The global corporate landscape is currently navigating a transition that transcends mere technological adoption. For decades, digital transformation was often viewed as a means to optimize existing processes—making a manual task faster or a physical record digital. However, the emergence of generative artificial intelligence (AI) has shifted the conversation from optimization to a fundamental reimagining of how value is created, and delivered.
Nandan Nilekani, Chairman of Infosys, has been vocal about this paradigm shift, asserting that AI is not simply a new tool in the toolkit but a force that is changing the very business and operating models of enterprises. According to Nilekani, the impact is universal; whether a company is an IT service provider, a consumer goods giant, or a global bank, the current wave of AI-driven change necessitates a structural pivot in how these organizations function.
This shift represents a move toward the “AI-first” enterprise. In this model, AI is not bolted onto existing workflows but is instead the core engine around which business processes are designed. For the global economy, In other words a transition from labor-intensive operating models to intelligence-augmented ones, potentially altering the cost structures and competitive advantages of industries worldwide.
As a financial journalist and economist, I have observed many “disruptive” cycles, but the velocity of the current AI integration is unprecedented. We are seeing a compression of the adoption curve, where the gap between the “AI-leaders” and “AI-laggards” is widening rapidly. The risk for companies is no longer just a loss of efficiency, but a loss of relevance as their fundamental operating models become obsolete.
Beyond Automation: The Structural Pivot of the AI-First Enterprise
To understand why AI is changing business models rather than just improving them, one must distinguish between automation and augmentation. Traditional automation replaced repetitive tasks. Generative AI, however, targets cognitive tasks—analysis, synthesis, and creativity—which were previously the sole domain of human expertise. When the “unit of work” changes from a human hour to an AI-generated output, the entire economic logic of a business changes.
For IT services, this is particularly acute. The traditional “time and material” billing model, which relies on the number of hours engineers spend coding or testing, is under immense pressure. As AI tools like Infosys Topaz enable developers to produce code faster and with fewer errors, the value proposition shifts from “effort” to “outcome.” Companies are now being forced to price based on the value delivered rather than the hours logged.
This structural pivot requires a complete overhaul of the internal operating model. It involves moving away from rigid hierarchical approvals toward fluid, AI-assisted decision-making. In an AI-first model, the primary role of the human worker shifts from “doer” to “orchestrator,” overseeing multiple AI agents that handle the execution of complex tasks.
Sectoral Impacts: From Banking to Consumer Goods
The assertion that every company will be affected is not hyperbole; the application of AI varies by sector, but the underlying shift in operating models remains consistent.
Banking and Financial Services
In the banking sector, AI is moving beyond chatbots and fraud detection. The operating model is shifting toward “hyper-personalization” at scale. Instead of offering a standard set of products to a demographic segment, banks can now use AI to analyze real-time data and create bespoke financial products for an individual customer in seconds. This changes the bank’s role from a utility provider to a proactive financial concierge, fundamentally altering the customer relationship and the revenue streams associated with personalized advisory services.
Consumer Packaged Goods (CPG)
For consumer goods companies, the change is most evident in the supply chain and demand forecasting. Traditional models relied on historical data and seasonal trends. AI-driven models now incorporate real-time signals—social media trends, weather patterns, and geopolitical shifts—to adjust production in real-time. This reduces waste and optimizes inventory, shifting the operating model from “forecast and push” to “sense and respond.”
IT and Professional Services
As mentioned, the professional services sector is facing an existential shift. The ability of AI to handle documentation, initial research, and basic coding means that the “entry-level” work that previously trained junior associates is disappearing. This creates a talent gap that companies must bridge through aggressive upskilling, moving their workforce toward high-level strategic thinking and complex problem-solving.
Scaling Intelligence with Infosys Topaz
To address these systemic changes, Infosys has introduced Topaz, an AI-first set of services and solutions. The goal of Topaz is not merely to provide AI tools but to help enterprises build the “cognitive core” necessary to support a new operating model. This involves integrating generative AI into the heart of the business—from the back-office ERP systems to the front-end customer interfaces.
The implementation of such a system typically follows a three-tier approach:
- Efficiency Gains: Using AI to reduce the cost of existing operations (e.g., automating customer support).
- Experience Enhancement: Using AI to improve the customer or employee journey (e.g., AI-driven personalized shopping).
- Business Model Innovation: Creating entirely new products or services that were impossible without AI (e.g., autonomous financial planning).
The challenge for most CEOs is that they often stop at the first tier. However, as Nandan Nilekani suggests, the true competitive advantage lies in the third tier—the ability to pivot the business model itself to leverage the unique capabilities of artificial intelligence.
The Economic Implications of the AI Transition
From an economic perspective, the widespread adoption of AI-first operating models suggests a shift in the “marginal cost of intelligence.” As the cost of generating a high-quality analysis or a piece of code drops toward zero, the value of the human “final mile”—the judgment, the ethics, and the strategic direction—increases.
This creates a fascinating economic paradox: while AI may reduce the need for certain types of labor, it increases the premium on high-level cognitive skills. We are likely to see a redistribution of value within the corporate hierarchy, where the ability to prompt, guide, and audit AI becomes the most valuable skill set in the organization.
the shift in operating models will likely lead to a consolidation of market power. Companies that successfully transition to AI-first models can operate with significantly lower overheads and faster pivot speeds than their legacy competitors. This “efficiency gap” could lead to rapid market share shifts, as AI-native companies can undercut incumbents on price while providing a superior, personalized experience.
Key Takeaways for Global Business Leaders
For executives attempting to navigate this transition, the following points summarize the core challenges and opportunities presented by the shift in operating models:
- Move Beyond the “Tool” Mindset: Stop asking “What can AI do for us?” and start asking “How would we build this company if AI were the primary worker?”
- Reevaluate Value Capture: If your revenue model is based on hours worked, it is at risk. Transition toward value-based or outcome-based pricing models.
- Prioritize Cognitive Upskilling: Focus training not on how to use a specific AI tool, but on the critical thinking and orchestration skills required to manage AI systems.
- Build a Data Foundation: AI is only as effective as the data it accesses. A shift in operating model requires a shift in data architecture—moving from siloed databases to a unified, AI-ready data fabric.
What Happens Next?
The trajectory of AI integration is moving toward “agentic” workflows, where AI agents do not just suggest actions but execute them autonomously across different software systems. This will further accelerate the change in operating models, as the need for human intervention in routine cross-departmental processes vanishes.
The next major checkpoint for the industry will be the upcoming quarterly earnings reports from the major global system integrators, where the market will look for concrete evidence of how “AI-first” strategies are translating into revenue growth and margin expansion. The ongoing discussions at the World Economic Forum regarding AI governance will likely provide a framework for how these new operating models can be implemented ethically and sustainably.
As we witness the dismantling of legacy business structures in favor of AI-driven agility, the question is no longer if your business model will change, but how quickly you can lead that change. I invite you to share your thoughts in the comments: Is your organization treating AI as a tool or as a new foundation?