The landscape of consumer products is undergoing an unprecedented transformation, driven not merely by shifting consumer preferences but by the invisible hand of artificial intelligence. A recent, groundbreaking EY report casts a stark light on this paradigm shift, revealing that for consumer products (CP) brands, the battle for consumer attention has moved beyond mere visibility to an entirely new frontier: AI-driven selection. This seismic change is accelerating brand consideration risk for companies ill-prepared to navigate a world where algorithms, not just advertising, dictate purchasing decisions. The report, based on a survey of over 850 CP executives globally including significant US representation, serves as a clarion call for strategic re-evaluation, underscoring that AI is not just a tool but the very architecture of future commerce.

The Era of AI-Driven Selection: Beyond Traditional Visibility

For decades, the mantra for consumer products brands has been "visibility is king." Shelf space, prime advertising slots, prominent display in physical and digital storefronts – these were the traditional battlegrounds for capturing consumer eyeballs and, subsequently, their wallets. However, as the EY report rigorously details, this traditional model is rapidly becoming obsolete. We are now firmly entrenched in the era of AI-driven selection, where the mechanisms of discovery and choice are increasingly outsourced to sophisticated algorithms.

What does AI-driven selection truly entail? It means that consumer interaction with products is no longer a direct search-and-find mission influenced primarily by brand advertising. Instead, it’s an orchestrated journey guided by AI systems embedded within e-commerce platforms, social media, search engines, and emerging retail media networks. These AI models analyze vast datasets – including past purchases, browsing history, stated preferences, real-time context, and even biometric data – to proactively recommend products. They don't just show you what you might like; they increasingly predict what you will select, often before you consciously realize your need.

Consider the pervasive influence of recommendation engines on major e-commerce sites, personalized search results on Google, or the curated product feeds on social media platforms. These are not passive displays; they are dynamic, intelligent systems designed to optimize conversions. The EY report highlights that platforms and retailers are now effectively the new gatekeepers of discovery. Their proprietary algorithms control the flow of information, determining which brands appear prominently, which products are suggested, and which offers are prioritized. This fundamental shift means that a brand might have an exceptional product, a compelling marketing campaign, and ample inventory, yet still struggle if its offerings are not algorithmically optimized for discovery within these powerful digital ecosystems.

The implications for brand consideration are profound. Historically, a consumer might browse several brands for a specific product category, weighing attributes, price, and reputation. In an AI-driven environment, the initial set of brands presented for consideration is significantly narrower, often curated by an algorithm. If a brand isn't among those initial suggestions, its chances of even being considered plummet dramatically. This accelerates brand consideration risk, pushing non-optimized brands to the periphery, regardless of their intrinsic quality or historical market standing. For consumer products executives, particularly in the competitive US market where digital commerce is deeply ingrained, understanding and adapting to this algorithmic reality is not merely an option but an urgent imperative for sustained competitiveness.

The Executive Conundrum: Acknowledging the Readiness Gap

The EY report paints a vivid picture of the paradox facing consumer products executives today: a clear recognition of AI's critical role juxtaposed with a significant deficit in actual capability. A striking 47% of executives surveyed believe that influencing algorithmic recommendations will be absolutely essential for their competitiveness within the next five years. This statistic alone underscores a widespread acknowledgement of the strategic importance of AI. These leaders understand that future market share hinges on their ability to integrate with, and even subtly direct, the autonomous systems shaping consumer choices.

However, the grim reality is that only a paltry 21% of these same executives report having the current capabilities to effectively deliver on this strategic imperative. This represents a substantial readiness gap – a chasm between strategic foresight and operational execution. This disparity is particularly concerning for US-centric operations, given the rapid pace of AI adoption and digital sophistication among American consumers and retailers. The competitive landscape in the US is unforgiving, and a delay in bridging this gap could prove fatal for brands.

What contributes to this significant readiness gap? Several factors are at play. Firstly, legacy systems and infrastructure often present formidable barriers. Many established CP brands operate on older technological stacks that are not designed for the real-time data ingestion, processing, and analytical demands of AI-driven strategies. Integrating AI capabilities into these systems can be complex, costly, and time-consuming.

Secondly, there's a critical talent shortage. Developing and deploying sophisticated AI strategies requires specialized expertise in data science, machine learning, AI ethics, and platform integration. The demand for such talent far outstrips supply, making it challenging for CP companies to build robust internal AI teams. Even when talent is acquired, there’s a need for broader organizational upskilling to ensure that marketing, sales, product development, and supply chain teams understand how to leverage AI tools and interpret AI-driven insights.

