This article was contributed by Koval Dmytro who works as a software engineer at Inoxoft.
Intelligent automation refers to advanced technologies that combine fundamental process automation with artificial intelligence and machine learning. Unlike basic robotic process automation (RPA) which follows rules-based scripts, intelligent automation leverages self-learning capabilities to make more complex decisions and complete higher-level tasks.
The concept of intelligent automation emerged in the early 2010s as an evolution of traditional business process automation. Early use cases focused on streamlining back-office processes in areas like finance, HR, and IT. Adoption accelerated through the late 2010s as capabilities expanded. According to Grand View Research, the global intelligent automation market size was valued at USD 7.34 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 19.6% from 2021 to 2028.
Intelligent automation is now being applied across a wide range of business functions and industries. A 2021 survey by Deloitte found that 52% of organizations already implement some form of intelligent automation. The most commonly automated processes include IT infrastructure/operations, finance/accounting, order processing/fulfillment, and customer service. As familiarity and capabilities continue to grow, adoption rates are projected to increase steadily in the coming years.
Understanding Intelligent Automation
Intelligent automation refers to advanced technologies that automate tasks and processes to deliver outcomes with minimal human intervention. Unlike traditional rigid automation, intelligent automation leverages technologies like artificial intelligence, machine learning, and natural language processing to handle unstructured data and adapt to changing environments.
There are several key types of intelligent automation technologies:
- Robotic Process Automation (RPA) – Software bots that mimic human actions and automate repetitive, rules-based tasks like data entry, invoice processing, email management etc. RPA bots can log into applications, enter data, move files, extract information and complete transactions.
- Machine Learning (ML) – Algorithms that can analyze data, identify patterns and make predictions without explicit programming. ML helps systems improve from experience and handle variability beyond hard coded rules. ML powers capabilities like image recognition, forecasting, recommendations and predictive analytics.
- Natural Language Processing (NLP) – Enables systems to understand, interpret and generate human language. With NLP, systems can parse text to extract meaningful insights, engage in conversational dialogs, summarize documents and more.
- Computer Vision – Technology that extracts information from visual inputs like images and videos. Computer vision enables capabilities like facial recognition, defect detection in manufacturing, reading text in images etc.
- Cognitive Agents – Software that can sense environmental contexts, adapt to changing conditions and complete tasks autonomously to achieve defined objectives. Chatbots that provide customer service are an example of cognitive agents.
Intelligent automation enables handling of unstructured data from multiple sources, contextual adaptation, straight through processing without human intervention and continuous optimization over time. It amplifies human capabilities by automating repetitive, tedious tasks so knowledge workers can focus on higher value strategic work. For organizations seeking to implement such automation effectively, partnering with a reputable Pega consulting partner can provide invaluable insights and support throughout the process, ensuring seamless integration and optimal utilization of automation technologies.
The Role of Intelligent Automation in Modern Enterprises
Intelligent automation is playing an increasingly important role in modern enterprises across industries. At its core, intelligent automation involves using software and AI technologies to automate repetitive, routine tasks that were previously handled manually. This allows organizations to unlock significant efficiency gains, cost savings, and other benefits.
There are several key ways that intelligent automation is impacting modern enterprises:
Automating Repetitive and Routine Tasks
Many tasks that workers have traditionally handled are highly repetitive and require little critical thinking or decision making. These include data entry, billing, invoicing, reporting, and more. By using intelligent automation tools like robotic process automation (RPA), organizations can configure software bots to carry out these mundane and repetitive tasks automatically based on predefined rules. This frees up human workers to focus their time and energy on higher value activities.
Improving Efficiency and Productivity
By deploying intelligent automation to handle repetitive and routine work, enterprises can achieve dramatic productivity gains. Software bots can operate 24/7 without rest, quickly process high volumes of transactions, and eliminate human errors. Workers are relieved of boring everyday tasks and can be assigned to projects requiring strategic thinking, creativity, and innovation. Overall efficiency and productivity across departments and the organization increases.
Reducing Costs and Errors
Intelligent automation also helps modern enterprises significantly reduce operational costs. Software bots cost a fraction of human labor to operate and maintain. Automation eliminates overhead costs associated with training, onboarding, facilities, and managing large teams of workers. It also drastically reduces error rates compared to manual processes, minimizing wastage and rework. Enterprises are able to get more work done at a substantially lower cost.
Enhancing Customer Experience
With intelligent automation streamlining back office processes, enterprises can deliver a smoother, faster, and more satisfying customer experience. Invoice queries, account changes, refunds, and other customer requests can be handled more quickly and accurately. Customers get the fast, seamless service they expect across all channels thanks to automated processes. This builds brand loyalty and retains valuable customers.
Benefits of Intelligent Automation
Intelligent automation can provide numerous benefits for modern enterprises across industries. Some of the key advantages include:
Time and Cost Savings
One of the biggest benefits of implementing intelligent automation is the potential for significant time and cost savings. By configuring software bots and AI agents to handle repetitive, rules-based tasks, enterprises can achieve processing efficiencies very quickly. This allows resources like employees to focus their time on higher-value work. Intelligent automation also reduces the need for additional staffing to manage increased workloads. Overall, most organizations achieve a return on their investment in less than 12 months.
Improved Accuracy and Compliance
Intelligent automation also leads to greater accuracy and consistency in task execution. Software bots are programmed to follow set rules and processes flawlessly every time. They are not susceptible to human error or judgment calls. This results in near 100% accuracy for automated tasks. Further, the detailed audit trails from bots can help demonstrate regulatory and internal compliance.
