Enterprise AIOps

Conclusion

This report’s objective was to provide insights into how an organization can progress from applying AI in a controlled lab environment to advancing the organization to an enterprise AI capability. As outlined, there are many factors that must be addressed to make this transition successful (scoping of AI projects, measuring success, embedding responsible AI, integration of the ops, monitoring AI model performance, and pursuing continuous improvement). Today, we are at the point of maximum hype and potential associated with AI. If organizations are not able to move AI models to production and realize the potential return on those investments, AI solutions are in danger of decreased adoption and longer horizons until organizations experience the full value AI can provide as a transformation capability.

Through the application of the approaches and frameworks outlined in this report, an organization can leverage lessons learned to reduce time and risk in making AI an enterprise capability for their organization. In many cases, organizations focus on one or a small number of these aspects, leading to challenges in establishing and scaling enterprise AI. Each area outlined in this report must be given attention and resources, must be integrated, and must be iteratively enhanced to realize the full potential of enterprise AI.

It is also critical to keep in mind that each topic needs to be improved iteratively, especially in light of the state of rapid technology development ...