A new form of shopping arrives: AI agents are no longer a novelty; they now anticipate needs, search, compare and even purchase on behalf of consumers.
Retailers must optimize for algorithms: As Kearney’s report warns, six in ten U.S. consumers expect to use an AI shopping agent within a year, meaning algorithms will decide which products surface first.
Agentic payments go mainstream: Partners such as Razorpay and OpenAI are piloting chat-based payments via UPI and ChatGPT, hinting at an imminent shift from user‐initiated to agent‑initiated commerce.
Introduction
Picture yourself sitting on a couch after a long day. You remember you’re out of milk and honey, but instead of reaching for your phone and tapping through multiple apps, you say, “Hey ChatGPT, restock my groceries.” Seconds later your digital assistant confirms: “Ordering milk and honey from BigBasket. Payment processed via UPI. Delivery by tomorrow at 9 AM.” What sounds like science fiction is quickly becoming reality. The emerging phenomenon, dubbed agentic commerce, puts AI agents at the center of the shopping journey – from search to purchase – transforming how consumers buy and how retailers sell.
In the last two years, search interest in agentic commerce has exploded. Google Trends data show that queries for “AI shopping agent” spiked by over 4,000 % in 2024–2025 (see chart below). Much of this momentum comes from generative AI tools like ChatGPT and voice assistants integrated into messaging platforms. Analysts have started asking: What happens when algorithms, not humans, become the primary gatekeepers of digital commerce?
Key Features & What’s New
At the heart of agentic commerce is the AI agent: software that can autonomously search, compare, recommend, and even purchase goods on behalf of a user. Kearney’s recent report breaks down how these agents work:
Anticipatory shopping: agents monitor your consumption patterns (via receipts, calendars or connected appliances) and anticipate when you’ll run out of essentials.
Personalized research: they comb through thousands of products, factoring in price, quality, reviews and your preferences to generate a shortlist.
Negotiated pricing: advanced agents submit quote requests to multiple merchants, pushing sellers into price competition.
Automatic purchase and logistics: once a choice is made, the agent completes the transaction, arranges delivery and tracks the order.
What’s truly new is that these agents can execute purchases without the user watching a web page. Razorpay’s pilot with OpenAI and NPCI exemplifies this transition. Users can tell ChatGPT to order groceries and pay via UPI; the system processes the payment through Razorpay’s stack, enabling frictionless agentic payments. BigBasket is one of the first merchants in the pilot. The integration relies on NPCI features such as UPI Circle and UPI Reserve Pay, and early testers report a seamless experience.
Another twist is the integration of AI agents directly into search and social platforms. Adobe found that traffic from generative search tools surged 4,700 % year‑over‑year, illustrating how consumers are increasingly finding products through AI suggestions rather than browsing store websites. As platforms like Klarna’s AI assistant, Google’s Shopping Graph and ChatGPT with browsing become default, retail traffic flows may shift away from traditional search engine optimization to algorithmic discoverability.
Business Model & Market Fit
Kearney categorizes agentic consumers into four segments:
Tech-forward early adopters (15 %): They embrace AI for convenience and novelty.
Price-sensitive pragmatists (35 %): They use AI to secure the best deals and reduce the time spent comparing products.
Privacy-conscious skeptics (30 %): They are wary of data sharing and prefer to supervise agent decisions.
Routine loyalists (20 %): They stick to known retailers and may only accept agent assistance for routine reorders.
From a business perspective, agentic commerce threatens to compress margins. Algorithms level the playing field, making price and quality more transparent and reducing consumers’ propensity to impulse‑buy extras. Retailers may also face higher fees from agent platforms that mediate transactions. Yet there are opportunities: brands that provide rich machine‑readable product data and reliable fulfillment can become “agent‑preferred,” securing prime placement in algorithmic recommendations. New business models such as subscription “agent boosters” (pay‑to‑play placements in agent recommendation engines) and dynamic bundling (agents packaging complementary products from multiple sellers) could emerge.
Developer & User Impact
From both developer and consumer perspectives, agentic commerce brings benefits and risks.
