IBM has launched a new service designed to help enterprises deploy agent AI at scale across their operations. The product, called IBM Enterprise Advantage, integrates IBM consulting services, pre-built AI agents and governance tools to help companies more quickly and securely deploy AI across the enterprise, the company said.
Enterprise Advantage allows organizations to integrate AI into existing workflows and systems and extend agent applications using their current cloud environment and AI models.
This is achieved by combining IBM’s consulting expertise with tools developed through our in-house AI-enabled delivery platform, IBM Consulting Advantage. IBM says its internal platform is used in more than 150 customer engagements and helps consultants improve productivity.
These experiences have shaped how Enterprise Advantage is now delivered to support customers’ own AI initiatives.
The service supports leading hyperscalers such as AWS, Google Cloud, and Microsoft Azure, as well as IBM watsonx, and a combination of open and proprietary models. This service allows companies to extend rather than replace existing technology investments.
Address the enterprise AI execution gap
A recent report from the IBM Institute for Business Value highlights the widening gap between AI ambition and execution. The report found that 79% of executives expect AI to deliver significant business value by 2030. However, only 24% said they had a clear vision of where that value would come from.
This report shows that many organizations do not yet have the operating models and systems needed to support AI at scale.
Enterprise Advantage is designed to address this gap by providing a structured way to build, integrate, and manage AI agents within your existing enterprise platform.
The main elements of the service are:
- Designing an enterprise AI platform: Enterprise Advantage allows companies to integrate AI capabilities into existing enterprise systems and workflows without requiring major infrastructure changes. IBM cited Pearson as an example of how the company is building personalized, AI-powered learning tools for organizations and individuals worldwide. The learning company is also developing a custom in-house AI platform to transform its business operations and drive growth.
- Multicloud and model flexibility: With support for leading cloud platforms and a combination of open and proprietary AI models, organizations can build on their existing technology investments.
- Pre-built AI agents and accelerators: Enterprise Advantage gives enterprises access to a catalog of reusable, industry-specific AI agents aimed at accelerating deployment timelines and reducing deployment risks.
- Built-in governance and controls: Enterprise Advantage supports built-in mechanisms for security, compliance, and lifecycle management when AI agents are deployed into production environments. An IBM research report notes that organizations are increasingly adopting a hybrid human-AI work model, introducing new governance and risk management requirements as AI becomes integrated into operations.
“Many organizations are investing in AI, but achieving real value at scale remains a huge challenge,” said Mohammad Ali, senior vice president and head of IBM Consulting. “We have solved many of these challenges within IBM by leveraging AI to transform our own operations, deliver tangible results, and provide proven strategies to help our customers succeed. Enterprise “By combining human expertise with digital workers and ready-to-use AI assets, Advantage offers this framework to its customers, enabling them to scale AI with confidence and achieve meaningful impact.”
What this means for ERP insiders
Enterprise AI is moving from experimental mode to operational mode. IBM’s launch reflects increasing pressure on large organizations to move beyond pilots and define how AI fits into their core operating models. The focus is on repeatable architectures that can scale across the enterprise and deliver value while working within existing systems.
Agentic AI raises the bar for architecture in ERP environments. As AI agents take on more orchestration and decision support roles, ERP systems must allow AI to work directly with live business data, rather than sitting outside of core processes. This makes platform design and interoperability more important than point functionality.
Governance becomes a prerequisite for scaling AI. IBM’s research highlights that risk, compliance, and oversight concerns have emerged as key constraints to AI adoption. For large enterprises, scalable AI now requires a governance framework built into the architecture from the beginning.