Greater Agility and Scalability
With intelligent automation, organizations gain much more flexibility and agility in their operations. New software bots can be deployed quickly as needed to handle growing workloads. The automation environment also makes it easy to update processes across the enterprise. This scalability allows firms to easily match operational capacity with changing business needs.
Employee Productivity Gains
By implementing intelligent automation for repetitive, mundane tasks, enterprises see higher employee productivity levels. Staff are freed from manual processing to take on more value-added work. Employees gain job satisfaction from focusing on core responsibilities that better utilize their skills and experience. Their output quality also goes up without the distractions of repetitive tasks. Together, these lead to higher employee productivity and engagement.
Challenges and Limitations of Intelligent Automation
Implementing intelligent automation comes with a unique set of challenges that enterprises need to be aware of. Here are some of the key limitations to consider:
Upfront Investment Required
While automation promises long-term productivity and efficiency gains, there is often a significant upfront investment required. The costs of software licenses, infrastructure, maintenance, and integration can add up quickly. Many automation tools are priced based on the number of bots or volume of transactions, which can become expensive as usage scales. Enterprises need to account for these costs and measure them against the expected return on investment.
Concerns Around Security and Governance
With bots interacting with multiple enterprise systems and sensitive data, security is a major concern. There is always a risk of bots making unauthorized changes or exposing confidential data if not properly secured and monitored. Companies need to establish strong access controls, auditing procedures, and oversight models to govern bot activities. Special care must be taken when providing bots access to personal and financial information.
Scaling Automation Across the Enterprise
While small isolated automation projects may be easy to handle, scaling bots across the entire enterprise comes with greater complexity. Integrating bots with legacy systems, inconsistent data formats, and distributed networks can be challenging. Companies struggle to maintain centralized oversight and control as usage expands. A clear scaling strategy and governance framework is essential.
Managing Change and Adoption
The transformative nature of automation requires major organizational change management. Employees may resist automation fearing job loss or lack the skills to work alongside bots. Insufficient employee training and preparation is a common cause for failed implementations. Leadership must communicate a strong vision, address concerns transparently, and invest in upskilling the workforce. A phased rollout gives employees time to adapt and build trust in automation.
Implementing Intelligent Automation Strategies
Successfully implementing intelligent automation requires careful planning and execution. Here are some key steps for enterprises to consider:
Assessing Automation Opportunities
- Conduct an automation opportunity assessment across the organization to identify processes that are repetitive, rules-based, high volume, and prone to errors – these are prime candidates for automation.
- Involve process owners to map out processes end-to-end to highlight pain points and steps that can be automated.
- Estimate potential cost savings and efficiency gains from automating each process.
- Prioritize processes based on ROI, impact on employees, and ease of implementation.
Building a Business Case
- Gather data on current process costs, cycle times, and errors. Compare to benchmarks for automated processes.
- Estimate implementation costs including software, integration, testing, training, and change management.
- Calculate ROI by comparing current vs future state costs and efficiency gains.
- Include qualitative benefits like improved data quality, compliance, customer experience.
- Outline risks and mitigation strategies. Build a phased rollout plan.
Choosing the Right Technologies
- Assess intelligent automation capabilities needed – RPA, AI, machine learning, NLP, computer vision, etc.
- Evaluate leading software platforms on features, ease of use, scalability, and integration.
- Start small – choose a well-defined pilot to test the technology and build credibility.
- Leverage cloud delivery models for speed and lower startup costs.
- Ensure the software can integrate with existing core systems.
Integrating with Existing Systems
- Set up a staging environment to test integration points.
- Leverage APIs and built-in connectors to minimize custom integration work.
- Develop libraries of reusable components for common integrations.
- Implement strong authentication, access controls and encryption to secure integrations.
- Monitor transactions and data flows to troubleshoot issues.
Managing Change
- Involve stakeholders early and get buy-in from IT, security, compliance and business teams.
- Provide extensive training on using and managing the automation tools.
- Start with non-disruptive back-office tasks to build confidence before advancing to customer-facing processes.
- Slowly expand scope while monitoring for errors and system glitches.
- Develop internal support resources and docs to drive adoption.
- Continuously measure benefits and gather user feedback to fine-tune the implementation.
Conclusion and Future Outlook
Intelligent automation has demonstrated its ability to drive significant efficiencies and cost savings for modern enterprises across industries. As the technology continues to evolve, we can expect even greater capabilities and more widespread adoption.
Intelligent automation has moved beyond basic task automation. When thoughtfully implemented, it can enable true breakthroughs in efficiency and productivity. As technology continues to evolve, intelligent automation will have an ever-growing role in augmenting human capabilities and transforming business operations. Organizations that strategically leverage IA solutions will be able to optimize their processes, reduce costs, and gain competitive advantages. With the power of AI, automation will become an essential driver of business innovation and success.
About the author
Koval Dmytro is a seasoned software engineer at inoxoft.com known for his expertise in developing cutting-edge technology solutions. With a passion for innovation and a keen eye for detail, he has successfully led numerous projects throughout his career. With over a decade of experience in the technology sector, Koval Dmytro remains committed to delivering high-quality products and services that meet the evolving needs of clients worldwide.
Last Updated on March 21, 2024 6:16 pm CET