Agentic Commerce and the Future of Retail and CPG

Shopping Behavior Is Shifting: Precision Spending and AI Adoption

Retail consumers are spending strategically and trying to balance value, brand trust, and experience. Economic pressure has led many to trade down to budget‑friendly brands, while others remain willing to spend more on trusted names. Health, nutrition, and sustainability priorities also influence spending patterns, making modern shoppers both pragmatic and purpose‑driven.

This precision spending creates rich behavioral signals that AI systems can use to tailor recommendations, personalize offers, and anticipate trade‑offs in real time. Already:

• 41% of consumers use AI to research products.
• 33% use AI to read reviews.
• 31% use AI to find deals and promotions.

These early uses of AI are laying the foundation for agentic commerce where AI agents venture beyond assistance to autonomous decision‑making and purchases on behalf of consumers.

Converged Commerce: Orchestrating Physical, Digital, and AI Experiences

Shopping interactions now span physical stores, social media, marketplaces, brand sites, and AI platforms. Consumers move fluidly across channels, often within a single decision. While 72% still shop in physical stores, digital touchpoints and AI‑enabled discovery are increasingly part of everyday behavior.

Consumers today want:

• Seamless omnichannel experiences
• Effortless discovery across digital and physical channels
• AI‑driven assistance that understands preferences, values, and budget constraints

AI’s role is evolving from reactive support to proactive orchestration. IBV identifies key AI agent types consumers desire most:

1. Deal Hunter Agents continuously monitor pricing, promotions, and loyalty rewards
2. Customer Service Agents provide 24/7 support across channels
3. Product Review Agents analyze credibility and align offerings with consumer values
4. Personal Shopper Agents shop and execute purchases based on individual style and preferences

Brands and retailers must ensure their products are visible, relevant, and discoverable within this evolving landscape, especially when these shifts are accompanied by potential operational disruptions like mergers or divestitures. Those that fail to solve fundamental data challenges up front risk being overlooked not just by people, but by AI agents trained on millions of options.

The Trust Imperative: Foundations of Loyalty and Advocacy

Trust has always been central to retail and CPG success, but in an AI‑driven world it becomes exponentially more critical. Consumers are willing to share data, but they do so with caution:

• 52% are comfortable sharing data, yet
• 83% express overlapping concerns about privacy, misuse, and unwanted outreach.

This reveals a trust paradox: personalization is expected, but not at the cost of control or transparency. Retailers must build trust on two fronts:

1. Product Transparency. Provide accurate, consistent, and machine‑readable product data so both consumers and AI systems can confidently find and recommend products.
2. Responsible Personalization. Use consumer data ethically, safeguard privacy, and communicate clear value exchanges for data sharing.

When done well, this trust fuels a trust‑loyalty‑advocacy cycle: consistent, transparent experiences earn loyalty; loyalty drives advocacy; advocacy strengthens data signals that improve AI recommendations and deepen consumer relationships.

What Retail and CPG Leaders Should Do in 2026

The vision for agentic commerce is clear. Execution is the differentiator. To turn AI ambition into operational impact, retail and CPG leaders should take pragmatic, high‑leverage actions:

• Build omnichannel experiences for people and machines. Design experiences that serve both human shoppers and AI intermediaries. Understand consumer intent at every touchpoint and integrate conversational, guided, and autonomous experiences to reduce friction and strengthen engagement.
• Make products AI‑discoverable. Prepare for Generative Engine Optimization (GEO) by structuring product data so that large language models and autonomous systems can interpret, compare, and act on product information. Maintain unified, accurate product metadata that feeds both human‑facing channels and machine‑readable APIs.
• Reinforce trust through transparency. Implement digital trust signals that satisfy both consumers and algorithms. Provide clear proof of product quality, sustainability credentials, and privacy safeguards. Ensure transparency works in both human and machine languages.
• Develop a unified data and governance strategy. Integrate structured and unstructured data across product, customer, and operational systems. Define clear AI interaction guardrails, ensure data integrity through auditing and lineage, and align with evolving regulatory and ethical standards worldwide.