For the greater part of the last decade, three tools have come to dominate this category: Ansible, Progress Chef, and Puppet. On a general level, here’s how they stack up:

• Ansible: Lightweight and easy to use; ideal for supporting a wide range of environments and configurations
• Progress Chef: Powerful, secure, and highly customizable; ideal for organizations that require an advanced automation platform
• Puppet: Feature-laden with a large user base and following; ideal for enterprises looking for a premium automation solution

In this article, we’ll delve into each of these automation tools’ respective strengths and shortcomings across eight key categories: pricing, ease-of-use, quality of support and documentation, breadth of features, integrations, supported environments, size/makeup of customer base, and community size.

Ansible vs. Chef vs. Puppet at a Glance

Best for Pricing: Ansible and Progress Chef

When it comes to pricing, premium versions of Ansible and Progress Chef fall in the same range—$137 to $140 a year per node—while Puppet Premium is on the high end at $199 a year per node. It’s worth noting that all three are all available as free open source projects, though the commercial versions come with additional features and support. Organizations with large scale deployments and complex infrastructures may be better suited with the premium versions.

Best for Ease of Use: Ansible

Ansible takes the lead in this category for myriad reasons. From its minimal setup/configuration and agentless architecture to declarative model and use of YAML syntax, the automation platform was designed to be lightweight and easy to use.

On the other hand, Progress Chef and Puppet are notorious for their steep learning curves and require users to learn a domain-specific language (DSL) and understand how to configure/deploy agents on target systems. That said, they also provide more granular control and flexibility for custom use cases at the cost of additional complexity.

Best for Support and Documentation: Ansible

Ansible boasts excellent documentation, featuring comprehensive guides, modules, and examples. Furthermore, its extensive user community contributes to the solution’s continuously expanding knowledge base. Progress Chef and Puppet also provide comprehensive documentation, albeit not as novice-friendly as Ansible’s materials. In terms of support, Puppet, Progress Chef, and Ansible all offer enterprise support as well as community-driven forums to assist users in resolving issues.

Best for Features: Three-way tie

When it comes to the breadth of features, all three automation tools provide robust capabilities.

• Ansible focuses on simplicity and ease of use, providing a wide range of modules for infrastructure provisioning, configuration management, and application deployment.
• Progress Chef emphasizes infrastructure as code, enabling users to define and manage their infrastructure using the Chef DSL.
• Puppet excels in configuration management, featuring a powerful domain-specific language for defining system states.

Ansible Feature Highlights

Some of Ansible’s more notable features include the following:

• Agentless Architecture: Ansible’s standout feature is its agentless architecture, which eliminates the need to install and manage agents on target systems. Ansible connects to remote systems using secure shell (SSH) or other remote protocols, making it lightweight, easy to set up, and non-intrusive to the existing infrastructure.
• YAML-based Playbooks: Ansible uses YAML-based playbooks to define and orchestrate automation tasks. Playbooks provide a human-readable and easily understandable format, allowing users to describe the desired state of systems and execute complex workflows using simple and concise syntax.
• Push-based Model: Ansible follows a push-based model, where the control machine pushes configurations and tasks to the target systems. This model enables real-time execution of tasks, making Ansible well-suited for ad-hoc automation, configuration management, and application deployment.
• Built-in Idempotence: Ansible enforces “idempotent” operations, which means that executing the same playbook multiple times results in the same desired state. This feature eliminates the risk of unintended configuration changes and allows for safe and predictable automation across various systems and environments.
• Extensibility and Integrations: Ansible offers a wide range of modules and plugins, allowing users to extend its functionality and integrate with existing tools and technologies. Ansible can seamlessly integrate with cloud platforms, networking devices, databases, and more, making it a versatile automation tool that can span across different infrastructure components.

Progress Chef Feature Highlights

Some of Progress Chef’s more notable features include the following:

• Chef Infra: Progress Chef’s Infra platform enables developers and operators to define infrastructure configurations using a declarative language, providing version control, repeatability, and consistency across environments.
• Chef Habitat: Progress Chef includes Chef Habitat, a tool that simplifies the packaging, deployment, and management of applications. Habitat provides a framework for creating platform-agnostic application artifacts, enabling easy application migration and seamless deployment across different environments.
• Chef Automate: Progress Chef offers Chef Automate, a comprehensive platform and dashboard that provides visibility and control across the entire automation infrastructure. The platform offers real-time analytics, compliance reporting, and a centralized dashboard for managing infrastructure, applications, and security.
• Cookbook Library: Progress Chef boasts an expansive community-driven cookbook library, which is a collection of pre-built, reusable configuration and automation scripts. This library enables developers to leverage the expertise and best practices of the Chef community, accelerating the deployment of infrastructure and applications.
• Enterprise Security and Compliance: Progress Chef places a strong emphasis on security and compliance and provides features like role-based access control (RBAC), encrypted data transmission, and built-in audit trails to ensure the security of your infrastructure. Additionally, Progress Chef helps organizations adhere to various industry standards and regulations and streamline the process of meeting security and governance requirements.

Puppet Feature Highlights

Some of Puppet’s more notable features include the following:

• Declarative Language: Puppet’s declarative language, Puppet DSL, enables users to describe the desired end state of their infrastructure and configurations, rather than focusing on the specific, step-by-step instructions, thereby simplifying the management and maintenance of complex systems.
• Puppet Forge: Puppet offers Puppet Forge, a vast repository of pre-built modules that provide ready-to-use configurations and automation scripts. This extensive library allows users to leverage community-contributed content, saving time and effort in developing and implementing configurations from scratch.
• IaC Support: Puppet supports IaC by allowing users to define and manage infrastructure configurations in a version controlled, tracked database or repository (e.g., GitHub) as if it were application code.
• Puppet Enterprise: Puppet provides Puppet Enterprise, a centralized and enterprise-grade solution for configuration management. The platform extends the capabilities of the open-source Puppet tool with additional features like a graphical user interface (GUI), RBAC, event inspector, and other advanced orchestration capabilities.
• Master-agent Architecture: Puppet’s master-agent model consists of a Puppet master server controlling and managing the configurations of multiple Puppet agent nodes. This architecture allows for efficient and scalable management of distributed systems, ensuring consistent configurations across a network of nodes.

Best for Integrations: Ansible

Ansible boasts an extensive range of integrations, with support for various cloud platforms, infrastructure providers, and third-party tools through its vast collection of modules. Similarly, Progress Chef and Puppet also offer integrations with popular cloud platforms and tooling.

Ansible Integration Highlights

Some of Ansible’s more notable integrations include the following:

• Cloud Platforms: Ansible integrates with popular cloud platforms like AWS, Microsoft Azure, Google Cloud Platform (GCP), and VMware vSphere, and supports automatic provisioning, deployment, and management of cloud resources, including virtual machines, networking, and storage.
• Version Control Systems: Ansible seamlessly integrates with version control systems like Git/GitHub, enabling users to store and manage Ansible playbooks and configurations in a version-controlled repository.
• Networking Devices: Ansible integrates with network devices from leading vendors like Cisco, Juniper, Arista, and F5 Networks, and provides modules specifically designed for network automation, enabling users to manage and configure network devices programmatically.
• Continuous Integration/Continuous Deployment (CI/CD) Tools: Ansible integrates with popular CI/CD tools like Jenkins, GitLab CI/CD, and Atlassian Bamboo, allowing users to incorporate Ansible automation into their CI/CD pipelines, as well as automate the deployment of applications and infrastructure as part of the continuous delivery process.
• Databases: Ansible provides modules for interacting with leading databases like MySQL, PostgreSQL, Oracle, and MongoDB, allowing users to automate tasks related to database provisioning, configuration, and data management.
• Container Orchestration Platforms: Ansible integrates with container orchestration platforms like Kubernetes and Docker Swarm, allowing users to automate the deployment and management of containers, including provisioning, scaling, and updating containerized applications

Progress Chef Integration Highlights

Some of Progress Chef’s more notable integrations include the following:

• Cloud Platforms: Progress Chef Automate integrates with major cloud platforms such as AWS, Microsoft Azure, and Google Cloud Platform (GCP). This integration enables users to automate the provisioning, configuration, and management of cloud resources and services.
• Container Orchestration Platforms: Progress Chef Automate integrates with container orchestration platforms like Kubernetes and Docker Swarm. It provides capabilities for managing and automating the deployment of containerized applications, including configuration management and scaling.
• CI/CD Tools: Progress Chef Automate integrates with popular CI/CD tools such as Jenkins, GitLab CI/CD, and Atlassian Bamboo. These integrations enable users to incorporate Progress Chef Automate into their CI/CD pipelines, automating the deployment and configuration of applications as part of the continuous delivery process.
• Monitoring and Logging Tools: Progress Chef Automate supports integration with monitoring and logging tools like Prometheus, Elasticsearch, and Splunk. These integrations allow security teams to collect and analyze data from Progress Chef Automate for monitoring, troubleshooting, and compliance purposes.

