Quick commerce, the promise of ordering essential items and receiving them at your doorstep in minutes, has rapidly evolved from a novelty to the default expectation in urban India. What once felt like a premium convenience is now embedded in everyday life. The true engine behind this shift is not hyperlocal warehouses or aggressive marketing alone. It is the systematic integration of artificial intelligence across the quick commerce value chain. AI now shapes operations, logistics, customer experience, and risk management. It has transformed from a differentiating advantage into the central backbone of modern quick commerce.
The Rise of AI in Retail and Q‑com
AI adoption in retail and e-commerce is accelerating at an unprecedented pace, and India is no exception. A 2025 EY India report suggests that generative AI and allied technologies could increase retail productivity by 35–37% over the next five years, with most organisations already running pilots and allocating dedicated budgets for AI integration. This momentum is reflected globally as well—the “AI in Retail” market is estimated at USD 14–14.5 billion in 2025, with projections showing it could expand to about USD 138.3 billion by 2035, nearly a ten-fold increase over the decade.
This technological groundswell has quickly permeated India’s quick commerce (Q-com) sector. A 2025 Bain & Company analysis forecasts that the Indian Q-com market will grow at a compound annual growth rate (CAGR) of over 40% through 2030. Expansion is being driven by dense urban demand, a broader product catalogue, and improved fulfilment infrastructure.
At the same time, retailers integrating AI, from supply-chain management and inventory planning to customer service, are reporting measurable gains. Many cite 5% to 15% annual revenue growth, along with 10% to 30% reductions in operational costs.
For quick commerce players, operating on thin margins and tight delivery windows, these improvements are not just helpful; they are vital. AI-powered demand forecasting, real-time supply chain visibility, and predictive delivery systems are rapidly becoming the standard. What began as pilot projects is now mission-critical infrastructure ensuring speed, reliability, and scalability.
Together, these trends paint a clear picture: AI is no longer optional. For quick commerce platforms, it is the core engine that enables speed, consistency, personalisation, and long-term profitability.
Behind the Scenes: Where AI Adds Value
Smarter Inventory and Demand Forecasting
Traditional retail or warehousing systems often struggle to keep up with fast-shifting demand patterns, especially in a dynamic market like quick commerce. With AI, dark stores and micro-warehouses can now leverage hyper-local data, including time-of-day purchasing patterns, regional preferences, weather changes, festival cycles, and even user behaviour trends, to anticipate demand spikes. This helps reduce stockouts, curbs wastage, and ensures high-velocity items are always ready for instant delivery.
Industry data supports this transformation. A recent study by McKinsey found that AI-driven supply chain management can improve forecasting accuracy by 20–50%, significantly reduce supply-chain errors, and boost overall operational efficiency. In some real-world cases, retailers have sharply cut slow-moving stock surplus while increasing the availability of fast-moving goods in just a few months.
Emerging academic research also validates this: a newly proposed “agentic AI” framework for smart inventory replenishment shows promising results in reducing stockouts and inventory holding costs while dynamically optimizing supplier selection and product mix.
Optimised Last-Mile Logistics
Last-mile delivery has always been the Achilles’ heel of quick commerce, thanks to high costs, unpredictable traffic, and fluctuating demand. AI-driven routing engines are changing this calculus. By ingesting real-time traffic data, courier availability, delivery density, and demand urgency, these systems dynamically assign couriers and delivery slots to optimise both cost and speed. The result: faster deliveries, lower fuel and labour costs, and fewer failed or delayed deliveries, all critical for maintaining razor-thin Q-com margins.
Some Indian quick commerce platforms have publicly highlighted their AI-enabled warehouse expansion and logistics automation as key enablers of faster, reliable delivery. As competition intensifies and consumer expectations sharpen, efficient AI-driven logistics will likely become a key differentiator.
Enhanced Customer Experience and Personalisation
On the customer-facing side, AI is transforming how users discover and order products. Smart recommendation engines, powered by user purchase history, time, location, and contextual signals, make reordering faster and more intuitive. Conversational AI agents, such as chatbots or voice assistants, now handle millions of customer interactions daily, resolving queries, processing refunds or returns, and addressing issues, often within seconds.
According to market data, AI-enabled support and automation are becoming the new standard. Many retailers report tangible improvements in customer satisfaction and retention as a result. In some cases, quick commerce firms in India reportedly use AI to suggest recipes based on items in a user’s cart, a subtle but effective method to increase basket size, drive engagement, and deliver more value. These kinds of AI-driven features illustrate how quick commerce is evolving to be not just fast, but intelligent and anticipatory.
When AI Fails Us: Risks, Misuse and Systemic Vulnerabilities
In the same way that AI enables scale and speed, it also opens doors to deception, fraud, and systemic risk. As quick commerce increasingly relies on automated verification systems (photos, delivery proofs, minimal human oversight), the boundary between real and fake begins to blur — especially now that AI tools are widely accessible.
