Published on: September 10, 2026
For decades, brands earned a place on the shortlist by winning a person’s attention. Now a fast-growing share of Indian consumers hand that job to an AI model instead, and the model was never in the room for your last ad campaign. It only sees what you built long before anyone typed a query: trust, structure, credibility. The brands that show up were legible to a machine months ago.
In this exclusive conversation, Anshul Garg, Managing Partner and Head of AI Experiences & Solutions (AI-X) at PDX India, part of Publicis Groupe India , argues that the same shift rewriting brand discovery is now rewriting how boards fund AI itself, as a P&L decision, not a marketing one.
Accenture finds nearly 30% of Indian consumers now use AI models as their first discovery channel. What happens to brand preference when the shortlist is built by a machine?
We are witnessing the biggest shift in consumer discovery since the advent of search. For years, brands competed for attention; now, they must compete for AI recommendation.
“As consumers increasingly rely on AI to discover products and services, the first shortlist is no longer built by people, it’s built by machines.”
AI models don’t respond to advertising alone; they respond to credible, structured, and contextual signals. They assess relevance, trust, expertise, customer sentiment, and product information before surfacing recommendations. This fundamentally changes how brand preference is created.
In the AI era, success will depend on whether a brand is machine discoverable, machine understandable, and machine recommendable. That’s why capabilities such as Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) are becoming strategic imperatives.
The competitive battleground is shifting from share of voice to share of recommendation. Brands that build the strongest ecosystem of trusted data, authoritative content, and digital credibility will increasingly influence consumer choice before traditional marketing even enters the equation.
AI is being sold as the fix for unit economics. With acquisition costs still climbing, which line of the P&L does it actually move?
The biggest misconception is that AI is simply a cost optimisation tool. In reality, it is a growth multiplier. The most successful organisations are using AI to transform both sides of the business: driving revenue while improving operational efficiency.
On one hand, AI enables hyper-personalisation, improves conversion, accelerates decision-making, and unlocks new revenue opportunities. On the other, it streamlines operations, automates workflows, and enhances productivity at scale. The real value lies not in doing the same work faster, but in fundamentally changing how businesses grow and compete.
As AI adoption matures, organisations must also recognise that AI has its own economics. Compute, model inference, orchestration, and token consumption are becoming strategic cost considerations. Just as cloud optimisation became a business discipline over the last decade, AI optimisation will define the next. The winners will be those who maximise business outcomes per AI investment, not just AI adoption.
You’ve partnered boards on growth for a decade. What does a CEO want from AI that a marketing brief usually leaves out?
The biggest difference is perspective. A marketing brief asks, “How can AI improve customer engagement?” A CEO asks, “How can AI create enterprise value?”
Today’s boards aren’t investing in AI for incremental productivity they’re investing in it to build a sustainable competitive advantage. They expect AI to accelerate decision-making, improve execution, unlock new growth opportunities, and make the business more agile and resilient.
We’re seeing AI evolve from a functional capability to an enterprise decision layer, informing everything from customer experience and pricing to operations, supply chains, and resource allocation. The conversation has moved beyond automation to transformation.
“The real measure of AI success isn’t how many copilots you’ve deployed, it’s how much better your business performs.”
CEOs evaluate AI through outcomes like revenue growth, profitability, speed to market, and decision quality. Ultimately, the question is no longer “Where can we use AI?” but “Where can AI create the greatest strategic advantage?”
You’ve worked across industrials and energy, in India and Europe, not only consumer brands. Where is AI landing faster outside consumer?
While consumer AI dominates the headlines, some of the most transformative AI adoption is happening in industrial and asset-intensive sectors. The reason is simple, AI delivers measurable business outcomes where complexity is highest.
We’re seeing AI move beyond operational efficiency to become a commercial growth engine. It is helping businesses improve demand forecasting, optimise pricing, prioritise high-value opportunities, personalise B2B engagement, and deliver more proactive customer experiences.
More importantly, AI is enabling industrial organisations to shift from selling products to delivering outcomes. By connecting commercial, operational, and customer data, AI helps businesses make faster, smarter decisions across the entire value chain from demand generation to after-sales service.
The difference is that success is measured in business impact, not engagement metrics. Whether it’s higher sales productivity, better asset utilisation, improved margins, or stronger customer retention, the ROI is tangible and measurable. That’s why AI adoption in these sectors is accelerating not because it’s innovative, but because it’s delivering real competitive advantage.
AI-X was built around the side to bottom funnel. What does AI change about earning loyalty that it doesn’t change about winning attention?
AI is changing both attention and loyalty, but the real competitive advantage lies in loyalty. Attention is becoming increasingly commoditised. Today, almost every brand can use AI to generate content, optimise media, and personalise campaigns at scale. Those capabilities are quickly becoming table stakes.
Loyalty, however, is built long after the first click or purchase. It comes from consistently delivering relevant, seamless, and personalised experiences across every customer interaction. That’s where AI creates lasting value.
At PDX, we’ve built AI-X around the mid-to-bottom funnel because this is where sustainable growth is created. AI enables brands to anticipate customer needs, recommend the next best action, empower sales and service teams with real-time intelligence, and orchestrate connected experiences across the customer lifecycle. The future of competitive advantage won’t be defined by who creates the most content, it will be defined by who builds the most intelligent customer relationships.
“In an AI-first world, attention can be automated, but loyalty must be earned.”
AI is the engine that helps brands earn it consistently and at scale.
What did running a P&L teach you that now decides where you deploy AI?
Running a P&L changes the AI conversation from technology to value creation. Every investment competes for capital, so the first question isn’t “Where can we deploy AI?” it’s “Where can AI create the greatest business impact?”
That means prioritising AI where it unlocks growth, improves margins, accelerates decision-making, or strengthens customer relationships not simply where it’s easiest to automate.
“Automation delivers efficiency, but better decisions create competitive advantage.”
That’s where AI creates disproportionate value by helping organisations make smarter commercial and operational decisions at speed and scale.
Ultimately, AI isn’t just a technology investment; it’s a business and capital allocation strategy. The organisations that lead won’t be those deploying the most AI, but those deploying it where it delivers the highest and most sustainable enterprise value.
