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‘Mindful Marketing’ is a series of pocket cartoons that apply the lens of humour and sarcasm to amplify the prevalent (mis)practices that hamper organizations in their marketing, branding and other initiatives.

I began this endeavour in early 2022 in collaboration with Arun Ramkumar, a cartoonist and brand designer. These cartoons are loved by the business community and widely shared in social media across the world.

 
 
 
 
 
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Highly rated Keynote Speaker
and Marketing Strategist.

Rajesh Srinivasan is a Modern Marketing Strategist, 2x Author and a Tedx Speaker. His mission is to Turn Organizations into Centres of Marketing Excellence.

A sought-after keynote speaker, Rajesh has delivered more than 150 speeches, workshops and mastermind sessions in the last five years and positively impacted more than 4500+ industry leaders.

As a Marketing strategy consultant, Rajesh works with the CEOs and business heads of start-ups and fast-growing companies and supports them in their go-to-market, brand positioning and growth strategy. He helps organizations take crucial decisions in innovation, new product development, creative, content development and media strategy.

Rajesh has delivered keynote sessions at the business conclaves like World Marketing Congress & The Economic Times Marketing Leaders’ Summit. He has been appointed as one of the Jury Board members for the Economic Times – Most Promising Tech Marketers’ Award – 2020 & 21.

Featured on: Business Today, The Week, India Today, and Business Standard.

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May 22
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The Long Tail of Brands – How the Aggregate of the Small Brands Is Rewriting the Rules of Market Share

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A Researcher’s Starting Point

I have spent over two decades studying brands — how they are built, how they die, and increasingly, how they are being disrupted in ways that traditional brand theory never fully anticipated.

What has fascinated me most, as both a practitioner and a researcher, is the speed at which the brand-building playbook has been rewritten in India over the last three to four decades. I entered this field when mass media was the unquestioned engine of brand equity. Television advertising was the moat. Distribution muscle determined winners. The FMCG giants — from Hindustan Unilever to ITC — had cracked a system that seemed almost impenetrable to any challenger without deep pockets and a patient balance sheet.

That system has not collapsed. But it is under a pressure that is structural, cumulative, and far more interesting than most brand commentary acknowledges.

In my research, I began noticing a pattern: a new class of brands was emerging — not one type, but four distinct operating models, each drawing from a different source of advantage. None of them individually threatens the established giants. But collectively, they are doing something that the Long Tail principle in economics predicts with precision: the aggregate of the niche is eating into the share of the few hits. I call this the Long Tail of Brands.

The aggregate of the niche is eating into the share of the few hits. This is the Long Tail of Brands.

The Scale of the Disruption: Key Data

$80B+

India D2C Market GMV (2024)

 $6–7B

Quick Commerce GMV (2024)

 52%

Indian consumers buying private labels (EY, 2025)

 

3.5–4.5M

Influencer creators in India

 ₹68.75B

Influencer mktg size by 2025

 85–90%

FMCG still sold offline (NielsenIQ)

The Principle That Explains Everything

In 2004, Wired editor Chris Anderson introduced a concept that would quietly revolutionize how we think about markets. He called it the Long Tail. His argument, drawn from the economics of digital distribution, was deceptively simple: in any market, the combined revenue of thousands of niche products, each selling in small quantities, can rival and eventually exceed the revenue of a handful of bestselling hits. Netflix understood it. Spotify built a business on it. Amazon embedded it into its architecture.

Twenty years later, the Long Tail has migrated from content to commerce. And nowhere is its disruption more visible, more structurally consequential, and less understood than in India’s fast-moving consumer goods landscape.

The hits are Hindustan Unilever, ITC, Marico, Dabur, Procter and Gamble. The tail is everything else: the D2C challenger selling turmeric latte mixes on Amazon, the Reliance Smart private label occupying eye-level shelf space, the Blinkit-born brand that exists only in the dark commerce supply chain, and the food creator on YouTube who sells his own line of knives to millions of subscribers who already trust his palate. Individually, none of these is a category threat. Collectively, they are a structural redistribution of market share.

The Architecture of the New Long Tail

To understand the disruption, it helps to map its four distinct operating models. Each draws from a different source of competitive advantage, and each exploits a different vulnerability in the established brand’s armour.