Puppet Integration Highlights

Some of Puppet’s more notable integrations include the following:

• Cloud Platforms: Puppet integrates with major cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and VMware vSphere. These integrations enable users to manage and automate the provisioning, configuration, and management of cloud resources.
• CI/CD Tools: Puppet integrates with popular continuous improvement/continuous deployment (CI/CD) tools such as Jenkins, GitLab CI/CD, and Atlassian Bamboo. These integrations allow users to incorporate Puppet into their CI/CD pipelines, automating the deployment and configuration of infrastructure and applications as part of the continuous delivery process.
• Version Control Systems: Puppet integrates with version control systems like Git and Subversion. These integrations allow users to manage Puppet code, manifests, and modules using version control, facilitating collaboration, change tracking, and version management.
• Networking Devices: Puppet supports integration with network devices from various vendors, including Cisco, Juniper Networks, Arista Networks, and F5 Networks. These integrations enable users to automate network device configurations, provisioning, and management.
• Container Orchestration Platforms: Puppet integrates with container orchestration platforms such as Kubernetes and Docker Swarm. These integrations enable users to manage and automate the configuration and deployment of containerized applications.

Best for Supported Environments: Three way tie

All three tools support a wide array of operating systems and environments. As an agentless solution, Ansible can manage heterogeneous environments effortlessly, including Windows, Linux, and network devices. Progress Chef and Puppet, with their agent-based approach, can manage a diverse range of systems and are well-suited for complex environments.

Best for Customer Base: Ansible 

Despite being the youngest of the three automation tools, Ansible has gained significant traction in recent years and has amassed a large, rapidly growing user base. The tool’s ease-of-use and extensive community support are key ingredients driving its popularity. Progress Chef and Puppet also have substantial customer bases, but more so with larger organizations and enterprises.

Best for Community Size: Ansible

Ansible has a thriving, active community that actively contributes to its wealth of resources in the form of blogs, forums, and GitHub repositories. Similarly, Progress Chef and Puppet also have active, sizable communities, though not as large or vocal as Ansible’s crowd.

Top 3 DevOps Configuration Tooling Alternatives

Chef, Ansible, and Puppet aren’t the only automation tooling players in the game. The following alternatives provide similar features and capabilities, and may warrant further investigation

if none of the leading three platforms suit your organization’s needs.

AWS CloudFormation (and Cloud Vendor-provided Tools)

If you’re looking to automate an AWS-based cloud infrastructure, AWS CloudFormation is a formidable alternative to Puppet, Chef, and Ansible. In fact, CloudFormation—like Ansible—enables you to define IaC using JSON or YAML templates However, CloudFormation naturally a more AWS-centric solution for describing and provisioning AWS resources like EC2 instances, load balancers, and databases, along with their configurations and dependencies. Unsurprisingly, similar services like Microsoft’s Azure Resource Manager (ARM) and Google Cloud Deployment Manager (GCDM) also offer automation capabilities that excel in their native vendors’ environments.

SaltStack

Another long standing alternative to Puppet and Progress Chef, SaltStack is an open-source configuration management and orchestration tool known for its scalability and performance. The tool uses a master-minion architecture that has a master node communicating with multiple minion nodes to manage configurations and execute tasks.

Terraform

While not a direct alternative to Ansible, Puppet, or Progress Chef, Terraform does possess some overlapping capabilities for infrastructure provisioning and management. The solution enables users to define infrastructure as code using a declarative language, and supports multiple cloud platforms—AWS, Azure, Google Cloud, as well as on-premises infrastructure. With Terraform, you can define the desired state of your infrastructure and execute plans to create, modify, or delete resources accordingly.

Bottom Line: Ansible vs. Chef. vs. Puppet

In short, Ansible excels in pricing, ease-of-use, and community size, making it an ideal choice for organizations seeking a user-friendly automation tool for basic IT environments. Progress Chef and Puppet provide more granular control mechanisms and robust features, making them suitable for complex environments with specific requirements. By carefully evaluating these three tools based on pricing, ease-of-use, documentation, features, integrations, supported environments, customer base, and community, you’ll be better positioned to make an informed decision that aligns with your organization’s needs and goals.

Organizations continue to face a critical decision when it comes to their IT infrastructure: fully embrace the cloud, or adopt an on-premises model? The question remains pertinent despite the fact that the cloud has been around for almost two decades now. This article will provide deeper context to this question with the goal of helping organizations make better-informed infrastructure decisions unique to their specific requirements and environments.

On-Premises vs. Cloud

As their name implies, on-premises environments have computing resources and systems that are physically located within an organization’s premises or facilities. This gives them direct control and ownership over their IT infrastructure, including the physical infrastructure, security measures, and network connectivity. This means they are also responsible for procuring, installing, configuring, and managing all the necessary components as well as ensuring their maintenance, upgrades, backups, and security.

In contrast, a cloud-based infrastructure involves the deployment and maintenance of servers, storage devices, networking equipment, and other hardware and software resources in the cloud service provider’s data centers. A cloud infrastructure is easier to deploy and manage initially, with no required upfront capital expenditures in hardware. Cost-wise, the cloud uses a metered, pay-per-use model, which—depending on scaling requirements and other factors—can be more cost-effective than on-premises.

Cloud Pros and Cons

The cloud has revolutionized the way organizations consume and manage data, applications, and IT resources. Some crucial benefits of the cloud include:

• Unprecedented Scalability: Cloud infrastructure offers unparalleled scalability, allowing businesses to scale resources up or down based on demand. This ensures optimal performance and cost efficiency.
• Significant Cost Savings: Cloud computing eliminates the need for capital expenditure on hardware, maintenance, and upgrades. Instead, businesses can opt for a pay-as-you-go model, reducing upfront costs and enabling predictable budgeting.
• Expanded Accessibility and Flexibility: Cloud services can be accessed from anywhere with an internet connection, providing seamless collaboration and remote access to resources. This flexibility is especially beneficial for distributed teams and remote work environments.
• Automatic Updates: Cloud providers take care of infrastructure updates and security patches, freeing up internal IT teams from routine maintenance tasks.
• Easier Disaster Recovery: Cloud-based backups and disaster recovery solutions offer data redundancy and high availability, minimizing downtime and ensuring business continuity.

Some potential drawbacks to consider when adopting cloud infrastructures include the following:

• Data Security and Privacy Concerns: Organizations that entrust sensitive data to a cloud service provider may raise security and privacy concerns with their own customers.
• Compliance Issues: Cloud service providers typically implement robust security measures; however, organizations must ensure compliance with relevant regulations and industry standards.
• Vendor Lock-in: Migrating from one cloud service provider to another can be challenging and costly, as organizations may become dependent on specific features or services offered by a particular provider.

On-Premises Pros and Cons

On-premises IT infrastructures provide organizations with significant benefits absent in cloud implementations, including the following:

• More Data Control: On-premises infrastructures provide organizations with complete control over their data and resources—a potential hard requirement in highly regulated industries or for organizations with strict compliance requirements.
• Lower Latency: On-premises infrastructures can offer lower latency, since data processing and storage occur locally.
• More Customization Options: On-premises allows organizations to custom-tailor their IT environments for their specific needs and integrate legacy systems seamlessly.

On-premises infrastructures also have their share of drawbacks:

• High Upfront Costs: Building on-premises infrastructure involves significant upfront costs, including hardware, software licenses, and dedicated IT staff.
• Maintenance and Updates: Organizations are responsible for maintaining and updating their own infrastructure, which can be resource-intensive and require skilled IT personnel.
• Scalability Challenges: Scaling on-premises infrastructures can be complex, time-consuming, and costly, requiring additional hardware purchases and configuration adjustments.
• Limited Accessibility: On-premises infrastructure may pose limitations for remote work and collaboration, restricting accessibility to data and applications.

Cloud vs. On-Premises: How to Decide

The choice between cloud and on-premises infrastructure ultimately depends on the unique needs and priorities of each organization. Here’s a look at how each solution measures up on key feature areas.

