising
significance of
industry clouds
Empowering
industries
ueled by the ever-growing need for speed
to market, automation, unified technology
experience and access to innovative
technologies like Generative AI, the public cloud
market is booming. Recent reports show that a
staggering 60% of corporate data now resides in the
cloud, compared to just 30% in 2015. However, as
companies worldwide embrace cloud solutions for data
management, many discover that general-purpose
offerings fall short of addressing their industry-specific
requirements.
This gap has paved the way for industry cloud
platforms, tailored to deliver solutions that fit the most
critical use cases within specific sectors. Unlike the
horizontal approach of generic clouds, industry cloud
platforms, also known as vertical clouds, are designed
with the unique needs of industries in mind. Instead
of a one-size-fits-all approach, they offer a curated
collection of cloud solutions and applications specific
to industry.
Why industry cloud?
Industry cloud solutions prioritize deep integration and
vertical alignment, catering to the specific business,
operational, legal, regulatory, and security needs of
an industry. Instead of aiming for broad applicability,
they focus on maximizing value within well-defined
industry parameters. Such parameters can range from
providing infrastructure stack compliant to regulatory
requirements to software with out-of-the-box business
processes aligned to the value chain.
Beyond just tailored solutions, industry clouds offer a
curated ecosystem of innovative tools, applications,
and datasets. These elements flawlessly integrate with
the full range of cloud services, including Software-
as-a-Service (SaaS), Platform-as-a-Service (PaaS),
and Infrastructure-as-a-Service (IaaS). This creates a
comprehensive, ready-to-deploy stack that empowers
companies with superior agility in managing workloads
and go-to-market.
F
Since industry clouds come pre-loaded with solutions
and best practices of specific industries, they
streamline implementation, saving businesses time
and effort. Additionally, cloud service providers
(CSPs) offering industry clouds have deep expertise
in industry-specific regulations, data protection, and
security needs. They integrate these compliance rules
directly into their cloud solutions, known as sovereign
clouds, which offer businesses greater control and
ownership over their data. This ensures data is stored
and managed according to local regulations and laws,
often including keeping data within specific countries
or regions. Sovereign clouds also address industry-
specific security and compliance requirements,
providing both infrastructure and tools for seamless
workload migration. It is a massive opportunity as the
focus on sovereign clouds can empower Indian data
center businesses to become leading indigenous CSPs.
Built upon technologies designed for immediate
business deployment, industry clouds promote shared
resources by allowing multiple organizations to
utilize the same underlying infrastructure. A shared
resource model promotes more efficient utilization of
computing resources, resulting in lower overall costs
for the organizations involved. CSPs offering industry
clouds further contribute to a smoother operational
environment by providing automatic software updates
and conducting behind-the-scenes maintenance,
significantly alleviating concerns and expenses
associated with downtime.
That said, industry clouds are neither a new concept
nor a new solution. But their relevance in the current
times, when being digital for legacy or a modern
business is an existential need, has steadily increased.
As a result, major CSPs rose to the top of the cloud
industry and smaller cloud providers became industry
specific. Major cloud service providers now offer
industry clouds as a solution for specific types of
enterprises. This is a logical progression in their
service portfolio as more customers subscribe to
cloud services and seek to maximize the value of their
investment.
“
Industry clouds deliver solutions
that fit the most critical use
cases within specific sectors.
In brief
The emergence of industry clouds reflects
a shift towards sector-specific growth in the
public cloud landscape.
Industry clouds prioritize vertical
integration, aligning with the unique
requirements of sectors such as BFSI,
healthcare, and telecom, offering a
comprehensive ecosystem.
Sovereign clouds address industry-specific
security and compliance requirements for
seamless workload migration.
03
Click here to watch
Digital
twins
Responsible
AI
Sustainable
coding
Unleashing next-gen
employee experience
AI-augmented
Abhinav Johri
Partner, Technology

16 17
Sector adoption
Prominent early adopters of industry cloud span
diverse sectors, such as telecommunications, IT
services, banking, and discrete manufacturing
industries. Following closely are professional services,
investment, and insurance sectors. Non-traditional
sectors such as media and agriculture are now
embracing industry clouds. Although agriculture
remains an emerging sector, its applications include
curated solutions for managing crop data, predicting
weather patterns, and optimizing a farm-to-fork supply
chain.
Below we discuss a few advantages of industry cloud
for various sectors.
BFSI: Banking-specific solutions enable
targeted upgrades of various banking
systems, minimizing risk. Payment
modernization solutions enhance efficiency
in operations teams with real-time payment
data, allowing customers to send money
seamlessly. Insurance-specific cloud
solutions offer advanced analytics that
combine financial, actuarial, investment,
and risk data to drive better decision-
making across the enterprise. By linking
legacy systems with modern applications,
insurance-specific solutions can also
reduce the need for physical property
inspections during underwriting while
improving accuracy through AI-powered
property analysis.
Energy utilities: These solutions support
the entire customer journey from lead
generation to field service, offering a
unified view of customers and connected
products for manufacturing clients. They
also enable the implementation of smart
grids, enhancing the monitoring and
control of energy distribution networks.
