Scaling AI value from isolated pilots to enterprise-wide adoption remains a primary hurdle for many organisations.
While experimentation with generative models has become ubiquitous, industrialising these tools (i.e. wrapping them in necessary governance, security, and integration layers) often stalls. Addressing the gap between investment and operational return, IBM has introduced a new service model designed to help businesses assemble, rather than purely build, their internal AI infrastructure.
Adopting asset-based consulting
Traditional consultancy models typically rely on human labour to solve integration problems, a process that is often slow and capital-intensive. IBM is among the companies aiming to alter this dynamic by offering an asset-based consulting service. This approach combines standard advisory expertise with a catalogue of pre-built software assets, aiming to help clients construct and govern their own AI platforms.
Instead of commissioning bespoke development for every workflow, organisations can leverage existing architectures to redesign processes and connect AI agents to legacy systems. This method helps companies to achieve value by scaling new agentic applications without necessitating alterations to their existing core infrastructure, AI models, or preferred cloud providers.
Managing a multi-cloud environment
A frequent concern for enterprise leaders is vendor lock-in, particularly when adopting proprietary platforms. IBM’s strategy acknowledges the reality of the heterogeneous enterprise IT landscape. The service supports a multi-vendor foundation, compatible with Amazon Web Services, Google Cloud, and Microsoft Azure, alongside IBM watsonx.
This approach extends to the models themselves, supporting both open- and closed-source variants. By allowing companies to build upon their current investments rather than demanding a replacement strategy, the service addresses a barrier to adoption: the fear of technical debt accumulation when switching ecosystems.
The technical backbone of this offering is IBM Consulting Advantage, the company’s internal delivery platform. Having utilised this system to support over 150 client engagements, IBM reports that the platform has boosted its own consultants’ productivity by up to 50 percent. The premise is that if these tools can accelerate delivery for IBM’s own teams, they should offer similar velocity for clients.
The service provides access to a marketplace of industry-specific AI agents and applications. For business leaders, this suggests a “platform-first” focus, where attention turns from managing individual models to managing a cohesive ecosystem of digital and human workers.
Active deployment of a platform-centric approach to scaling AI value
The efficacy of such a platform-centric approach is best viewed through active deployment. Pearson, the global learning company, is currently utilising this service to construct a custom platform. Their implementation combines human expertise with agentic assistants to manage everyday work and decision-making processes, illustrating how the technology functions in a live operational environment.
Similarly, a manufacturing firm has employed IBM’s solution to formalise its generative AI strategy. For this client, the focus was on identifying high-value use cases, testing targeted prototypes, and aligning leaders around a scalable strategy. The result was the deployment of AI assistants using multiple technologies within a secured, governed environment, laying a foundation for wider expansion across the enterprise.
Despite the attention surrounding generative AI, the realisation of balance-sheet impact is not guaranteed.
“Many organisations are investing in AI, but achieving real value at scale remains a major challenge,” notes 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 gradually moving away from the capabilities of specific LLMs and towards the architecture required to run them safely. Success in scaling AI and achieving value will likely depend on an organisation’s ability to integrate these solutions without creating new silos. Leaders must ensure that as they adopt pre-built agentic workflows, they maintain rigorous data lineage and governance standards.
See also: JPMorgan Chase treats AI spending as core infrastructure
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