Finally, a lack of strategic foresight and clear investment roadmaps can hinder progress. While executives recognize the importance of AI, translating that recognition into actionable strategies with dedicated budgets and clear KPIs remains a challenge for many. The rapid evolution of AI technology means that strategies must be agile and continuously updated, demanding a level of organizational flexibility that many large enterprises struggle to achieve.

This readiness gap exposes consumer products brands to heightened risk. Those that cannot effectively influence algorithmic recommendations will find their products increasingly invisible to consumers, leading to declining sales, eroding market share, and ultimately, a significant loss of competitive edge. The urgency for closing this gap cannot be overstated; it's a race against time for brands to adapt or face obsolescence in the new AI-driven economy.

Strategic Imperatives: Navigating the Algorithmic Landscape

Given the pressing challenges and the acknowledged readiness gap, what strategic imperatives must consumer products brands embrace to thrive in this AI-driven era? The EY report points to several critical pathways, with partnerships with retailers and platforms emerging as a paramount priority, alongside a renewed focus on data mastery.

Partnerships as the New Pillar: Collaborative Influence

A staggering 77% of executives surveyed by EY prioritize strengthening partnerships with retailers and digital platforms. This statistic is not merely an indicator of tactical importance; it signifies a fundamental rethinking of traditional brand-retailer relationships. In the age of AI-driven selection, these partnerships evolve from transactional agreements to strategic alliances, where collaboration becomes key to influencing the algorithms that mediate consumer choice.

What do these deeper partnerships entail?

• Data Sharing and Collaboration: Brands need to move beyond simply sharing product data. They must explore opportunities to share consumer insights, demand signals, and even collaborate on anonymized data analytics with their retail partners. This shared data ecosystem can help both parties understand consumer behavior better, refine AI models, and tailor recommendations more effectively. For instance, a CP brand could work with a major e-commerce platform to feed its first-party consumer data (with privacy compliance) into the platform’s recommendation engine, ensuring that its products are surfaced to the most relevant consumer segments.
• Joint AI Development and Experimentation: Progressive brands are not just looking to influence existing algorithms; they are seeking opportunities to co-develop or co-pilot AI initiatives with retailers. This could involve joint ventures to build custom recommendation models, develop innovative retail media formats optimized for AI, or even test new AI-powered shopping experiences. Such collaborations provide brands with unique insights into algorithmic mechanics and a direct channel to embed their brand attributes into the AI's decision-making logic.
• Optimizing for Retail Media with AI: Retail media networks, often powered by advanced AI, are growing exponentially. Partnerships here mean understanding how retailer-specific algorithms prioritize ads and sponsored content, and then optimizing brand campaigns accordingly. This goes beyond traditional banner ads; it involves leveraging AI for dynamic pricing, personalized ad delivery, and intelligent budget allocation based on real-time performance within the retailer's ecosystem.
• Gaining Algorithmic Visibility and Feedback: Effective partnerships offer brands a feedback loop on how their products are performing algorithmically. Retailers can provide insights into algorithmic rankings, product visibility scores, and conversion rates driven by AI recommendations, allowing brands to refine their product data, content, and promotional strategies.

These collaborative efforts are crucial because retailers and platforms possess the proprietary data, the consumer touchpoints, and the engineering capabilities to deploy AI at scale. By aligning with them, CP brands can gain unprecedented access and influence, ensuring their products remain discoverable and desirable in an increasingly automated marketplace.

Data and Analytics Mastery: The Foundation for Influence

Underpinning successful partnerships and effective algorithmic influence is an unwavering commitment to data and analytics mastery. The EY report implicitly stresses that without robust data capabilities, any attempt to navigate the AI landscape will be futile.

