Pressure to scale AI deployment is mounting as executives push for adoption and competitive threats loom. While recent years have seen extensive AI experimentation, particularly with generative AI (GenAI), transitioning to standardized development and deployment necessitates changes in core enterprise processes.
The IDC paper titled "Four Areas of Change Required to Scale AI Adoption in the Enterprise,” sponsored by Wipro and Red Hat, provides insights into the necessary transformations for enterprises to effectively scale AI adoption.
Key Areas of Change
- Application Development: Enterprises should centralize AI model development and operations tools to improve efficiency, collaboration, and deployment rates
- Data Management: Enterprises need to invest in infrastructure and data quality processes to effectively supply enterprise data to AI applications, with techniques like Retrieval-augmented generation (RAG) playing a crucial role in managing dynamic data sets.
- Governance: Enterprises need new governance approaches to effectively manage AI development, deployment, and risk, with collaborative efforts from AI/ML engineers, application developers, data scientists, and IT operations teams.
- Infrastructure: AI influences enterprise decisions on workload placement, considering factors like data gravity, security, user location, model size, and infrastructure options, necessitating flexible architectural choices for future adaptability.
This document delves into how changes in key areas are essential for scaling up AI adoption in enterprises. Using Red Hat OpenShift AI and Red Hat Enterprise Linux AI, Wipro has built an Enterprise AI Platform designed to speed the development and delivery of AI applications for enterprises.
Download the full IDC document to learn how strategic investments in these areas of change can help enterprises transition from AI experimentation to achieving business outcomes with AI applications.
