Intelligent Automation: How AI Boosts Business Performance

Artificial Intelligence is reshaping business process automation, making workflows faster, smarter, and more efficient. Business process automation isn't new, but the introduction of AI has changed what’s possible. Now, by applying AI to standard automation, businesses can streamline all kinds of tasks.

Across numerous industries, companies that choose to automate their repetitive tasks through an IA assistant stand to see plenty of benefits, including increased efficiency, cost savings and an improved customer experience. And their human employees can have more time to focus on the more strategic and creative aspects of their jobs.

What is Intelligent Automation?

Intelligent Automation is software technology that incorporates AI and technologies like Machine Learning (ML) and Robotic Process Automation (RPA) to automate complex workflows. It spans the entire automation journey — discovery, automation, optimization — seamlessly connecting and interacting with humans, systems, and data.

IA goes beyond basic automation, incorporating advanced approaches like cognitive computing and data mining to enable smarter, more adaptable decisions.

Intelligent Automation is the next step in a company's digital evolution, as it not only automates simple and repetitive steps, but also adapts to changes occurring in the organization and learns.

Email marketing is one example of Intelligent Automation. Companies can use customers’ data to send them personalized messages, which have been automatically triggered by specific behaviors or dates, such as birthdays or anniversaries.

What is the difference between Robotic Process Automation (RPA) and Intelligent Automation (IA)? 

RPA is meant for simpler, repetitive tasks - requiring bots that follow narrow, pre-defined instructions, and are incapable of adapting to new environments or making decisions. 

IA can handle more complex tasks that require inference, predictions and decision-making abilities - all of which is made possible by combining robotic process automation, artificial intelligence and other related technologies.

There is no Intelligent Automation without Robotic Process Automation. Together with Artificial Intelligence, RPA creates Intelligent Automation and, thus, capabilities that it did not have before - it’s able to understand and learn, as well as plan and solve more complex tasks independently, gaining experience and improving its work.

What is the difference between Artificial Intelligence (AI) and Intelligent Automation (IA)?

Artificial Intelligence is a tool for efficient problem-solving, while Intelligent Automation combines several tools and technologies (including AI) to automate tasks, workflows, and processes. 

Intelligent Automation uses automation technologies like AI, Business Process Management (BPM) and Robotic Process Automation (RPA) to simplify organizational processes, streamline and scale decision-making, and improve operational efficiency.

How does Intelligent Automation work?

By combining Artificial Intelligence, Robotic Process Automation and Business Process Management, Intelligent Automation can speed up business processes while reducing production costs.

To achieve end-to-end automation capabilities, Intelligent Automation harnesses AI technologies such as computer vision, natural language processing (NLP), machine learning (ML), and generative AI, making it possible to automate processes that rely on unstructured data or complex information sources, or that don’t follow a structured workflow.

Intelligent process automation integrates advanced AI techniques, including machine learning (ML) and natural language processing (NLP) capabilities, with robotic process automation to perform advanced tasks for which it isn’t necessarily preprogrammed. It can also infer the business context behind data and learn from its experiences, making it much more flexible and adaptable than older forms of automation.

Components of Intelligent Automation

Intelligent Automation is a combination of integration, process automation, AI services, and RPA technologies that work together to execute repetitive tasks and augment human decision-making. Intelligent Automation can include NLP, ML, cognitive automation, computer vision, intelligent character recognition, and process mining.

• Artificial Intelligence (AI): AI is the foundation of intelligent automation, enabling systems to process information, identify patterns, and make decisions without human intervention. This ability allows businesses to automate decision-making processes, enhance efficiency, and improve accuracy in ways that rule-based automation cannot.
• Robotic Process Automation (RPA): Software bots that can perform repetitive tasks, such as data entry, invoice processing, and customer service responses, with high accuracy and speed. In Intelligent Automation, RPA provides necessary functionalities to handle tasks without any human involvement. It mainly acts as a task handler to perform predefined processes within intelligent automation.RPA frees up human workers from low-value, repetitive tasks and improves the efficiency by automating them.
• Business Process Management (BPM): It's a concept that utilizes tools and technologies to manage, analyze, and optimize processes and workflows in business organizations and achieve higher operational efficiencies in operations. The primary job of business process automation is to identify and eradicate inefficiencies by reassigning tasks that are time-intensive or prone to human error to AI automation.
• Automation Tools: There are software tools and applications designed and used to automate business processes and workflows. These tools also improve functionalities of existing processes and provide seamless communication among various processes and systems across the organization. Among them: 

-Natural language processing (NLP): The ability of machines to understand, interpret, and generate human language, enabling them to interact with humans through chatbots, voice assistants, and other conversational interfaces.

-Computer vision: The ability of machines to interpret and analyze visual data from the real world, enabling them to perform tasks, such as object recognition, image and video analysis, and autonomous navigation.

-Intelligent character recognition: The ability of machines to recognize and interpret handwritten or printed text, enabling them to automate tasks, such as data entry and document processing etc.

