The consumer products industry is undergoing a fundamental shift. Traditional sales and marketing operating models, designed for a linear and predictable environment, are increasingly insufficient in a world where artificial intelligence (AI), algorithms, and platform-led interactions shape consumer demand. The EY State of Consumer Products Report 2026 highlights that while many companies pursue incremental optimization, true competitive advantage will stem from a structural redesign of commercial decision-making processes. This requires senior marketing and CX leaders to move beyond isolated functional improvements and embrace an integrated, AI-driven commercial model.
The Algorithmic Shift: Redefining Consumer Influence
The landscape of consumer demand is no longer solely driven by brand campaigns or direct retailer relationships. Instead, it is increasingly mediated by algorithms, digital platforms, and AI agents acting on behalf of shoppers. This shift means that brands now compete not only for attention or physical shelf space, but also for selection by systems that operate upstream from direct consumer interaction.
Historically, consumer products companies optimized pricing, promotions, media, and product portfolios as distinct functions. However, this fragmented approach, termed the “Optimization Trap” by EY, is no longer sufficient. While companies improve individual decisions, they fail to connect these decisions across sales and marketing functions, leading to a “Decision Gap” between insight and coordinated action. The EY Consumer Products Dynamics Research 2026 indicates that 64% of companies report little to no improvement from their sales and marketing transformation initiatives in areas such as commercial decision speed and effectiveness, or brand impact at the point of purchase.
This new reality is illustrated by consumer behaviors. The EY AI Sentiment Index 2026 reveals that a meaningful minority of consumers are already comfortable delegating purchasing decisions to AI or using AI to support their choices. For instance, in a scenario where a consumer needs laundry detergent and requests “sensitive skin, under $10,” an AI agent instantly evaluates options based on historical purchases, pricing, and retailer platform prioritization. The agent selects a product, bypassing traditional search, browsing, or campaign influence. This process signifies that a brand can be neither rejected nor selected if it never enters the AI’s decision set.
The implication for leaders is clear: influence is shifting upstream. Retailer and platform algorithms interpret signals like product data, pricing, availability, and consumer feedback. Brands must actively shape these signals, integrate data effectively with retailer ecosystems, and ensure consistent product information to influence automated recommendations and substitutions. The challenge is not merely to optimize existing processes faster, but to fundamentally rethink how decisions are made and governed in this system-driven market.
Summary: Consumer demand is increasingly shaped by algorithms and AI agents. Companies stuck in siloed optimization are failing to connect decisions across functions, resulting in lost value as influence shifts upstream. Brands must adapt their strategies to influence platform algorithms directly.
Bridging the Divide: Integrated Decision-Making and Operating Models
While AI and advanced analytics are technically ready to transform commercial decision-making, most organizational operating models are not. The EY Consumer Products Dynamics Research 2026 highlights that only 11% of executives report their sales and marketing teams operate as a unified growth engine, and just 15% state that commercial data is fully integrated to drive cross-functional decisions. This disconnect results in friction, delays, and rework, hindering speed to market and overall commercial outcomes.
Closing this “Decision Gap” requires more than just stronger analytics or faster tools; it demands an integrated way of working where commercial decisions are treated as a connected capital allocation challenge. This involves managing pricing, promotion, media investment, and portfolio strategy as interdependent choices, rather than separate functional silos. Key enablers for this shift include shared decision infrastructure, common performance metrics, cross-functional accountability, and strong leadership alignment around commercial priorities.
Barriers to transformation are often organizational rather than technological. The report reveals that “decision rights, governance, and approvals slow execution” is the leading barrier (42%), followed by “lack of sustained leadership alignment and sponsorship” (36%). These organizational impediments are 1.4 times more impactful than technology and data foundations in limiting transformation speed. This underscores that the primary task is to rewire organizations around AI, not simply overlay AI on existing, outdated processes.
For example, Kraft Heinz successfully reshaped its innovation decisions by building an integrated system that links idea generation, evaluation, and planning into a continuous, data-driven process with AI at its core. This approach substantially increased portfolio value and reduced required approvals through streamlined governance. Similarly, Asahi Europe & International connected revenue growth decisions with AI, leading to a 60% year-on-year growth in daily tool usage and faster analysis of hundreds of promotions within 24 hours. These cases demonstrate that integrating data and decisions across functions, with clear ownership and aligned incentives, is crucial for realizing AI’s potential.
What to do:
- Define Core Commercial Decisions: Identify the three to five commercial decisions that drive the most growth (e.g., pricing, promotion, media, assortment).
- Establish Integrated Governance: Assign shared ownership, KPIs, and governance structures for these core decisions across sales, marketing, and supply chain. Ensure decisions are made once and executed consistently without rework.
- Align Incentives: Structure incentives to reward cross-functional collaboration and shared outcomes, moving beyond functional silos.
- Invest in Data Integration: Prioritize building an external data platform that integrates with retailers, APIs, and data ecosystems to embed brands in decision systems.
What to avoid:
- Optimizing in Silos: Do not improve individual functions without a clear plan for how decisions connect across the entire commercial model.
- Technology-First Approach: Avoid deploying AI solutions without first redesigning the underlying operating model and governance.
- Ignoring Decision Rights: Do not defer conversations about who owns which decisions and how trade-offs are governed.
