
Recent strides in the automation of many business processes and operations, have been achieved through the implementation of software robotics, or Robotic Process Automation (RPA).
RPA is robotics software (often referred to as “bots”) concerned with automating routine, rule-based tasks that do not require understanding or interpretation of data. A bot follows scripts or workflows to perform tasks that would otherwise be performed manually by a human, whereas AI involves learning, acquiring information and the rules to use that information to reach approximate or definite conclusions or predictions.
Intelligent Automation (IA) aims to combine RPA with AI so that AI enhances RPA, achieving automation objectives with greater efficiency and the ability to automate for more complex processes.
The line between Artificial intelligence and RPA can be blurred due to misleading categorisation of RPA under the umbrella of AI. RPA has also been on an upwards trajectory in technological advancements, since the 1980’s and 90’s. Though AI is more popularly known, RPA has gained traction, especially in recent years with rule based chatbots, bots in call centres, automatic response messages, monitoring bots that can be used to monitor inventory and materials management, bots that are able to create work schedules for resources – and many more rule-based automations.
In the rush of businesses to explore these new areas of innovation the distinction between the two technologies can be left out, with some organisations referring to their use of RPA as AI to appear more innovative and marketable. This is referred to as “AI washing. ‘AI washing’ is when a company overstates the role or capability of AI in their software to tap into the AI hype.
With growing expectation for businesses to adopt AI for efficiency and optimisation companies may label RPA as AI to try to meet customer expectations. This is not to say that these same companies are not using basic AI integrations in their processes but that there has not been a full integration of Intelligent Automation throughout their organisation. The extent to which an organisation is using RPA or AI enhanced RPA is important to verify.
Further examples of how RPA is used in Work Management would be in scheduling and dispatching where bots can automate the allocation of tasks by assessing technician schedules, ensuring optimal routing and task assignment to match technician skills and location with job requirements, optimising workforce deployment.
Bots are used in inventory and materials management to categorises products and monitors stock levels, automating reorder processes based on thresholds, and managing supplier communications to maintain inventory health.
Bots can be programmed to automate the sending of appointment confirmations, updates, and feedb
ack requests, ensuring consistent engagement without manual intervention and automatically update CRM systems with changing customer data. Customer onboarding can be automated using bots. Network management processes (such as events) and incident and diagnosis management can be partially automated.
Bots are being used to streamline the creation, updating, and closing of service orders, automating invoice generation, reducing errors, and improving cycle times.
To further enhance the automation of these processes RPA and AI can work together synergistically and should be used together for optimisation and increased efficiency.
As previously mentioned, RPA is used to automate repetitive tasks that don’t require decision making, which is highly effective for certain types of tasks, however, AI steps it up by handling processes that require some judgement or decision making. With AI applying reason, it can adjust the rules for RPA scripts so to automatically navigate circumstances that fall out of the scope of the original rules set for the bot to follow. AI can alter the bot’s algorithms to adjust to variant operations.
IA allows businesses to automate far more complex operations. Processes that require understanding and analysing, such as natural language processing problem solving. For example, AI- enhanced chatbots can manage complex customer queries and integrate with RPA systems to update records or process transactions in the background.
IA still requires high level human intervention to oversee and ensure data quality and system compatibility.
RPA is a significant part of discussing AI Integration into Work Management systems, because when enhanced by AI the technologies dramatically transform business operations by not only automating tasks but entire processes, and the sharing of data across an organisation to inform decision making.
With further use of generative AI, Intelligent Automation can lead to the automatic replication and adjusted re-application of business processes to suit various circumstances in varying industries, with minor human intervention, through the analysis of historical data within the organisation.