By leveraging advanced analytics and
connecting various technology platforms,
energy utilities can gain insights into
risks and operational costs, optimize
assets, manage portfolios, implement
proactive maintenance, and more. This
ultimately leads to faster returns on IoT
investments and accelerates smart factory
transformation.
Healthcare: Protecting sensitive patient
data is paramount for healthcare
companies. Cloud solutions tailored to
the healthcare sector provide a secure
and patient-centric cloud-based platform
that enables remote monitoring, real-time
patient data collection, and innovative
patient engagement techniques, improving
the overall clinical experience. These
solutions leverage advanced analytics for
population health management, predictive
analytics, and personalized medicine,
all while adhering to strict healthcare
regulations to ensure patient data privacy
and security.
TMT (technology, media and
entertainment, and telecom): Industry
clouds offer specialized infrastructure
services that streamline the software
development lifecycle with integrated
tools for coding, testing, and deployment.
They also feature built-in cybersecurity
capabilities for advanced threat detection,
vulnerability assessments, and compliance
management, helping TMT organizations
protect their IT assets. For telecom
companies, specific cloud solutions provide
tools for optimizing network performance,
managing traffic, and ensuring high
availability, enabling them to deploy new
services such as 5G-enabled applications.
While each industry — and the companies within them
— is unique, it is crucial to identify the vertical CSP
that provides solutions suited to their business needs,
minimizing data risk. As the industry cloud technology
is still in nascent stages, companies should thoroughly
evaluate solutions before selecting a vertical cloud
provider to safeguard their data and access best-in-
class services.
Summary
Industry clouds empower organizations
to integrate components from
Cloud Service Providers (CSPs),
focusing on vertical integration for
various sectors. They offer tailored
solutions, emphasizing vertical
alignment with business, legal, and
security requirements. They create
comprehensive ecosystems with
modernized tools seamlessly integrated
into SaaS, PaaS, and IaaS. These clouds
streamline business processes, featuring
pre-configured solutions, and adhere to
industry-specific regulations. Notable
sectors adopting industry clouds include
telecommunications, banking, energy
utilities, healthcare, and technology.
The technology is still evolving, urging
companies to carefully assess solutions
for data protection and optimal services.

18 19
Digital twins:
Creating
intelligent
industries
Digital
twins
f the consumer metaverse was creating all
the buzz in 2022, the year that followed
has seen Generative AI (GenAI) take center
stage. But away from the limelight, digital twins, the
foundation of the enterprise metaverse, has matured
as a technology and its use cases are going up in
multiple sectors.
In fact, the global digital twin market had reached
almost US$9 billion in 2022, with 29% of global
manufacturing companies having either fully or
partially implemented their digital twin strategies
— an increase from 20% in 2020, according to
research agencies. However, it is projected to soar
to US$137.67 billion by 2030, which translates into
a CAGR of 42.6%, as reported by Fortune Business
Insights. The Asia-Pacific region is projected to grow
faster, at a CAGR of 45.9%, led by countries such as
South Korea, Japan, India, and China.
Unlocking efficiency
While there are many ways in which digital twin
technology can be used, three main types have
emerged, depending on purpose and scope.
The first is the component digital twin that helps in
experimenting with new component designs before
choosing the final one for production. These twins
utilize digital counterparts to analyze, predict, and
optimize the performance of individual components.
They excel at assessing factors such as stress and
strain in mechanical components, electrical load
in electrical ones, or flow characteristics in fluid
components, thus preventing premature malfunctions
or breakdowns.
The second type can be termed as the system digital
twins, and they provide a predictive analysis by
offering insights into how components interact and
perform together. They serve as comprehensive
digital replicas crucial for predictive analysis and
understanding of the complex interplay among various
components.
I
Finally, the process digital twins intricately model
specific segments or entire manufacturing processes,
providing highly detailed virtual representations.
These advanced models play a key role in optimizing
processes, increasing productivity, reducing defects,
and ultimately adding significant value to operations.
It is critical to identify use cases relevant for the
business and ensure early prioritization based on
business benefits, availability of technology and
skills. In addition, change management’s impact to
mitigate adoption risk is a critical consideration as
organizations embark on this journey.
Diverse digital twin applications
Digital twins’ applications vary by sector due to
factors such as technology, network connectivity,
skills, interoperability, data standardization, and
governance. But OEMs are the biggest adopters,
especially in new manufacturing operations. The
automotive sector holds more than 15% of the market
share of digital twin adoption, with significant demand
in the electric vehicle (EV) segment. In fact, a global
EV major employs component digital twins to monitor
vehicle parts in real time and predict issues before
they manifest. This enhances the overall lifecycle,
safety, and performance of its EV cars.
Other early and heavy adopters include
manufacturing, healthcare, infrastructure, smart
cities, and agriculture sectors.
Manufacturing: Organizations are using
virtual replicas of production lines,
machinery, and factories to simulate
and optimize processes, improving
production planning, minimizing
downtime, and reducing maintenance
costs. A global aviation company is
using component digital twins to predict
99.9% of anomalies in its jet engine parts,
while a manufacturing company is using
process digital twins to optimize various
parameters, resulting in a reduction of
“
Digital twins will redefine
industries but require
meticulous implementation.
