Intelligent enterprises embed AI into core systems, values, and behaviors.
AI is no longer just a technology to be integrated—it must become a foundational capability within the enterprise. True value is unlocked when AI is embedded deeply into the core of systems, workflows, and decision-making processes. The shift from integration to immersion must not just be AI-enabled but AI-native—systems that learn continuously and adapt intelligently. Many organizations still treat AI like any other software product—something to install, configure, and plug into existing systems.
“AI needs to be embedded in the architecture, platforms, and principles—and absorbed by the people.” ~ Metesh D Bhati, EVP & Chief Digital and AI Officer, Protean eGov Technologies
But AI demands a different approach—one that starts with reimagining platforms and infrastructure to enable intelligent workflows and real-time decisions. It requires moving from digital-first to AI-first thinking. A major opportunity lies in the data enterAI Belongs in the Fabric of Business, Not Just the Stack Intelligent enterprises embed AI into core systems, values, and behaviors. prises already possess. Instead of choosing off-the-shelf large language models (LLMs), organizations gain more by building small, domain-specific models trained on proprietary data. However, they must overcome challenges such as the lack of standardized ontologies, missing semantic layers, and inconsistent data governance.
Governance must also be rethought. Compliance—especially with evolving regulations like the DPDP Act—should not be treated as a checklist item. Data governance must be compliant by design, with ethics, explainability, and accountability built into the architecture from the ground up. Equally critical is absorption. It is not enough to adopt AI—the enterprise must absorb it. That means aligning AI with people, ethics, and evolving business models. Intelligence must not only live in systems but also shape how decisions are made and trust is built across stakeholders.
At an operational level, deploying AI presents complex, practical challenges. Latency in data ingestion, drift in model performance, and weak alignment between ML metrics and business KPIs are common friction points. Many monitoring systems fail to reflect the domain-specific context in which AI operates. To overcome this, mature teams are building observability layers to track drift, ensure lineage, and generate insights across the full AI lifecycle. Ultimately, AI success hinges on mindset, architecture, and accountability—and how deeply it is absorbed into the fabric of the enterprise