• Leveraging Demand Signals: AI thrives on data. CP brands must invest in technologies and processes to capture, analyze, and act upon real-time demand signals. This includes everything from search trends, social media sentiment, competitive pricing, weather patterns, and supply chain disruptions. AI models can then use these signals to predict future demand with greater accuracy, informing production, inventory, and promotional strategies. This data-driven foresight allows brands to optimize for algorithmic relevance, ensuring products are available and promoted precisely when and where AI predicts consumer interest will peak.
• Measuring ROI in an AI-Driven Environment: Traditional ROI metrics for marketing and sales may not fully capture the value created by AI-driven initiatives. Brands need to develop new frameworks for measuring the return on investment in AI capabilities. This could involve tracking metrics like algorithmic visibility scores, share of recommendations, AI-influenced sales attribution, and the efficiency gains from AI-optimized supply chains. Clear ROI measurement helps justify continued investment and refines AI strategies.
• Ethical Considerations in Data Use: As brands collect and utilize more consumer data, ethical considerations and privacy compliance become paramount. Trust is a non-negotiable asset. Brands must ensure transparency in data collection, provide clear opt-out options, and adhere strictly to regulations like GDPR and CCPA. Building consumer trust in how AI uses their data is essential for long-term brand loyalty and avoids potential regulatory pitfalls.

Mastering data and analytics empowers CP brands to understand the mechanics of algorithmic selection, identify opportunities for influence, and measure the impact of their AI strategies. It transforms guesswork into data-driven decision-making, ensuring that every investment in AI translates into tangible competitive advantage.

The Dawn of Agent-Driven Commerce: Preparing for the Future of Buying

While current AI-driven selection focuses on recommendations and search, the EY report hints at an even more profound transformation on the horizon: agent-driven commerce. This future state, characterized by "AI buying journeys," envisions a world where autonomous AI agents – sophisticated digital assistants – proactively orchestrate purchase decisions on behalf of consumers, moving beyond mere suggestions to active execution. This evolution is building on rapid advancements in real-time voice APIs and powerful reasoning capabilities seen in models like OpenAI's GPT-5, enabling complex, multi-turn interactions and decision-making by AI.

Imagine an AI agent, deeply understanding a consumer's dietary needs, budget, and sustainability preferences, not just recommending a brand of cereal but actively searching, comparing, and ordering it from a retailer, perhaps even negotiating on price or delivery time. This is the essence of agent-driven commerce, where the AI doesn't just guide; it acts.

The report highlights a concerning "low readiness for agent-driven commerce" among CP executives. Despite progress in leveraging AI for demand signals and ROI measurement, many brands are not yet equipped for a world where an AI might be their primary "customer." This exposes significant gaps that urgently need addressing.

To thrive in an agent-driven commerce world, brands must become "agent-ready." This involves several critical steps:

• Structured Product Data (Semantic Markup): AI agents require highly structured, granular, and semantically rich product data. This means going beyond basic descriptions to provide comprehensive attributes, certifications, ingredients (in detail), sustainability metrics, allergen information, user reviews, and even manufacturing processes, all in a machine-readable format. Brands need to invest in robust Product Information Management (PIM) systems and adhere to industry-standard semantic web markups (e.g., Schema.org) to ensure their products are intelligible to AI.
• Clear Value Propositions for AI Agents: Brands must articulate their unique selling propositions (USPs) in a way that AI agents can easily understand and prioritize. If an agent is looking for "the most sustainable option" or "best value for money," a brand needs to have its data clearly reflect these attributes. This may involve optimizing product content not just for human readers but for algorithmic interpretation.
• Seamless Integration with AI Shopping Assistants: Brands will need to explore APIs and integration points with emerging AI shopping assistant platforms. This could mean developing direct integrations, participating in specific AI agent marketplaces, or providing data feeds optimized for these new consumer interfaces. The goal is to ensure the brand's offerings are discoverable and actionable by AI agents.
• Proactive Engagement with AI Platforms: Just as brands engage with retailers, they will need to engage with developers and providers of AI agent technologies. This involves understanding their roadmaps, participating in pilot programs, and providing feedback to ensure brand-friendly features are incorporated into these evolving platforms.
• Brand Identity Beyond Visuals: In a world where AI agents might make purchases without direct human browsing, traditional visual branding (packaging, logos) still matters for post-purchase recognition and loyalty, but initial consideration might be driven by data points. Brands need to build their identity through consistent quality, clear value attributes, and strong reviews that algorithms can interpret.

The rapid progress of real-time voice APIs, coupled with advanced reasoning capabilities, means that agent-driven commerce is not a distant fantasy but an imminent reality. Brands that fail to become "agent-ready" risk being bypassed entirely by AI-orchestrated purchases, facing an ultimate form of brand consideration risk. The time to prepare for this future is now, not when it becomes ubiquitous.

US-Centric Implications: A Market at the Forefront of Change

The insights from the EY report carry particularly significant US-centric implications, reinforced by EY's major US operations and the substantial US representation within the CP executive survey. The United States stands at the vanguard of AI adoption and digital commerce, making its consumer products market a crucial testbed and an early indicator for global trends.