In brief
The global digital twin market, valued at
nearly US$9 billion in 2022, is projected to
reach US$137.67 billion by 2030.
Component twins analyze, optimize
designs; system twins predict component
interactions; process twins reduce defects
and enhance productivity.
Digital twins are applied across sectors with
significant adoption by original equipment
manufacturers (OEMs) and in new
manufacturing operations.
Organizations should develop concrete
roadmaps, conduct proof-of-concept
(POC) projects, and ensure full-scale
implementation with continuous monitoring.
04
Responsible
AI
Empowering
industries
Sustainable
coding
Unleashing next-gen
employee experience
AI-augmented
Ram Deshpande
Partner, Technology

20 21
defective products by 75%. An oil company
has deployed process digital twins to
optimize the drilling process on its oil rigs,
resulting in reported savings of up to US$1
million per day.
Healthcare: A medical center in the US
is developing digital twins of patients’
kidneys to improve surgical outcomes and
provide enhanced training for surgeons.
Similarly, an Indian healthcare company is
utilizing this technology to develop patient-
specific heart models, enabling treatment
simulation and evaluation without invasive
procedures. Personalized treatments are
now possible by creating virtual patient
models for precise diagnosis and treatment
planning. To improve skills and minimize
errors, surgeons are using process digital
twins to simulate complex procedures.
Infrastructure and smart cities: Digital
twins are being utilized to simulate human
behavior, including crowd dynamics
in urban environments or emergency
scenarios, addressing critical political
and societal decision-making needs. The
Survey of India is actively creating digital
twins of major cities that accurately mirror
the urban landscapes and physical assets.
These detailed models not only aid in
city planning and policymaking, but also
enhance disaster management efforts.
Recently, the Indian government launched
Sangam, an initiative focused on digital
twins for future infrastructure planning and
design, leveraging innovative integration of
advanced technologies such as AI and 5G.
Agriculture and precision farming: While
manufacturing and healthcare were early
adopters of digital twin, agriculture is an
emerging sector. Digital twins empower
farmers with data-driven insights for
precision farming. Virtual models of
crops and farmland help optimize
irrigation, fertilization, and pest control
or can continuously monitor and predict
a milch animal’s poor health, equipment
malfunction, soil dryness, or temperature
change. With a significant portion of the
Indian population reliant on agriculture
for employment, adopting digital twins in
this sector has the potential to modernize
traditional farming practices and bolster
food security.
A comprehensive table below describes more industry applications and various
use cases that are getting implemented.
Discrete manufacturing
Sector Type of digital twin Use cases
Process and batch manufacturing
Healthcare
Logistics
Construction
Education
Public services
Telecommunication
Product digital twin
Process digital twin
Patient digital twin
System digital twin
Project digital twin
Campus digital twin
City digital twin
Infrastructure digital twin
Product design and prototyping
Process optimization
Patient monitoring
Supply chain optimization
Project planning and simulation
Campus planning and optimization
Urban planning
Network planning
Process digital twin
Facility digital twin
Asset digital twin
Student digital twin
City digital twin
Asset digital twin
Production optimization
Quality control
Surgical planning
Warehouse management
Resource management
Student performance monitoring
Emergency response planning
Performance monitoring
Asset digital twin
System digital twin
Biological digital twin
System digital twin
Building digital twin
Learning environment digital twin
Infrastructure digital twin
System digital twin
Predictive maintenance
Supply chain integration
Drug development
Fleet tracking and optimization
Building lifecycle management
Virtual learning environments
Infrastructure monitoring
Fault detection

22 23
Summary
Digital twins have matured as a
foundational technology, with diverse
applications across industries. Digital
twins, categorized into component,
system, and process types, optimize
efficiency and predictive analysis. Major
adopters like automotive and healthcare
leverage digital twins for enhanced
outcomes, while infrastructure and
smart cities utilize it for urban planning,
enhancing disaster management, and
future infrastructure planning and
design. However, challenges such as
infrastructure upgrades, security, and
skill shortages must be addressed for
widespread adoption. With GenAI and
IoT integration, digital twins are poised
to reshape industries, demanding
meticulous planning and collaboration
for successful implementation.
Challenges in adoption
While digital twins offer significant opportunities, there
are several challenges to successful implementation.
Upgrading infrastructure and enhancing connectivity,
particularly in rural areas, is crucial as unreliable or
slow internet access hinders real-time data exchange.
Security and regulatory compliance also pose hurdles,
with rising cyber threats necessitating prioritization
of cybersecurity measures. Interoperability and
standardization across OEMs is critical for adoption
and success of digital twin solutions. The shortage
of skilled professionals in data analytics, simulation
modeling, and cybersecurity underscores the
importance of aligning educational programs with
market demands. Moreover, the substantial upfront
costs can be especially daunting for small and
medium-sized enterprises (SMEs). Demonstrating
return on investment and integrating with legacy
systems add to the complexity, requiring meticulous
planning and execution. Addressing these challenges
comprehensively is vital for widespread adoption of
digital twin technology in India.
Building a future roadmap
As the ecosystem matures, digital twins will redefine
industries and innovation. Integration of GenAI
and Internet of Things (IoT) will enhance predictive
capabilities, further boosting effectiveness. We can
expect increased collaboration between technology
providers, businesses, and research institutions.
