How AI agents are changing the rules of demand generation

AI agents are redefining demand generation in CPG

AI shopping agents are becoming a central player in how consumers discover, compare, and buy products. Instead of scrolling through websites or reading reviews, many people now rely on generative AI systems to make decisions for them. These agents research, compare, and even execute purchases automatically. It’s a complete rewiring of demand generation in the consumer packaged goods (CPG) industry.

The numbers tell the story. Bain & Company data shows that 30% to 45% of U.S. consumers already use AI to support shopping decisions, and 64% have used or are open to using AI to make a purchase. By 2030, this market, what Bain calls “agentic commerce”, could reach $300 to $500 billion in annual U.S. revenue, representing up to 25% of total e-commerce. That’s a clear sign that AI-driven transactions are moving from edge cases to everyday behavior.

For business leaders, this means one thing: the customer of the future may not be a person but an algorithm. The brands that succeed will be those that make themselves visible, understandable, and relevant to these agents. That demands structural changes inside organizations. Marketing, data, and technology teams must work as one, an ecosystem that can anticipate how both people and machines make buying decisions. The future of growth depends on it.

The shift from browsing to prompts reconfigures the shopper experience

The old model of product discovery, searching, clicking, comparing, is dissolving. Consumers now issue prompts such as “reorder groceries for my family next week” or “set up a clean skincare routine for dry skin.” AI systems then interpret those instructions, find products, and execute transactions almost instantly. This is the new reality of shopping: faster, smarter, and mostly invisible to the human eye.

This shift changes everything about how companies build relationships with their audiences. If a product isn’t recognized or recommended by AI, it effectively doesn’t exist in the marketplace. According to Bain & Company, 44% of online buyers already start their journey using a large language model or a combination of AI and search engines. It’s a structural shift in digital traffic, moving influence from traditional SEO and digital ads to AI interpretation and recommendation logic.

For C-suite leaders, the implication is clear. Competing in this new ecosystem requires focusing less on capturing direct clicks and more on becoming part of the AI thought process. Products must be defined in ways that machines can easily recognize and evaluate, through data-rich attributes, precise descriptions, and clear user intent signals. The opportunity here is significant: companies that act now can shape how AI engines understand their brands, giving them greater control over future market visibility.

Brand building must adapt to be both humanly compelling and machine legible

Brand building has entered a new phase where success depends on how well a brand communicates with both people and machines. Human buyers still care about emotional connection, trust, and storytelling. However, AI systems rely on structured information, tangible attributes, verified claims, and consistent online data, to determine which products to recommend. In this dual environment, vague positioning or generic marketing messages lose impact quickly.

Brands must therefore focus on clarity. Every product feature, claim, and description must be specific, relevant, and machine-readable. This means rethinking website content, product pages, and even advertising language to ensure they are designed for algorithmic interpretation as much as for human engagement. Companies that clearly define what makes their products stand out, whether that’s sustainability, performance, or practicality, will gain higher visibility in AI-driven recommendations.

Executives should also pay close attention to how sentiment and feedback are captured. AI systems learn from user reviews, expert commentary, and online discussions. Positive narratives embedded in this ecosystem can enhance both brand credibility and AI recognition. In effect, every digital mention contributes to visibility in agentic commerce. Precision of language, well-structured data, and consistent brand expression must now coexist within the same strategy. That combination ensures a brand connects emotionally with people while staying relevant to machine logic.

Innovation must be mission-driven and differentiated

AI-driven commerce changes how innovation happens. Consumers no longer search for static categories like “breakfast foods” or “facial creams.” They use intent-driven prompts—“healthy breakfast for busy mornings” or “skincare for dry skin”—forcing brands to align product development around distinct missions. The future belongs to products designed to serve specific use cases that AI agents can identify as superior for those missions.

For business leaders, this requires disciplined investment in innovation that connects directly to real consumer needs as expressed through data. The key lies in how AI interprets prompts and external feedback. When companies analyze those signals, they gain real-time insight into evolving demand. This insight enables product teams to design offerings that both solve real problems and stand out in AI comparison engines. The standard for innovation is rising, only products that outperform peers in clear, data-led ways will win algorithmic preference.

L’Oréal offers an early view of this shift. Its AI-powered Beauty Genius platform personalizes skincare routines using facial scans and customer inputs. The tool doesn’t just enhance personalization; it feeds data back to the company, revealing what customers want and where gaps exist. This feedback loop links product performance with innovation priorities, accelerating learning and adaptation.

Executives should expect this model to expand rapidly. Mission-driven innovation powered by AI insights will define competitive advantage. The winners will be companies that align R&D, marketing, and digital systems around clear data signals and consumer intent, moving faster and more precisely than those that still rely on traditional category thinking.

Commercial strategy requires hybrid partnerships with AI platforms and retailers

The commercial model for consumer packaged goods (CPG) is entering a new hybrid phase. Discovery is increasingly driven by AI agents, while retailers continue to manage the transaction and fulfillment process. This shift forces companies to think beyond traditional distribution strategies and form deeper partnerships with both large language model (LLM) platforms, such as ChatGPT, Claude, or Gemini, and established retail ecosystems.