Cost

Because cloud service providers handle hardware maintenance, software updates, and security, on-premises solutions may seem costlier; however, once on-premises IT infrastructure is established, the ongoing costs can be lower compared to long-term cloud usage. Additionally, cloud computing costs can easily skyrocket if not properly configured and managed. However, for organizations that need to scale their resources according to fluctuating demand, the cloud’s pay-as-you-go pricing model can result in more predictable monthly costs, if optimized correctly.

Ease of Implementation

To implement a cloud-based infrastructure, organizations must select a cloud service provider, migrate applications and data, and configure the necessary resources. Over the years, standard best practices for migrating from on-premises to the cloud have emerged, and cloud providers offer extensive documentation, support, and tools to facilitate the migration process. However, organizations should nonetheless carefully plan and execute their cloud migrations to ensure minimal disruption and optimal performance.

Implementing on-premises infrastructures also require significant planning, hardware procurement, installation, and configuration; however, in this case organizations must allocate resources for building and maintaining the infrastructure, including skilled IT personnel for ongoing management.

Security

Cloud service providers invest heavily in security measures, including data encryption, access controls, and threat detection systems. They employ dedicated security teams and adhere to industry standards and compliance regulations. However, organizations must also take responsibility for securing their applications, data, and user access through proper configuration and robust security practices.

When it comes to on-premises, organizations are left to their own devices and have direct control over their security measures. They can implement specific security protocols, firewalls, and intrusion detection systems tailored to their requirements. However, this also means that organizations are solely responsible for ensuring the effectiveness of these security measures and staying up to date with the latest threats and vulnerabilities.

Compliance

Cloud service providers often offer compliance certifications and attestations to demonstrate their adherence to industry-specific regulations and security standards. This is crucial for organizations operating in highly regulated industries or handling sensitive data; however, firms must nonetheless ensure that their specific cloud-based IT assets are properly configured, and that any additional security measures are in place to meet specific compliance requirements. On-premises infrastructure allows organizations to maintain full control over compliance and regulatory requirements. They can implement customized security controls and monitoring processes to meet specific compliance standards.

Data Accessibility

Cloud services enable universal accessibility, allowing users to access data and applications from any location with an internet connection. This flexibility is particularly beneficial for remote workforces, enabling seamless collaboration and productivity. On-premises infrastructures may pose limitations on accessibility, especially for remote or geographically distributed teams. Organizations must establish secure remote access mechanisms to enable remote access to on-premises IT resources.

The Hybrid Cloud: Best of Both?

In some cases, organizations may opt for a hybrid cloud approach that combines elements of both cloud and on-premises infrastructures. This model allows organizations to leverage the scalability and flexibility of the cloud while maintaining sensitive data or critical applications on-premises. For many organizations, hybrid cloud environments provide the best of both worlds, allowing for a balance of cost efficiency, flexibility, and data control.

Cloud and On-Premises Use Cases

The choice between cloud and on-premises infrastructures depends on the specific needs, priorities, and circumstances of each organization. The following are several ideal use cases for cloud and on-premises IT infrastructures, with factors such as cost, scalability, data control, compliance requirements, and security all come into play when making an informed decision.

Cloud

• Startups and Small Businesses: The cloud offers a cost-effective solution for startups and small businesses, eliminating the need for substantial upfront investments in infrastructure and IT personnel.
• Scalability and Bursting: Organizations with fluctuating workloads or seasonal demand can benefit from the scalability offered by the cloud. They can easily scale resources up or down as needed, optimizing costs and performance.
• Collaboration and Remote Work: Cloud services enable seamless collaboration among distributed teams, facilitating remote work and improving productivity.

On-Premises

• Highly Regulated Industries: Organizations operating in industries with strict compliance requirements (e.g., finance or healthcare) often go with on-premises to maintain full control over data security and compliance.
• Supporting Legacy Systems: Organizations with legacy systems may go with on-premises in order to integrate and coexist with their existing environment seamlessly.
• Data Sensitivity: Organizations handling highly sensitive data (e.g., government agencies or defense contractors) may need to keep their data on-premises to minimize risks associated with external data storage.

Bottom Line: Cloud vs. On-Premises

The choice between cloud and on-premises infrastructure ultimately depends on the unique needs and priorities of each organization. Cloud computing offers scalability, flexibility, and cost savings, but it requires careful consideration of issues related to data security and potential vendor lock-in, to name a few. On-premises infrastructures provide more data control, customization options, and lower latency, but come with higher upfront costs and limited accessibility. The hybrid cloud approach can be an ideal solution for organizations seeking a balance between cost efficiency and data control. Ultimately, organizations should assess their specific requirements, compliance needs, budget, and long-term goals to determine the most suitable infrastructure model for their organization.

FAQ

What is the cloud?
The cloud refers to the delivery of computing services over the internet, allowing businesses to access and utilize resources such as storage, servers, databases, and software applications on-demand, without the need for physical infrastructure.

What does on-premises mean?
On-premises refers to hosting all hardware, servers, and applications within an organization’s own premises or data center, managed and maintained by its own IT staff.

What are the main benefits of the cloud?
The cloud offers several benefits, including scalability, cost savings, flexibility and accessibility, automatic updates, and streamlined disaster recovery options.

What are the benefits of on-premises infrastructure?
On-premises infrastructure offers complete control over data, lower latency due to the localization of IT resources, and more customization options.

Which is more cost-effective, the cloud or on-premises IT infrastructure?
The cost-effectiveness of the cloud versus on-premises infrastructure depends on various factors such as the size of the organization and workload demands, to name a few. The cloud offers cost savings in terms of upfront capital expenditure and ongoing maintenance, as organizations only pay for resources used. However, on-premises involves higher upfront costs but may result in greater long-term savings once the infrastructure is established.

Is the cloud less secure than on-premises?
Cloud providers implement robust security measures to protect data, including data encryption, access controls, and threat detection systems. However, organizations must also ensure proper configuration and adopt additional security measures to meet specific compliance requirements and protect their applications, data, and user access.

Which option is better for compliance and regulatory requirements?
Both cloud and on-premises infrastructures can be designed to meet compliance and regulatory requirements. Leading cloud service providers typically provide compliance certifications and attestations, whereas on-premises allows organizations to maintain full control over compliance by implementing customized security controls and monitoring processes.

Can I have a mix of cloud and on-premises infrastructure?
Yes, organizations can adopt a hybrid cloud approach that combines elements of both cloud and on-premises infrastructure. The hybrid cloud model allows organizations to leverage the scalability and flexibility of the cloud while maintaining sensitive data or critical applications on-premises.

What are some typical use cases for the cloud and on-premises infrastructure?
Cloud computing is suitable for startups and small businesses, organizations with fluctuating workloads, and collaboration and remote work environments. On-premises infrastructures may be more ideal for organizations that are operating in highly regulated industries, heavily reliant on legacy systems, or handling highly sensitive data.

To drive growth and maintain a competitive edge, organizations are continuously adopting new technologies to survive in today’s fast-paced digital ecosystems. Automation has emerged as a powerful tool for achieving these ends, enabling firms to streamline processes, enhance efficiency, and promote innovation. By leveraging automation, they can optimize their operations, reduce human error, and unlock new possibilities for growth.

This article explores automation—its benefits, implementation strategies, and its transformative potential across various industries.

What is Automation?

Automation refers to the process of automating repetitive tasks, workflows, and processes within an organization. It involves using software tools and technologies to replace manual, time-consuming activities with automated processes that require minimal human intervention. Automation can encompass a wide range of activities across different departments. For example, IT automation deals with automating server provisioning, software deployment, network configuration, system monitoring, and data backups, whereas customer service automation may involve using chatbots and other automation technologies to manage large caseloads.

Regardless of the department or organizational function involved, the primary goal of automation is to improve operational efficiency, reduce costs, enhance productivity, and increase service quality levels. By automating routine tasks, organizations can free up their staff’s time to focus on more strategic initiatives, such as innovation, problem-solving, and improving customer experiences.

The Role of AI in Automation

Artificial Intelligence (AI) is at the forefront of prominent innovations in automation, and the presence of AI-powered automation is ubiquitous across domains and industries. However, organizations in practice have been using automation for years, long before AI and machine learning (ML). For example, enterprises in heavy industries and manufacturing have been using automation technologies like assembly lines and mechanical robots to streamline their operations for decades. Infrastructure automation in IT involves the use of specialized software to deploy cloud-based servers and networks automatically, also without the use of AI.