 

Model Source of Advantage Core Risk Scale Potential
D2C / Marketplace Zero-friction distribution on Amazon/Flipkart/Meesho No mental availability; high brand mortality High volume in aggregate; low individually
Retailer Private Label Distribution dominance + margin incentive + staff push Dependent on retailer staying relevant Highest structural threat — grows with retail
Q-Commerce Native Brand Dark store speed; urban convenience demand Platform dependency; platform may launch rival label Limited to urban q-commerce user base
Influencer / Creator Brand Parasocial trust; pre-built community; low CAC Audience loyalty tied to one person’s reputation Fast launch; harder to scale without diluting trust

1. The D2C Availability Play

India’s direct-to-consumer brand ecosystem has moved with remarkable speed. A KPMG report valued the Indian D2C market at approximately $12 billion in 2022, projecting it to surpass $60 billion by 2027 at a CAGR of 40 per cent. What is more striking is that this projection has already been overtaken by reality — the market crossed the $80 billion mark in 2024 and is on track to exceed $100 billion by 2025.

The numbers are impressive. The strategic reality is more nuanced, and this is where I find most commentary gets it wrong.

Most Indian D2C brands are not brand-building exercises. They are distribution arbitrage plays. The entry barrier that once protected established brands — physical distribution reach — has been digitally dissolved. What remains missing for most of these brands is mental availability: the brand’s salience and ease of recall at the moment of purchase. Performance advertising generates transactions. It does not build brands.

D2C Market: Key Numbers (Sources: KPMG, IBEF, D2CStory)
  • Indian D2C market valued at ~$12B in 2022 (KPMG Report)
  • Crossed $80 billion GMV in 2024 — surpassing earlier projections
  • Projected to exceed $100 billion by 2025 (D2CStory Research)
  • Over 800 D2C brands in India as of 2024 (Indian Retailer)
  • Growing at 40% CAGR — 3x the broader retail market growth rate

2. The Retailer’s Private Label Muscle

The second strand of the Long Tail is more structurally dangerous than the D2C wave, because it combines distribution dominance with margin incentive. This is the disruption I find most underappreciated in strategic discussions about FMCG.

Costco’s Kirkland Signature is the starkest illustration of what private label can become at scale. Kirkland-brand items generated $56 billion in revenue in fiscal year 2022-23 — a level that would make it, as a standalone company, larger than Nike, Coca-Cola, and United Airlines (Fortune). By 2025, Kirkland Signature had grown to $90 billion in sales, covering over 600 products and accounting for roughly a third of Costco’s total revenue.

In India, private labels currently constitute approximately 10-12 per cent of organized retail. But the EY India Retail Report (2025) signals the acceleration: 52 per cent of Indian consumers are now opting for private label products, with 70 per cent acknowledging that these brands are increasingly offering better quality. Crucially, 74 per cent of consumers say private label options are more prominently displayed — often at eye level — reflecting a deliberate strategic push by retailers.

Reliance Retail — which officially operated 19,340 stores as of March 2025, nearing 20,000 — Apollo Pharmacy, and MedPlus are not merely distribution channels. They are brand builders operating at the exact moment of highest purchase intent.

Private Label: India vs Global (Sources: Indian Retailer, EY, Fortune)
  • India private label: ~10-12% of organized retail (Indian Retailer, 2023)
  • 52% of Indian consumers buying private labels in 2025 (EY Report)
  • 70% say private label quality has improved significantly (EY Report)
  • Private labels = 90% of India apparel retail; 40% of online grocery (Indian Retailer)
  • Kirkland Signature: $90B in sales in 2025 — larger than Nike (Fortune / Tasting Table)

3. Quick Commerce and the Platform Trap

India’s quick commerce sector has expanded with extraordinary velocity. The industry’s GMV rose from $500 million in FY 2021-22 to $3.34 billion in FY 2023-24, growing at an annual rate of 73 per cent (Chryseum Report via India Briefing). By 2024, quick commerce GMV had reached $6-7 billion, accounting for two-thirds of all e-grocery orders and approximately 10 per cent of e-retail spend in India.

Blinkit leads the market with approximately 45-46 per cent share, processing roughly 600,000 daily orders. Swiggy Instamart follows with 25-27 per cent and Zepto holds approximately 21-29 per cent (DemandSage, 2026).

