How forward-thinking brands are using AI not just to move faster, but to make more money
Consumer brands have always competed on product, distribution, and marketing. The new frontier is operational intelligence — and the brands that figure out AI first will have a structural P&L advantage that compounds over time.
This isn’t a prediction. It’s already happening. Based on conversations with founders and a dedicated session with Google’s marketing team and research by world’s leading CPG companies, we’ve mapped where AI is actually moving the needle — across three distinct layers of a consumer business.
Part 1: How Brands Produce Content
The traditional model: hire more people to make more content to reach more customers. The AI model: multiply output with the same team.
One consumer health brand we spoke to, with a 7-million-subscriber YouTube channel as its primary acquisition engine, is the clearest proof point we’ve seen.
2 places where AI has made most impact:
- AI handles deep research cutting manual research time by 70%
- video editing and transcription : AI compressed the TAT from days to hours a full day’s work to seconds), and transcription.
The same team that was producing around 32 videos a month is now publishing 2,000 reels a month, 60X growth in output with no additional hiring.
But this isn’t just an isolated example. Industry data confirms the pattern: AI tools are reducing content production time by 60–80% across marketing organizations. Brands deploying AI recommendation engines are seeing 150% conversion rate increases and 50% growth in average order values in mature deployments.
Our advice to companies: be nuanced in your use of AI so that you don’t peddle AI slop. The successful brands will not replace creative judgment, they’ll use AI to eliminate everything around it, so their teams can spend more time on the work that actually requires them. Brand ethos and taste will still remain in the hands of humans, for everything else AI will accelerate production at lower costs.
Brands who are able to crack this, accelarate great content production and drive down their CACs.
Part 2: How Brands Show Up in Front of Customers
Once you have generated amazing content, you need to show up where the customers are. With the rise of chat based searches, that game is ever evolving.
Google processes over 5 trillion searches annually, and the nature of those searches is changing fast. Google Lens now handles 25 billion monthly queries. Voice search is accelerating in India-first markets. And AI-powered search is shifting the interface from query-and-scroll to decision-making assistance : consumers aren’t just looking for information anymore, they’re asking AI to help them decide.
The GEO shift. There is a massive shift happening in how organic brand building is done. The old world optimized for keywords. The new world optimizes for context. What gets cited in AI-generated answers is no longer about keyword density, it’s about whether your content demonstrates genuine Experience, Expertise, Authoritativeness, and Trust (E-A-T):
- Experience: documenting real outcomes — clinical results, customer journeys — not generic category claims
- Expertise: citing qualified professionals who validate your claims
- Authoritativeness: building credibility across a content ecosystem, not a single channel
- Trust: transparent pricing, honest practices, clear contact information
The content that drove brand-building over the last decade, lifestyle photography, punchy captions, awareness campaigns, is largely invisible to AI systems. What gets cited is substantive, specific, and human-first.
The platform visibility problem: Instagram content is invisible to LLMs because of walled gardens. YouTube is openly accessible to all AI algorithms. For brands that have been investing in Instagram reels over YouTube, this is a structural discoverability gap that will only widen.
Our advice to companies: We feel this is a very in time opportunity for brands to get previously paid discoverability on searches for effectively zero marginal cost acquisition once you’re in the set. The brand that shows up first in an AI-generated answer for a high-intent query has won without spending a rupee on that click. GEO today is where SEO was nearly 25 years ago – the early movers have an unfair advantage in the long run at lower costs.
Part 3: How Brands Operate End-to-End
This is where the transformation goes deepest and where most consumer brand leaders are still underinvested. The consumer brands doing this well aren’t just using AI for marketing. They’re rebuilding their operating model around it.
Customer experience, orchestrated by AI. One brand we work with has rebuilt every customer-facing surface — diagnosis, conversation, prioritisation, care — around AI it built in-house. Their AI chatbot handles pre- and post-sales conversations at scale; cost per chat has dropped by 88% , while chat QA scores have exceeded human benchmarks. A FICO-style propensity model scores every retention customer from 0 to 100, then automatically sequences calls, WhatsApp messages, and app nudges in order of conversion probability functioning as a 24/7 growth team. Their nutrition support function, which previously required significant human bandwidth, now serves 1,000+ people per day at $1 per day in total operating cost.
Engineering velocity, multiplied. 70% of new app code at this same company is now AI-written. Every engineer works with AI across boilerplate, refactors, tests, and migrations — with visible reductions in PR turnaround and cycle time across squads.
Finance and analytics, made self-serve. They built in-house Claude plugins for their product analysts and finance team, integrating directly with their ERP, BI tools, and data layer, with no middleware costs. Daily MIS reports are auto-generated and auto-distributed. Almost all of this is done by individual team members using AI as a co-pilot not by any outside agency.
Supply chain and demand forecasting. At the macro level, the benchmarks are consistent: McKinsey finds AI reduces forecasting error by up to 65% and improves supply chain efficiency by 20%. Unilever used weather-based AI demand forecasting to increase ice cream sales 30% in key markets. Danone pushed forecast accuracy above 90%, significantly cutting waste. For consumer brands carrying inventory or operating on thin margins, these numbers translate directly to cash.
Our advice to companies: : AI in operations doesn’t just cut cost, it can improve quality simultaneously. Across engineering, finance, and customer operations, the brands eliminating entire cost layers (middleware, manual reporting, QA headcount) while improving output quality are building a structural margin advantage that isn’t visible in a single quarter but compounds meaningfully over time. We encourage every founder to mandate AI usage across their teams. Maybe Friday sessions where teams can take turns to present how they used AI in their work? We believe it’s best implemented when it comes top down.
The Common Thread
Across all three layers, the same pattern holds: the brands winning with AI aren’t treating it as a campaign or a tool experiment. They’re treating it as infrastructure and building in-house rather than buying off-the-shelf where it matters most.
The adoption gap is real. 66% of global CPG firms are deploying generative AI, but only 16% of initiatives have scaled enterprise-wide and just 25% deliver expected ROI. The failure modes are predictable: tools without integration, teams without habits, and initiatives that start with technology instead of a business problem.
The brands that pick a few high-leverage workflows, embed them deeply, and build before expanding will compound their advantage. The window to move first is still open but it’s closing faster than most brand leaders realize.