That said, AI is a force multiplier for these organizations’ automation efforts. In the case of manufacturing, enterprises these days are using AI/ML-based diagnostic tools and digital twins to anticipate and remediate issues before they turn into business disruptions and failures. AI-based solutions also allow manufacturers to glean critical insights for optimizing processes and workflows. Similarly, the advent of AI has allowed IT departments to integrate infrastructure automation tools with intelligent agents to build automated fault tolerance into their environments and applications.

Automation Benefits By Category

Automation provides myriad benefits across a wide range of industries, but for most enterprises, the biggest draw is cost savings—specifically, reducing costs associated with manual labor, human errors, and system downtime. By minimizing the need for human intervention, businesses can lower labor expenses and redirect resources to higher-value activities. Additionally, automation can improve resource utilization, optimize system performance, and reduce the risk of costly errors or outages.

Operational Excellence Through Automation

Automation can help enhance efficiency and productivity by eliminating the need for manual intervention in repetitive tasks, allowing organizations to achieve greater efficiency and productivity. By automating processes, businesses can accelerate workflows, minimize errors, and ensure consistent results. This in turn results in faster response times, smoother operations, and increased overall productivity. However, in order to maintain sustainable growth through innovation, firms must achieve operational excellence to remain competitive.

IBM defines operational excellence as “an approach to business management that emphasizes continuous improvement across all aspects of the business and within all business processes by creating a culture where management and employees are invested in business outcomes and empowered to implement change.”

In the pursuit of operational excellence, automation also promotes a culture of continuous improvement and learning. As teams automate repetitive tasks, they gain insights into process inefficiencies, bottlenecks, and potential optimization opportunities. This knowledge can drive process reengineering efforts, leading to further efficiency gains and innovation.

Automating Reporting

Across all industries, teams rely on accurate and timely reporting to maintain continuous situational awareness regarding their respective projects and initiatives. To this end, reporting can be automated by pulling data from multiple systems, consolidating it, and presenting it in a predefined format. For example, sales and marketing departments can leverage robotic process automation (RPA) to assemble data from various systems—including Customer Relationship Management systems, marketing automation platforms, and social sentiment and analytics platforms, to name a few—merge the results into a consolidated report, and automate the report’s delivery to relevant stakeholders on a regular basis.

Security and Compliance Automation

Automation plays a crucial role in strengthening security and ensuring compliance with industry regulations. By automating security measures and functions like intrusion detection systems, vulnerability scanning, and log analysis, organizations can identify and respond to threats quicker and more effectively. Furthermore, automation helps organizations maintain consistent security controls and enforce compliance policies across their IT infrastructure.

Automating Integrations

In order to keep up with shifting business demands and requirements, organizations must remain agile and flexible with their tools and processes. However, in many cases these tools and processes are unable to scale accordingly or lack newly-required features. Automation can facilitate seamless integration between different systems and applications within an organization’s infrastructure as well as provide mechanisms to easily integrate with other systems and tools. This interoperability promotes data sharing, collaboration, and the development of interconnected ecosystems that drive digital transformation.

Robot Process Automation (RPA)

Robotic process automation describes technology that enables organizations to automate repetitive and rule-based tasks through the use of software robots. These robots mimic human interactions using software algorithms, allowing them to perform tasks such as data entry, data extraction, and process automation. RPA is designed to work across various applications and systems, interacting with user interfaces, performing calculations, and making decisions based on predefined rules.

Use Cases For Automation

Rendering Financial Decisions

As the underlying mechanics of AI are mathematical, it’s not surprising that banks and financial institutions use machine learning and AI algorithms to analyze large volumes of financial data to detect patterns, predict market trends, and automate trading processes. On the consumer and B2B front, banks are incorporating automation into their lending practices to provide advanced financial analytics and credit assessments. AI allows firms to service consumers and businesses with solutions better suited to their unique situation and requirements.

Improving Customer Service and Support

Customer service organizations are using automation to streamline support processes by automating repetitive tasks such as responding to frequently asked questions, generating standard email responses, or updating customer records. By enhancing the efficiency and effectiveness of customer support operations with AI, firms stand to improve response times and enhance customer satisfaction.

Automating Insurance Processes

The insurance industry was one of the early adopters of automation and has pioneered many applications of AI across the enterprise. For example, AI is crucial for helping risk and insurance professionals develop more comprehensive policies for better claims and underwriting outcomes. On the customer service side, many insurance carriers have automated the claims management process, from initial reporting to settlement.

Automating Retail Operations

According to McKinsey, almost a third of all sales and sales operations tasks can be automated with today’s technologies. And judging by the current AI adoption levels  among sales organizations, this percentage is certainly on the rise. AI automation is used in retail for inventory management, demand forecasting, and personalized marketing. Recommendation systems leverage AI algorithms to analyze customer preferences and browsing history, enabling personalized product recommendations. And in physical storefronts, robots and automated checkout systems are now being employed to streamline in-store operations.

Optimizing Shipping and Logistics

Automation plays a crucial role in shipping and logistics by optimizing route planning, managing fleet operations, and improving supply chain efficiency. Firms can use AI algorithms to analyze data from various sources like weather conditions, traffic patterns, and shipment details to make real-time decisions and improve delivery schedules.

Bolstering Accounting Data Integrity

For accounting departments, automation eliminates the risk of manual mistakes, ensuring accuracy and reliability in routine tasks. By enforcing standardized processes, organizations can achieve consistent results and reduce the likelihood of data integration errors that can lead to delays and costly fixes. For example, accounting and finance professionals are now using RPA to automatically extract data from invoices and input it into the organization’s accounting software, eliminating the need for manual data entry and reducing errors.

Streamlining IT Operations

IT teams these days more often use automation tools for  automatically rolling out IT infrastructure components. This modern approach is preferred over setting up and configuring servers and IT infrastructure manually, especially when it comes to cloud-native environments.

Popular IT automation tools like Ansible, Puppet, and Chef provide a mechanism for managing and scaling complex infrastructures effectively. They bring consistency, repeatability, and control to configuration management, resulting in improved efficiency, reduced errors, enhanced security, and streamlined operations.

Forecasting Energy and Utilities Usage

According to recent statistics, the global AI energy market is anticipated to top $7.7 billion by 2024. The energy sector is applying AI-powered automation technologies to myriad different industry processes, leading to improved worker safety levels and streamlined emergency response efforts. On the inventory side, firms operating in the energy sector are using automation for demand forecasting and energy optimization. Energy providers are using AI algorithms to analyze historical data to predict energy consumption patterns, optimize energy distribution, and identify potential equipment failures, leading to cost savings and improved reliability.

Bottom Line: Automation

Automation is a powerful enabler for businesses seeking to optimize their operations, reduce costs, and drive innovation. By eliminating repetitive tasks and streamlining workflows, organizations can achieve enhanced efficiency, productivity, and accuracy. The benefits of automation span cost reduction, improved reliability, scalability, security, and compliance. As technology continues to advance, the scope and impact of automation will no doubt follow suit. Organizations that embrace automation will position themselves for success in an increasingly competitive and dynamic business environment, where efficiency, agility, and innovation are key to thriving, not just surviving, in today’s business landscape.

And while city-wide monitoring may not be a new phenomenon, the mass proliferation of Internet of Things (IoT) devices and systems in recent years has resulted in the smart city designation: a city that uses IoT, sensors, and data extensively to improve infrastructure, energy use, utilities, public services, and more.

The global IoT in smart cities market size is projected to hit $260 billion by 2025, at a compound annual growth rate (CAGR) of 18.1% during the forecast period, according to research from Markets and Markets.

This market growth is driven by a combination of factors, most notably an increasing number of government programs and public-private partnership (PPP) initiatives for developing IoT smart city advanced services to enhance the daily lives of citizens. The rising adoption of connected solutions and smart technologies is also expected to drive market growth in this category.

IoT deployments in smart cities are primarily aimed at improving sustainability in the face of urbanization challenges, supporting the implementation of smart intercity transportation networks, optimizing water management efforts, and improving the lighting and heating efficiency in buildings and public structures.

Here, we explore some of the ways that smart cities use innovative digital technologies on top of traditional networks and services to improve the lives of residents and visitors alike:

See more: Industrial Internet of Things (IIoT) Market Size & Forecast

5 Examples of IoT in Smart Cities

1. Smart Parking Lots

Many smart cities have implemented intelligent parking solutions capable of monitoring for available public parking spaces.

By using underground sensors to detect whether a parking space is occupied, cities can save drivers the hassle of hunting for parking spaces, while reducing emissions and traffic. Users can typically access parking notifications and guidance via a smartphone app and/or website; more commonly, the technology is used for displays in multi-level parking structures.