The strategic vulnerability of q-commerce native brands, in my assessment, is structural dependence. A brand that exists only on Blinkit has surrendered its distribution destiny to Blinkit. Blinkit’s parent already has an inventory-led pivot that supports private label plans. When the platform becomes a competitor in your category, a single-channel brand has no alternative to absorb the volume loss. Amazon did the same with Amazon Basics. The platform playbook is always the same.

Quick Commerce: Market Reality (Sources: India Briefing, DemandSage, Mordor Intelligence)
  • GMV: $500M (FY22) → $3.34B (FY24) → $6-7B (2024) — growth rate: 73% annually
  • Blinkit: ~45-46% market share; ~600,000 daily orders
  • Swiggy Instamart: 25-27% | Zepto: 21-29% (DemandSage, 2026)
  • Market projected to reach $9.95 billion by 2029 (India Briefing)
  • Blinkit’s parent has active private label pivot underway (Mordor Intelligence, 2026)

4. The Influencer Brand: Borrowed Trust, Real Revenue

The fourth strand of the Long Tail is the most psychologically sophisticated, because it inverts the conventional sequence of brand building. And it is the one I find most fascinating as a researcher.

Traditional brand building follows a known arc: invest in mass communication, build salience and recall, convert that recall into purchase behaviour at the point of availability. The time horizon is long. The spend is enormous: HUL’s advertising and promotion expenditure in FY24 was Rs 6,489 crore — a 32 per cent jump year on year (HUL Financial Results, FY24). The influencer brand compresses this arc to near zero, because it begins from an already established trust relationship.

I know of a chef with a YouTube channel and a large subscriber base who developed his own line of kitchen equipment including knives. He did not need a marketing campaign. He needed only a product that met the expectation his years of content had already set. His audience had watched him use a knife, describe its weight, explain why blade angle matters. When he launched his own line, he was converting a community into a customer base. This is what the marketing literature calls parasocial trust, and it is enormously powerful.

Globally, the creator economy reached $205 billion in 2024, projected to pass $250 billion in 2025. Goldman Sachs estimates it could reach $480 billion by 2027. In India, the influencer ecosystem spans 3.5 to 4.5 million creators (Kofluence, 2024-25). The influencer marketing industry in India is projected to reach Rs 68.75 billion by 2025 — a 439 per cent growth from Rs 12.75 billion in 2022 (GrabOn / EY data).

Creator Economy: Key Numbers (Sources: Goldman Sachs, GrabOn, Kofluence, Grand View Research)
  • Global creator economy: $205B (2024) → projected $250B+ (2025) (Grand View Research)
  • Goldman Sachs: creator economy to potentially reach ~$480B by 2027
  • India influencer market: ₹12.75B (2022) → projected ₹68.75B by 2025 (+439%)
  • India’s influencer ecosystem: estimated 3.5–4.5 million creators (Kofluence 2024-25)
  • Instagram hosts 1.8–2.3M Indian creators; YouTube: 500K–700K (Kofluence)

Why the Aggregate Matters More Than the Individual

The strategic error conventional brands make is evaluating each of these challengers individually and finding them manageable. In my research, this is the single most important blind spot I observe in how FMCG strategists are framing the competitive landscape.

If 200 D2C brands each take 0.2% of category revenue, the established market leader has lost 40 percentage points of aggregate share. The loss is invisible at the individual level. It is existential at the aggregate level.

The established brand’s volume stagnates. Its production cost per unit rises. Its ability to fund the advertising that sustains mental availability erodes. The cycle is not dramatic. It is slow, structural, and very difficult to reverse. This is the Long Tail’s real mechanism: not disruption through a single challenger, but attrition through a thousand small ones.

Why the Consumer Is Complicit

India’s Gen Z population — those born between 1997 and 2012 — numbers 377 million, making it the largest generation ever to live in India (BCG + Snap Inc., October 2024). This cohort is entering peak consumption age this decade. They did not grow up in a media environment where brand preference was shaped by television advertising watched as a household event. They formed preferences through YouTube content, Instagram Reels, and creator communities.

A 2024 SheerID survey of Indian Gen Z students found that 74 per cent discover new brands through social media, while 37 per cent learn about new brands through peer recommendations. The influencer’s recommendation and the peer review carry more authority with this cohort than the 30-second commercial.

The brand that spent decades building mental availability through mass media finds that the mental space it occupied in the millennial consumer’s mind was simply never constructed in the Gen Z consumer’s mind in the first place. This is not a communication problem. It is an architecture-of-attention problem — and in my view, it is the most consequential shift the FMCG industry has not yet fully reckoned with.

