A Systems Approach to Effective AIOps Implementation

Artificial Intelligence in IT Operations, or AIOps, has gained considerable attention and expectations over the past few years. However, implementing AIOps in organizations is challenging. This research aims to guide effective enterprise-level AIOps implementation by building a general framework using systems thinking methodologies. The framework proposed builds a structure on the rubric of socio aspect, technical aspect, socio-technical intersection, system dynamics, and environmental factors of AIOps implementation. Each aspect has its corresponding methodology from systems thinking theory. This research is beneficial and critical to organizations wanting to implement or in the process of implementing AIOps. First, this research helps to outline the whole problem space, including both socio and technical aspects. Second, it proposes a comprehensive framework that can be used as a reference for guiding AIOps implementation in real-world scenarios. Based on the actual situation of each organization, companies can build their own AIOps reference models using this framework. The framework bridges gaps between various teams, enabling effective cross-disciplinary collaboration. The framework also provides a big picture and a way to think holistically to all AIOps-related stakeholders and keep their expectations aligned. Moreover, with the systems thinking methodologies embedded in the framework, organizations can guide effective planning, communication, and risk management throughout the AIOps implementation process.

Name thesis:degree_name

Master

Department dc:contributor.department

System Design and Management Program.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Hua, Yunke

Advisor dc:contributor.advisor

• Rhodes, Donna H.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139422

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139422