The global smart parking systems market size was $4.42 billion in 2020 and is projected to increase at a CAGR of 21.5% between 2021 and 2028, according to Grand View Research.

Smart parking system vendors are currently experiencing a sharp decrease in demand due to the ongoing pandemic, with extended lockdown measures and steep drops in traffic to blame.

2. Smart Waste Management 

One of the more active areas of smart city innovation involves IoT-based waste management.

These solutions are designed to optimize waste collection processes, reduce the operational costs, and increase the efficiency of waste management as well as mitigate environmental issues related to waste disposal efforts.

For example, waste containers equipped with level sensors automatically notify a centralized waste management platform when waste levels are exceeded; this in turn dispatches a truck driver via smartphone to service the containers.

The smart waste management market was valued at $1.77 billion in 2020 and is anticipated to hit $6.52 billion by 2026, at a CAGR of 25.68% during the forecast period, according to Mordor Research.

3. Smart Traffic Control Systems

To reduce the waste in time and money caused by traffic delays, many city governments are leveraging IoT to automate and optimize the city’s traffic control systems.

These solutions typically consist of a network of sensors installed at intersections for measuring traffic volume and adjusting stop-and-go times accordingly.

For example, Pittsburgh, Pennsylvania is deploying smart traffic signals at one-third of its 610 intersections. Since starting the project, the city has experienced a 41% decrease in intersection wait times and 21% percent reduction in vehicle emissions. Cities like Dallas are also implementing IoT-enabled traffic management systems for improving road congestion management efforts.

The global intelligent traffic management system market size in 2020 was valued at $9.12 billion and is expected to increase at a CAGR of 11.9% from 2021 to 2028, according to Grand View Research.

The ongoing pandemic and its impact on drivers and commuters has severely impacted global demand for traffic management systems.

See more: Top Industrial IoT (IIoT) Trends

4. Smart Street Lightning

IoT-based connected lighting is another highly active smart city domain that helps municipalities increase energy efficiency and reduce energy and maintenance costs.

Smart lights automatically adjust their brightness levels based on street activity as well as transmit maintenance data to anticipate outages and enable faster response times.

Chicago projects that its connected street light program will result in an annual savings of $10 million in energy costs. Miami has some of the most connected street lights in the world — an achievement that has saved the city 44% in energy costs annually, compared to traditional street lights. Paris recently retrofitted its aging streetlight infrastructure with 280,000 connected IPv6-based LED streetlights. Using a Wi-SUN Alliance-supported IEEE 802.15.4 wireless RF mesh architecture, the connected streetlights were implemented as network-as-a-service deployments, saving the city 70% on annual streetlight energy costs.

The global smart lighting market size was $10.79 billion in 2020 and is projected to reach $45.47 billion in 2028, at a CAGR of 19.7% over the forecast period, according to Emergen Research.

Aside from smart street lighting scenarios, increased use of smart lighting in security use cases and for reducing overall power consumption are some key factors behind the increase in the global smart lighting market.

5. Smart Utility Meters

Utility companies serving municipalities are installing IoT-based solutions for automating and optimizing city-wide energy use.

For example, smart meters attached to city buildings are connected to a smart energy grid and enable the utility company to track energy consumption, improve energy flow management, and more. This also enables them to carry out administration operations remotely, such as disconnecting service, implementing new pricing, installing new load management programs, and troubleshooting local power infrastructure.

Additionally, they allow the utility company to pinpoint exact outage locations and restore operations faster and more efficiently.

The global smart meter market size was valued at $21.79 billion in 2020 and is expected to hit $54.34 billion by 2030, at a CAGR of 10.10% from 2021 to 2030, according to Allied Market Research. 

Conclusions

Cities are in constant evolution and flux. Smart cities are designed to be responsive and agile to the continuously shifting needs of their inhabitants. By adopting the latest IoT-based smart city technologies, city planners and administrators can access the critical data and necessary insights to improve the lives of their citizens and tackle their most pressing issues. 

See more: Best IoT Platforms & Software

However, self-checkout kiosks and hyper-personalized in-aisle advertising displays are just a small sampling of the current customer-facing retail IoT innovations.

Behind the scenes, in the stock rooms, warehouses, and fulfillment centers, IoT innovations have also radically transformed the way customers’ orders are assembled, handled, and shipped.

IoT in Retail Today 

The pandemic has caused a seismic shift in demand for IoT in retail. For example, customer-facing IoT solutions for enhancing physical in-store experiences are giving way to solutions for optimizing the back-end fulfillment and support process.

According to a study by Microsoft, almost nine out of 10 (87%) retailers regarded the use of connected technologies inside their stores as a critical component of their success, with 92% having already implemented IoT in their stores, in some shape or form. That said, retail IoT adoption efforts can be drawn out and complex, with 88% of retailers experiencing failure in one or more of their IoT projects implementations.

Still, retail IoT adoption is accelerating as consumer demand for online shopping and e-commerce continues to surge. With in-person shopping experiences shuttered for the greater part of the last couple years, online shopping and web-based retail activity has surged globally in the face of the pandemic. That said, retail brick-and-mortar establishments are gearing up to receive physical, in-store customers again. To re-open to the so-called “new normal,” a new category of IoT solutions have emerged for improving customer hygiene and safety during their visits (e.g., enforcing social distancing, detecting the use of face masks, maintaining crowd control, and more).

According to Grand View Research, the global retail IoT market size is anticipated to hit $182.04 billion by 2028, at a compound annual growth rate (CAGR) of 26% over the forecast period. This growth is due to continued consumer demand for more seamless shopping experiences and smart payments coupled, with the commoditization of sensors and IoT hardware and other drivers.

5 Examples of IoT in Retail

IoT is a key enabler of smart retail — the hybridization of brick-and-mortar shopping modes with modern technologies and innovations. It’s worth noting that while in the past smart retail was driven by the quest for new operational efficiencies and ways to improve customer satisfaction, these days public health risk reduction and customer safety are now also crucial IoT drivers.

From smart signage to robot customer service agents, the following are some leading examples of IoT in retail:

1. Personalizing the In-Store Experience with Beacons

Introduced by Apple, beacons are minuscule Bluetooth devices capable of sending push notifications and providing location-based services to mobile devices based on their proximity/location. Popular brick-and-mortar retailers, such as Urban Outfitters, CVS, Timberland, and Macy’s, have been early adopters and innovators in the retail beacon space. For example, they use the technology to offer limited-time discounts, announcements/invitations to special events, and other promotions while customers are physically in the store.

2. IoT-Enabled Inventory Tracking Solutions 

IoT-powered tracking systems enable end-to-end product and inventory visibility and awareness, from the time they first enter the warehouse to the time they arrive at the purchaser’s residence or place of business. These IoT solutions provide both customers and retailers continuous situational awareness regarding the status and whereabouts of their orders, resulting in reduced support requests/calls and improved customer satisfaction. And as automated self-service storefronts enable more sales volume at the front-end of retail operations, IoT-enabled inventory and warehouse management solutions on the back end enable retailers to scale with customer demand. The growth in IoT adoption to support retail back-end operations is also on the rise. A report by Grand View Research predicts that the global IoT in warehouse management market will reach $19.06 billion by 2025.

See more: How the Internet of Things (IoT) is Used by Volkswagen Group, BigBelly Solar, Deep Sky Vineyards, Citizen Care Pod, and Canutillo Independent School District: Case Studies

3. Automated Self-Service Storefronts

Fully automated stores use a variety of solutions such as motion-activated smart cameras, shelf monitoring systems, beacons, and other IoT technologies to provide a fully automated, end-to-end in-store shopping experience — guests need only walk in, select the items they wish to purchase, and walk out. Payment is automatic, and customers receive an electronic receipt on their mobile devices. Though Amazon Go is currently the leading player in this category — with 29 automated stores in operation — competitors are steadily gaining ground on the market leader in response to global market demand for automated storefronts. According to a recent survey by SOTI, 73% of shoppers surveyed prefer self-service technologies (e.g., self-checkout, price checking, product assistance) over interacting with a live customer service staff member, a 10.6% increase from the previous year’s survey. Suffice to say, the pandemic has significantly increased demand for retail IoT solutions in this space.

4. Reducing In-Store Customer Wait Times

IoT solutions are being deployed by brick-and-mortar retailers to help customers better manage in-store wait times. For example, stores often use IoT-enabled stationary sensors to track customer movements during checkout, alerting store employees automatically when a new aisle should be opened. This is especially critical during the pandemic, when long lines and crowds pose an elevated public safety risk. 

