To ensure a common understanding, artificial intelligence (AI) can be defined as the process of developing systems capable of performing complex tasks by learning and understanding languages, recognizing images, and resolving problems (SYDLE, 2022). AI also encompasses the ability to recognize patterns across different contexts, such as language, sound, and images.

This unique ability, similar to our human cognition, involves understanding a certain pattern, associating it with existing knowledge, and confirming the consistency of our knowledge base. It improves the uniformity of memory and allows predictions regarding the evolution or depreciation of a particular pattern (GARG & GOEL, 2021).

## AI definition, perspectives and uses

AI has been widely applied across various types of organizations, with the financial sector serving as a notable example. Figure 1 illustrates some examples of AI usage in this sector (adapted from BDE, 2019).


Figure 1: Examples of Artificial Intelligence usage in the financial sector. Source: BDE, 2019 (adapted)

As depicted, AI allows for the distinction between its learning capacity and the objective of a single learning process, even if it is applicable and comprehensive.

Studies also highlight the significant use of AI in conjunction with Robotic Process Automation (RPA), which aims to automate routine processes. By incorporating learning processes into solutions, organizations can optimize efficiency and business processes, resulting in technological transformations and effective problem-solving approaches (DAVENPORT & RONANKI, 2018).

Over time, as learning processes evolve and data structures are applied by data scientists and machine learning developers, the results become increasingly impactful. Machine learning models (ML) play a crucial role in this advancement (DAVENPORT & RONANKI, 2018).

NASA provides an example of successful implementation, where routine process automation was employed in the accounts payable department to reduce costs. As a result of this intelligence-led project, the agency expanded the use of RPA in more complex applications. The key lesson learned was that implementing AI in routines does not need to be complex from the outset (DAVENPORT & RONANKI, 2018).

Moreover, AI applications extend beyond detecting financial risks. They offer opportunities for internal services and customer-oriented services in various sectors (BDE, 2019).

In conclusion, the described works highlight the potential of ML models to detect and address transactional inconsistencies, empowering companies' governance to make informed decisions. These decisions may include blocking suspicious operations to mitigate fraud risks (GARG & GOEL, 2021).

From a business perspective, AI is viewed as a key driver for future leadership, as indicated by Deloitte's research (2020). Figure 2 illustrates the belief in AI as a crucial element in future business leadership (adapted from DELOITTE, 2020).


Figure 2: Belief in AI as a key to future business leadership. Source: DELOITTE, 2020 (adapted)

## Conclusion

In conclusion, it is evident that AI will play a crucial role in future business leadership. However, its successful integration requires a strategic and consistent approach to Agile Governance. Organizations must thoroughly understand AI and its potential applications within their internal processes. Embracing AI as a native task for running business processes and facilitating growth in the global market will be essential for businesses in the coming years. It is no longer a turnkey solution but a fundamental element for sustainable success and competitiveness.