Artificial Process Automation: The Artificial Intelligence Leap

What is Artificial Process Automation? Discover how APA is being implemented in business operations by integrating AI and machine learning to create intelligent and autonomous processes that improve efficiency and enable real-time decision-making.

• Understanding the current context of APA

Automation has been the key to business process development for decades. Cost savings, error reduction, and the ability to focus efforts on tasks that truly add value to the business are advantages that companies cannot overlook.

Throughout history, technology has driven the evolution of automation, incorporating new tools like robots, sensors, specialized hardware, and software that have helped companies transform their business processes to maximize their efficiency and performance.

In Spain, 29% of companies have increased their performance and profits thanks to investments in Artificial Intelligence (AI) and automation, according to KPMG's "Global Tech Report 2023". In recent years, artificial intelligence and machine learning have gained relevance in society, and it was expected that organizations would gradually adopt this technology. According to IBM reports, 44% of organizations are actively incorporating artificial intelligence into their operations (Flowable, 2023).

Integrating these technologies into automation endows processes with intelligence and opens the door to automating tasks that previously required human intervention. This combination of artificial intelligence and automation is intended to allow decisions within a workflow to be determined automatically and in real time by the process itself.

• What is Artifial Process Automation? 

APA (Artificial Process Automation) involves applying artificial intelligence techniques and machine learning algorithms to business processes.

Until now, process automation relied on a rigid framework of predefined rules that dictated how a system should behave or how a robot should act in pre-programmed scenarios.

With the advent of APA, the processes themselves will possess this intelligence, allowing processes to behave independently without the need for humans to anticipate these situations or program these tasks.

This, combined with the potential of machine learning, allows the process to learn from its behavior over time and improve its efficiency based on the knowledge of decisions it has made in various situations.

In addition to these technologies, it's important to highlight the potential of Machine Learning. When we talk about Machine Learning, we are referring to the process itself learning from its behavior over time and enhancing its efficiency. This is based on the accumulated knowledge of the decisions the process has made in various situations over time.

The use of these technologies in automation marks a turning point, as processes will gain autonomy and make real-time decisions, triggering different actions within the same workflow and providing more precise results for each situation.

• Technologies and Use Cases 

Nowadays, there are numerous artificial intelligence techniques that can be used in process automation. For example, Natural Language Processing (NLP). NLP allows systems to interpret human language and generate text automatically. In this way, it makes possible tasks such as information extraction, content generation, entity recognition or sentiment detection.

Companies in various industries can use NLP to extract information from unstructured documents and automate tasks such as fraud detection, invoice processing, stock risk calculation, automatic response generation in customer service, trend and threat identification, extraction of relevant information from social media or articles to make decisions about markets or competitor products, claim categorization and processing, and the automation of regulatory or clinical document analysis for designing an automatic recommendation system.

Another widely used tool is predictive models that assist in decision-making by anticipating future events. Incorporating these models into processes can give them capabilities such as predicting product demand based on historical data and making real-time decisions like automatically ordering stock replenishments.

Other techniques that can be used in intelligent process automation include computer vision, which allows machines to interpret and understand visual content from images or videos; data mining, which involves discovering significant patterns and useful knowledge from large data sets; and cognitive computing, which involves creating computer systems capable of simulating the human brain's functioning to process information and learn similarly to a human mind.

These techniques can be applied in monitoring orders to make decisions regarding delays or cancellations, thus maximizing inventory use. Additionally, they are very useful in analyzing images and videos to improve quality in production lines and enhancing the functioning of autonomous vehicles, among other applications. 

Conclusion: The Future

The use of artificial intelligence in company business processes is revolutionizing how humans interact with them. These techniques enable processes to make real-time decisions without needing to be exclusively programmed for them. Process automation will not only handle repetitive tasks or orchestrate processes; the truly revolutionary aspect is transferring the decision-making responsibility to the process itself, avoiding the long wait times that human involvement can cause.

Although challenges remain, such as data privacy, companies' lack of experience with artificial intelligence, resistance to change, or the need for human supervision in critical processes, the operational cost, time, and effort savings are significant. Improving the accuracy and quality of processes positively impacts companies' efficiency, also increasing their productivity.

The rapid advancement of AI and machine learning heralds a future where new techniques will increasingly replace the need for human interactions in company processes, regardless of the industry, bringing us closer to comprehensive process automation.