Additionally, IoT technologies enable the faster retrieval of items from stock rooms and warehouses. In the past, employees would be tasked with combing through aisles of inventory manually, leading to extended customer wait times and potential purchase abandonment.

5. In-store Robot Customer Attendants

Like automated, self-service storefronts, robot customer service agents were once relegated to science fiction. These days, in-store robots are becoming increasingly common. These robots rely on a constellation of in-store IoT sensors and wireless technologies to help customers locate specific products, take physical counts of available inventory, find misplaced items, and more. This enables human customer service professionals to focus on providing higher-level customer satisfaction efforts and activities.

According to a report by Coherent Market Insights, the global retail robot market size was $7.1 billion in 2020 and is anticipated to top $55.8 billion by 2028 at a CAGR of 30.1% over the forecast period. The high cost of developing and manufacturing retail robots translates to a higher price tag to the retailer — significant roadblocks hindering widespread adoption. The vast array of required components (e.g., sensors, radios, actuators, cameras, microcontrollers, and touch screens) also make for a significantly complex final bill of materials (BOM). However, the commoditization of sensor hardware and IoT components in recent years has enabled leading robotics vendors to bring more cost effective retail robots to market.

Conclusions

Though online shopping continues to supplant in-store purchasing experiences, brick-and-mortar retailers will nonetheless continue to undergo IoT-enabled automation.

Consumers can expect to see even more spectacular IoT innovations on the retail floor and at the point of sale.

However, it’s conceivable that most future storefronts in urban areas will be automated, with some specialized in-person retailers focusing on select groups of clientele. Retail IoT will, therefore, increasingly shift to improving the customer experience on the fulfillment side, allowing for orders to be processed and shipped faster and with less errors.

See more: Best IoT Platforms & Software

In fact, the financial sector was already involved in developing innovations around Bayesian statistics, a staple of machine learning, as early as the 1960s. These seminal use cases were based around monitoring stock markets and making predictions for investors. Today, this legacy continues with AI-powered robo-advisers designed to give automated, algorithm-based financial planning services with minimal to no human assistance.

Modern finance has since diversified its AI use, including the streamlining of internal business processes and improvement of the overall customer experience. Both finance pros and customers are likely to have AI encounters on a regular basis, since most routine service-related issues are handled/resolved using some degree of AI-powered automation. This trend is likely to accelerate in order to meet rising customer demands for faster, more convenient, and secure financial experiences.

AI in Finance Today

AI in fintech had a market value of $7.91 billion in 2020 in 2020 and is expected to reach $26.67 billion by 2026, at a compound annual growth rate (CAGR) of 23.17%, according to Mordor Intelligence.

The anticipated growth is fueled by continuing advances in automated trading technologies and algorithms as well as relatively newer applications for smarter fraud prevention, more effective risk management, faster customer support, such as chatbots and through agent call routing, and tighter ongoing compliance with finance industry regulations. 

See more: Artificial Intelligence Market

5 Examples of AI in Finance

1. Automating Process Management and Back-end Operations

Forward-thinking companies navigate massive amounts of data with technology. In the case of finance, the automation of transaction processing and back-end operations has enabled organizations to scale to fulfill the demands of an always-connected, global economy. By utilizing AI and natural language processing (NLP), firms can automate the ingestion of accounts receivables/payables, invoices, and accounting requests in structured and unstructured formats.

2. Optimizing Trading Activity for Better Returns

Finance was an early AI innovator, focusing on the optimization of investor trading decisions. These days, both quantitative and algorithmic trading rely significantly on AI. In the case of quantitative trading, AI and statistical methods are used to surface investment opportunities but not necessarily place orders automatically. In contrast, algorithmic trading involves fully automated systems that perform analysis and open/close positions on a trader’s behalf. These systems can process large data sets and identify patterns faster and more efficiently, enabling better predictive capabilities and more accurate estimations of future market patterns.

See more: Artificial Intelligence: Current and Future Trends

3. Enhancing the Personalized Banking Experience

The majority of banking customers have already become accustomed to regular encounters with AI, since routine service-related banking issues are more often handled or resolved with some degree of artificial intelligence. AI-enhanced banking experiences span all platforms used by a customer, from customized offers and alerts via a bank’s website and mobile app to faster/smarter customer service call routing and problem resolution. Across these platforms, conversational AIs are taking the front line to provide personalized financial advice and guidance, customized to the unique profile and requirements of each customer. 

See more: Conversational Artificial Intelligence (AI) Market

4. Creating More Effective Fraud Detection Measures

Unsurprisingly, finance and banking enterprises are considered upper-echelon targets for cyber criminals. Industrial-grade cybersecurity and fraud detection measures are therefore the norm when it comes to preventing malicious actors from getting the upper hand. For example, AI is used for detecting and connecting anomalous spending patterns among credit customers, which in turn can inform broader data breach investigations.

See more: Trends in Artificial Intelligence (AI) in Cybersecurity

5. Informing Credit Decisions

In the past, three credit reporting agencies, Equifax, TransUnion, and Experian, provided the data behind the vast majority of consumer credit decisions globally. This effectively left most of the world’s population unaccounted for, since credible but “unbankable” consumers from developing nations or impoverished regions lack formal access to global credit-granting institutions. AI has changed this dynamic by allowing banks to use behavioral attributes, such as phone information, bills/payment records, and social media information to create machine learning (ML) models for credit risk and worthiness.

See more: Top Performing Artificial Intelligence Companies

The recent accelerated COVID-19 vaccine development efforts are just a few examples of how AI-driven medical innovations can be critical to everyone’s well-being.

That said, drug discovery is just one of the many health care/medical fields and specialties that AI has transformed.

AI in Health Care Today

The market size for health care AI and cognitive computing reached $6.7 billion in 2021, at a compound annual growth rate of 40 percent, compared to $811 million back in 2015, according to a recent Frost & Sullivan report.

Some areas of heightened growth include AI applications in medical imaging diagnosis, AI-based solutions for optimizing hospital workflows and enhancing care delivery as well as use cases for reducing patient treatment times, complexity, and costs.

See more: Artificial Intelligence Market

5 Examples of AI in Health care

1. AI for Accelerated Drug Discovery

The expedited response of vaccine researchers to the pandemic was aided by AI.

AI algorithms have helped to break new ground in accelerating the discovery of new molecular combinations, tracing toxicity potentials, identifying active mechanisms, and a myriad of other drug discovery applications.

Interestingly, in Moderna’s case, AI helped to both speed up coronavirus vaccine development and automate other key systems and processes in the company.

2. AI-Supported Medical Imaging Analysis

The timely, accurate assessment of a patient’s condition is critical for effective treatment and recovery.

To this end, AI has brought about major advances in case triaging or determining when cases are urgent or non-urgent.

For example, radiologists and cardiologists are using AI-based solutions to automatically review images and scans. This enables them to quickly identify key insights and prioritize emergency cases.

AI-assisted diagnostic imaging is widely considered one of the most promising clinical applications for AI in health care.

See more: Artificial Intelligence: Current and Future Trends

3. AI-Enabled Medicine Cost Reduction

In a crisis such as the pandemic, heightened urgency makes the speed of drug development a high-priority concern.

Drug research and discovery budgets under normal circumstances are heavily allocated to experimentation-related activities and processes.

Through the use of AI, such as convolutional neural networks, predictions can be automated regarding complex processes, including the binding of molecules to proteins. Because AI-enabled solutions can analyze hints and signals from vast quantities of experimental measurements faster than teams of researchers could on their own, safe and effective drug candidates can be identified in less time and with significant cost reductions.

Other innovations improve long-term efficacy as a cost-reduction measure.

For example, solutions like the digital pill combine personalized, AI-based tools with standard drug prescriptions for better patient response to drugs, increased adherence, and improved management of chronic medication intake.

4. AI-Based Mental Health Treatment

A plethora of AI-powered apps for iOS and Android are available for managing and enhancing users’ psychological well-being.

Though highly popular, these solutions have mostly been of consumer-grade quality and limited to mobile device use. Recently, companies like Kernel have emerged with medical-grade software/hardware solutions that use AI/machine learning (ML) to quantify and understand the human brain for more accurate mental health assessments and treatment.

Google’s DeepMind Health has also developed a technology that merges ML with system neuroscience to build neural networks that mimic the human brain. Partnering with clinicians, researchers, and patients, Google aims to apply its AI prowess in solving real-world health care problems. 

5. AI for Diagnosing Cancer More Accurately

As cancer is the leading cause of death worldwide, a myriad of oncology-related AI solutions have taken on the multifaceted, complex challenge of diagnosing and treating the disease.

