Agentic Commerce in India Could Add $3T by 2047: What Retail Teams Should Do

Agentic commerce is moving from concept to market reality

Today’s signal from India is not just another AI keynote claim. At India AI Impact Summit 2026, InMobi CEO Naveen Tewari said agentic commerce could unlock nearly $3 trillion in economic value by 2047. Whether that exact number lands high or low, it sets a clear direction: AI agents are being positioned as a real commercial interface, not a side feature.

For e-commerce operators, this changes priorities. The winning question is no longer "Should we test AI shopping journeys?" It is "Which parts of our current stack break when autonomous decision-making enters discovery and checkout?" That shift affects catalog design, payment orchestration, governance, and measurement all at once.

If your team is still deciding where to start, begin with operational basics before advanced orchestration. This AI shopping assistants article is a useful baseline reference.

Why the 2047 forecast matters for your 2026 roadmap

A long-range number like 2047 can feel abstract, but strategy decisions happen this quarter. The right reading is not the headline amount. The right reading is that major players now see agent-led commerce as structurally inevitable, and they are building the rails early.

Three practical implications stand out:

1. Commerce UX becomes model-mediated. Product pages still matter, but the first "reader" may be an AI agent before a human shopper.
2. Conversion shifts upstream. If agents shortlist options earlier, your attribute quality matters more than last-mile persuasion copy.
3. Trust and auditability become conversion factors. Merchants that can explain recommendations and payment flows will earn both user confidence and regulator confidence.

This is also where many teams underestimate timeline risk. AI pilots are fast, but AI operations are slow if your catalog is inconsistent. We covered this execution gap in AI checkout readiness for 2026, and the same constraint appears here: bad inputs create expensive downstream friction.

Payment infrastructure is becoming the bottleneck and the moat

The strongest near-term signal was not just forecasts. Mastercard announced a fully authenticated agentic commerce transaction in India and tied it to tokenized, governed payment flows across issuers, aggregators, and merchants. That matters because many AI shopping demos fail at handoff: users leave the assistant interface to complete payment elsewhere.

In other words, the market is now testing end-to-end agentic checkout, not only discovery.

For retailers, this creates a concrete checklist:

• Define where AI agents are allowed to act autonomously and where explicit user confirmation is required.
• Align fraud, chargeback, and consent rules with new agent-initiated payment patterns.
• Ensure payment event data can be joined with catalog and merchandising data for post-purchase analysis.
• Plan rollback logic for failed or ambiguous agent actions.

If your catalog and payment systems are disconnected, you will not debug this quickly. Tools like Lasso can help by centralizing product data normalization before teams layer agentic experiences on top. The value is less about "more AI" and more about fewer operational blind spots.

Product data quality will decide who captures agentic demand

Agentic commerce amplifies the old retail truth: the merchant with better structured product data wins more discovery surfaces. The difference is speed. AI agents can evaluate many candidates instantly, so incomplete attributes are penalized faster than in traditional search.

Focus on four data layers first:

1. Decision attributes: size, compatibility, materials, delivery windows, returns constraints.
2. Disambiguation fields: model names, variant IDs, region-specific naming, compliance labels.
3. Commercial context: real-time price, stock confidence, promotion validity, substitution logic.
4. Evidence fields: ratings, review summaries, proof points that can be referenced by assistants.

Most teams already hold this data somewhere, but not in one reliable schema. That is why preparation usually starts with mapping and enrichment rather than with a new chatbot. If this is currently painful in your organization, review use cases and compare with your existing feed process in our product feed management guide.

Governance, transparency, and explainability are now commercial requirements

Today’s summit coverage also emphasized investment scale and policy coordination. That is useful context, but day-to-day operators need a simpler principle: if you cannot explain an agentic recommendation or transaction, you cannot scale it safely.

A practical governance model should include:

• A clear decision log for what the agent saw and why it ranked products.
• Human escalation thresholds for sensitive categories and high-value orders.
• Locale-aware policy rules for disclosure, consent, and retention.
• Monitoring that tracks outcome quality, not just click-through rate.

This is where many e-commerce AI projects stall. Teams report impressive pilot metrics but cannot defend consistency under audit. The fix is operational discipline, not another model switch.

What to do in the next 90 days

You do not need to wait for every market standard to settle before acting. The best teams will treat today’s agentic commerce signal as a trigger for foundational work that pays off regardless of platform direction.

A focused 90-day plan:

1. Audit your top 500 revenue SKUs for missing or conflicting decision attributes.
2. Define agent permissions by journey step: discovery, compare, cart, checkout, post-purchase.
3. Instrument a shared dashboard for conversion, override rate, failed autonomous actions, and refund deltas.
4. Pilot one category where structured data is already strong and margins tolerate experimentation.
5. Document governance in plain language for legal, finance, and operations alignment.

If you want to shorten this path, Lasso can help teams import, standardize, and enrich product data so agentic journeys run on cleaner inputs from day one. For rollout planning, see pricing or talk with the team via contact.