Strategic Impact of Edge Computing on Business Operations and Innovation

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 Table of Contents
1. Introduction..............................................................................................................................3
2. Top Emerging Technology: Edge Computing.........................................................................3
3. Edge Computing..........................................................................................................................4
3.1 Brief Introduction and Overview...........................................................................................4
3.2 Description of the Technology (Technical View)..................................................................5
3.3 How Does Edge Computing Impact Businesses?..................................................................9
3.3.1 Opportunities and Threats...............................................................................................9
3.3.2 Business Environment: Industry, Size, and Models......................................................11
3.3.3 Revenue Generation vs. Cost Reduction......................................................................11
3.4 Market Size and Future Outlook..........................................................................................12
Conclusion.................................................................................................................................14
4. References:................................................................................................................................16
 1. Introduction
Information and Communication Technologies (ICT) have witnessed paradigm shifts

over the last twenty years, revolutionizing business operations, competition, and value provision

(George et al., 2023). New technologies like cloud, social media analytics, and machine learning

have spurred new models besides bringing new efficiencies and smartness (Perifanis & Kitsios,

2022). New technologies have disrupted traditional industries by rearranging value chains,

changing expectations about customers, and enabling real-time, data-based decision-making

(Rhett Power, 2021).

There is an evolving environment under which edge computing is the leading technology,

owing to the rampant proliferation of smart devices and connectivity, as well as the Internet of

Things (IoT). In comparison to conventional cloud configurations, under which data is stored on

far-off center servers, edge computing places computation next to data sources, which helps

decrease responses, uses less network bandwidth, and safeguards privacy, all crucial for firms

that need speed and need to comply with rules. The main goal of this assignment is to review

edge computing, its role in the development of ICT, and what it means for businesses, the

workplace, and competition.

2. Top Emerging Technology: Edge Computing

Overview and Rationale for Selection

George et al. (2023) have found that, because of its features, edge computing has grown

in significance and overtaken limitations in the cloud design. This includes placing computing

power near where the data is located, on the outside of the network. This architecture helps cut
latency, ensure efficient use of bandwidth, secure data, and allow for instant analysis, which are

essential for current businesses that need to respond fast and operate without central control.

The main reason for using edge computing is the growth of its applications and its

breakthrough influence in various industries. It is anticipated that more than 50% of enterprise

data will be generated and processed outside the central locations of clouds by 2025, according

to Gartner's prediction (2023). It is a reflection of a fundamental shift in the digital strategy of

businesses to edge-powered architectures. Similarly, as per a report published by Statista (2024),

the global edge computing industry will surpass USD 274 billion by 2028, growing at a

compound rate (CAGR) of more than 38%.

Compared to cloud computing, which centralizes processing at data center locations,

edge computing enhances the ability to process data locally (Perifanis & Kitsios, 2022). This

shift is most beneficial to manufacturing, health, and logistics sectors, which have decision

latency that may create inefficiency within operations or create safety risks. With digital

transformation being undertaken by businesses, edge computing is a technological trend and a

strategic enabler for agility, customer centricity, and differentiation.

3. Edge Computing

3.1 Brief Introduction and Overview

Edge computing is an extension of what was previously thought to be legacy distributed

architectures. It traces back to content delivery networks (CDNs), used in the 2000s to retain

content near customers. The term " edge computing " arose sometime between 2015, as IoT

device deployments were starting to outpace that of conventional center-based cloud systems for

real-time data processing (Ficili et al., 2025), arose the term "edge computing". As companies
rely more on embedded systems and networked devices, the limitations of center-based clouds—

most notably, latency, data sovereignty, and bandwidth—have come ever more sharply into view.

Edge computing mitigates those limitations by supporting data processing and decision-making

at the edge, speeding digital programs across industries.

Source: https://www.forbes.com/sites/rhettpower/2021/12/05/how-to-ensure-emerging-

technology-will-benefit-your-business/

3.2 Description of the Technology (Technical View)

Edge computing in Information and Communication Technologies (ICT) mainly aims to

build local architectures that process and examine data (Cao et al., 2020). Contrary to how cloud

computing usually works, edge computing puts the processing power near where data is

generated. This structure change is because many businesses want low-latency, efficient, and

real-time analysis for their different business applications. This becomes important in cases

where a quick reaction is critical, as with autonomous systems, industrial automation, healthcare,

and smart cities.

Architecture and Components

 There are several layers in edge architecture, and each completes its specific activity in

the chain of operations. Edge devices include sensors, actuators, smart cameras, and machines

connected to the internet to collect raw data directly from the world (Ficili et al., 2025). Even

though they are not powerful in computations, they are essential for gathering data.