Companies like PathAI develop ML-based solutions for both helping pathologists make more accurate diagnoses and developing effective methods for highly individualized cancer treatments.

AI-powered breast cancer screening is another active health care space that combines advanced computer imaging with ML-powered detection of suspicious features as well as predictions regarding likelihood of malignancy. Dutch spin-off Screenpoint Medical, for instance, says its AI systems for assessing mammograms can perform better diagnoses than the average mammography radiologist.

AI health care solution providers are also taking on cancer at the molecular level. For example, German biotechnology firm Evotec recently partnered up with AI drug discovery firm Exscientia to apply AI techniques to small molecule drug discovery. The partners have announced the start of a phase 1 clinical trial for a novel anti-cancer molecule. 

See more: Top Performing Artificial Intelligence Companies

With Microsoft, Cisco, Intel, NATO, the U.K. government, the U.S. government, the European Parliament, and thousands of other high-profile customers falling victim to the attackers, the incident illustrates how security failures in today’s integrated cloud environments can have such a devastating and widespread impact, catching even the most well-protected organizations off guard. 

Cloud security failures on several levels ultimately led to the successful supply chain attack. Initially, cyber criminals were able to infiltrate SolarWinds’ internal systems by first compromising its Microsoft 365 email services and Azure cloud infrastructures. This ultimately led to attackers being able to move laterally through the cloud, accomplishing their mission to hide malware in SolarWinds software updates to customers.

Amazon Web Services (AWS) also came under fire for enabling the cyber criminals to utilize its infrastructure-as-a-service (IaaS) resources to carry out the attack. This reveals an interesting facet of cloud security that makes it unique from other cyber disciplines: with instantly provisioned and scalable IT resources, effective cloud security becomes not just a matter of protecting the business from being attacked — it’s also about not being used unwittingly to attack others.

Cloud Security Today

With the rise of software-defined networks (SDN), DevOps, and cloud automation platforms, the line between application and infrastructure security has been gradually blurring.

For this reason, cloud security is a unique discipline, as traditional security approaches fail to keep up with rapidly evolving agile environments and DevSecOps processes. Developers working in cloud-based environments frequently use containerization software, like Docker, for application portability as well as Kubernetes to orchestrate (e.g., spin up/down en masse) and cluster those containerized applications.

These environments specifically require cloud security solutions versus traditional security platforms designed to protect on-premises IT networks and assets.

Additionally, with many enterprises using a mix of public cloud-based infrastructure/apps and private cloud or on-premises IT assets, hybrid cloud security is also a rapidly growing space.

Cloud Security Market

The global cloud security market is worth $34.8 billion in 2021 and is expected to hit $67.6 billion by 2026, increasing during that period at a compound annual growth rate (CAGR) of 14.2%, according to ResearchAndMarkets.com.

The shift of the global workforce to the home office — and subsequently the public cloud — has resulted in a spike in cyber crime as malicious opportunists seek to prey on less-experienced internet and email users as well as enterprises as their IT and security staff are busy transitioning to a global remote workforce.

Benefits of Cloud Security

In the past, cloud security solutions were primarily designed to address the needs of cloud-native applications and/or virtual infrastructures.

Newer cloud-based offerings provide unified security management and orchestration for both on-premises networks and cloud infrastructures.

Not only does this streamline administration and allow for cloud-based management of on-premises IT resources, it also enables organizations to leverage the metered resources to quickly scale up protective measures against active cyber attacks or malware campaigns.

Cloud Security Use Cases

Public cloud providers such as AWS, Google Cloud, and Microsoft Azure enable developers to quickly spin up the virtual infrastructure resources needed to build their applications on top of the cloud.

This makes modern software development arguably the most prominent use case in the cloud security space. Indeed, several vendors market products on the cloud application side of affairs — most notably cloud access security broker (CASB), container security, and zero-trust access solutions.

Secure access service edge (SASE) in particular has seen a dramatic increase in adoption during the global pandemic, mainly due to the massive shift toward working from home.

With employees using unmanaged devices to access corporate IT environments in droves, the limitations of legacy virtual private network (VPN) and traditional remote access quickly manifest themselves in poor application performance, availability issues, and the emergence of security gaps.

Use cases abound for general enterprises looking to leverage the cloud for providing remote workers a more secure, rapid response to corporate network resource demands.

Cloud Security Vendors

The following are 10 leading cloud security vendors, from cloud security monitoring services to security for hybrid cloud environments:

Cloud Passage

Halo, Cloud Passage’s flagship platform, improves the security of private/public/hybrid cloud deployments by automating cloud security and compliance controls.

Forcepoint

Forcepoint develops secure access service edge (SASE) solutions, among others, as well as a cloud access security broker (CASB) solution: a cloud/on-premise hardware or software device that sits between users and cloud service providers to monitor for security issues.

Carbon Black

Now a part of VMware, Carbon Black offers a cloud-native platform for endpoint protection. The solution features a wide array of features, from threat hunting to its next-generation antivirus (NGAV)—AI-powered malware detection/prevention..

CrowdStrike

CrowdStrike was the first to develop a cloud-native platform for endpoint protection and data breach detection. Other features include custom tailored threat intelligence and advanced malware detection.

Aqua Security

Acqua focuses on security monitoring for cloud environments—virtual and serverless infrastructure, microservices, containers, and more.

Check Point

Security behemoth Check Point made its entry in the cloud security arena with its CloudGuard platform for public cloud security. The solution provides threat protection and security intelligence for public cloud services such as Amazon Elastic Kubernetes Service and Amazon Elastic Container Service, to name a few.

Palo Alto Networks

The recent acquisitions of Twistlock and Puresec position Palo Alto Networks solidly in the container and serverless security space. Leading cloud-native app developers have already found these two vendors’ offerings indispensable; now, they’ve joined the cybersecurity giant’s lineup of leading solutions, from the cloud to the edge.

Qualys

Qualys offers a platform for cloud security that includes cloud agents, scanners, sensors, SaaS connectors, and more for comprehensive visibility and insights regarding potential security gaps and vulnerabilities.

Zscaler

Zscaler is focused on developing cloud-native security solutions such as its Zscaler Internet Access—a security stack as-a-service delivered completely via the cloud, and Zscaler’s Private Access (ZPA), a cloud service that enforces zero-trust access for private applications in the public cloud or on-premise data center.

CyberArk

A leader in identity security, CyberArk is known for its Privileged Access Manager, designed specifically for mitigating account exploitation risk in AWS public cloud/hybrid environments.

Conclusions

Incidents like the SolarWinds data breach illustrate how integral cloud security is to the software ecosystems of today.

In an age of software as-a-service (SaaS), integrations, and APIs, enterprises must adopt a layered cloud security strategy leveraging some (or most) of vendor technologies.

Cloud security solutions must also be multi-faceted and capable on several levels: to protect the growing number of hybrid cloud deployments as well leverage AI/ML to counter increasingly sophisticated cyber attackers.

And while the cloud has heralded the arrival of infrastructure-as-a-service (IaaS) and software-defined networks (SDN), at the end of the day, these services are delivered out of physical data centers, albeit distributed and dispersed across the globe. For traditional firms with on-premises IT infrastructures, network security continues to increase in complexity in order to keep up with ever-sophisticated cyber attackers. 

Whether on premises or in the cloud, IT infrastructures are prone to the same security issues and are equally exposed to cyber threats. However, with today’s networks supporting a vastly greater number of users, devices, and Internet of Things (IoT) sensors, the enterprise attack surface’s composition has evolved significantly even when compared to just a few years ago.

Network Security Today

Today’s network security offerings range from traditional intrusion detection and prevention systems (IDPS) to next-generation firewalls (NGFW) and unified threat management (UTM) devices that serve multiple security functions in one appliance.

More recently, the network detection and response (NDR) category has emerged to describe solutions that utilize artificial intelligence (AI)/machine learning (ML) and other non-signature based methods to detect advanced threats lurking in the network.

These solutions are also designed to work well with other tools for streamlined remediation, threat hunting, and more.

Network Security Market

The network security market is anticipated to increase from $27.39 billion in 2021 to $60.38 billion in 2028, at a compound annual growth rate (CAGR) of 12.0% during the forecast period.

Drivers such as the global shift to working from home and the resulting spike in remote access have resulted in higher enterprise network traffic and number of varying devices — this in turn translates to more risk exposure to threats like ransomware and bothers.