Organizations should develop a concrete roadmap,
understanding advantages and challenges, evaluating
requirements and resources, partnering with experts,
conducting POC projects, and testing and reviewing
efficacy. Full-scale implementation with continuous
monitoring and maintenance will ensure sustained
functionality.

24 25
Responsible
AI: Building
a sustainable
framework
Responsible
AI
ecently, a Hong Kong multinational company
lost over $25.6 million because of a deepfake
video made using AI. The video avatar looked
so credible that employees were convinced that they
were talking to their CFO during a conference video
call and proceeded to execute a series of transactions.
Not only did the imposter appear authentic, but it also
sounded convincing. In India, too, there are several
reported cases of deepfake videos and AI-generated
voices, including a recent case of a woman falling
victim to an AI-generated voice fraud and losing
money. In early 2024, two separate deepfake videos
of star Indian cricketers went viral on social media.
In the videos, their voices have been manipulated
to promote an online game and a betting app. The
intense competition surrounding AI development, with
countries and companies vying for supremacy, has
raised crucial discussions about responsible AI. The
ascent of large language models (LLMs) is giving rise
to urgent questions on the boundaries of fair use.
The concept of responsible AI is not new. Back in
2016, Big Tech companies banded together to
establish a partnership on AI, laying the groundwork
for ethical AI practices. However, as the GenAI
landscape evolves, fresh and complex challenges are
emerging.
GenAI risks
Risks associated with GenAI, especially in LLMs,
include model-induced hallucinations, ownership, and
technological vulnerabilities such as data breaches,
as well as compliance challenges arising from biased
and toxic responses. There have been recent examples
of authors being credited with non-existent articles
and fake legal cases have been cited by GenAI tools.
Inadequate control over LLMs trained on confidential
data can lead to data breaches, which, according to a
recent EY survey, is the single biggest hurdle to GenAI
adoption in India.
Toxic information and data poisoning, intensified
by insufficient data quality controls and inadequate
R
cyber and privacy safeguards, adds another layer
of complexity, diminishing the reliability of GenAI
outputs and jeopardizing informed decision-making.
Additionally, the broader spectrum of technology risks
of deepfakes to facilitate crime, fabricate evidence
and erode trust necessitates proactive measures for
secure GenAI adoption.
Potential intellectual property rights (IPR) violations
during content and product creation also raise legal
and ethical questions about the origin and ownership
of generated work.
Other risks include:
• Bias and discriminationMisuse of personal data
• Explainability
• Misuse of personal data
• Predictability
• Employee experimentation
• Unreliable outputs
• Limitations of knowledge
• Evolving regulation
• Legal risks
Building guardrails against risks
To capitalize on the competitive advantage and
drive business, GenAI models and solutions are
implementing safety guardrails to build more trust.
The tech giants have created the frontier model
forum. Its objectives include advancing AI safety
research, identifying best practices, and collaborating
with policymakers, academics, civil society, and
companies. The forum aims to ensure that AI
developments are handled responsibly and deployed
responsibly. A model’s performance is evaluated and
measured against designated test sets and quality
considerations. Model monitoring and performance
insights are leveraged to maintain high quality
standards.
“
Without adequate controls, adopting
AI poses regulatory, reputational,
and business risks to organizations.
In brief
Responsible AI aims to identify and mitigate
bias, ensuring that AI systems make fair and
unbiased decisions.
Organizations must revamp AI policies,
establish trusted frameworks, and undergo
trust assessment to adopt AI responsibly.
Global AI regulations vary in scope
and approach, reflecting the growing
recognition of the need to govern AI
technologies responsibly.
Collaboration among stakeholders is crucial
for navigating this complex landscape and
realizing the potential of GenAI responsibly.
05
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Digital
twins
Empowering
industries
Sustainable
coding
Unleashing next-gen
employee experience
AI-augmented
Kartik Shinde
Partner, Cybersecurity

26 27
With various models evolving, implementing robust
data governance policies that comply with privacy
regulations will help companies mitigate risks.
There are seven key domains to establish a robust
framework and governance processes that align with
industry-leading standards of responsible AI. These
are business resiliency, security operations, model
design development, governance, identity and access
management, data management, and model security.
Such a framework assesses an organization’s existing
policies, procedures, and security standard documents
to determine the adequacy of governance processes
and controls associated with GenAI and evaluates
implementation effectiveness.
Regulations so far
The growing need for AI regulations has resulted in
a complex and diverse array of global regulations
to navigate AI risks. China has been a forerunner
in designing a new law that focuses on algorithm
recommendations, including generative and synthetic
algorithms. EU’s AI Act is the first major legislation
to stress on a risk-based approach. It categorizes AI
applications into different risk levels, ranging from
unacceptable to low and with high-risk applications
subject to more stringent requirements. The law
prohibits AI systems that pose an ‘unacceptable
risk,’ such as those utilizing biometric data to deduce
sensitive traits like individuals’ sexual orientation.
Developers of high-risk applications, such as the ones
that use AI in recruitment and law enforcement, must
show that their models are safe and transparent.