Benefits
Time savings: Delegating shopping tasks to an agent frees users from endless browsing.
Enhanced personalization: Agents learn preferences and health restrictions, leading to better recommendations.
Optimized spending: By comparing prices across vendors, agents drive down costs and reduce impulsive purchases.
Accessible payments: Integrations like Razorpay with UPI make payments seamless, benefitting unbanked and rural users in India.
Risks & Opportunities
Privacy and data exploitation: Agents need data to operate effectively. A misconfigured agent could overshare personal information or be manipulated by malicious actors.
Algorithmic bias: If training data favor certain brands or categories, agents could perpetuate discriminatory pricing or limit consumer choice.
Disintermediation: Retailers lose direct relationships with consumers, making it harder to build loyalty.
New developer opportunities: Building agent optimization services (similar to SEO but for agent ranking) and agent compliance tools (ensuring agents follow regulatory guidelines) could be lucrative.
Comparisons
A key difference between agentic commerce and traditional e‑commerce lies in who holds the power of choice. The table below contrasts the two:
| Feature | Traditional e‑commerce | Agentic commerce |
|---|---|---|
| Discovery | User manually searches and browses websites | Agent searches, filters and recommends based on preferences |
| Price comparison | User uses comparison sites or coupon codes | Agent negotiates prices across vendors in real time |
| Checkout | User manually enters payment details | Agent executes payment via integrated stack (e.g., UPI) |
| User experience | Visual web pages and ads | Conversational interface, voice commands |
Community & Expert Reactions
Retail consultants are sounding the alarm. “Retailers need to become agent‑preferred or risk invisibility in the algorithmic era,” warns Kearney’s report. Pricing strategist Daniel Kost (“How AI is transforming commerce,” Oct 2025 webinar) notes that “algorithmic gatekeepers compress margins and shift loyalty from brands to platforms. Sellers must treat algorithms as their new customers.” Consumer forums, meanwhile, show mixed feelings: some early adopters on subreddits celebrate never having to shop again, while privacy advocates worry about agents over‑sharing personal data.
Risks & Challenges
Regulation and compliance: Governments may regulate agentic platforms to prevent anti‑competitive behavior and protect consumer rights. Transparent data practices and audit trails will be essential.
Security vulnerabilities: Compromised agents could perform unauthorized purchases or leak financial credentials. Robust authentication and encryption are paramount.
Ethical dilemmas: When an agent misinterprets a user’s constraints (e.g., dietary restrictions) and orders harmful products, who is liable? Responsibility frameworks must evolve.
Cultural barriers: Adoption rates differ by region; some cultures may resist autonomous purchasing due to trust issues.
Road Ahead
Over the next two years, expect agentic commerce to move from pilot to mainstream. As smartphone manufacturers integrate AI agents into operating systems, and messaging platforms embed payment stacks, the frictionless flow from chat to checkout will become standard—mirroring the trajectory of Gemini 2.5 Computer Use toward full OS-level integration.
Retailers will invest in machine‑readable catalogs, agent optimization strategies, and algorithm audit tools. Consumers will come to expect concierge‑like service from their digital assistants, raising the bar for personalization and reliability.
The shift will also spawn new regulatory questions. Will governments require agents to display “fair price” comparisons? Could antitrust laws classify dominant agent platforms as gatekeepers subject to special obligations? Observers point to Europe’s Digital Markets Act as a blueprint. In any scenario, the winners will be those who adapt early—brands that make their products agent‑friendly and consumers who learn to harness agents’ power.
Final Thoughts
Agentic commerce isn’t just a new feature; it signals a fundamental change in digital trade. The excitement around ChatGPT‑powered grocery shopping and algorithmic deal‑hunting hints at a future where shopping becomes invisible—embedded in our conversations, calendars and home devices. It’s not the convenience alone that’s revolutionary; it’s how quietly this shift redistributes power from brands to algorithms, from websites to conversational agents. For businesses and consumers alike, the question isn’t whether to adopt AI shopping assistants but how to shape them responsibly.
