Scaling AI from isolated pilot projects to widespread enterprise adoption presents a significant challenge for many organizations. While experimentation with generative AI models has become commonplace, the industrialization of these tools—integrating them with essential governance, security, and existing infrastructure—often hits roadblocks. To bridge the gap between investment and tangible returns, IBM has introduced a novel service model focused on helping businesses assemble, rather than solely build, their internal AI capabilities.
### Embracing Asset-Based Consulting
Traditional consulting models typically rely on extensive human capital to address integration challenges, a process that can be both time-consuming and costly. IBM is positioning itself to disrupt this paradigm by offering an asset-based consulting service. This approach merges standard advisory expertise with a library of pre-developed software assets, enabling clients to construct and manage their AI platforms more efficiently.
Rather than commissioning custom development for every new workflow, organizations can leverage these existing architectures to re-engineer processes and connect AI agents to their legacy systems. This methodology allows companies to realize value from new agentic applications without requiring fundamental changes to their core infrastructure, existing AI models, or preferred cloud environments.
### Navigating Multi-Cloud Environments
A persistent concern for enterprise leaders is the risk of vendor lock-in, particularly when adopting proprietary platforms. IBM’s strategy acknowledges the complex, multi-vendor reality of modern enterprise IT landscapes. The service is designed to support a heterogeneous foundation, compatible with major cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure, in addition to IBM’s own watsonx platform.
This flexibility extends to the AI models themselves, accommodating both open-source and proprietary options. By enabling companies to build upon their current technology investments rather than mandating a complete overhaul, this service directly addresses a key adoption barrier: the fear of accumulating technical debt when shifting between technology ecosystems.
The underlying technical framework for this offering is IBM Consulting Advantage, the company’s internal delivery platform. Having been utilized in over 150 client engagements, IBM reports that this platform has enhanced the productivity of its own consultants by as much as 50 percent. The underlying principle is that if these tools can accelerate delivery for IBM’s internal teams, they should offer similar speed and efficiency to clients.
The service also provides access to a marketplace of industry-specific AI agents and applications. For business leaders, this signifies a shift towards a “platform-first” mindset, where the focus moves from managing individual AI models to overseeing a cohesive ecosystem of both digital and human resources.
### Actively Deploying a Platform-Centric Approach to Scale AI Value
The effectiveness of this platform-centric strategy is best demonstrated through its real-world application. Pearson, the global learning company, is currently using this service to build a customized AI platform. Their implementation integrates human expertise with AI assistants to manage routine work and decision-making processes, offering a clear example of the technology in an operational setting.
Similarly, a manufacturing firm has adopted IBM’s solution to formalize its generative AI strategy. For this client, the focus was on pinpointing high-value use cases, rigorously testing targeted prototypes, and aligning leadership around a scalable strategy. This led to the deployment of AI assistants utilizing multiple technologies within a secure, governed environment, establishing a foundation for broader enterprise-wide expansion.
Despite the considerable attention surrounding generative AI, the realization of significant balance-sheet impact is not inherently guaranteed.
“Many organizations are investing in AI, but achieving real value at scale remains a major challenge,” commented Mohamad Ali, SVP and Head of IBM Consulting. “We have solved many of these challenges inside IBM by using AI to transform our own operations and deliver measurable results, giving us a proven playbook to help clients succeed.”
The conversation is steadily evolving from a focus on the capabilities of specific large language models to the critical architecture required to deploy them safely and effectively. Success in scaling AI and achieving its intended value will likely hinge on an organization’s ability to integrate these solutions without creating new operational silos. Leaders must ensure that as they adopt pre-built agentic workflows, they maintain stringent standards for data lineage and governance.
Original article, Author: Samuel Thompson. If you wish to reprint this article, please indicate the source:https://aicnbc.com/16309.html