While India is developing its own law, the Ministry
of Electronics and IT (Meity) has recently issued an
advisory to AI platforms to take permission before
launching AI products in the country. The government
has asked intermediaries to tag any potentially
deceptive content with distinctive metadata or
identifiers to trace its source and thus aid in tracking
misinformation or deepfakes and its creators.
Meanwhile, in the US, the AI Executive Order directs
agencies to move toward adoption with safeguards in
place.
The G20 nations have also committed to promoting
responsible AI in achieving the Sustainable
Development Goals (SDGs). Additionally, 28 countries,
including India, China, the US, and the UK, signed
the Bletchley Declaration AI summit, pledging to
address AI risks and collaborate on safety research.
Also, HITRUST has released the latest version of
the Common Security Framework comprising areas
specifically addressing AI risk management. Along
with the global agreements, Responsible AI needs local
regulations as well.

28 29
Artificial Intelligence (AI) evolution has triggered multiple
regulations across the world
Scoring Factors
AI regulations
Data Regulations (data,
cyber and privacy)
Strategy, roadmap and
investment
Infrastructure and
Tooling
Skill and Education
Rising global guidelines/regulations on Responsible AI signal urgency
EU
• EU Parliament voted
on draft AI law
• Date June 2023
• Publication: the EU
Artificial Intelligence
Act (AIA)
• Date: April 2021
Germany
• Publication: AI Cloud
Service Compliance
Criteria Catalogue
(AIC4)
• Date: Feb 2021
UK
• UK Launches AI
regulation roadmap
• Publication:
Guidance on AI and
data protection;
• Date: July 2020
Algeria
• Presented national
strategy on research
and innovation in AI
• Date: Jan 2021
Ethiopia
• Finalizing the
preparation of
national policy
on AI
• Date: June
2023
Saudi Arabia
• SA proposes
AI regulation
via the new
Intellectual
Property Law
• Date: May
2023
South Africa
• Launches “AI for
Africa” blueprint
in collaboration
with other African
nations
• Date: Nov 2021
Canada
• Publication: the Digital
Charter Implementation
Act, Bill C-27
• Date: June 2022
Mexico
• Law for the Ethical
Regulation of Artificial
Intelligence for the
Mexican United States
US
• Biden Executive Order
• Date: Oct 2023
Australia
• Royal Commission
Report into
Robodebt Scheme
• Date: July 2023
Indonesia
• MCI is drafting ethical
guidelines for privacy
protection
• Date: Aug 2023
New Zealand
• NZ government
releases Digital
Strategy for
Aotearoa
• Date: Sep 2022
S Korea
• PIPC publishes guidelines on
personal data processing in AI
• Date: Aug 2023
Japan
• Amendment that allows level
four automated driving
Vietnam
• Instructs
cross border
platforms to
use AI and
remove toxic
content
• Date: June
2023
Sri Lanka
• Announces 1 Billion fund
for AI
• Date: Sep 2023
Malaysia
• Considering a
new law to label
AI generative
products either
“AI-generated” or
“AI-assisted”
• Date: July 2023
Singapore
• Singapore and the EU
signed a Digital Partnership
• Date Feb 2023
• Publication: the Model AI
Governance Framework
• Date: Jan 2019
Philippines
• University of Philippines
released draft set of AI
regulations
• Date: July 2023
Thailand
• ETDA proposes three new
AI laws
• Date: Sep 2023
UAE
• UAE launches
Generative AI
guide
• Date: April
2023
Egypt
• Egypt’s National
Council for AI
announces the launch
of “Egyptian Charter
for Responsible AI”

30 31
Summary
Building trust in AI systems is essential
for their acceptance and adoption. The
risks associated with GenAI, particularly
in Large Language Models (LLMs),
include model-induced hallucinations,
ownership disputes, and technological
vulnerabilities such as data breaches,
along with compliance challenges
due to biased or toxic responses.
Intellectual property rights violations,
bias, discrimination, and legal risks
are additional concerns. To address
these risks, safety guardrails are
being implemented, and regulations
are evolving globally. Responsible AI
adoption involves redesigning policies,
establishing trusted frameworks,
forming ethics boards, training
employees, and implementing robust
security measures.
While governments are working on regulations, Big
Tech and industrial bodies are implementing their own
set of safeguards, including continuous monitoring
and auditing, investing in cyber security measures,
red-teaming GenAI models, using frontier AI models,
reporting inappropriate uses and bias, watermarking
on audio and visual content, and so on.
Responsible AI adoption: key steps
While countries are framing global agreements and
regulations and models are implementing guardrails,
organizations must consider several key points in
adopting AI safely and responsibly.
• Redesign AI policies and design standards.
• Build a trusted AI framework for your organizational
needs: Decide the type of AI appropriate for your
organization, ensuring ethics, social responsibility,
accountability and reliability. Creating trust in AI
will require both technical and cultural solutions.
This framework should emphasize bias, resiliency,
explainability, transparency, and performance.
• Form GenAI ethics board: Ensure a diverse mix
of legal experts, technology leaders, security
innovators, and human rights scholars.
• Perform HITRUST Assessment: Conduct HITRUST
certification assessment to demonstrate assurance
of the security and operational controls within the
AI system.