US consumers are among the most digitally savvy and adoptive of new technologies. They readily embrace online shopping, voice assistants, and personalized digital experiences. This high level of consumer readiness translates into a fertile ground for the rapid proliferation of AI-driven selection and, subsequently, agent-driven commerce. What might be nascent trends in other markets are accelerating quickly in the US.

For US CP brands, this means:

• Intensified Competition: The US market is characterized by fierce competition across nearly every consumer product category. The introduction of AI as a primary selection mechanism will only amplify this intensity. Brands that gain an early lead in algorithmic influence will consolidate market share, leaving late adopters struggling to catch up.
• Advanced Retailer and Platform Ecosystems: Major US retailers (e.g., Amazon, Walmart, Target) and tech platforms (e.g., Google, Meta) are global leaders in AI investment and deployment. These entities are rapidly integrating AI into every facet of their operations, from supply chain and logistics to retail media and customer experience. US CP brands must therefore contend with highly sophisticated AI environments, demanding equally sophisticated responses.
• The Race for Data and Talent: The demand for AI expertise and high-quality data scientists is particularly acute in the US, given the concentration of tech giants and innovative startups. US CP brands face a significant challenge in recruiting and retaining the talent necessary to build their AI capabilities. Moreover, the sheer volume of consumer data generated in the US necessitates advanced data management and analytics strategies.
• Evolving Regulatory Landscape: While the US currently has a more fragmented approach to AI regulation compared to, for example, the EU's comprehensive AI Act, the landscape is rapidly evolving. States are implementing privacy laws, and federal discussions on AI governance are ongoing. US CP brands must navigate this dynamic regulatory environment, ensuring their AI strategies are compliant and ethically sound to maintain consumer trust and avoid legal repercussions.
• Opportunity for Innovation: Despite the challenges, the US market also presents unparalleled opportunities for innovation. Brands willing to experiment with new AI-powered product development, personalized marketing at scale, and novel retail experiences can carve out significant competitive advantages. The openness to technological adoption among US consumers and businesses fosters an environment where bold AI strategies can yield substantial returns.

The structural disruption predicted by 71% of executives surveyed is not a distant threat but a present reality in the US consumer products market. This goes far beyond general AI adoption; it signifies a fundamental rewiring of how brands connect with consumers and how value is created and captured. US CP brands must recognize their unique position at the forefront of this change and act decisively to leverage AI for enduring success.

From Disruption to Opportunity: Strategies for CP Brands to Thrive

The EY report unequivocally states that AI represents a structural disruption, not merely another technological advancement. This distinction is crucial: it’s not about overlaying AI onto existing business models, but fundamentally reimagining how CP brands operate. For those willing to embrace this challenge, disruption morphs into unprecedented opportunity.

1. Reimagining Brand Building in the AI Era:
When algorithms mediate discovery, how does a brand build equity and loyalty?

• Focus on Unique Value and Emotional Connection: Brands must differentiate themselves beyond functional attributes that AI can easily compare. This means emphasizing unique brand stories, values, and emotional benefits that resonate deeply with consumers. When an AI agent presents options, a human consumer's ultimate choice might still be swayed by a strong emotional connection or brand narrative that the AI itself cannot fully process.
• Experiential Marketing and Community Building: Investing in immersive brand experiences, fostering vibrant online communities, and creating compelling content that goes beyond product features are vital. These strategies build brand affinity that transcends algorithmic recommendations, creating a pull factor that even the most sophisticated AI cannot ignore.
• Brand Purpose and ESG (Environmental, Social, Governance): AI agents, and increasingly consumers, are factoring in ethical considerations. Brands with clear, authentic ESG commitments and purpose-driven initiatives will stand out. This data, when properly structured, can be a powerful differentiator when an AI agent or consumer evaluates options.

2. Investing in Comprehensive AI Capabilities:
This goes beyond tactical AI deployments to a holistic strategic commitment.