Benefits of Network Security

Today’s enterprise networks tend to be a mix of on-premises, cloud (IaaS), and sensor/IoT networks at the edge. Now more than ever, comprehensive network security is critical to ensuring that excessive cyber risk exposure doesn’t impact the business. This means being able to collectively monitor and mitigate the cyber risk exposure of these heterogeneous network environments via a single pane of glass.

Additionally, the ML-powered insights provided by today’s network security solutions enable businesses to shore up their defenses against continuously evolving cyber threats. 

Interestingly, many of today’s network security offerings are cloud-based and can therefore scale in real-time to the firm’s needs as well as secure IaaS environments natively. Several vendors use a similar deployment architecture consisting of network security devices deployed on premises or in the cloud, managed by a cloud-based security orchestration platform.

Network Security Use Cases

The enterprise IT security market is fairly saturated with numerous players in many cases offering overlapping solutions.

IDPS and UTM are commonly bundled into a single device (i.e., the NGFW) these days, though a handful of vendors develop special intrusion detection (IDS) sensors for special use cases (e.g., for monitoring vessel networks).

The IoT and industrial control system (ICS) network monitoring space is experiencing a surge in demand — especially for use in environments like manufacturing facilities, energy stations, automotive assembly lines, and more.

With many of these industries undergoing digital transformation, the need for monitoring the supporting networks for threats is being addressed by a growing number of players. That said, the operational technology (OT) network security space is still relatively new, with room for new disruptors.

More than a few vendors market their solutions under the NDR category. These offerings focus on network traffic analysis use cases for the modern enterprise. More often, these centralized NDR platforms combine threat detection with security workflow automation, auto-remediation, threat hunting, and more.

Network Security Providers

The following are 10 leading vendors that provide solutions for network security, from UTMs and smart firewalls to AI-powered network anomaly detection and NDR solutions.

ExtraHop

ExtraHop provides continuous network monitoring through its Reveal(x) EDR platform. It’s cloud-based solution features auto-discovery and auto-classification capabilities for real-time visibility into IT infrastructures both on-premise and virtualized/cloud-based.

Fortinet

A leader in network security, Fortinet has developed its flagship FortiGate NGFW with advanced UTM capabilities in a unified smart firewall device, enabling customers to more easily monitor/protect their networks via a single pane-of-glass.

Sophos

An established name in network security, Sophos offers a NGFW with enterprise-grade firewall capabilities as well as advanced features for protecting against modern cyber threats such as advanced malware, drive-by downloads, botnets, and more.

Tenable

Tenable is perhaps most well-known for its Nessus vulnerability scanner; however, its solutions for enterprise network visibility are also popular for detecting, identifying, and mitigating threats across an organization’s entire IT computing fabric — be it on premises, in the cloud, or in an industrial/operational technology (OT) environment. 

Claroty

A leader in OT network security, Claroty has developed a platform that provides industrial enterprises with comprehensive network visibility, monitoring, and threat detection. Its solution was designed to monitor both standard IT networks as well as OT environments with industrial control systems (ICS) and supervisory control and data acquisition (SCADA) systems present.

Vectra

Vectra’s platform monitors both on-premise and cloud-based networks with AI-powered capabilities for advanced threat detection and risk awareness. By using machine learning (ML) to enrich the network metadata collected, organizations can perform more contextual, accurate assessments regarding their network risk.

Fidelis Cybersecurity

The Fidelis Network is an NDR platform that provides monitoring for both individual endpoints as well as entire networks. Like Vectra (and many others), the solution uses AI/ML for enriching network metadata in network security analytics and threat hunting use cases.

VMware

VMware is most well-known as the leader in the virtual machine space, but it also offers a range of enterprise cybersecurity solutions. Its NSX platform (previously Lastline Defender) was designed to detect advanced network threats and security incidents in both on-premise and cloud networks

Forescout

While Forescout offers a range of traditional enterprise network monitoring tools, the company has positioned itself as a security provider for the “enterprise of things” — or enterprise networks supporting a combination of IT, OT, and IoT devices. 

Darktrace

Darktrace’s AI-powered cybersecurity suite enables enterprises to automatically detect network anomalies and advanced threats — malicious signals that may be difficult to recognize using standard network monitoring tools and IDPS solutions.

Conclusions

Network security continues to evolve as organizations become more heterogeneous in their IT environments.

From the cloud to the edge, modern enterprises require solutions that are both streamlined for centralized management yet flexible enough to monitor any type of network deployment, be it virtualized/cloud-based or on premises, traditional computing or IoT sensor-based.

And as malicious actors continue to evolve in their capabilities, network security controls must also be highly adaptable. To this end, today’s leading AI/ML-powered NDR platforms are redefining the next generation of network security solutions.

See more: Best Threat Intelligence Platforms for 2021

Whether they were first to market with the autonomous cloud is up for dispute, but one thing is for certain: as cloud databases become increasingly prevalent, Oracle faces an ever-expanding arena of competitors for the cloud database crown.

Cloud Databases Today

Market research firm IndustryARC predicts that the global cloud database market will reach $39.1 billion by 2026, at a CAGR of 31.4% between 2021-2026. This growth is attributed to the data explosion brought on by an onslaught of mobile and IoT devices, expanding cloud database adoption in sectors such as banking and education, and the overall growing popularity of database-as-a-service (DBaaS) — metered databases that are managed and scaled in the cloud.

5 Trends in Cloud Databases

The majority of the following trends deal with so-called “cloud-native” databases: databases deployed and delivered strictly through the cloud. Per the economic advantages of the cloud, many of the advanced computing and IT resources required by these newer cloud databases could only be acquired cost-effectively through a metered, time-shared usage model.

From the rise of autonomous cloud databases to increased graph database adoption, the following are five trends in cloud databases to keep track of in the near future.

1. Increasing Graph Database Adoption

Graph databases are a type of cloud-native, NoSQL database technology specially designed to focus on the relationships between data, as well as the data itself. Structurally, a graph databa se consists of nodes (circles) connected by edges (lines)— these represent data points and relationships, respectively. 

Graph databases are becoming an increasingly popular cloud database option, as they don’t require rigid design and data structure formalities typical of standard relational databases. Leading solutions in this category include Neo4j and Amazon Neptune, to name a few.

2. Rise of Multi-Cloud Database Clusters

In a general cloud context, multi-cloud deployments enable firms to leverage several cloud vendors to achieve better performance, scalability and availability. The importance of this capability has become more apparent over the years as large swathes of the internet have experienced outages due to failures on the part of public cloud service providers.

New cloud database offerings such as MongoDB Atlas offer users the ability to run database-driven applications on several cloud infrastructures simultaneously — for instance, across AWS, Google Cloud Platform, and Microsoft Azure, allowing for multicloud high availability at the database level.

See more: Cloud Database Market 2021

3. Fully-Managed (Autonomous) Cloud Databases

As mentioned previously, Oracle’s early vision of a self-managing database capable of automated patching, upgrades, and tuning is now manifest in its Autonomous Database product. Not to be outdone, Microsoft’s Intelligent Insights enhances Azure SQL cloud databases with AI-powered features like proactive monitoring, automatic detection of issues, and self-tailored performance insights, to name a few. As more small and medium-sized organizations move their databases to the cloud, cloud-native solutions will at least need to become easier to manage — if not fully automated.

4. Legacy Database Products Becoming Cloud-Based

Well-established relational database products have started their migration to the cloud, with long-standing customers heavily invested in these early database technologies following suit. For example, IBM’s Db2 product, first released back in 1983, has been recently revamped as IBM Db2 on Cloud: a fully managed SQL database solution that runs in the cloud on a metered, pay-as-you-go basis.

5. More Streaming/Time-Series Database Deployments

According to the latest figures, over 26 billion IoT devices are currently churning out massive volumes of data: sensor readings, weather conditions, machine operating levels, patient health monitoring statistics, and a myriad of other telemetry types for various use cases. These data types are uniquely structured and tend to be immutable (i.e., the data is never updated, only stored) — think temperature readings regularly recorded over a period of time. Because the cloud databases optimized for this typically have their data “streamed in” and time stamped, they are referred to as, appropriately enough, time-series or streaming databases.

Vendors like InfluxDB and Prometheus are currently experiencing heightened demand as time-series databases come into vogue with expanding IoT adoption and the arrival of Industry 4.0.

Conclusion 

Despite their merits, cloud databases aren’t ideal for every organization. In many cases, strict data governance restrictions or compliance measures require data management and security to remain on premises. Firms operating under these constraints have only traditional database options to select from; that said, these same firms are likely to already benefit from cloud databases, albeit indirectly through the use of third-party SaaS applications.