Edge Node/Gateways: These devices are between the edge and the central cloud,

functioning as mid-level parts of the computing network. They obtain information from multiple

sources and analyze it in an initial way. Being responsible for translating protocols, making local

choices, and reformatting data makes edge gateways lower the amount of raw data sent over the

network to servers further away.

Edge Servers: More capable processing nodes positioned at the network edge but enabled

to conduct computationally demanding operations like machine learning inference, video

analysis, and event correlation in real-time. On-premises or regional data center locations often

host these servers in industrial and enterprise contexts to deliver high-capacity and responsive

local processing.

Cloud or Core Systems: Though edge computing distributes most functionality, cloud

systems still have an essential place in architecture. Centralized systems are employed when

there is a need for extensive computational resources, for instance, for training deep learning

models, for storing historical data, and for coordinating worldwide operations (Perifanis &

Kitsios, 2022). They still have to control distributed edge resources through centralized

management platforms.

How Edge Computing Works

The edge computing procedure generally entails four primary stages:

 Data Gathering: Edge devices continuously produce raw data from interactions within the

environment—temperature sensor readings, video streams from security cameras, or location

data from GPS devices. This data serves as the source for edge analytics.

Local Processing: Once data is gathered, it is locally processed by edge nodes or servers.

This involves running analytic algorithms, detection of anomalies, and application logic.

Processing data locally at the source significantly improves latency, which is essential for real-

time applications like industrial robots or telemedicine (Hartmann et al., 2022). It also minimizes

dependency on high-bandwidth backhaul links, thus saving cost and bandwidth.

Selective Syncing: Only central servers receive key insights or summarized data. The

model for selective syncing prevents unwanted data from clogging the cloud infrastructure.

Selective syncing increases data privacy as it prevents the transmission of unwanted data over

networks, which is helpful for industries operating under regulated rules, i.e., financial and health

industries.

Feedback Loop: The central systems can create new policies, insights, or machine

learning models based on the aggregated data. They send them back to edge nodes to fine-tune

the processing locally. This two-way movement keeps the edge systems in sync with enterprise-

wide targets and changing company rules.

Technical enablers

Several innovations make edge computing effective:

AI on the Edge: With the introduction of TensorFlow Lite, PyTorch Mobile, and ONNX,

sophisticated analytics at the edge is now possible on limited devices. Therefore, edge systems

can find defects locally in assembly lines or notice faces from video surveillance even if they are

not connected to the cloud.

 Hardware Acceleration: Dedicated hardware, like NVIDIA’s Jetson Nano, Intel’s

Movidius Neural Compute Stick, and Google's Coral Edge TPU, provides high compute and low

power utilization. The hardware is tailored for the execution of AI inference workloads and real-

time applications in systems that have limited energy or space.

Orchestration Platforms: Distributed edge node fleets must be managed through advanced

orchestration solutions. Docker and other containerization technologies, coupled with edge-

native orchestration offerings like Kube Edge, Open Horizon, and AWS Greengrass, enable

businesses to deploy, upgrade, and manage software on thousands of edge sites cost-effectively.

They help manage resource assignment, workload distribution, and failure recovery at scale.

5G Networks: 5G incorporation is a major driver for edge computing. 5G features ultra-

low latency (under 1 millisecond) and high bandwidth (10 Gbps), which makes it particularly

suitable for real-time applications like autonomous transport, smart grids, and immersive media.

With 5G and edge computing, data can be processed within milliseconds, creating dynamic and

responsive digital services opportunities.

Resilience and Autonomy

Another unique aspect of edge computing is that it can operate independently, even in the

case of temporary disconnection from the master cloud. Think, for example, of a distant

industrial site or a natural disaster: Edge nodes can continue to monitor operations, perform data

processing, and apply essential control logic whether external networks are up or not. This makes

edge computing especially beneficial for mission-critical use, where system latency loss or

downtime can have far-reaching operational or safety consequences (Hartmann et al., 2022).

The technology design for edge computing is an attractive substitute for cloud-hosted

centralised computing by placing computation at data points of creation. Edge computing

enables organizations to provide increased speed, effectiveness, and flexibility in a fast-

expanding digital environment through its hierarchical design paradigm, intelligent processing,

and reliance on enablers like AI, 5G, and container orchestration.

3.3 How Does Edge Computing Impact Businesses?

Edge computing significantly affects many areas of a business, supporting improvements in

operations and contributing to new strategies.

