Overview of Intelligent Automation

Intelligent automation (IA), which combines robotic process automation (RPA) with artificial intelligence (AI), is a workflow optimization process that many organizations are implementing to more effectively streamline operations.
As advanced technologies like artificial intelligence come to the fore, many customers are expecting faster and higher-quality outputs from the brands they trust. In response, enterprises are increasingly looking for ways to automate their business processes – hence the rise of intelligent automation.
In this guide, we’ll take a closer look at what intelligent automation is, how it works, the benefits that come with using it, and how it can be applied to common business processes and automation scenarios most effectively.

TABLE OF CONTENTS
ToggleWhat Is Intelligent Automation?Business Use Cases for Intelligent AutomationBenefits of Intelligent AutomationBest Practices for Getting Started With Intelligent AutomationBottom Line: Intelligent Automation in Modern Enterprise Workflows

What Is Intelligent Automation?
Intelligent automation is an advanced automation process that merges artificial intelligence and machine learning with robotic process automation to automate business process workflows and create intelligent, robotic agents that can take over some of an organization’s workflow-based tasks.
Robotic process automation bots alone can handle a number of automated business tasks. But they don’t possess the additional human-like capabilities to go beyond routine training and take on new tasks that require cognitive and sensory capabilities.
When combined with RPA, artificial intelligence and machine learning give bots the algorithmic knowledge to comprehend and execute automated tasks at a deeper level. The training data involved in IA is typically a large set of data from various sources and in diverse formats: both structured and unstructured.
In essence, this sophisticated AI training gives RPA-powered machines the capacity for decision intelligence, or at least the context to make data-driven decisions that are largely independent from regular human intervention.
But for bots to move beyond simple and routine task automations, they often require more than basic AI and ML algorithmic training. For example, many upper level bots are trained with deep learning, neural networks, and natural language processing so they can understand human language and generate unique content on a range of topics.
To give IA machines the ability to “see” or interact with their surroundings, many of these bots also receive training based on computer vision and optical character recognition (OCR). With this training in particular, intelligently automated machines can take on tasks in retail, manufacturing, and other settings that typically require a pair of eyes and sensory skills.
Intelligent Automation vs. Robotic Process Automation
Robotic process automation is one component of intelligent automation. When you work strictly with RPA, you can train bots to handle simpler tasks on a routine schedule. Despite this simplicity, these RPA bot can be remarkably productive – in many cases they can handle a large portion of a staff member’s routine tasks.
However, to accomplish higher-level tasks, you need the deeper contextual and cognitive capabilities that come with artificial intelligence. Intelligent automation combines the best of both AI and RPA technologies to meet its automation goals. The challenge, of course, is that developing these bots requires more investment, and in some cases may also require tech support that has advanced training.
Intelligent Automation vs. Hyperautomation
Hyperautomation is more focused on transforming overall business strategies to wholly incorporate smart automations across infrastructure, departments, and projects. It is more all-encompassing (though the technology remains nascent) and has a greater capacity to fully transform a workplace.
Intelligent automation technologies can be used to achieve hyperautomation goals, but hyperautomation itself typically involves more organization-wide strategy and planning to get up and running. It necessarily requires a longer period to fully implement, and also needs a trained support staff to maintain and adjust each of the many elements as workflow progresses.
Business Use Cases for Intelligent Automation
Intelligent automation can be incorporated into a range of business use cases and industries. With the right training and monitoring in place, many organizations are beginning to bring IA into their workflows in the following ways:

Customer service and contact center agents: Some organizations are creating more sophisticated robotic call center agents to handle calls and chats without sounding so scripted; IA tools may also be used to more efficiently manage call logs, score leads, personalize marketing campaigns, and make recommendations based on buyer history.
Smart manufacturing and supply chain management: IA-powered robots can take on human tasks — or even chains of tasks — on factory production floors and make adjustments to their performance based on real-time training and feedback. They can also use applied predictive analytics and computer vision/machine vision to manage quality and maintenance schedules for both factory machines and manufactured products, while also considering how these changes impact supply chain schedules and logistics.
DevOps: Intelligent automation is particularly effective for automating software testing and recommendations and actions for CI/CD. It can also be used to manage cybersecurity efforts in DevSecOps scenarios.
Cybersecurity management: IA bots can handle the full cybersecurity management lifecycle, not only detecting vulnerabilities and issues on a massive scale but using predictive analytics and smart recommendations to actually make the necessary improvements and handle threat response activities themselves.
Insurance: In complex and tedious insurance workflows, like claims and risk management, IA bots can comb through large amounts of data quickly and automate tasks like claim intake and settlement. When these tasks are automated at scale, it can increase insurance company productivity and reduce the chance of risky or erroneous claims.
Human resources and recruitment support: Certain key aspects of recruitment and HR can be automated with IA agents, including onboarding and payroll processing tasks.
Healthcare: IA in healthcare can handle some of the back-office administrative tasks of a healthcare facility, following automated workflows while adhering to cybersecurity and compliance requirements for data processing. IA has also been used to manage large-scale tasks in public health, like COVID-19 vaccination distribution and tracking.
Consumer self-service technologies: While many of these technologies are still fairly new, self-driving cars, smart checkout kiosks, and similar self-service technologies are made smarter with the help of IA.