For executives, the challenge is balancing visibility across both ends of this dual environment. Brands must integrate content and data directly into AI-driven discovery tools while still collaborating with retailers to ensure accuracy, fulfillment speed, and pricing alignment. Companies that ignore either front risk becoming invisible in digital discovery or losing control over post-sale operations.

Unilever provides a clear direction here. The company has already integrated content systems that connect directly with major retailers such as Amazon, Walmart, and Alibaba, as well as newer AI shopping interfaces. This approach allows Unilever’s teams to manage how their products appear just in search results, and in automated recommendations produced by AI across multiple platforms. For business leaders, this demonstrates that agility in data-sharing, adaptive content creation, and retail alignment can directly protect market relevance.

At the strategic level, brands must also decide how to engage with AI ecosystems commercially. Should products be sold directly through an AI platform, or would it make more sense to maintain the relationship with retailers? Each path has implications for pricing structures, margins, and long-term control over customer experience. The key is to maintain flexibility and seek a business model that allows the brand to remain visible to algorithms while profitable in execution. Companies that optimize this balance will dictate terms in the emerging agent-driven marketplace.

CPG operating models need integration and speed

Legacy organizational structures are not built for the speed and complexity of agentic commerce. Most CPG companies operate with fragmented teams and disconnected responsibilities, marketing, sales, and technology often run in parallel rather than in sync. Moving forward, this separation will slow progress. To operate effectively in an AI-influenced environment, companies need tighter integration, real-time data sharing, and a unified view of execution.

The next step is establishing a global AI commerce function, small, empowered, and cross-functional. This team should oversee ecosystem monitoring, data operations, partnerships, and the rollout of best practices across global markets. From there, local execution squads in leading markets such as the U.S., India, and China can execute rapid tests, interpret prompt-driven analytics, and adapt campaigns or pricing strategies based on algorithmic performance.

The focus should shift from incremental workflow changes to organizational speed and clarity of ownership. Teams must know who is responsible for AI monitoring, agent partnerships, and content optimization. Governance should enable faster trade-offs among competing priorities without creating bureaucratic barriers.

To measure effectiveness, businesses must evolve their key performance indicators beyond traditional engagement metrics. Tracking clicks or impressions no longer reflects performance in AI-driven environments. Success now depends on measuring how often an AI agent selects the brand, how that choice converts, and how efficiently it occurs. Companies that adopt these metrics early will build internal discipline for operating in real time, reinforcing a culture of speed and adaptability essential for staying visible in automated commerce.

Immediate “no‑regrets moves” are essential for CPG firms

The transition to AI‑driven commerce is no longer theoretical, it is underway. Every CPG company now operates in an environment where algorithms influence purchasing decisions as much as people do. Waiting to adapt carries clear risk: invisibility in algorithmic selection means lost sales and a shrinking presence in consumers’ digital journeys. That’s why brands need to take immediate, practical steps that build readiness for AI agents and secure long‑term visibility.

The first step is to build what can be called an “agent‑ready” brand. This means ensuring all product details, descriptions, specifications, and consumer benefits, are clear, structured, and machine‑readable. Claims must be concrete and verifiable through reviews or expert commentary. Language should reflect specific consumer missions rather than vague brand statements. Every digital property, from product listings to websites, should communicate directly with both consumers and AI systems scanning the market for relevance.

Next, companies need operational precision. AI agents process logic, not assumptions. That requires brands to define purchase conditions through clear, executable rules: pricing, returns, guarantees, shipping times, and availability. All of these need to be expressed in data formats intelligible to AI systems. Brands should also start experimenting with sponsored agent recommendations and building mission‑specific product bundles to improve placement in AI‑led searches.

Equally important is establishing agile, cross‑functional teams that combine marketing, sales, data, and technology leadership. This is how companies can move in real time as conditions change. The process works best when executives directly sponsor these teams and empower them to experiment, measure, and adjust without delay.

For business leaders, these “no‑regrets” actions have one shared outcome: readiness. The market is already evolving toward agentic commerce, and those who adapt early will have data advantages, better AI visibility, and stronger relationships with both machines and consumers. The goal is not to predict where the market will settle, but to stay flexible and relevant as it forms. Early movers will own the momentum and set the pace for the rest of the industry.

Recap

The rise of AI agents is not a distant scenario, it’s a live transformation already shaping how demand is created and captured. For business leaders, the core challenge is not simply understanding the technology but organizing around it. The brands that succeed will be those that treat AI not as a tool but as a channel for influence, discovery, and growth.

This shift will pressure every function, marketing, sales, data, operations, to operate faster and with greater alignment. Decisions will rely more on machine logic, and success will depend on how well your teams can anticipate and integrate those signals. A fragmented approach will slow progress; a connected, agile one will unlock advantage.

Now is the moment to act with precision and intent. Define what makes your brand distinct, express it in human and machine language, and build systems that adapt as the algorithms evolve. The future of demand will be decided by the brands that move first, move clearly, and stay ready to evolve again.