The evolution of IT architecture to effectively integrate Artificial Intelligence requires a fundamental shift in design, drawing inspiration directly from the human brain, as explored by IBM Technology. AI, at its core, is a massive undertaking to mimic human intelligence. Understanding the brain's "body plan"—a biologist’s term for an architecture—is vital to restructuring enterprise IT for success in AI.
The initial paradigm of AI development, driven by GPT-driven large language models, involved AI essentially "swallowing the internet," processing vast data to create models with a strong understanding of text and images. However, this framing is not practically useful inside an organization, where the focus is on specific, applicable data. The current approach of "jamming AI into the existing enterprise" of data, applications, SaaS, and networking—what IBM Technology calls the "plus AI paradigm"—is associated with a severe failure rate: 90% plus failure for AI initiatives. To reverse this trend and achieve an 80% plus success rate, a new organizational structure is needed, demanding greater separation and organization for data sources, tools, and executive capabilities that interact strategically with AI.
The architecture of the human brain provides the blueprint for this new structure. The brain is organized into three regions: the lower brain, midbrain, and upper brain. The lower brain processes primitive data and responses, such as temperature monitoring. The midbrain focuses on connectivity, handling data exchange, deciding what to ignore, what to store in memory, and facilitating communication across the left and right hemispheres. The upper brain, which contains the most "real estate", includes the frontal area, responsible for executive functioning—the pilot that integrates experiences and decides what to do next. This upper region is also crucial for processing sensory data (auditory and optical) and engaging in long-term strategic thinking.
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One of the brain's most remarkable capabilities, and one difficult to replicate, is integration. It seamlessly combines memories of different senses—like the smell of salt, the sound of waves, and the taste of food—into a single coherent memory. Crucially, the brain achieves its efficiency by ignoring almost everything it encounters, filtering out about 99.8% of all data. It excels at storing only things that stand out and are likely to be important in the future, such as remembering if a Ferrari went zooming past.
To move enterprise IT from the failing "plus AI paradigm" to an AI-ready architecture, IBM Technology recommends a shift away from the traditional, API-based "star structure". Traditional IT involves executive functioning applications (such as CRM, HRIS, and financial systems), a data layer (often a data lake), and a network connecting them. The old API-based paradigm relies on structured integrations that easily break if anything is left to chance.
The solution requires two new architectural components: a new orchestration layer and the Model Context Protocol (MCP). The orchestration layer is designed to spawn "armies of AI agents". To integrate this layer, each executive functioning application and partitioned area of the data layer must be transformed into an MCP service. The MCP service exposes these applications as sets of "tools" (what can I do) and "data sources" (what do I know about).
This new structure directly mirrors the brain's organization: The orchestration layer acts as the frontal lobe or forebrain, the executive functioning center. The individual applications become specialized "organs"—for instance, CRM as the auditory center, HRIS as the olfactory system, and the financial system as the upper strategic part of the brain. The AI agents spawned by the orchestration layer function like synapses, structures that connect neurons, allowing the frontal lobe (orchestration layer) to activate different specialized organs (applications) to achieve complicated goals and acceptable outcomes. This requires breaking down the data lake into an AI-ready data layer.
By mirroring biological intelligence—its integration, compartmentalization, and organized structure—the enterprise can transition to a successful AI-ready posture. This evolution is critical because biological intelligence is highly compact and runs on very low power, offering a model for how the enterprise can achieve highly integrated and successful AI functioning.