A stark example: in late 2025, a customer of Swiggy Instamart allegedly used a generative AI tool, Nano Banana, a part of the Gemini AI system, to manipulate a photo of an egg tray. The original tray had only one cracked egg, but after applying the prompt “apply more cracks,” the AI allegedly transformed the image to show over 20 broken eggs. The customer then submitted this doctored image as “proof” and reportedly received a full refund. This incident raised serious questions about the robustness of current refund and verification protocols in quick commerce.
This incident has sparked a wider debate: when AI-powered scams (deepfake product photos, manipulated images, fake returns) become easy and inexpensive to execute, trust, the bedrock of quick commerce, is undermined. For Q-com platforms that build their value promise on both speed and reliability, such incidents pose major threats. If careless or malicious actors misuse AI, platforms might be forced to tighten verification (e.g. unboxing videos, stricter checks), which could slow down delivery or degrade user convenience.
Beyond single incidents, there’s a broader ethical dimension. The use of AI in decision-making, from which customers to prioritise, how discounts are offered, and which products are surfaced, raises valid questions about fairness, transparency, and data privacy. A 2024 academic study on AI in retail highlighted growing consumer concerns over privacy and algorithmic fairness, even as AI adoption accelerates.
When AI Works: Positive Outcomes from Thoughtful Integration
AI in quick commerce is not only about headlines and risks. There are documented examples of brands using AI responsibly to deliver tangible business results. For example, a recent study on generative AI use in online retail found that integrating GenAI across product descriptions, search, and customer workflows led to a ~16% uplift in sales, credited solely to smoother user experience and higher engagement.
Across India’s commerce ecosystem, AI-led supply-chain transformation is accelerating. Modern brands understand that speed and efficiency must be built into the core operations. For instance, fragrance brand Fraganote, which recently raised $1 million in funding specifically to strengthen its fulfillment capabilities, exemplifies this approach.
Instead of relying on guesswork, the brand actively incorporates technology to overcome quick commerce challenges. They use software integration and their own prediction models to plan ahead for raw materials and anticipate demand shifts, which are critical in a market where fragrance trends change with seasons (like different scents trending in winter versus summer). Furthermore, to meet the customer expectation of instant gratification, they leverage technology to “identify the best sellers pin code wise” and stock those high-demand items specifically in local fulfillment centers.
This granular, data-driven approach to inventory management ensures high availability where it matters most, reducing stockouts and maximizing delivery speed. These innovations show how AI is strengthening the backbone operations that quick commerce increasingly depends on, validating the co-founder’s focus on operational discipline. As Garima Kakkar, Co-founder of Fraganote, notes in an interview: “You have to tie it up with tech, you have to have enough stock, and how efficiently you can rotate your working capital and inventory… this is where the magic really happens in getting this right.“
These examples show a core truth: when AI is woven thoughtfully into operations, it enables scalability without sacrificing service quality, transforming quick commerce from a gamble on speed to a reliable, intelligent, and efficient service.
The Road Ahead: 2026 and Beyond
Looking ahead, quick commerce in India is likely to divide into two paths: platforms that treat AI as a superficial add-on and platforms that embed it deeply as core infrastructure. The latter are expected to emerge as winners. Analysts expect that, over the next few years, AI-first operating models in which AI powers forecasting, routing, inventory replenishment, and customer interactions will become the norm for leading quick commerce firms. As demand surges and order volumes spike, platforms that use AI to anticipate demand, manage stock, and optimise delivery will gain a strong competitive edge.
At the same time, customer expectations will evolve. Speed remains important, but consumers will increasingly demand trust, consistent quality, and transparency even if it means a slight trade-off in convenience or delivery time. Platforms that fail to ensure reliable verification, ethical AI practices, or transparent operations risk loyalty erosion. Regulatory scrutiny around data privacy, algorithmic fairness, and secure grievance redressal is also likely to increase.
In this environment, ethical and privacy-conscious AI adoption can become a key differentiator. Platforms that prioritise fairness, transparency, and accountability will build long-term trust and sustained market share. Quick commerce may not just be about instant delivery. It could evolve into a service that is intelligent, reliable, and deeply human-centred, blending data, technology, and trust into a seamless retail experience.
To conclude, the rapid rise of quick commerce has been powered by ambition: to compress time, deliver convenience, and deliver items at speed. AI has provided the scaffolding to turn that ambition into reality. But as recent incidents show, speed without integrity is brittle. In a world where even a single cracked egg can be faked with a prompt, trust becomes fragile.
For quick commerce platforms that recognise this and treat AI not as a stopgap but as the structural backbone, the opportunity is profound. They have the potential to build a future where ordering daily essentials in minutes is not just possible but reliable, safe, and scalable. For consumers, that means convenience with confidence. For quick commerce platforms, it means a new identity, from fast delivery startups to intelligent logistics infrastructure.
If executed right, this could redefine retail for a generation.