74%

India Gen Z discover brands via social media (SheerID 2024)

 37%

Learn of new brands through peer recommendations

 40%

India Gen Z would switch brand loyalty for a better offer

The Enduring Advantage the Conventional Brand Still Holds

Having outlined the disruption at length, let me be equally direct about what the analysis does not mean. It is not a death notice for established brands.

Physical distribution in India remains the most defensible moat in consumer goods. India has approximately 13 million traditional kirana retail outlets spread across urban, semi-urban, and rural geographies (AICPDF / Business Standard, 2024). Offline channels — traditional trade and modern trade combined — account for over 85% of FMCG sales at the all-India level, with e-commerce still limited to 11-13% even in metros (NielsenIQ, Q1 2025). This is not a transitional figure that will normalize toward a 50-50 split in the near term.

HUL’s products are sold in around 9 million retail outlets across the country — a figure cited on Unilever’s own website — a reach built over fifty years that no marketplace listing can replicate. It is a physical infrastructure that took decades and billions of rupees to construct. The entry barrier online is low. The entry barrier offline is not. This asymmetry is the conventional brand’s most durable strategic advantage — and the one most underweighted in brand disruption commentary.

There is also the matter of customer equity. The established FMCG brand carries decades of accumulated trust. When a consumer in a Tier 2 town reaches for a Dove soap, she is not executing a considered rational decision. She is completing a habit loop formed by consistent product experience over years. That habit loop is not broken by the existence of a D2C product on a platform she does not regularly use.

The Offline Moat: Why It Still Holds (Sources: NielsenIQ, HUL Investor Reports)
  • Offline channels (traditional + modern trade) account for 85%+ of FMCG at all-India level (NielsenIQ Q1 2025)
  • India has approximately 13 million kirana retail outlets (AICPDF / Business Standard, Oct 2024)
  • HUL products sold in around 9 million retail outlets nationwide (Unilever.com, primary source)
  • Online shoppers = ~332 million out of 1.4B population (IAMAI-Kantar 2024)
  • Physical distribution remains the highest entry barrier in FMCG

The Intelligent Response: The Two-Board Game

The question for established brands is not whether to take the Long Tail threat seriously. The question is where to compete and how to adapt without abandoning the structural advantages that still define their position.

HUL’s investment in influencer marketing and category-specific creator programs is not a capitulation to the new model. It is an extension of the established brand’s mental availability machinery into the spaces where the next generation of consumers is forming preferences. The brand that waits for Gen Z to migrate to television is not a strategist. It is a spectator.

The intelligent conventional brand plays what I call a Two-Board Game:

Board 1: Protect the Physical Moat Board 2: Win the Digital Mind
Deepen kirana and general trade penetration Invest in creator partnerships and influencer content
Defend eye-level shelf presence vs private labels Build brand salience in short-form video formats
Train trade channels to counter staff push of private labels Develop Gen Z-native community touchpoints
Expand rural distribution beyond Tier 2 cities Use data from D2C experiments to inform product innovation

Building mental availability through creator content is a legitimate evolution of the advertising function. It does not mean abandoning physical distribution in favour of e-commerce-first strategies — which is the mistake some mid-size FMCG players have made while chasing digital metrics and allowing their general trade presence to erode quietly.

A Quick Summary

The Long Tail of brands is real, growing, and consequential. D2C brands competing on availability, retailers leveraging private labels, q-commerce native brands dependent on platform goodwill, and influencer-founded labels converting parasocial trust into purchase intent are collectively exerting a structural share pressure on established FMCG brands that no single challenger could achieve alone.

Long Tail Strand Threat Level Conventional Brand’s Best Defence
D2C Marketplace Brands Medium — aggregate clutter erodes share Mental availability through advertising + offline distribution
Retailer Private Labels High — combines shelf, margin & staff push Customer equity, product quality, and trade partnerships
Q-Commerce Native Brands Medium — platform-dependent and fragile Own physical distribution; q-commerce is a niche slice of total FMCG
Influencer / Creator Brands Medium-High for Gen Z categories Adopt influencer marketing; build creator partnerships at scale

 

After thirty years of studying how Indian brands are built and broken, my conclusion is this: the brands that will lose are not the ones facing Long Tail disruption. They are the ones that mistake disruption for inevitability and stop competing.