• Train employees: Deliver AI risk management
training and ensure technical skill development for
employees.
• Put in place a new data privacy and security
architecture.
• Implement technology and data quality controls:
Evaluate controls implemented for AI risk
management and review current state to ascertain
applicability of the National Institute of Science
and Technology AI Risk Management Framework
security and privacy requirement. Deploy tools to
monitor cyber and data poisoning attacks, data
privacy, monitor for hallucinations, manage third-
party risks, prompt injections and malicious attacks.
Navigating the complex landscape of responsible AI
requires a multifaceted approach. While technological
advancements offer immense potential, mitigating
associated risks necessitates proactive collaboration
among governments, organizations, and global
communities. Establishing trusted AI systems,
fostering responsible AI development practices, and
prioritizing human-centered design are essential
steps toward harnessing the power of GenAI for a
sustainable and equitable future. The journey toward
responsible AI will require continuous learning,
adaptation, and a commitment to ethical and inclusive
practices.

32 33
Unleashing
next-gen
employee
experience with
digital and AI
Unleashing next-gen
employee experience
he next-generation employee experience
is pivotal for organizations striving to
attract, retain and nurture top talent in a
competitive landscape. Creating a single, consumer-
grade experience for the organization’s workforce,
leveraging digital technologies, will positively impact
every HR process and dimension of an employee’s
work life. Employee experience (EX) remains at the
core of the Chief Human Resources Officer’s agenda
and a top focus for organizations today.
Companies that invest in EX witness compelling value
compared to those that do not. They have been
shown to have four times higher average profits and
two times higher average revenues. Moreover, they
are 11 times more likely to be featured on employee
review sites as best places to work and more than two
times as often among the World’s Most Innovative
Companies. Additionally, their teams are 21% more
productive, and employees are 60% more likely to stay
with their employer, as per EY analysis.
With the market evolving, the HR tech space is
exciting, brimming with intense activity from
thousands of vendors currently active. In 2023 alone,
the space witnessed more than US$4 billion in startup
funding and around 300 funding rounds. In addition,
the space also witnessed more than 200 mergers and
acquisitions according to various reports.
Deep personalization is key
EX design uses the lens of ‘significant moments’
and ‘personas’ to envision the entire work life of an
employee along with processes of an organization.
It personalizes the design completely through these
personas, weaving the hundreds of significant
moments relevant to the persona as a digital journey.
The personalization goes deeper by aligning fully with
the employees’ context, needs, objectives, behaviors,
and personal preferences.
Thus, instead of a generic “one size fits all” approach,
the EX design is akin to a personalized work design
that constantly adapts, evolves, and improves for
T
each organization and employee. With AI entering the
HR space, EX personalization is only getting further
accelerated.
How AI aids a quantum jump in
HR and EX
AI is revolutionizing every aspect of HR and EX.
With Generative AI (GenAI) entering the sector,
CoPilots abound, and every AI dimension is evolving
exponentially with tremendous business impact. While
individual AI and digital dimensions are powerful
by themselves, in combination, they are even more
potent. Conversational AI, NLP are one example of
how technologies work great together and build upon
each other. Another illustration is the combination
of GenAI, ML and analytics, among numerous other
possible combinations.
A few additional examples of HR Process use cases
leveraging AI include:
Robotic Process Automation (RPA):
Robotic Process Automation (RPA) goes
beyond basic automation. Intelligent
Bots can handle repetitive, manual, high-
volume tasks and offer a wide range of
use cases. This allows employees, HR
personnel, and managers to dedicate
their attention to more value-added
activities, thereby enhancing EX. RPA has
hundreds of use cases across all functions
including examples such as conducting
pre-employment checks, processing
department changes, drafting employment
contracts, payroll streamlining,
administering leave, reducing exit
process redundancies, enabling timesheet
submissions, managing tax deductions,
enrolling in benefits, and many more.
Conversational AI, chatbots, and virtual
assistants: These are fairly common now
and have a big impact on engagement
and EX. These technologies are evolving
“
AI can revolutionize employee
experience, placing humans at the
center and shaping the future of work.
In brief
The explosion of HR vendors and
capabilities has led to a profusion of
platforms within organizations. In this
context, creating a single, consumer-grade
digital employee experience is essential
for fostering an engaged and empowered
workforce.
Digital and AI technologies, including GenAI,
Intelligent Automation, Conversational AI,
Big Data, social and immersive technologies,
offer significant value across the entire hire-
to-retire spectrum.
Organizations investing in EX witness
higher profits, revenues, productivity, and
employee retention compared to those that
do not prioritize EX.
06
Digital
twins
Responsible
AI
Empowering
industries
Sustainable
coding
AI-augmented
Ajay Gachhi
Partner, Technology

34 35
and now going up the maturity curve
to include learning capabilities beyond
frequently asked questions (FAQs), such as
problem-solving abilities, providing advice,
incorporating NLP, handling both text and
voice interactions, managing complex
transactions, offering advanced features,
advanced voice recognition, supporting
multiple languages, conducting sentiment
analysis, and many other evolving
advanced features. They have the potential
to reduce handling times by 50%, save a
majority of processing costs, eliminate
errors, maintain full auditability and 24*7
availability.