Summary: Integrating sales, marketing, and supply chain into a single commercial decision system is critical. Organizational barriers like unclear decision rights and misaligned incentives hinder this more than technology. Leaders must prioritize governance, shared KPIs, and cross-functional alignment to fully leverage AI.
Strategic Imperatives for an AI-Driven Commercial Model
The shift to an AI-driven commercial model requires leaders to make deliberate strategic choices, rather than allowing their operating models to evolve informally. The EY report outlines eight key implications that translate into actionable priorities for senior marketing and CX leaders.
1. Focus on Integrated Value Creation: Recognize that the most significant value sits in the hand-offs between functions, not within individual functions. Define cross-functional decisions crucial for growth and assign shared ownership, KPIs, and governance to ensure these are executed once and consistently.
2. Prioritize Decision Velocity over Insight Accumulation: The biggest challenge is converting insight into actionable decisions at speed. Identify decisions that drive growth, measure the time from insight to action, and simplify governance to reduce cycle time. For example, a telecom provider could use AI to identify churn risks and automate targeted retention offers (e.g., 10% discount on monthly bill; 3-month commitment) within minutes, rather than days.
3. Define AI’s Role and Accountability: Clarify which decisions will be automated by AI, which will remain human-led, and how accountability will be maintained. Align incentives and decision rights to reflect AI’s practical use. In retail, this could mean AI automates dynamic pricing within predefined guardrails (e.g., price changes within +/- 5% of base price; real-time competitive matching), with human oversight for strategic promotions or new product launches.
4. Map Shifting Influence Upstream: Understand where influence is moving in your categories—from traditional channels to retailer platforms, algorithms, and AI agents. Prioritize investment in capabilities that influence these upstream environments, including robust product data, consistent pricing, and platform visibility. For a B2B SaaS company, this means optimizing product listings and feature descriptions on marketplace platforms (e.g., AWS Marketplace, Salesforce AppExchange) to rank highly in algorithmic recommendations, rather than solely relying on direct sales outreach.
5. Embrace Integration as Differentiation: While efficiency gains from AI will be widely available, true advantage will come from how effectively commercial decisions are connected and aligned. Integrate trade, media, and pricing into a single commercial investment model with shared metrics and a common view of ROI, ensuring these levers are planned and optimized together. A financial services firm could integrate its marketing spend, customer acquisition incentives, and product pricing models into a unified platform, optimizing for customer lifetime value (CLTV) rather than siloed channel ROIs.
6. Redefine Roles Towards Judgment and Orchestration: As decisions become more connected and AI-supported, the value of execution declines, and the value of judgment and coordination increases. Redefine core sales and marketing roles around decision-making, strategic influence, and orchestration, rather than task execution. This involves equipping teams with advanced analytical skills and a deep understanding of AI outputs, moving from report generation to strategic interpretation and cross-functional collaboration.
7. Clarify Ecosystem Ownership and Partnerships: Define what capabilities must remain in-house for value creation (e.g., core consumer understanding, strategic pricing decisions) and where to partner deliberately. Protect direct access to consumer data through direct channels or robust data agreements. For a healthcare provider, this might mean owning patient outcome data and treatment protocols, while partnering with AI diagnostic companies for specific image analysis or predictive analytics, with clear data sharing agreements and consent policies in place.
8. Act Decisively on Governance and Data Ownership: Do not defer key decisions around governance, automation, and data ownership, as these will define your future commercial model. Make these choices explicit now, rather than allowing them to evolve informally and create future constraints. Establish clear data governance policies (e.g., data ownership for customer profiles; data retention periods) and escalation paths for data discrepancy issues between marketing, sales, and product teams.
What ‘Good’ Looks Like in an AI-Driven Commercial Model:
- Unified Commercial View: All commercial functions operate from a single, integrated data platform with common KPIs (e.g., customer acquisition cost, gross margin, market share, customer lifetime value).
- Automated Decision Support: AI-driven systems provide prescriptive recommendations for pricing, promotions, and portfolio adjustments, automating routine decisions within defined thresholds (e.g., promo discount limits of 15% off MSRP) and escalating exceptions to human strategists.
- Empowered Commercial Teams: Sales and marketing professionals shift focus to strategic relationship building, complex problem-solving, and orchestrating cross-functional initiatives, supported by real-time insights from AI tools.
- Agile Experimentation: A culture of continuous testing and learning, with rapid iteration of commercial strategies based on performance data (e.g., A/B testing marketing creative; weekly review of pricing elasticity models).
- Strong Data Governance: Clear policies and controls for data ownership, quality, privacy, and security, ensuring trusted facts inform all decisions.
Summary
The consumer products industry is at an inflection point. The rise of AI and algorithmic commerce demands a fundamental transformation of sales and marketing operating models. Companies that continue to operate within fragmented, siloed structures risk falling into an “Optimization Trap,” improving individual functions while losing relevance in an ecosystem where demand is shaped upstream. Reclaiming relevance requires senior leaders to embrace connected decision-making, integrate data and processes across the commercial model, and redefine roles to emphasize judgment and orchestration. The technology is ready; the challenge is organizational. Acting with clarity and intent now—by establishing robust governance, aligning incentives, and fostering a culture of cross-functional collaboration—will determine competitive advantage in the AI-driven world of 2026 and beyond. Inaction is not a neutral choice; it is a decision to fall behind.
Source: EY State of Consumer Products Report 2026, EY Consumer Products Dynamics Research 2026, EY AI Sentiment Index 2026.
