We’ve built a digital economy on a fundamental misconception: that reaching someone’s eyes is the same as capturing their decision. For the past decade, we’ve optimized for impressions, clicks, and views, the metrics that flatten the richness of human attention into binary events. Today, with 886 million Indians online and India’s digital economy projected to contribute nearly one-fifth of national income by 2030, we can no longer afford this reductionism. The emerging challenge is not access to attention; it is the ability to translate attention into outcomes and more critically, to build operating systems where this translation happens systematically, at scale, and with trust as the foundation.
The attention economy in India is no longer nascent. It now contributes approximately 2.3% of GDP, with projections reaching 4.5% by 2030. But scale obscures complexity. India added 238 million online shoppers by the end of 2025, with 28 million new consumers joining since 2024. Gen Z, who are representing 40% of e-commerce shoppers, spends 1.5 times more on lifestyle, beauty, and electronics than older cohorts, and discovers products almost entirely through social media, video content, and influencers. They spend an average of 4 hours daily on smartphones, where 58% of their internet time occurs. Yet enterprises continue measuring success through impressions and click-through rates. The misalignment between how consumers engage and how we measure that engagement is not a measurement problem; it is a strategic problem.
Attention: From Eyeballs to Outcomes
The first myth we must dispel is the false equivalence between attention and effectiveness. Recent research from India’s largest multi-platform attention study reveals a sobering reality: a mere 5% increase in genuine attention (defined as true visual focus, not mere exposure) can yield up to 2X gains in brand perception. More provocatively, attention metrics are 8X better than view-through rates at predicting brand recall, and 4X better at predicting brand favorability. This is not a marginal improvement. This is a structural shift in how we should measure digital effectiveness.
But here is the critical nuance: not all attention is equal.
Fleeting attention = under one second,builds recall.
Sustained attention = between three and nine seconds,creates deeper cognitive engagement and confidence.
Beyond nine seconds, the return diminishes. For India’s Gen Z, this means a well-crafted Instagram Reels introduction captures attention in the critical first two seconds. A YouTube pre-roll ad that respects the viewer’s context and intent sustains it through the critical nine-second window. A WhatsApp message that answers a specific question seals the outcome.
Most digital transformation programs in India still rely on funnel-based attribution,a methodology designed for simpler, linear customer journeys. In reality, the Indian digital consumer operates across 5-6 touchpoints before conversion, often across multiple devices and contexts. Traditional last-touch attribution assigns all credit to the final interaction, rendering earlier moments invisible and consequently under-invested. The solution lies in multi-touch attribution models augmented by machine learning,models that assign probabilistic credit to each touchpoint based on its actual contribution to the conversion outcome.
For enterprises, this requires three operational shifts.
Operating Models: From Experiments to Embedded AI
Here is where many digital transformation narratives break down. Enterprises run GenAI pilots and show promising ROI in controlled sandboxes. They celebrate in board meetings. And then they struggle to scale. Why? Because pilots operate in isolation, and scaling requires organizational restructuring that goes far beyond technology.
The data is stark. According to recent Ecosystem research commissioned by Snowflake, 77% of Indian enterprises cite proving return on investment as their biggest challenge in AI deployment. Even more telling: only 23% of Indian organizations have fully integrated AI into their business strategy, despite significant pilot activity. This reveals a chasm between experimentation and enterprise-wide deployment. Among global enterprises with India operations, 92% are piloting or scaling AI initiatives,yet 70% lack structured frameworks to measure ROI.
An AI-powered operating model is architecturally distinct from one merely augmented with AI tools. In a traditional model, humans execute sequentially:
collect data → analyze → decide → execute.
In an AI-augmented model, machines assist:
AI analyzes → human validates → human decides → AI executes.
But in an AI-powered model, the structure inverts:
AI provides options with confidence scores → human validates high-confidence paths → AI executes most paths autonomously → humans intervene only on exceptions.
This requires flatter organizations, cross-functional ownership of outcomes, and genuine data maturity.
The data readiness crisis is non-negotiable. Sixty percent of Indian enterprises cite data quality as a roadblock; 54% cite security, and 50% cite accessibility. Organizations that use DPDP Act compliance as a catalyst to unify and govern their data architectures will emerge stronger. Those that treat it as a checkbox exercise will find their AI initiatives brittle and unreliable.
The investment reality is sobering: 95% of organizations allocate less than 20% of their IT budgets to AI. This gap between conviction and commitment is becoming the defining factor in how quickly enterprises extract measurable returns. Yet the imperative is clear. According to EY’s AI research, enterprises are moving away from measuring AI success purely through cost reduction and productivity metrics, toward a five-dimensional ROI model: time saved, efficiency gains, business upside, strategic differentiation, and resilience. Only 8% of Indian enterprises can measure AI ROI today,which means first-movers have an asymmetric advantage.
Trust: The Operating System of Scale
For years, we’ve discussed trust as a consumer issue,how do we build trust so customers share data and transact? This framing has inverted. Trust is now an operational issue. It is the infrastructure enabling AI-powered organizations to function reliably. It is the mechanism allowing enterprises to deploy algorithms to millions of decisions without organizational collapse.
The Digital Personal Data Protection Act, which entered the enforcement phase in 2025, is widely viewed as a compliance burden. In reality, it is a blueprint for operating model change. The Act’s core mandates,explicit consent, breach notification, data retention limits, and accountability,require enterprises to map data flows, classify processing, and establish clear ownership. When you do this work, you simultaneously build infrastructure for responsible AI. Organizations face penalties up to INR 250 crore for non-compliance, but the real value emerges operationally: enterprises with clear data governance and explainable AI suffer fewer customer disputes, regulatory actions, and algorithmic failures.
India’s AI Governance Guidelines (2025) extend this thinking, requiring model documentation, explainability for critical algorithms, and human-in-the-loop validation. These sound bureaucratic, but they force discipline that reduces operational risk. Organizations that document their AI systems, audit them for bias, and maintain human oversight are less likely to face regulatory scrutiny or customer backlash. [linkedin]
The Integrated Imperative
These three imperatives, attention-to-outcome measurement, AI-powered operating models, and trust architecture,form a cycle. Strong measurement reveals which customers are valuable and why. This intelligence feeds AI systems optimizing for those customers. Those systems operate with transparency that maintains trust. This generates performance data that feeds back into measurement, creating continuous refinement.
India’s digital economy by 2030 will not be built by companies best at capturing attention. It will be built by organizations best at converting attention into outcomes, doing so systematically through AI, and maintaining trust as their operating system.
The path is clear. The imperative is now. The time to build is today.
Note: This content was created by the author in an individual capacity. It is not affiliated with, nor does it represent the views of, the author’s employer or any third-party brands to which they provide services.