Machine learning and deep learning
tools: These are self-learning technologies
across machine learning methodologies
like supervised, unsupervised, reinforced,
and deep learning. Their use cases are in
almost every process, and a few examples
include enhancing employee engagement,
recommending career paths, analyzing
learning patterns, matching résumés to job
descriptions, standardizing job roles, and
targeted sourcing.
HR analytics: Deeply relevant and
actionable insights with consumer grade
interface dashboards enable great EX and
hold relevance across every HR function.
It includes adoption of intelligent and
on-demand reports, dashboards, KPIs,
balanced scorecards, and predictive and
prescriptive analytics, alongside ETL
(Extract, Transform, Load) tools, data
science and big data.
Social and collaboration: As interactions
within teams profoundly affect EX,
organizations are using next-GenAI tools in
social recruitment, learning, collaboration,
internal networks, knowledge
management, and immersive virtual
working for hybrid and remote workers.
Gamification: This approach is being
increasingly employed to enhance EX,
engagement and participation. Applicability
and usage examples include rewards for
recognition, training, learning, recruitment,
knowledge management, simulations,
employee engagement, incentivization
of process adherence and performance,
points, badges, encouraging collaboration,
leaderboards and microlearning based
games.
AR/VR/XR-based immersive
technologies: Immersive onboarding
experience through VR and AR augmented
by GenAI is used for familiarizing
candidates with culture, teams, work
environment and tasks. They hold benefits
in job training immersions, 3D modeling,
visual overlays in process steps, allowing
safe learning simulations for dangerous
and complex activities, recruitment in job
scenarios, workplace virtual conferencing
and collaboration, AR adding dynamic
digital elements to conferencing, 3D
avatars, spatial audio and advanced
workforce engagement systems, among
others.
IoT and connected enterprise: Some
examples include smart workplaces,
wearables, safety management, employee
health, fitness trackers, automated
attendance tracking, headset simulations,
bias-free hiring, sentiment tracking, and
productivity analysis.
Blockchain in HR: With the potential
to reshape HR technology, use cases
encompass résumé validation, background
verification, smart contracts, learning
education repositories, cross-border
payments, international assignees,
intellectual property, automated claims
and many more.
Apart from these, there are additional examples of
process use cases in HR with AI which are causing a
deep impact on EX:
• Recruitment: AI tools can be used extensively in
candidate relationship management, automated
sourcing, talent branding, contextual search,
strategic workforce planning, social and network
recruiting, JD creation and updates, candidate
chatbots, video interviews with auto grading,
candidate skill matching, résumé parsing, effective
screening and predicting candidate success.
• Learning: Next-GenAI tools can be used to draw
personalized learning paths, automate future
skilling and reskilling recommendations, enhance
continuous learning, microlearning, AR/VR/XR
simulations, gamification, social learning, mobile
learning, adaptive learning, curated content,
intelligent learning needs analysis, AI learning
coaches, learning retention, proficiency mapping,
just in time learning, effectiveness measurement
and assessment generators.
• Talent management and marketplace:
Automated identification of skill and job gaps,
skill-based resource management, career insight
recommendations, succession and performance
analysis, real-time continuous performance
feedback, bias-free evaluations, automated career
pathing.
Challenges and future
With the advent of GenAI, the EX landscape is
undergoing a profound transformation. Amidst the
plethora of tools available, organizations must strike
a delicate balance, ensuring that the fundamental
human touch inherent to HR remains central, while
leveraging technology to enhance it. This necessitates
a nuanced approach, wherein ethical and responsible
usage of AI, potential bias and fairness, safety, data
privacy, compliance, transparency, trust, consent,
intellectual property and security are taken into
consideration.
The HCM platforms space has become highly diverse,
driven by evolving maturity, super specialization and
intense digital innovation. Below is a summary of the
different categories of HCM platforms. Given that
organizations often deploy multiple platforms, there
is an urgent need to deliver a single consumer-grade
employee experience across all.
These platforms have come to the fore since they
allow all the diverse platforms to work together
and give a single, seamless, consumer-grade
employee experience. Some examples include
ServiceNow, Applaud and Microsoft Viva.
Experience layer platforms
Every mature organization requires these
foundational systems since these platforms hold
the key masters, employee data and core HR
processes apart from catering to the entire hire
to retire lifecycle. Examples of Tier 1 vendors
include SAP SuccessFactors, Oracle HCM,
Workday and DarwinBox, among others, along
with local HCM platforms.
Foundational HCM platforms
These platforms leverage AI and digital
capabilities to offer specialized HR use cases
across every process but especially talent
management. Learning innovative digital
vendors include Eightfold.AI, Skyhive, Knewton
and Saberr. Additionally, these vendors might
also be GenAI vendors (OpenAI, Google AI,
Anthropic), blockchain vendors (Bitwage,
CareerBuilder, HireRight, Chronbank, Blockchain
Helix), RPA vendors (Automation Anywhere,
UIPath, BluePrism, etc.), or virtual agents
(Amelia, IBM Watson, for example).
Specialized digital and AI
vendors for HR
These are specialized platforms designed to get
feedback, work as listening posts and facilitate
360° employee engagement. Some of these
vendors include Qualtrics, Glint, CultureAmp
and Perceptyx.