This smart kiosk uses intelligent automation to support a variety of customer-service use cases. Source: ADERA
More on a similar topic: Generative AI: 15 Enterprise Use Cases You Can Implement
Benefits of Intelligent Automation
Intelligent automation delivers a range of benefits not only to the business leaders enacting the strategy but also to the workers interacting with the technology and the customers on the receiving end of production.
These are some of the most common benefits that come from bringing intelligent automation into business workflows:

Optimized productivity: IA goes beyond simple automation and focuses on larger and more impactful automation scenarios. This technology supports faster and higher quality product and service delivery, which benefits the business and gives customers a better experience overall.
Avoiding error-prone human task work: Because intelligent automation can tackle more complex workflows and processes than traditional automation can, IA is one of the most effective ways for organizations to automate task work that human workers most frequently make mistakes on.
Affordable and scalable process automation: If you have the resources and commitment to scaling your process automations, intelligent automation can easily be scaled to meet your new requirements. While additional compute or processing resources may be necessary as you scale, you likely won’t have to invest in entirely new technologies or infrastructure to grow your IA footprint.
Compatible with various industries and technologies: Intelligent automation is flexible enough to work across industries, sectors, and project types because of its algorithmic training and sensory depths. IA technology is also often integrable with an organization’s other automation and process management technologies, including CRMS and ERPs.
Backfilling in environments with worker shortages: Although intelligently automated machines are not currently equipped to take on all human tasks, their training is sophisticated enough so that they can truly backfill human roles in a variety of project and workplace settings and scenarios. This is especially helpful for organizations that are having trouble filling certain roles on their teams.
A more unified operational model: Intelligent automation is designed to work with modern cloud and AI technologies but also legacy and hybrid technologies; it provides a way to intelligently integrate best practices and tools across this stack, which can facilitate more unified technical operations and strategy for the organization.

Best Practices for Getting Started With Intelligent Automation
Intelligent automation is a complex and multifaceted automation strategy that requires buy-in, dedicated training and change management, thoughtful planning, and ongoing strategic pivots.
To get the most out of your intelligent automation initiatives, follow these best practices for getting started:
Involve All Relevant Company Stakeholders
Data scientists, automation engineers, and other IT team players should be involved from the start in customizing IA to fit the organization, but other business leaders and stakeholders should also be involved when intelligent automations are first being discussed to ensure the technology meets organization-wide demands and gets buy-in from all departments and project teams.
Set Goals and Consider Your Most Important Use Cases
At this stage, seek out employee feedback on tedious task work that could be automated or otherwise handed off; don’t simply ask managers, but be willing to talk to employees who are in the weeds of the organization’s most tedious tasks.
Additionally, consider your budget and any tools or resources you may still need to get started, as well as any measurable goals or outcomes you hope to achieve with intelligent automation.
Invest in Flexible, Integrable IA Tools
A long list of AI and RPA tools are on the market today, but not all of them effectively combine the strengths of both technology types to achieve intelligent automation. Research the options that are available on the market, paying particularly close attention to any advanced technologies and features that meet your needs. Also, pay close attention to how — or if — these platforms will integrate with your other business process management tools.
Automation Anywhere is an example of an intelligent automation and RPA platform that gives administrators accessible, hands-on control over bot automations. Source: Automation Anywhere
Test and Monitor Automations At All Stages of Development and Deployment
At all stages of intelligent automation, test how automations are performing and if they are meeting their intended purpose. It’s especially important to quality-test automations that affect customer-facing interactions, such as intelligent customer service agents or autonomous devices.
QA specialists or automation engineers on your team are likely the best fit to test how automations are performing, and different types of automation testing and monitoring tools can supplement their work.
Follow AI Ethics and Ethical Best Practices
Because intelligent automation is so heavily entwined with artificial intelligence, it’s important to consider the ethical implications of the data you’re using and where and how you apply artificial intelligence in your workflows. Ensure all of your most sensitive data — particularly PHI and PII — is securely stored separately from these technologies, and frequently audit your IA tools and results to ensure data is being used ethically.
If the tools you’re using aren’t transparent enough to give you this kind of visibility, consider switching up your toolset or strategy to create more visibility. Taking this step will help you to protect your consumers’ data as well as any other sensitive business data from unauthorized access and usage.
Learn more: Generative AI Ethics: Concerns and Solutions
Bottom Line: Intelligent Automation in Modern Enterprise Workflows
Intelligent automation has gained steam in recent years, not only because it offers exciting prospects for modern business productivity but also because it’s actually possible now.
Advanced technologies like generative AI and computer vision are becoming more accessible and optimized for the everyday user. Most enterprises already use some kind of automation technology in their daily work and are familiar with the organizational change management, training, and ongoing commitment that comes with automating business tasks.
In short, this is a dynamic time for intelligent automation and a great time to get started with the technology. For the best possible results, follow the best practices listed above and don’t lose sight of the people who need to be involved. Especially as this technology and its capabilities evolve, you’ll want to ensure that all relevant stakeholders in your business receive the upskilling training they need to keep up with the technology and take on new and more challenging tasks that are beyond the robots’ purview – for now.
Read next: 10 Best Machine Learning Platforms
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