 

About the Author

Rajesh Srinivasan is a brand and marketing strategy consultant, 4x Amazon Bestselling author of Mindful Marketing, and keynote speaker with over two decades of experience across India, the US, and Asia. He has worked with clients including Tata Steel (Durashine), ITC Ltd., Kangaro Group, and holds an IIM Lucknow Executive Management qualification (Batch Topper). He is a recipient of CMO Asia’s Most Admired Marketer 2025 and served on advisory and jury panels at Economic Times Tech Marketers Awards, and Realty Plus.

 

Mar 30
0

Becoming an AI Strategist: How to Amplify Your Experience Without Surrendering Your Thinking

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Becoming an AI Strategist: How to Amplify Your Experience Without Surrendering Your Thinking

For most of my career, expertise felt like a form of accumulation. Years of pattern recognition, intuition sharpened through repetition, judgment earned through failure — these were the currencies that mattered. Then generative AI arrived, and with it came a question that I suspect unsettled many experienced professionals more than they were willing to admit publicly: Will this dilute what I’ve spent years building?

It is a reasonable fear, and I want to take it seriously rather than dismiss it with the kind of breathless optimism that dominates most AI discourse. The concern isn’t irrational. We have watched enough technological waves wash over industries to know that tools don’t simply augment; they sometimes displace. And for those of us whose value proposition has always rested on depth of thinking, nuance of judgment, and the ability to read a room that no data set can fully capture, the arrival of systems that can produce fluent, confident-sounding output in seconds is genuinely worth interrogating.

What I have discovered, however, through sustained use rather than casual experimentation, is that the fear, while understandable, is built on a misreading of what AI actually does in the hands of someone who brings real intellectual capital to the interaction. AI does not replace strategic thinking. It amplifies it. But that amplification is entirely conditional on what you bring through the door.

The Gap Nobody Is Talking About Honestly

If you survey the landscape of people engaging with AI today, two dominant profiles emerge, and neither is quite where the real value lies. The first group, let’s call them the tool-first users, have mastered the mechanics: they know their way around prompts, they follow the latest model releases, they can chain workflows and automate tasks with impressive efficiency. What they often lack is the business acumen, the domain depth, and the strategic context to direct those capabilities toward outcomes that actually move the needle. They are technically fluent but strategically thin.

The second group is made up of experienced domain professionals: marketers, strategists, operators, finance leaders who have spent decades accumulating exactly the kind of contextual intelligence that the first group lacks. They understand markets, they understand human behavior, they understand the particular texture of their industry. But their engagement with AI tends to be superficial: a prompt here, a summarization task there, perhaps some content assistance. They are treating a thinking partner like a search engine.

The opportunity, and I would argue the professional imperative of this decade, lies in the intersection between these two profiles. The person who can bring deep domain expertise, structured thinking, and genuine intellectual frameworks to their engagement with AI, and who understands enough about how these systems work to direct them purposefully, is operating in a category that does not yet have a widely accepted name. I have started calling it the AI Strategist, not as a job title but as a description of a mode of working.

What Changed When I Stopped Treating AI as a Tool

My own shift came through a specific, somewhat accidental discovery. Early in my experiments with generative AI, I was doing what most people do: asking questions, generating drafts, testing capabilities. The outputs were adequate but unmistakably generic, the kind of content that could have been produced by anyone asking roughly similar questions. It confirmed my initial suspicion that AI would produce a flattening effect on intellectual work.

Then I began doing something different. Instead of asking questions, I started bringing frameworks. I fed the system my mental models, my accumulated understanding of specific market dynamics, my particular way of structuring a brand problem or a consumer insight. I stopped treating it like a search query and started treating it like a briefing document for a capable but context-starved analyst.

The difference was not incremental. It was categorical. The output didn’t just improve in quality; it improved in ways that were distinctly shaped by the thinking I had brought in. It reflected my frameworks back at me, extended them in directions I hadn’t considered, surfaced implications I would have reached eventually but would have taken longer to articulate. The work was still mine. But it moved faster, cut deeper, and covered more ground than I could have managed working alone.

That is when the underlying principle became clear to me: AI without context is a generative engine pointed at nothing in particular. AI with deep, structured, expert context becomes something considerably more interesting, a genuine thinking partner that extends the range of what a single expert can accomplish.