3.3.1 Opportunities and Threats

Table 1: Opportunities and Threats of Edge Computing

Opportunities Description

Real-Time Decision-Making Enables instantaneous data analytics and

autonomous actions in latency-sensitive

environments (e.g., manufacturing,

autonomous vehicles) (Alzu'Bi et al., 2024).

Bandwidth Optimization Reduces the volume of data transmitted to

cloud servers, lowering bandwidth costs and

enhancing efficiency.

Improved Security and Compliance Limits data transmission over external

networks, enhancing privacy and supporting

regional regulations like GDPR (Alzu'Bi et

al., 2024).

Innovation Enablement Facilitates the development of advanced

services such as predictive maintenance,

 AR/VR applications, and smart infrastructure.

Resilience Ensures continued operation during

connectivity failures, making systems more

reliable and fault-tolerant.

Threats

Infrastructure Complexity Involves managing diverse edge nodes and

network layers, increasing IT and operational

complexity (Rancea et al., 2024).

Cybersecurity Risks Distributed edge devices may be attack

vectors if not adequately secured and

monitored.

Lack of Standardization A fragmented ecosystem with varying

platforms and protocols hinders

interoperability and integration.

Scalability Challenges Expanding and maintaining many edge

devices requires robust orchestration and

lifecycle management (Alzu'Bi et al., 2024).

Vendor Lock-in Reliance on proprietary edge platforms can

reduce system flexibility and increase long-

term costs.
3.3.2 Business Environment: Industry, Size, and Models

Edge computing is rapidly gaining acceptance within most industries that utilize its

capability to address domain-related problems. Edge is employed for real-time inspection, robot-

based manufacturing, and predictive repair to enhance the Industry 4.0 movement within the

manufacturing sector. Edge is used for remote diagnosis, real-time patient monitoring, and

portable imaging for the healthcare industry, enabling quick intervention and lessening the load

on centralized systems (Rancea et al., 2024). In the retail sector, edge computing is used for

behavior analysis, smart shelving, and queue control, making operations more efficient and

enabling a better user experience. Edge-enabled fleet monitoring, autonomous systems, and

dynamically optimized routes aid transportation and logistics companies. Edge is similarly used

for the energy and utility industry for remotely monitoring, detecting faults ahead of time, and

real-time grid operations.

Early on, edge computing was seen mainly by large corporations that could afford and

sustain distributed systems. Nowadays, though, edge-as-a-service models have enabled the

technology to be more widely available for SMEs and mid-sized businesses, making it more

democratic. Edge computing is scalable and can support different types of business models.

Operationally intensive organizations benefit from increased throughput and minimized

downtime (Perifanis & Kitsios, 2022). Customer-focused models use edge for real-time

personalization, while subscription models, particularly SaaS and IoT, utilize it for consistent,

location-based service quality and increased user interaction.

3.3.3 Revenue Generation vs. Cost Reduction

Edge computing achieves substantial business value through two purposes: streamlining

operations and opening up new sources of revenue. On the cost-savings end, edge computing
lessens the dependency on central cloud-based infrastructure, thus decreasing bandwidth

consumption and, by extension, the related cost. Through localized data processing,

organizations can significantly reduce the cost of perpetual data streaming to cloud servers

(George et al., 2023). Edge computing also facilitates proactive servicing through real-time

monitoring and analysis, thus enabling businesses to detect likely equipment failure before it

occurs, reducing expensive downtime. More importantly, keeping sensitive data at the edge

increases data security and minimizes the chances of data breaches, particularly for industries

that are stringently regulated, such as healthcare and the financial sector.

In generating revenue, edge computing allows organizations to launch novel digital

offerings, such as innovative products that provide real-time feedback, adaptive properties, or

integrated analytics. Edge frameworks support hyper-personalization and location-based

services, improving customers' experience and satisfaction. Edge structures also simplify data

monetization by aggregating contextual intelligence from user behavior or operational data,

enabling businesses to create new intelligence-based offerings. Overall, edge computing is a

means for cutting operational costs and a strategic enabler for product line extension, better

customer experience, and capturing new market opportunities.

3.4 Market Size and Future Outlook

Current Market Dynamics

As of 2024, the global edge computing market is valued at approximately USD 53.6

billion and is projected to grow at a CAGR of 38.2% over the next five years (Gartner, 2023).

Significant investment is driven by:

Increasing IoT Adoption: The fast growth of Internet of Things (IoT) devices generates

enormous data at the network edge. Edge computing allows data to be processed in real time and
locally, decreasing latency and bandwidth consumption and making time-critical applications

possible for industries like manufacturing, healthcare, and smart infrastructure (Alzu'Bi et al.,

2024).

