Master's – Business Process Changes on the Implementation of Artificial Intelligence

Tipo de evento: 
Defesa
Data e hora: 
09/12/2020 - 14:00 to 17:00

 

Oscar Do Amaral Adorno 

Master's – Business Process Changes on the Implementation of Artificial Intelligence

Advisor: Prof. Dr. Paulo Tromboni de Souza Nascimento

Comission: Profs. Drs. Leonardo Augusto de V. Gomes, Ana Lúcia de Figueiredo Facin and Edson Carlos Germano

Link YouTube: https://youtu.be/6ZG4FhEcYgA

Abstract*

The process of digital transformation is a phenomenon that will affect all organizations. In business that have already started digital transformation process, artificial intelligence (AI) solutions begin to appear. What challenges and business processes changes are there or are already underway in Brazilian companies in this journey? Projects from this nature promote new challenges and generate organizational changes in operational and administrative processes. This study analyzes the challenges and investigates the changes needed to implement digital transformation projects, with a greater focus on Artificial Intelligence initiatives.
The research question raised was: What are the main changes and impacts on business processes in the adoption of artificial intelligence? In addition, this work sought to analyze in greater detail: (1) What are the Artificial Intelligence initiatives in the company? Are they grouped under sole responsibility? In what organizational structure? (2) What are the main challenges in implementing AI? (3) What are the main impacts on business? (4) How does the integration of artificial intelligence solutions interact with current operations and business processes?
In order to seek to answer the research questions, a multiple case study was carried out with companies from different sectors that have a high degree of maturity in digital transformation and innovation. The companies belong to the following industries segments: Telecommunications & Technology, Professional Services, Logistic Services, Chemical and Financial Services. The challenges and changes were identified through content analysis with a semi-structured protocol for interviews and data were collected publicly available and provided by the companies.
This work aims to contribute to the academic literature by improving the understanding of the potential effects of Artificial Intelligence technologies in organizations (management of business processes, dynamics of transformations, patterns and organizational structures and management). In addition, from the content analysis by multiple case studies, a comparison was made between AI projects from companies of distinct economy sectors to find similarities and differences.
The main business process changes found in the companies were in the service areas with a focus on technical service and customer solutions. In addition, there were AI initiatives to internal processes automation in the tax, audit and security areas.
In the case of solutions related to customer service, an area for management, monitoring and training cognitive solutions were structured mostly with teams from the call centers.
Regarding the business impacts, with the process’s automation, there was a productivity growth, an increase in the service level in attendance and a reduction in interactions in the call centers. Moreover, a case with marketing effects relating the company to Artificial Intelligence was detected.
The AI ​​initiatives were led mainly by teams in the areas related to technology, which are: Digital, Digital Transformation, Data & Analytics and Data Management - Algorithms and Innovation.
The main challenges found in implementations were the prioritization and selection of AI projects, obtaining support from senior management and financial resources to carry out the digital initiatives. From the human resources point of view, the scarcity of specialized AI teams and their high turnover were identified as obstacles, as well as cultural and integration barriers between departments. Likewise, there were systemic difficulties in integrating specific departmental systems, scalability of solutions and risks related to databases and systemic environments changes.
The improvement of professional and academic work in this field has great relevance at this moment, as professionals and academics are beginning to understand the transformative potential of Artificial Intelligence technologies in our society.

*Abstract provided by the author

 

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