When a company embarks on a promising and rewarding growth path, the investments it needs to make are not just about hiring new resources or purchasing innovative technologies. In order to optimize activities for greater profitability and better performance, it becomes crucial to consider intelligent automation of both simple and complex processes as a critical element to invest in. How much to automate, or how much autonomy to give to a process, is a decision to be made based on business objectives and available resources. Knowing the degree of autonomy a process has can help us think about the way forward.
What are the steps to move from simple automation (RPA) to intelligent automation (IPA)?
Automating a process means transforming an activity performed by a human being into a flow comprehensible to a machine. With Robotic Process Automation (RPA), I can program a software bot (short for robot – a software agent that acts autonomously) to simulate human actions that do not require cognitive input. This is a deterministic ‘Even/If’ bot – do this if that happens. So I need to predict all the activities involved in a process and translate them into an information language so that the software can act autonomously. In this first phase, there are no variations for the bot to adapt to autonomously. Anything outside the initial programming will not receive a response and may interrupt the process or generate an error.
If we do not want this to happen, we must rely on artificial intelligence and its machine-learning capabilities. Intelligent Process Automation (IPA) is the gateway to probabilistic programming, where software is trained with more variables depending on the level of autonomy we want to achieve. Software used for cognitive automation goes through a training phase based on data from past experience and the analysis of new data.
In the blog, you can find insights into the difference between RPA and IPA and the respective advantages of a deterministic or intelligent solution. This article, on the other hand, focuses on the various enhancements that take a process from functioning solely on the cognitive input of a human; to acquiring some autonomy, although the human still needs to play a controlling role; to becoming a fully autonomous process.
Step 1: Robotic Process Automation for simple, repetitive tasks
Every employee performs simple, routine tasks on a daily basis, often taking up the first part of the day. These are the precious minutes, accompanied by a cup of coffee, that our brains need to start cognitive processes. Reading emails, for example, is one of them. However, there are other, far more time-consuming processes that are not functional and only risk making us make mistakes. These are all activities that require a low level of concentration. So why not delegate these activities to automation? Robotic Process Automation is perfect for repetitive tasks involving structured data.
RPA bots follow pre-defined rules and instructions. An example of an automated process using a deterministic RPA bot is the processing of employee expense claims. Typically, this process requires employees to fill out paper or electronic forms with all the necessary information and send them to the HR or administrative department for review and approval. This is a time-consuming activity that requires little or no mental effort.
Instead, consider the steps an automated software would take:
- accesses the employee’s mailbox, automatically identifying messages containing claims and downloading the attachments;
- extracts the relevant information from the forms and enter it into a dedicated system;
- verifies that the requests comply with pre-defined company policies and reports any discrepancies or deficiencies to the employee;
- after review, the bot generates reports or approval documents that are sent to managers;
- once approved, the bot updates internal systems and sends notifications to employees.
It is evident, how automation reduces processing times, eliminates errors, and, most importantly, frees up resources from non-productive activities. Of course, we can only use RPA for simple, straightforward actions, but this also makes it quick to implement in business systems.
Step 2: Cognitive automation for training and process adaptation
The next step after pure automation requires the support of artificial intelligence. We are already at Intelligent Process Automation (IPA). Instead of structured data based on rules and instructions, the software will have to interpret e-mails, documents, and images. In these cases, machine learning will complement the work of the programmer to automate more complex processes that require a certain degree of training and adaptation.
Cognitive automation is based on the analysis of past experience. By continuously incorporating historical and new data, the automated process adapts to the feedback it receives. Take, for example, the forecasting of demand for products or services. Using advanced machine learning algorithms and access to historical sales data, a predictive model can be created that analyses past patterns to identify trends and behavior.
The model can be trained to understand variables that influence demand, such as seasonality, promotions, special events, and economic conditions. These predictions can be used to guide business decisions, such as production planning, inventory management, defining marketing strategies, and forecasting resource requirements.
