IBM has announced IBM Enterprise Advantage, a new consulting service designed to help companies build, govern, and operate an enterprise-scale intelligent AI platform. This initiative combines industry expertise with
IBM’s premise is clear: scaling AI within an organization is not just about “connecting a model,” but involves redesigning processes, integrating existing data and systems, and establishing an operational governance to deploy agents without starting each project from scratch. Under this framework, Enterprise Advantage is presented as a “first-of-its-kind” service (according to IBM) with an asset-based approach—meaning it relies on shared standards, reusable components, and ready-to-use artifacts—aimed at accelerating the journey from conception to deployment.
An “agnostic” approach: no forced changes to cloud, models, or infrastructure
One of the key messages of the launch is that organizations can use Enterprise Advantage to re-design workflows, connect AI to existing systems, and scale intelligent agents without needing to switch cloud providers, models, or core infrastructure. The official communication mentions compatibility with Amazon Web Services, Google Cloud, Microsoft Azure, as well as the IBM watsonx ecosystem, and support for open source and proprietary models.
Practically, IBM aims to position the service as an orchestration and governance layer on top of the company’s heterogeneous environment: multiple clouds, various tools, and different teams working under increasingly strict security and compliance requirements. The idea is significant: when capable agents that can perform actions—beyond just answering questions—are involved, access control, traceability, and safeguards become prerequisites rather than afterthoughts.
From internal platform to client offering: IBM Consulting Advantage’s “playbook”
IBM links Enterprise Advantage to IBM Consulting Advantage, its internal AI-assisted delivery platform within IBM Consulting. According to the company, this platform—with a growing marketplace of industry-specific agents and applications—has already supported more than 150 client projects and demonstrated productivity increases of up to 50% for its consultants, enabling faster results.
The strategic takeaway is clear: IBM is attempting to “productize” its method, translating the approach it has internally proven into a client-facing solution. Instead of merely selling consulting hours, it offers a model where part of the value is packaged as reusable assets—templates, agents, implementation standards, governance components—that are adapted to each client’s context. This aligns with the evolving nature of professional services around AI—moving away from artisanal customization towards more industrialized solutions.
Case examples: Pearson and a manufacturer adopting a “platform-first” strategy
IBM cites Pearson as an example of adoption: the education company is leveraging the service to build a tailored platform combining human expertise with agent assistants designed to support daily work and decision-making. IBM does not disclose public technical details, but the case demonstrates the goal: integrating assistants into routine operations without turning them into disconnected pilots.
Additionally, IBM describes a case involving a manufacturing company (unnamed) that used Enterprise Advantage to develop its generative AI strategy: identifying high-value use cases, prototyping, and aligning leadership around a “platform-first” approach. Under this framework, the client was deploying assistants with multiple technologies in a secure, governed environment—laying the groundwork to extend adoption throughout the organization.
“Many invest, few scale”: the challenge of delivering real value at the enterprise level
Mohamad Ali, Senior Vice President and Head of IBM Consulting, summarizes IBM’s diagnosis with a phrase repeated across many leadership committees: Investing in AI is relatively easy; extracting scalable value is the hard part. IBM claims to have addressed some of these challenges within its own operations and now applies this approach to clients—“combining human expertise with digital workers and ready-to-use assets.”
For many organizations, the bottleneck is no longer model availability but operation: integrating with legacy systems, managing identities and permissions, quality control, risk management, monitoring, and governance. IBM frames Enterprise Advantage as a way to navigate the agent market while simultaneously creating an internal platform with controls and standards from the outset.
Availability and positioning: a “now available” service
IBM states that Enterprise Advantage is available immediately, emphasizing that this is not a laboratory concept but a fully developed commercial offering built upon IBM Consulting Advantage’s prior learnings.
Frequently Asked Questions
What is agentic AI, and why does it complicate governance within companies?
Agentic AI refers to assistants that do not just respond but can execute tasks and coordinate actions within workflows. This elevates the need for access control, traceability, auditing, and safeguards, since the impact extends beyond informational responses to operational execution.
What does “asset-based consulting” mean in Enterprise Advantage?
IBM describes Enterprise Advantage as a service that combines consulting with reusable assets—such as standards, components, and resources—to accelerate the development and operation of an internal AI platform.
Can Enterprise Advantage be used without changing cloud providers or models?
According to IBM, the service is designed to scale agentic applications without requiring changes in cloud provider, model, or core infrastructure. It supports compatibility with AWS, Google Cloud, Microsoft Azure, IBM watsonx, and both open-source and proprietary models.
What types of organizations typically need a governed internal platform for assistants and agents?
Generally, companies with multiple business units, compliance requirements, and a need for standardized deployments (security, identity management, system integration) tend to seek a “platform-first” approach to prevent each assistant from becoming an isolated project.