This discovery did not remain a private intellectual exercise for long. I began integrating it directly into my client consulting engagements, not as a productivity shortcut layered onto existing work, but as a genuine strategic layer within the work itself: market analysis, brand positioning, consumer intelligence. The results were sufficiently compelling, and the pattern sufficiently repeatable, that I have since formalized this thinking into a dedicated practice: AI Strategy workshops for business leaders and domain experts who want to move beyond surface-level engagement with these tools and start directing them toward real business outcomes. What I have consistently found, both in client work and in workshop rooms, is that the bottleneck is almost never the technology. It is almost always the absence of structured thinking and rich context on the human side of the interaction. Fix that, and the tools perform at a categorically different level.

The Architecture of an AI Strategist’s Work

What distinguishes the AI Strategist from the casual user is not technical sophistication, though some technical literacy helps. It is the discipline of bringing structured thinking to every interaction, treating each engagement with the system as you would treat a strategic brief rather than a search query.

Context is the fundamental input, and it encompasses far more than most people realize. It includes your understanding of the industry, yes, but also the specific constraints of the business you’re working on, the customer profile you’ve developed through years of direct engagement, and crucially, the mental models and frameworks through which you’ve learned to interpret market signals. A prompt stripped of this context will produce output that is, at best, a competent generic response. A prompt saturated with it will produce output that carries the distinctive shape of your thinking.

Structure matters just as much. The instinct of most users, particularly early on, is to ask vague questions and hope for insight. The AI Strategist operates more like a research director commissioning an analysis: the problem is defined precisely, the constraints are made explicit, the desired output is specified not just in form but in the level of analytical depth required. This is not a technical skill. It is a thinking skill, the same skill that separates a good strategic brief from a muddled one.

And then there is the question of systems rather than one-off interactions. The most sophisticated practitioners are not having individual conversations with AI; they are building repeatable workflows that encode their expertise, their context, and their frameworks into the architecture of the system itself. The difference is the difference between hiring a consultant for a one-time engagement and building an institutional capability.

A Concrete Illustration

Let me make this tangible rather than theoretical. I built what I’ve taken to calling a Customer Insights Generator, a system designed to surface the kind of deep, unfiltered consumer intelligence that traditional research methods often miss or sanitize. It draws on publicly available customer reviews and social media signals, pulled through appropriate tools, and layers that raw material against internal customer data and, critically, against my own frameworks for interpreting consumer behavior.

The output is not a summary of what customers said. It is an analysis of what customers mean: the frustrations beneath the stated complaints, the desires driving the purchase decisions, the language patterns that reveal how they actually think about a category rather than how they respond to a survey question. For a brand making a product decision, the difference between those two levels of insight is often the difference between a successful launch and an expensive miss.

This is not a tool doing the strategic work. The strategic architecture, the decision about which signals to prioritize, the frameworks for interpretation, the business questions that the system is designed to answer, all of that reflects accumulated judgment. The AI extends the reach of that judgment. It does not substitute for it.

The Transition That Is Actually Underway

There is a broader shift happening in professional work that I think deserves to be named directly. We are moving, across disciplines and industries, from a model where individual expertise is expressed through the direct execution of tasks to a model where expertise is expressed through the design and direction of intelligent systems. The craftsman analogy that governed knowledge work for most of the 20th century, mastery demonstrated through doing, is being supplemented by something closer to the conductor model: mastery demonstrated through orchestration.

This is not a diminishment of expertise. If anything, it raises the stakes for genuine depth. Generic knowledge, easily retrieved by anyone with a search engine, was already under pressure before AI. What AI makes newly valuable is the kind of contextual, experiential, framework-grounded judgment that cannot be prompted out of a system that doesn’t have it. The professional who brings twenty years of hard-won domain understanding to their AI interactions will consistently produce work that someone with two years of experience and the same tools simply cannot replicate. The leverage is real, but it runs in the direction of depth, not around it.

Where to Begin

For those who recognise themselves in this argument but feel uncertain about where to start, my honest advice is to resist the impulse to master the technology before applying it to real work. The practitioners who are getting the most out of AI are not the ones who studied it most thoroughly in the abstract; they are the ones who brought their most pressing, most complex real problems to it and worked through them iteratively.

