What is automation for business?

2026-01-21 · Source: IBM Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Novice, quick

Summary

IBM Research's VP of AI and Automation, Nick Fuller, defines automation within an enterprise context, emphasizing its role in driving value, top-line growth, profitability, and productivity for mission-critical applications and infrastructure. This includes applications running on data centers, as well as physical assets like pumps and wind turbines in specific industries such as manufacturing, energy, and oil and gas. The research focuses on integrating AI, particularly large language models (LLMs) and agentic AI, across all phases of the delivery lifecycle for these applications and critical infrastructure, addressing aspects like development, resiliency, security, compliance, and cost.

Key takeaway

For AI Architects and MLOps Engineers tasked with optimizing enterprise operations, understanding automation as an AI-driven, lifecycle-spanning approach is crucial. You should evaluate how LLMs and agentic AI can be integrated into your development, security, and compliance workflows to improve the productivity and resiliency of mission-critical applications and infrastructure.

Key insights

Enterprise automation leverages AI, LLMs, and agentic AI to enhance mission-critical applications and infrastructure across their entire lifecycle.

Principles

• Automation drives value, growth, and productivity.
• AI is central to modern automation innovation.

In practice

• Apply AI to application development.
• Enhance infrastructure resiliency with automation.

Topics

• Business Automation
• Enterprise Applications
• AI Innovation
• Large Language Models
• Agentic AI

Best for: AI Architect, MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Research.