Employee engagement, listening
and feedback vendors

36 37
Summary
Progressive businesses prioritize
human-centric approaches, focusing
on enhancing employee experiences
(EX) with digital technologies and
AI. Investing in EX yields significant
benefits, including higher profits,
revenues, productivity, and
employee retention. The HR tech
space is thriving, with processes
undergoing transformation on
the basis of many AI-driven and
other tools such as robotic process
automation (RPA), conversational
AI, machine learning, HR analytics,
social collaboration, gamification,
immersive technologies, IoT, and
blockchain. These tools optimize
several functions in recruitment,
learning, talent management, and
marketplace. However, as AI technology
evolves, organizations must balance
technological adoption with ethical
considerations to create an engaging
and enriching employee journey.
Integration of digital EX across various
HR platforms is essential for fostering
an empowered workforce.
These specialized platforms are increasingly
deployed at organizations. Vendors include
HealthifyMe, Belong and Textio among others.
Employee wellness, diversity
and inclusion:
These platforms are integrated into work
to foster higher productivity. Examples
include Office 365, Meta Workplace, Google
Workspace, Slack, Teams, Miro and Mural.
Productivity, communication and
collaboration platforms
Though every HR platform has some reporting
embedded in its scope, it is frequently
transactional, siloed and limited. A specialized
analytics platform is frequently enterprise
wide and goes far beyond in aspects like
data management, ETL, data warehousing,
dashboarding, visualization, reporting,
predictive/prescriptive analytics, collaboration,
scalability, customization and flexibility. Some
examples of these platforms include SAS,
Business Objects, Tableau, Visier, and PowerBI.
HR Analytics Platforms
These thrived through a process focus,
created specialized white spaces and digital
AI innovations and are now competing with
core HCM platforms. The learning segment
has morphed into several sub categories like
LMS, learning experience platforms (Edcast,
Degreed, Percipio, and other), learning content
(for example, Coursera, Udacity, Skillsoft),
learning gamification (Gametize, Axonify, for
instance), assessment (Talview, Mettl, etc.)and
microlearning (Grovo, Edustream, for example).
Recruitment process has similarly created
subareas like applicant tracking, candidate
relationship management (Smashfly, Beamery,
Talemetry, to name a few), candidate experience
(Ideal, AllyO, Mya), sourcing (Arya, Entelo),
assessment and screening (Hirevue, Pymetrics,
Harver, for example). Similar Best of Breed
vendors exist for every process of the HR value
chain, including talent development, skills,
compensation, payroll and onboarding.
Best of Breed HR platforms

38
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Elante offices, Unit No. B-613 & 614
6th Floor, Plot No- 178-178A
Industrial & Business Park, Phase-I
Chandigarh - 160 002
Tel: + 91 172 6717800
Chennai
6th & 7th Floor, A Block,
Tidel Park, No.4, Rajiv Gandhi Salai
Taramani, Chennai - 600 113
Tel: + 91 44 6654 8100
Delhi NCR
Ground Floor
67, Institutional Area
Sector 44, Gurugram - 122 003
Haryana
Tel: +91 124 443 4000
3rd & 6th Floor, Worldmark-1
IGI Airport Hospitality District
Aerocity, New Delhi - 110 037
Tel: + 91 11 4731 8000
4th & 5th Floor, Plot No 2B
Tower 2, Sector 126
Gautam Budh Nagar, U.P.
Noida - 201 304
Tel: + 91 120 671 7000
Hyderabad
THE SKYVIEW 10
18th Floor, “SOUTH LOBBY”
Survey No 83/1, Raidurgam
Hyderabad - 500 032
Tel: + 91 40 6736 2000
Jaipur
9th floor, Jewel of India
Horizon Tower, JLN Marg
Opp Jaipur Stock Exchange
Jaipur, Rajasthan - 302018
Kochi
9th Floor, ABAD Nucleus
NH-49, Maradu PO
Kochi - 682 304
Tel: + 91 484 433 4000
Kolkata
22 Camac Street
3rd Floor, Block ‘C’
Kolkata - 700 016
Tel: + 91 33 6615 3400
Mumbai
14th Floor, The Ruby
29 Senapati Bapat Marg
Dadar (W), Mumbai - 400 028
Tel: + 91 22 6192 0000
5th Floor, Block B-2
Nirlon Knowledge Park
Off. Western Express Highway
Goregaon (E)
Mumbai - 400 063
Tel: + 91 22 6192 0000
3rd Floor, Unit No 301
Building No. 1
Mindspace Airoli West (Gigaplex)
Located at Plot No. IT-5
MIDC Knowledge Corridor
Airoli (West)
Navi Mumbai - 400708
Tel: + 91 22 6192 0003
Pune
C-401, 4th Floor
Panchshil Tech Park, Yerwada
(Near Don Bosco School)
Pune - 411 006
Tel: + 91 20 4912 6000
10th Floor, Smartworks
M-Agile, Pan Card Club Road
Baner, Taluka Haveli
Pune - 411 045
Tel: + 91 20 4912 6800
Tech trends
podcast
Cooming soon
Series 2

Tech Trends 2024 40
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