Scaling the value of AI from individual pilots to enterprise-wide implementation remains a key hurdle for many organizations.
Experimentation with generative models has become ubiquitous, but the industrialization of these tools (i.e., wrapping them in the necessary governance, security, and integration layers) often stalls. To address the gap between investment and operating returns, IBM has introduced a new service model designed to help companies build their in-house AI infrastructure, rather than purely building it.
Adopt asset-based consulting
Traditional consulting models typically rely on human labor to solve integration problems, a process that is often time-consuming and capital-intensive. IBM is one company trying to change this by offering asset-based consulting services. This approach combines standard advisory expertise with a catalog of pre-built software assets, aimed at helping clients build and manage their own AI platforms.
Instead of commissioning bespoke development for each workflow, organizations can leverage existing architecture to redesign processes and connect AI agents to legacy systems. This method helps enterprises achieve value by extending new agent applications without changing their existing core infrastructure, AI model, or preferred cloud provider.
Managing multicloud environments
A common concern for business leaders is vendor lock-in, especially when adopting proprietary platforms. IBM’s strategy recognizes the reality of heterogeneous enterprise IT environments. The service supports multi-vendor infrastructure compatible with Amazon Web Services, Google Cloud, and Microsoft Azure, alongside IBM watsonx.
This approach extends to the model itself, supporting both open source and closed source variants. The service addresses the adoption barrier of concerns about accumulating technical debt when switching ecosystems by allowing companies to build on current investments rather than requiring alternative strategies.
The technical backbone of the product is IBM Consulting Advantage, an internal delivery platform. IBM uses this system to support its work with more than 150 clients and reports that the platform has increased the productivity of its consultants by up to 50%. The premise is that if these tools can accelerate delivery to IBM’s own teams, they should be able to provide similar speed to clients.
This service provides access to a marketplace of industry-specific AI agents and applications. For business leaders, this suggests a “platform first” focus, shifting attention from managing individual models to managing an integrated ecosystem of digital and human workers.
Aggressively deploying a platform-centric approach to extend the value of AI
The effectiveness of such a platform-centric approach is best demonstrated through active adoption. Pearson, a global learning company, is currently using this service to build a custom platform. Its implementation combines human expertise and agent assistants to manage daily tasks and decision-making processes, demonstrating how the technology works in a real-world production environment.
Similarly, a manufacturing company adopted IBM’s solution to formalize its generative AI strategy. For this client, the focus was on identifying high-value use cases, testing targeted prototypes, and aligning leadership around a scalable strategy. The result is an AI assistant that uses multiple technologies within a secure and controlled environment, laying the foundation for widespread expansion across the enterprise.
Despite the attention to generative AI, realizing balance sheet impact is not guaranteed.
“Many organizations are investing in AI, but achieving true value at scale remains a huge challenge,” said Mohammad Ali, SVP and head of IBM Consulting. “We have solved many of these challenges within IBM by using AI to transform our own operations, deliver measurable results, and provide proven strategies to help our clients succeed.”
The conversation is gradually moving away from specific LLM features and toward the architecture needed to run LLM securely. Success in scaling AI and achieving value depends on organizations being able to integrate these solutions without creating new silos. Leaders should ensure that they maintain rigorous data lineage and governance standards when adopting pre-built agent workflows.
See: JPMorgan Chase treats AI spending as core infrastructure
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