Pick one use case that matters: a customer insight challenge, a strategic communication problem, a market analysis that needs to happen faster than your current process allows. Bring to it the full weight of your domain knowledge and your frameworks. Structure the problem carefully before you prompt. Evaluate the output against your own judgment, push back on what rings false, build on what rings true. Do that enough times on enough real problems, and you will develop an instinct for the collaboration that no course can teach as efficiently.

The fear that AI will reduce intellectual originality comes, I am convinced, from a specific pattern of use: the passive, low-context, low-structure engagement that produces outputs which genuinely do look and feel like they could have been written by anyone. That experience is real, and it is a reasonable basis for concern. But it is not a property of the technology. It is a property of the approach.

Used with the full weight of your experience, your frameworks, and your contextual understanding brought deliberately to bear, AI becomes something quite different: a multiplier of the originality you’ve spent years cultivating rather than a substitute for it. The future of professional expertise will not belong to those who know the most tools, nor to those who refuse to engage with them. It will belong to those who understand how to bring their deepest thinking into genuine collaboration with these systems, and who have the strategic clarity to direct that collaboration toward outcomes that matter.

That, in the end, is what it means to become an AI Strategist.

Mar 23
0

The Discipline of Focusing on Leading Indicators

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A few years ago, I was speaking with a farmer in my home town. It was just before harvest season, and I asked him a seemingly obvious question: “Are you expecting a good yield this year?”

He smiled and said something that stayed with me.

“If I start worrying about the harvest now, I’m already too late.
The harvest was decided months ago—when I chose the seeds, prepared the soil, and managed the water.”

That insight captures a fundamental truth about business.

Most of us fixate on the harvest.
Very few obsess over the soil.

Let me explain.

Outcomes vs Drivers

In business, market share, revenue, brand equity, customer satisfaction—these are lagging indicators. They are outcomes (harvest). They reflect decisions and actions that have already played out.

Yet, they dominate most leadership conversations.

But the most effective business leaders I’ve worked with—as a strategy consultant and board advisor—operate differently. They focus on leading indicators: the underlying levers that produce those outcomes.

They don’t chase the number.
They design the system that makes the number inevitable.

A Simple Example: The Illusion of “Chasing ₹250 Crore”

Take a common ambition:
“We want to reach ₹250 crore in revenue.”

On the surface, it sounds like a clear goal. But revenue, by itself, is not actionable. You cannot “do” revenue.

What you can do is influence the drivers that create it.

Leaders who understand this shift their focus to questions like:

  • Which market segment is structurally aligned with our product?
  • What is the specific need-state or context we are solving?
  • How do we craft a compelling narrative that resonates with that segment?
  • Is our pricing architecture aligned with perceived value?
  • Are we investing in the right media channels for efficient reach and frequency?
  • Is the product available in the right distribution channels, at the right moments of consumption?

These are leading indicators.

When these are designed well, revenue is no longer a target to chase—it becomes a byproduct.

What Separates Effective Leaders

The leaders who consistently outperform don’t get distracted by dashboards full of outputs.
They go one layer deeper.

They ask:

“What must be true in the system for this number to move?”

And then they work relentlessly on those variables.

This requires a different mindset:

  • From reporting numbers → to engineering drivers
  • From reviewing performance → to designing causality
  • From short-term reaction → to structural intervention

Example from Brand Management

In brand discussions, I often see teams fixated on brand awareness scores or market share shifts.

But strong brand builders don’t start there.

They focus on:

  • Distinctive brand assets (colors, shapes, cues that aid recall)
  • Category entry points (when and why consumers think of the category)
  • Consistency of messaging across touchpoints
  • Share of voice vs share of market

These are leading indicators of mental availability.

If these are built deliberately, brand recall and preference follow naturally.

Trying to directly “increase awareness” without strengthening these inputs is like trying to increase harvest without tending to the soil.

The Deeper Insight

Lagging indicators are seductive because they are visible and measurable.

But they are also too late.

By the time they move, the underlying system has already been set in motion.

Leading indicators, on the other hand, require:

  • Understanding the cause – the underlying drivers that produce the desired effect through better diagnosis
  • Then align all the tactical efforts on those drivers
  • Measure those leading indicators diligently.

This give you something far more valuable: control over outcomes.

In every business I’ve worked with, the turning point comes when leadership shifts its attention:

From: “Why are our numbers not improving?”

To: “Which drivers are we not getting right?”

That is the moment strategy begins to work.

Because in the end, outcomes are not managed.
They are designed.