• Data is the key piece of the intelligent automation ecosystem. IA uses raw data to train its systems and set up intelligent workflows. Over time, IA can also continue learning and improving using data from interactions.

Use cases for Intelligent Automation

One of the best parts of intelligent automation is its adaptability. Whether you work in manufacturing or marketing, in sales or in human resources, odds are you can benefit from intelligent automation. Here are just a few common use cases of how AI is reshaping business automation today. 

1. Customer service

AI-driven chatbots and AI assistants are reshaping customer interactions by offering instant responses and seamless experiences. Companies implementing business process automation with AI are seeing reduced human manual intervention, improved accuracy, and faster decision-making.Chatbots and virtual assistants, for example, can handle customer inquiries and provide support. If an issue is too complex and they can’t, they direct it to a human agent in the appropriate department. AI can also analyze customer feedback and interactions to detect sentiment and improve response strategies. This is called sentiment analysis.

2. Finance and accounting

The finance and accounting departments of businesses use intelligent automation to accelerate a range of workflows. With invoice processing, it can extract data from invoices and match them with purchase orders. With expense management, it can categorize employee expenses and flag anomalies for review. And with financial reporting, it can consolidate data from multiple sources, generate reports, and ensure compliance with regulations.

3. Recruitment and workforce management

AI-powered automation in HR simplifies tasks like resume screening and candidate matching by quickly analyzing large volumes of resumes to identify top candidates, reducing hiring time and improving match accuracy. Organizations can digitize and streamline key business processes such as employee onboarding, performance appraisals, leave management, training tracking, and expense claims, enhancing efficiency and accuracy across HR and business operations.

4. Insurance

With IA, the insurance industry can virtually eliminate the need for manual rate calculations or payments and can simplify paperwork processing such as claims and appraisals. Intelligent automation also helps insurance companies adhere to compliance regulations more easily by ensuring that requirements are met. In this manner, they are also able to calculate the risk of an individual or entity and calculate the appropriate insurance rate.

5. Healthcare

The healthcare industry is using intelligent automation with natural language processing (NLP) to provide a consistent approach to data collection, analysis, diagnosis and treatment. The use of chatbots in remote healthcare appointments requires less human intervention and often a shorter time to diagnosis. AI also plays a key role in managing compliance by standardizing medical documentation and tracking patient referrals, significantly reducing the risk of errors and delays. Additionally, AI analytics can optimize processes like patient flow management and hospital resource allocation, ensuring better operational efficiency.

6. Manufacturing

With IA, manufacturers are able to more effectively predict and adjust production to respond to changes in supply and demand. They can streamline workflows to increase efficiency and reduce the risk of error in production, support, procurement and other areas. Predictive analytics also help maintenance engineers anticipate and address errors before they affect your equipment. With the use of robots they are able to reduce the need for manual labor and improve defect discovery, providing a higher quality product to customers at a lower cost to the business. 

7. Transportation and logistics

IA is designed to improve efficiency, reduce costs and streamline operations. IA analyzes real-time data, such as traffic conditions and weather, to optimize delivery routes – reducing fuel consumption, travel time and emissions. This results in minimized delivery times and costs, reduced shortages and overstocking, as well as increased transparency through real-time shipment tracking.

8. Fraud Detection 

IA software can be used to detect and prevent fraud, analyzing transaction data in real time to flag suspicious activities and take the necessary steps to protect both companies and their customers. It can also be used to assess creditworthiness and calculate risk profiles for loan or insurance applicants, as well as streamline the approval process with automated document verification.

Benefits of Intelligent Automation

No matter how it is used, Intelligent Automation can benefit a company in all kinds of ways. At its most basic level, even for small businesses, IA increases productivity. Since it takes over processes typically performed by humans, you have more time to focus on higher-value efforts. But the benefits of Intelligent Automation extend so much further.

• Increase productivity: Automate repetitive tasks so employees can focus on high-value strategic work.
• Accelerated Productivity: IA-enabled processes are more efficient and can be deployed 24/7. Intelligent automation frees employees to tackle more value-driven, challenging, and creative responsibilities that adhere to the organization’s goals.
• Improved decision-making: Provides real-time insights and data analytics, enabling better decision-making on the fly and more informed business strategies.
• Superior data accuracy: Data errors are common across processes when handled manually, resulting in unnecessary rework. IA solutions can eliminate data errors that creep in through manual operations. Enhanced accuracy minimizes the potential for costly mistakes, and fosters trust in the integrity of data-driven decision-making.
• Improve customer experience: Providing a higher quality, more reliable product to market faster, or getting answers to queries faster (or immediately) provides a richer, more positive experience for the customer and thereby a competitive advantage for the company. Chatbots can offer personalized customer support 24 hours a day. The faster they can resolve their issues, the less likely they’ll consider a competitor.
• Save money: Reducing manual work lowers labor costs, minimizes errors, and eliminates inefficiencies in operations. Shrinks the time needed for common tasks, eliminating the need to pay skilled employees for mundane work and automating tasks that can include combing vast amounts of data and documents.

