Scaling AI Value: Transforming Beyond the Pilot Phase Purgatory
Scaling AI value from isolated pilots to enterprise-wide adoption remains a primary hurdle for many organisations. Despite the widespread experimentation with generative models, the practical implementation of these tools into existing governance and integration layers often faces significant delays.
IBM is responding to this challenge by introducing a new service model aimed at helping businesses assemble their internal AI infrastructure, rather than solely relying on building bespoke solutions. This approach involves adopting asset-based consulting, where IBM combines traditional advisory strategies with a catalogue of pre-built software assets, enabling clients to design and manage their own AI platforms more efficiently.
Instead of developing new workflows from scratch, organisations can leverage existing architectures to redesign processes, integrating AI agents with legacy systems. This strategy aims to deliver value by promoting the scaling of new AI applications without requiring extensive modifications to core infrastructure or existing AI models.
Another critical concern for enterprise leaders is vendor lock-in. IBM’s service supports a multi-vendor environment, accommodating platforms from Amazon Web Services, Google Cloud, Microsoft Azure, and IBM watsonx. This flexibility helps mitigate fears of becoming tied to a single provider, enabling businesses to build on prior investments rather than pursuing complete ecosystem overhauls.
Central to this offering is the IBM Consulting Advantage platform, which has reportedly improved consultant productivity by up to 50 percent across over 150 client engagements. The capability of this platform to enhance delivery speed for internal teams suggests it could provide similar benefits for client operations.
The real-world effectiveness of this platform-centric approach is demonstrated through active deployments. For instance, Pearson has utilized this service to create a custom platform that merges human expertise with AI assistants for managing daily tasks and decision-making. In a manufacturing context, another client has adopted IBM’s solutions to establish a structured generative AI strategy, identifying key use cases and aligning leadership around a scalable plan for integration.
Despite growing interest in generative AI, the challenge of reaping financial benefits remains significant. As Mohamad Ali, SVP and Head of IBM Consulting, emphasizes, while many organisations are investing heavily in AI, achieving large-scale value continues to be a critical obstacle. Success may increasingly hinge on leveraging these solutions without creating additional silos, reinforcing the importance of meticulous data governance and lineage as workflows evolve.
For detailed insights into IBM’s strategy and the associated implications, refer to the following resources:
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