AI Practitioner Journey

The Hidden Cost of Intelligence: How AI Adoption Reshapes Enterprise Infrastructure AI adoption brings strategic value, new capabilities, and competitive advantage. But quietly beneath the excitement lies something every enterprise eventually feels: a rapidly expanding and increasingly complex infrastructure footprint . Unlike traditional IT systems—where compute, storage, and network costs scale predictably—AI consumption grows in sharp spikes. GPU workloads fluctuate, vector databases multiply, pipelines expand, and observability tooling becomes mandatory. Without guidance, this creates AI infrastructure sprawl that is expensive, opaque, and hard to reverse. As an Enterprise Architect, I’ve observed that this pattern is not random. AI infrastructure grows in distinct stages that mirror an organization’s corporate lifecycle . Understanding these stages helps in designing AI infrastructure that is scalable, efficient, and value-aligned. AI introduces a new consumption model that c...

From Curiosity to Capability AI has evolved from a promising technology into a strategic enabler across all industries. Yet, as adoption accelerates, one persistent question remains: How exactly should organizations use AI? For some, it begins by bringing context to AI — feeding it data to uncover patterns and insights. Others focus on bringing AI to context — embedding intelligence directly into business operations. Both are valid starting points, but they represent only part of a much broader picture. From an enterprise perspective, AI now operates at multiple levels — as a learner, actor, advisor, creator, and increasingly, as an orchestrator and governor of intelligence across systems. Understanding these AI use case types  help leaders structure their AI strategy, plan investments, and align initiatives with real business value. The table below outlines ten foundational archetypes of AI use cases that together capture the full spectrum of enterprise AI maturity. Table: Ten ...

Introduction Artificial Intelligence (AI) is reshaping how organizations approach growth, efficiency, and decision-making. Yet adoption is not a single leap—it is a staged journey. From an Enterprise Architecture (EA) perspective, success comes from aligning goals, people, process, data, and technology so AI adoption supports business priorities in a sustainable and responsible way. The AI journey can be structured in five phases : Experimentation, Evaluation, Planning, Leverage, and Optimize. Each phase builds on the previous one, creating a pathway from initial trials to practical, scalable impact. Phase 1: Experimentation This phase is about curiosity and exploration. Small pilots test AI capabilities in controlled areas, such as process optimization, analytics, or customer engagement. The goal is to learn quickly, identify opportunities, and understand limitations. Insights from experimentation set the foundation for systematic evaluation. Phase 2: Evaluation Evaluation separates e...

Artificial Intelligence has shifted from being an experimental tool to a core enabler of business strategy. However, many organizations jump into AI without a structured plan, leading to fragmented initiatives and wasted investments. To succeed, an AI strategy needs three pillars: strong foundation blocks, clear results, and a practical roadmap. Let’s break these down. 1. The Foundation Blocks of an AI Strategy AI success is not only about data; it’s about how your organization is structured, how technology operates, and how fast you can adapt. Here are the key building blocks: a) Organizational Structure AI is changing the DNA of companies, and traditional org charts won’t cut it: Tech-Embedded Business Functions: Every business unit will have AI-driven roles; IT will be fully integrated with operations. Cross-Functional AI Squads: Data scientists, ML engineers, business analysts, and domain experts working in agile pods. AI Governance Board: A central body to manage complianc...

  "Architects who use AI will replace those who don’t."   This quote captures the reality of modern enterprise technology. Industry surveys, such as Gartner’s 2025 predictions , indicate that 70% of organizations will use AI-driven decision-making in IT governance and architecture . As organizations grow more complex, AI is now an integral part of Enterprise Architecture (EA) strategy. The Case for AI in Enterprise Architecture Enterprise Architecture exists to provide clarity, structure, and alignment between business goals and IT capabilities. However, the digital era introduces challenges: Explosion of complexity : Applications, APIs, microservices, and cloud services create sprawling ecosystems. Data overload : Organizations sit on massive volumes of data , yet struggle to extract actionable insights. Pace of change : Strategic decisions can’t wait for weeks of analysis— predictive and prescriptive capabilities are now essential. This is where AI steps in as a...

Enterprise Architecture (EA) has long been criticised for being either too rigid or too abstract. But I see this as not a problem with EA itself—but mainly the one size fit all approach that is followed. Organisations like any other live ecosystem goes through a lifecycle. Renowned valuation expert Aswath Damodaran's corporate lifecycle framework offers a powerful lens through which we can reimagine the strategic role of EA. EA is a shape-shifter , adapting its value, focus, and tools based on where a company is in its business lifecycle. Enterprise Architecture Across the Corporate Lifecycle The table below highlights how EA adapts its focus, addresses stage-specific challenges, and utilizes different tools as a company moves through Damodaran’s lifecycle stages: Lifecycle Stage Business Focus EA Challenges EA Focus Areas Key Tools & Methods Start-up (Idea Business) Prove viability, rapid iteration Lack of structure, tech founder-driven, resource constraints Define core capa...

In today’s technology race, organisations are constantly challenged to include AI element in business strategy. Amid the existing complexity of systems, applications, and operations, adding a new technology approach can become daunting. Can the the role of the Enterprise Architect (EA)  become the catalyst in this journey - or EA role is still an expensive overhead. Question is  To EA or not to EA? What Is Enterprise Architecture, Really? With over 25 years of IT experience and around 15 years in IT architecture field, I do have a vantage point for Enterprise Architecture. Enterprise Architecture is a discipline —a structured approach to aligning IT strategy with business goals. It involves creating holistic views of an organisations processes, data, applications, and infrastructure to ensure coherence, efficiency, and adaptability. The Enterprise Architect , then, becomes the bridge between strategy and execution—someone who can navigate the big picture while understandin...

 First post on the blog The best way to do something is....to do it. Putting thoughts on a blog is more suitable approach for me. It allows me to iterate through my ideas. This blog is about next journey in my professional career. At present, I am fascinated by the number of possibilities that AI technology have opened up. Of course, more choices means more confusion. At such a crucial juncture, I felt creating a blog for my though process will help me first put down the ideas, then iterate through them and when needed refer back so that continue to build on those base ideas. Isn't that what unsupervised learning do !! I am not a perfectionist. I am here to enjoy the journey :-)