Challenges of Intelligent Automation

Intelligent automation presents many challenges due to the complexity of the technology and its continuous evolution, and that artificial intelligence is still fairly new as an everyday enterprise software tool.

• Workforce Adaptation: Adopting any new technology comes with human challenges, including organizational resistance to change. Your staff may not fully understand the benefits of the new technology, be hesitant to adjust the workflows they’re used to, or feel that their job is at risk of replacement by automation. Without collaboration from everyone involved, your organization won’t be able to use Intelligent Automation to its fullest potential.
• Integration Complexity: Integrating Intelligent Automation into your existing IT infrastructure requires careful thought and planning, especially if your tech stack is particularly complex. Not every solution will come with off-the-shelf integrations for your existing platforms, so building custom integrations may be required — and the more platforms you use, the more integrations you’ll need.
• Inaccurate Data: A common barrier to the implementation of intelligent automation toward driving better business decisions is the inaccuracy of the underlying data that feeds into these automation systems. For example, if an intelligent system needs to change your marketing expenses on the fly, but the underlying data for making this decision is incorrect due to broken processes, then the outcome will be incorrect too.
• Data Security and Privacy: More than ever, data privacy is a critical issue for maintaining consumer trust. Failure to manage data properly can lead to privacy violations and data breaches, fines, and legal action. 
• The Lack Of Ready-Made Tool Kits: Intelligent automation combines automation and AI. There’s a lot more work to do to provide tool kits, including verticalized templates, workflows and implementation APIs. 
• Unrealistic Expectations: There’s a misconception that automation, particularly AI-driven technology, is a catch-all solution for every problem. While automation can deliver tremendous benefits, it’s just one ingredient in the recipe for efficiency — and without extensive testing, validation, and ongoing improvement, it can stagnate and slow productivity just like any other technology.

Tips for successful Intelligent Automation implementation

Implementing IA successfully requires careful planning, strategy, and execution.

1. It is crucial to define clear objectives and scope. For this identify the specific processes or tasks that would benefit the most from automation and set measurable goals to track progress. Clearly articulate the expected outcomes (cost savings, improved accuracy, or enhanced customer experiences). Automation projects often fail simply because expectations are not well-aligned with what a particular automation solution can do. 
2. Run a comprehensive cost-benefit analysis to assess the economic viability of business automation. Engage subject matter experts from relevant departments to gain insights into pain points, inefficiencies, and potential bottlenecks. Analyze the feasibility of automating each process, considering factors such as complexity, variability, and data availability. 
3. To ensure a smooth implementation, involve key stakeholders from the beginning. Engage process owners, IT teams, business leaders, and employees who will be directly affected by the automation. Understand their concerns, gather their input, and address any resistance or misconceptions.
4. Begin the implementation of Intelligent Automation with standard, rule-based and mature processes. Before proceeding with Intelligent Automation implementation, make sure that you are prioritising business processes that are ready for automation and can offer measurable benefits to your workforce. Try not to fall into the trap of excessive simplification or placing the automation project wholly in the realm of business initiatives.
5. Choose the Right IA Tools and Technologies. Evaluate different vendors and solutions based on factors such as scalability, ease of use, integration capabilities, and support services. Ensure that the chosen tools are compatible with existing systems and can seamlessly integrate with other technologies in your technology stack.
6. Implement strong access controls, encryption, and data privacy protocols to protect sensitive information. Regularly audit and monitor IA systems to identify and address any potential security risks.
7. It’s always a good idea to test before issuing a full-scale IA deployment. During testing, you’ll monitor your automation to identify any problem areas and ensure everything runs smoothly and aligns with your pre-defined goals. Take note of any slowdowns or infrastructure limitations. Keep track of key metrics such as accuracy, processing times, human intervention, etc.
8. Release your IA into its planned performance areas and continuously monitor it, making tweaks where necessary. Deployment is only half of automation – the other half is checking in on it to make sure it’s achieving your organizational goals.
9. Intelligent automation is not a one-time implementation; it requires continuous improvement and optimization. Monitor and analyze IA performance, gather feedback from users, and proactively identify areas for enhancement. Regularly update and refine automation workflows to adapt to changing business needs. 

CONCLUSIONS

Intelligent Automation can take your business to the next level by empowering your team to enhance processes, minimize mistakes and obtain more time for activities that generate profits. But to fully maximize Intelligent Automation, you need to implement it right.

Overall, the future of Intelligent Automation looks promising, with the potential to revolutionize how businesses operate and deliver value in an increasingly digital and competitive landscape.

Intelligent automation will continue to evolve with advancements in artificial intelligence (AI) technologies. IA solutions will become more tailored to specific industries and use cases. This customization will enable organizations to address unique challenges and capitalize on industry-specific opportunities more effectively. 

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