5G Network Expansion: The 5G network utilization offers ultra-low latency and high-

speed connections that substantially augment edge computing capacity. They facilitate

sophisticated applications like autonomous cars, augmented reality, and remote robotics via

seamless, high-bandwidth communication among edge devices and cloud or core networks.

Artificial Intelligence: Integration Edge computing is increasingly adopting AI models

that can be executed directly on the local hardware. This enables real-time decision-making,

anomaly detection, and automation at the point of data generation, minimizing the reliance on

cloud infrastructure and increasing responsiveness in mission-critical applications like healthcare

and industrial control systems (Perifanis & Kitsios, 2022).

Data Localization: Laws and national governments are implementing data sovereignty

policies that compel the processing and storing of specific data within national borders. Edge

computing facilitates compliance with these policies by processing data locally, thus improving

privacy, minimizing legal risk, and preventing expensive cross-border data transfer.

Vendor Ecosystem and Investment Trends

 Cloud Providers: AWS (Greengrass), Microsoft Azure (IoT Edge), and Google (Anthos)

are heavily investing in edge-capable platforms.

 Telecoms: Partnering with infrastructure providers to deploy Multi-Access Edge

Computing (MEC).

 Hardware Manufacturers: Intel, NVIDIA, and ARM are releasing edge-specific chipsets.

 Startups: Focused on edge AI inference, real-time orchestration, and federated learning.

  Regulatory Influence: Data sovereignty and compliance mandates (e.g., EU’s Digital

Markets Act, China’s Cybersecurity Law) are accelerating enterprise adoption of edge

computing as a solution for in-country processing and regulatory adherence.

Outlook and Forecast

In the next 5–10 years, edge computing will become a core infrastructure component in

most digitally mature enterprises. It will be a key enabler for next-generation digital ecosystems

through seamless convergence with technologies like Artificial Intelligence (AI) and 5 G.

Convergence will enable autonomous systems like driverless cars, bright production lines, and

smart infrastructure to process data locally in real-time, with little latency and utmost reliability.

Edge computing also enables sustainability initiatives by minimizing requirements for ongoing

data transmission to central data centers, reducing energy usage for bandwidth-intensive

workloads. Local processing at the edge translates into better resource usage, fewer carbon

emissions, and better environmental outcomes. Edge computing also enables deploying

microservices at scale through edge-native frameworks (Cao et al., 2020). These frameworks

underpin containerized, modular applications deployable dynamically across distributed systems,

delivering better agility, scalability, and resilience. As a result of these changes, edge computing

fortifies the base for successful, sustainable, and scalable shifts in industry. In the future, hybrid

models will be used, with businesses relying on their edge and cloud infrastructure. As a result,

we can see advances in edge orchestration platforms, data marketplaces, and real-time analytics

services.

Conclusion

Edge computing is a paradigm shift in data processing, decision-making, and service

delivery for businesses. It goes beyond most inherent flaws in traditional cloud computing
models using decentralized processing and real-time response. It enables operational efficiencies

and strategic innovations for numerous industries. Hence, it is a central part of the next-

generation ICT ecosystems. Despite the challenges of complexity, security, standardization,

growing edge platform maturity, more advanced orchestration capabilities, and the convergence

of edge, 5G, and AI networks foretell a robust and enduring edge computing future. Edge

computing will be one of the main drivers of continued long-term success for companies that

want to be responsive, resilient, and customer-centric.

 4. References:

Alzu'Bi, A., Alomar, A. A., Alkhaza'Leh, S., Abuarqoub, A., & Hammoudeh, M. (2024). A

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https://doi.org/10.26599/TST.2023.9010080

Cao, K., Liu, Y., Meng, G., & Sun, Q. (2020). An overview of edge computing research. IEEE

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Ficili, I., Giacobbe, M., Tricomi, G., & Puliafito, A. (2025). From Sensors to Data Intelligence:

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https://doi.org/10.3390/s25061763

Gartner (2023). Top Strategic Technology Trends for 2023. https://www.gartner.com

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computing: A survey of industry perspectives and predictions. Partners Universal

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IoT solutions: A literature review for strategic implementation. Information, 13(9), 427.

https://doi.org/10.3390/info13090427

Rancea, A., Anghel, I., & Cioara, T. (2024). Edge computing in healthcare: Innovations,

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Rhett Power (2021). How to Ensure Emerging Technology Will Benefit Your Business. Forbes.

https://www.forbes.com/sites/rhettpower/2021/12/05/how-to-ensure-emerging-

technology-will-benefit-your-business/