(CLP) Programs

Appleton Greene corporate training programs are all process-driven. They are used as vehicles to implement tangible business processes within clients’ organizations, together with training, support and facilitation during the use of these processes. Corporate training programs are therefore implemented over a sustainable period of time, that is to say, between 1 year (incorporating 12 monthly workshops), and 4 years (incorporating 48 monthly workshops). Your program information guide will specify how long each program takes to complete. Each monthly workshop takes 6 hours to implement and can be undertaken either on the client’s premises, an Appleton Greene serviced office, or online via the internet. This enables clients to implement each part of their business process, before moving onto the next stage of the program and enables employees to plan their study time around their current work commitments. The result is far greater program benefit, over a more sustainable period of time and a significantly improved return on investment.

Appleton Greene uses standard and bespoke corporate training programs as vessels to transfer business process improvement knowledge into the heart of our clients’ organizations. Each individual program focuses upon the implementation of a specific business process, which enables clients to easily quantify their return on investment. There are hundreds of established Appleton Greene corporate training products now available to clients within customer services, e-business, finance, globalization, human resources, information technology, legal, management, marketing and production. It does not matter whether a client’s employees are located within one office, or an unlimited number of international offices, we can still bring them together to learn and implement specific business processes collectively. Our approach to global localization enables us to provide clients with a truly international service with that all important personal touch. Appleton Greene corporate training programs can be provided virtually or locally and they are all unique in that they individually focus upon a specific business function. All (CLP) programs are implemented over a sustainable period of time, usually between 1-4 years, incorporating 12-48 monthly workshops and professional support is consistently provided during this time by qualified learning providers and where appropriate, by Accredited Consultants.

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Executive summary

Intelligent Automation

History

Process automation dates back to the Industrial Revolution when manufacturing and sales shifted from being done by hand to machines. Organizations are now able to operate consistently and efficiently thanks to business procedures. Process automation lowers the costs and hazards related to using human labor while enabling increases in production volume and quality. In the past, heavy industries such as chemical, power generation, and pharmaceutical have benefited hugely from automating their manufacturing and business functions. This trend has continued in the modern digital age, where businesses employ digital technology and software tools to automate time-consuming and repetitive operations. Business process automation uses technology to perform tasks that human effort would otherwise carry out. It aims to increase efficiency, reduce errors, and streamline operations, freeing up human resources to focus on more strategic and value-added activities. Examples of business processing include automating payroll processing, customer service management, and order processing.

1960s–1970s: The Dawn of Automation
The 1960s and 1970s witnessed the emergence of early process automation systems during the mainframe era. These systems, though primarily used to automate simple repetitive tasks such as payroll processing and inventory management, marked a significant leap forward in improving the efficiency and accuracy of business operations. Despite being custom-built, expensive, and requiring specialized knowledge to operate and maintain, these systems demonstrated the potential of process automation in reducing manual labor and enhancing productivity.

Fact: By the 1970s, IBM’s mainframes were handling payroll processes for major corporations, reducing payroll processing times by over 80%.

1980s: Enterprise Software Revolution
The 1980s marked a significant shift in the evolution of process automation. This era saw the rise of enterprise software and associated processes designed to integrate various business functions like finance, HR, and customer services. These systems provided a more comprehensive approach to automating business processes, allowing organizations to streamline operations across different departments and functions. More importantly, this period marked a transition from custom-built, expensive systems to more standardized, off-the-shelf solutions that could be implemented across multiple industries. This evolution in process automation not only improved the efficiency and accuracy of business operations but also made the technology more accessible and cost-effective, paving the way for a more efficient and streamlined future.

Case Study: SAP introduced one of the first Enterprise Resource Planning (ERP) systems in the 1980s, revolutionizing how businesses managed their resources, finances, and customer data. By integrating various business functions into a single system, SAP reduced the duplication of data and manual effort, which helped companies save millions annually.

1990s: Workflow Automation and Orchestration
In the 1990s, workflows and orchestration in tools and processes became the focus, and organizations could reengineer their business processes to improve efficiency and optimize performance. Orchestration tools allow businesses to automate and manage complex business processes more effectively.

Example: In 1993, GE implemented a workflow automation system that cut administrative processing time by 50% and reduced error rates, driving better customer service and cost savings.

2000s: Rise of Robotic Process Automation (RPA)
Subsequently, in the 2000s, “bots,” or software agents, came into being. These bots could replicate labor-intensive human actions such as data entry, transaction processing, and customer service. These bots often interacted with multiple systems and tools to automate tasks. It gave rise to Robotic Process Automation, which gained widespread adoption due to its ability to automate processes without requiring significant changes to existing IT infrastructure. It allowed businesses to achieve higher levels of efficiency, accuracy, and scalability with relatively low levels of investment.

Fact: The global RPA market grew to $1.4 billion by 2019, with industries such as banking and insurance automating claims processing, data handling, and customer interactions, resulting in cost savings of up to 40%.

Present Day: The Era of Intelligent Automation
Recent times have witnessed a significant evolution in process automation, marked by the integration of artificial intelligence (AI) and machine learning (ML). This convergence has given rise to Intelligent Automation, also known as Intelligent Business Process Automation (IBPA). Unlike traditional process automation, which is rule-driven and implements business logic and decision actions as rules in various tools, Intelligent Automation systems can learn from data, make decisions, and adapt to changing conditions. This shift has opened up new possibilities for automating complex, decision-driven processes and has been a driving force behind digital transformation initiatives in many organizations.

Case Study: UiPath, a leader in RPA and intelligent automation, helped Zurich Insurance automate their claims handling process, reducing handling time by 40% and improving customer satisfaction.

Today, Intelligent Automation stands as a beacon of digital transformation, reshaping the landscape of business operations and competition in the modern world. Its transformative power inspires optimism for the future, promising a new era of efficiency and innovation. This technology is not just a buzzword but a transformative force that is reshaping industries across the board, from finance and healthcare to manufacturing and retail.

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Current Position

Today, Intelligent Automation is not just a buzzword but a transformative force reshaping industries across the board. From finance and healthcare to manufacturing and retail, Intelligent Automation is combining the power of Robotic Process Automation (RPA), AI, ML, Natural Language Processing (NLP), and other advanced technologies to automate complex, cognitive tasks that were once the exclusive domain of human workers. This shift is not just about automating repetitive tasks; it’s about enhancing decision-making, improving customer experiences, and driving business innovation.

As shown below, several key technologies are driving the adoption of Intelligent Automation.

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Robotic Process Automation (RPA)
RPA is a foundational technology within IA, automating structured, rule-based tasks such as data extraction, transaction processing, and report generation. It is especially effective for automating high-volume tasks with low complexity, freeing up human workers for more strategic activities.

Fact: McKinsey estimates that RPA can automate up to 45% of workplace activities, resulting in increased productivity and significant cost reductions in operations.

Intelligent Document Processing (IDP)
Intelligent Document Processing (IDP) automates the extraction and organization of data from paper-based documents—such as claim forms, legal contracts, and healthcare records—integrating them into business processes. This technology is aligned with RPA but focuses on digitizing and processing documents to reduce manual labor and improve efficiency.

Example: In the insurance industry, IDP has revolutionized the way health insurers process claims. By using optical character recognition (OCR) and AI, companies can reduce the time spent on claims processing by over 50%, resulting in faster payouts and fewer errors.

Natural Language Processing (NLP)
NLP enables machines to understand and respond to human language, powering applications such a