• Talent Acquisition and Upskilling: Prioritize hiring data scientists, machine learning engineers, AI ethicists, and strategists. Equally important is upskilling existing employees across all departments to become AI-literate, enabling them to leverage AI tools and interpret AI-driven insights effectively.
• AI for Personalization at Scale: Utilize AI to deliver hyper-personalized product recommendations, marketing messages, and customer service experiences. This builds direct relationships with consumers that can strengthen brand loyalty even when initial discovery is algorithmically driven.
• AI for Supply Chain Optimization: Leverage AI for demand forecasting, inventory management, logistics optimization, and predictive maintenance. This ensures products are available when AI recommends them, preventing stock-outs that could lead an AI agent or consumer to choose a competitor.
• AI-Powered Product Innovation: Use AI to analyze consumer feedback, market trends, and competitive landscapes to identify unmet needs and accelerate product development cycles. This ensures brands are always offering fresh, relevant products that align with emerging consumer desires.

3. Cultivating Agile Innovation and Experimentation:
The AI landscape is rapidly evolving. Brands must adopt an agile mindset.

• Pilot Programs and A/B Testing: Launch small-scale AI initiatives, test hypotheses rigorously, and scale successful pilots. Continuous experimentation with new AI tools, platforms, and strategies is crucial.
• Fail Fast, Learn Faster: Embrace a culture that encourages calculated risks and views failures as learning opportunities. The speed of AI development means that waiting for perfect solutions will lead to falling behind.

4. Prioritizing Ethical AI and Trust:
Building and maintaining trust with both consumers and the platforms that host AI agents is paramount.

• Transparency and Explainability: Be transparent about how AI is used, especially in personalization and recommendation. Work towards explainable AI models where possible, so consumers and partners can understand the rationale behind AI decisions.
• Data Privacy and Security: Implement robust data governance frameworks, adhere to all privacy regulations, and prioritize cybersecurity to protect sensitive consumer data. Breaches of trust in the AI era can be catastrophic.

By adopting these strategies, CP brands can transform the structural disruption of AI into a powerful engine for growth, innovation, and sustained competitive advantage. It requires courage, investment, and a willingness to rethink foundational business practices, but the alternative is irrelevance.

The Urgency of Transformation: Why Now is Critical

The EY report's finding that 71% of executives agree AI represents a structural disruption, far beyond general adoption statistics, is perhaps its most profound revelation. This isn't merely another technological wave; it's a fundamental reshaping of market dynamics, consumer behavior, and the very essence of competitive advantage in the consumer products industry. The urgency for transformation cannot be overstated.

This structural disruption means that the competitive landscape is not just changing; it is being rebuilt from the ground up. Brands that perceive AI as an optional add-on or a distant future concern are fundamentally misjudging the speed and depth of this shift. Those who adapt swiftly and strategically will establish an unassailable lead, benefiting from early-mover advantages in algorithmic influence, data accumulation, and agent-readiness. Conversely, brands that delay their AI transformation risk being permanently marginalized, their products rendered invisible by algorithms, and their market share eroded by more agile competitors.

The time for deliberation is over; the era of decisive action has begun. Consumer products executives, particularly in the US, must recognize that their brands' future hinges on their ability to integrate AI into the core of their strategy, operations, and culture. This means moving beyond pilot projects to enterprise-wide transformations, investing significantly in AI talent and infrastructure, forging deep partnerships with retailers and platforms, and proactively preparing for the advent of agent-driven commerce.

The imperative is clear: secure your brand's future by embracing AI as the defining force of the modern consumer products landscape.

Securing Your Brand's Future in the AI Era

The EY report on AI reshaping consumer products selection and accelerating brand consideration risk serves as a pivotal moment for the industry. It vividly illustrates that the age of AI-driven selection is not a distant vision but our immediate reality, demanding a fundamental shift from brands competing for visibility to brands vying for algorithmic influence. The significant readiness gap among CP executives underscores the urgent need for strategic action, particularly given the rapid pace of AI adoption in the US market.

To thrive in this transformative era, consumer products brands must prioritize three critical areas: cultivating deep, collaborative partnerships with retailers and platforms to collectively influence algorithmic recommendations; mastering data and analytics to understand, predict, and respond to AI-driven consumer behaviors; and proactively becoming "agent-ready" to prepare for the imminent rise of agent-driven commerce.

This structural disruption presents both formidable challenges and unparalleled opportunities. Brands that commit to reimagining their brand building strategies, investing comprehensively in AI capabilities, fostering agile innovation, and upholding ethical AI practices will not only mitigate brand consideration risk but will also forge new pathways to growth and enduring relevance. The future of consumer products is intrinsically linked to AI, and only those brands that strategically embrace this powerful force will secure their place in the hearts, minds, and shopping carts of tomorrow's consumers. The time to act is now, to ensure your brand is not merely seen, but intelligently selected.