# Towards Enterprise-Ready AI Deployments Minimizing the Risk of Consuming AI Models in Business Applications

**Authors:** Aleksander Slominski, Vinod Muthusamy, Vatche Ishakian  
**arXiv:** [1906.10418](https://arxiv.org/abs/1906.10418)  
**Date:** 2019-06-25  
**Summary page:** https://www.summarizepaper.com/en/paper/1906.10418v1/

## Key points

- Authors address the risk of integrating AI models into business applications without thorough consideration
- Propose an approach leveraging AI techniques to gain insights and manage deployment in production environments
- Emphasize the importance of implementing control mechanisms to mitigate risks and ensure reliability of AI-driven processes
- Highlight significance of incorporating machine learning methodologies and microservices architecture for optimizing AI deployments in enterprise applications
- Advocate for a systematic approach towards deploying AI models aligned with organizational goals and regulatory requirements

## Summary

In their paper titled "Towards Enterprise-Ready AI Deployments: Minimizing the Risk of Consuming AI Models in Business Applications," authors Aleksander Slominski, Vinod Muthusamy, and Vatche Ishakian address the inherent risk associated with the stochastic nature of artificial intelligence (AI) models when integrated into business applications without thorough consideration. They propose an approach that leverages AI techniques to not only gain insights into the utilization of these models but also to effectively manage their deployment within production environments. By acknowledging the unpredictability of AI models, the authors highlight the importance of implementing control mechanisms to mitigate potential risks and ensure the reliability and stability of AI-driven business processes. Through their research, they emphasize the significance of incorporating machine learning methodologies and microservices architecture in optimizing AI deployments for enterprise applications. Overall, this paper offers valuable insights into how organizations can strategically harness AI technologies while minimizing uncertainties and maximizing operational efficiency. With a focus on enhancing business processes through intelligent automation, the authors advocate for a systematic approach towards deploying AI models that align with organizational goals and regulatory requirements. The keywords artificial intelligence (AI), machine learning, microservices architecture, business process optimization, and risk management underscore the key themes explored in this study, providing a comprehensive framework for enterprises looking to leverage AI capabilities effectively in their operations.