The cognitive automation of this process enables more accurate and efficient forecasts than manual data analysis. Furthermore, since the model can be continuously trained with new data, it can adapt to changes in the market and improve over time. As can be seen, even a small amount of artificial intelligence can significantly improve the level of autonomy and provide more concrete support for more complex processes.
Automation is a powerful engine that allows us to accelerate processes and maximize the efficiency of human resources. We can choose the degree of autonomy, but its implementation is indispensable to remain competitive. Click To Tweet
Step 3: Digital assistants for language processing
Starting with cognitive automation using AI and ML techniques, we can move on to simulating human activities such as language processing.
In fact, cognitive automation systems are able to understand and process human language (NLP = Natural Language Processing). The human resource will thus be able to interact with the software, which in turn will be able to extract meaning from written or spoken texts and provide an ‘intelligent’ response. NLP enables the automation of tasks related to chatbots, voice assistants, and language-based analytics.
Language understanding and user interface are also elements that allow customers to interact with our company in an automated way. If used correctly and with a strategy to improve the customer experience, the virtual assistant can significantly improve our work and our relationships with customers by reducing the time required for the activities involved.
The most relevant example is the use of a chatbot to improve the customer experience in a business.
Imagine a customer visiting your company’s website looking for information or assistance with one of your products/services. The average user does not have time to browse through the menu or read pages of FAQs, let alone send an email and wait at least the next day for a response. Today, if we don’t get a quick response, we risk losing the potential customer or receiving a negative review for inefficient customer service.
What to do if you do not have the resources to improve customer service? We can rely on the virtual assistant powered by an intelligent bot. The customer can type their questions or problems into the chat and the virtual assistant will provide immediate and relevant answers, such as: providing information about the company’s products or services, solving common problems, giving directions, or even helping with order scheduling or account management.
This provides a 24/7 service, allowing customers to receive answers and assistance even on Sundays or during off-peak hours. Waiting times are reduced and customer satisfaction is often increased. In addition, the answers provided by a self-learning virtual assistant will become increasingly precise and accurate as it adapts to new data it stores over time.
Users are increasingly appreciative of this new form of communication, just look at the success of ChatGPT.
Step 4: Autonomous agents for decision making
The highest level of intelligent automation involves complex decision-making processes. These are those processes in which deep data analysis, including deep learning, provides a broad overview on the basis of which to make analyses or predictions to direct short-term and long-term activities.
This deep analysis capability, which includes multiple variables and related factors, allows bots, or rather digital agents, to make decisions autonomously. We can anticipate the intervention of a human if we do not trust it enough or if the process is particularly delicate, but theoretically, the AI is capable of completing the process autonomously.
This type of support is particularly suited to human resource management, supply chain optimization, financial planning, and risk analysis. Thanks to its ever-increasing processing capacity and knowledge, the bot is able to provide timely and accurate recommendations and suggestions to support the decision-making processes of business executives.
By embedding software with intelligence into machines, we will get intelligent robots that can interact with their environment, learn from their surroundings and perform tasks that require cognitive skills. These robots will be able to adapt to changing circumstances and will also have the ability to cope with dynamic situations.
The factory is one context in which this level of automated intelligence would greatly optimize the production process. Imagine a robotic system based on artificial intelligence controlling the production process of specific components or products. The machine, equipped with advanced sensors, would collect real-time data on working conditions, the performance of the machine itself, and the characteristics of the materials used. Based on these measurements, it would dynamically adapt the process to changing production conditions, identifying anomalies and quality problems and making the necessary corrections. It would also optimize production planning and organization by analyzing customer requirements, delivery times, and available resources. It is not the mechanical arm that moves the objects, but a much more complex robot.
A process designed in this way would be able to maximize resource utilization, reduce waste and improve overall efficiency. In this final stage, the role of human resources would be to monitor everything to ensure safety, proper management of operations, and to make strategic decisions.
It is important to remember that, as has been emphasized, intelligent automation should only provide support. The final decisions should remain in the hands of human resources. This is because the business environment and ethical aspects require human judgment and appropriate oversight, which we cannot delegate to an algorithm. Emotional aspects, intuition, and creativity are key elements in decision-making, and no software has yet managed to emulate them.
