For many organisations, AI progress stalls in an uncomfortable middle ground. Experiments with generative models are easy to launch, but converting those pilots into enterprise-wide systems that deliver measurable returns remains a persistent challenge.
The issue is rarely model capability. Instead, the friction appears when companies attempt to operationalise AI—adding governance, security, and integration layers that allow these tools to function reliably inside real business environments. To address this gap, IBM has introduced a service model that focuses on assembling AI infrastructure rather than building everything from scratch.
Rethinking the consulting playbook
Traditional consulting approaches rely heavily on custom development and manual integration, which can be slow and expensive. IBM’s asset-based consulting model aims to change that dynamic by combining advisory services with a library of pre-built software components.
Rather than designing every workflow from the ground up, organisations can reuse established architectures to connect AI agents to existing systems. This allows companies to scale agentic applications without reworking core platforms, replacing models, or committing to a single cloud provider.
Avoiding lock-in across clouds and models
Vendor lock-in remains a major concern for enterprise leaders, particularly as AI stacks grow more complex. IBM’s approach reflects the reality of modern IT environments, which are rarely uniform.
The service supports deployment across Amazon Web Services, Google Cloud, Microsoft Azure, and IBM watsonx. It also accommodates both open- and closed-source models, enabling organisations to build on existing investments instead of abandoning them. This flexibility reduces the risk of technical debt accumulating as AI strategies evolve.
At the centre of the offering is IBM Consulting Advantage, the internal platform used by IBM’s own teams. According to the company, the platform has supported more than 150 client engagements and increased consultant productivity by up to 50 percent. The idea is simple: tools that accelerate delivery internally should be capable of doing the same for clients.
From managing models to managing ecosystems
The service includes access to a marketplace of industry-specific AI agents and applications. This reflects a broader shift in enterprise thinking—from deploying individual models to managing an integrated ecosystem of digital and human workers.
By standardising how agents are deployed, governed, and monitored, organisations can focus less on infrastructure overhead and more on outcomes.
Early examples of AI at scale
Real-world deployments offer a clearer picture of how this approach works in practice. Pearson, the global learning company, is using the service to build a custom platform that blends human expertise with AI assistants to support daily operations and decision-making.
In manufacturing, another client has used the platform to formalise its generative AI strategy. The engagement focused on identifying high-impact use cases, testing targeted prototypes, and aligning leadership around a scalable roadmap. The result was the deployment of AI assistants across multiple technologies within a secured, governed environment—creating a foundation for broader rollout.
Architecture over algorithms
Despite widespread investment, AI does not automatically translate into balance-sheet impact. As IBM Consulting’s Mohamad Ali notes, achieving value at scale remains a major hurdle for most organisations.
The conversation is gradually shifting away from individual large language models and toward the architecture required to run them responsibly. Long-term success will depend on whether organisations can integrate AI into their operations without creating new silos, while maintaining strong data lineage, governance, and oversight.
For enterprises stuck in pilot-phase purgatory, the path forward appears less about smarter models and more about building the right foundations to support them.
Source: https://www.artificialintelligence-news.com/news/scaling-ai-value-beyond-pilot-phase-purgatory/
