Stord: The AI-First Imperative: Reshaping E-Commerce for 2026 and Beyond

Artificial intelligence is rapidly transforming the e-commerce landscape, moving beyond incremental improvements to fundamentally redefine consumer interactions and operational capabilities. The Stord 2026 State of AI in E-Commerce Report highlights this shift, revealing that AI will not merely optimize existing models, but will revolutionize how brands connect with customers and execute across their value chains. For senior marketing and CX leaders, understanding this evolution is critical for building durable competitive advantage. The future of e-commerce belongs to brands adopting an AI-first mentality, seamlessly integrating intelligent automation from initial customer intent through to final delivery and post-purchase support.

The Agentic Shift: Consumer Behavior and Brand Performance

Consumer adoption of AI for online shopping is accelerating, signaling a decisive move towards “agentic commerce” where AI intermediates discovery, comparison, and purchasing. In 2024, 38% of consumers used generative AI for online shopping, a figure that surged to 51% in 2025. This 34% year-over-year increase underscores AI’s rapid embedding in the customer journey, with approximately 45-50 million U.S. consumers regularly using AI for shopping. This trend is particularly pronounced among younger demographics, with 37% of Gen Z and 29% of Millennials actively using AI, compared to only 5% of Baby Boomers.

This generational divide reveals distinct expectations. Younger cohorts view AI assistance as a natural extension of digital interaction, valuing speed, convenience, and personalization. Conversely, 50% of Baby Boomers express skepticism, citing distrust of AI recommendations or concerns about sharing personal information. However, AI recommendations demonstrably influence conversion, with 20% of consumers reporting they are more likely to convert when a product or store is recommended by AI. This propensity is even higher among Gen Z (38%) and Millennials (34%). The report projects a “zero-click buying era” by 2026, where AI agents autonomously complete purchases based on user preferences and context.

For brands, AI adoption is widespread yet shallow. While 88% of organizations used AI in at least one core function in 2025 (up from 78% in 2024), only 7% have reached a fully scaled stage. This maturity gap is significant, as organizations that achieve structural transformation by deeply integrating AI are outperforming the market, realizing 40% higher revenue and a 30% increase in Customer Lifetime Value (CLV). Furthermore, 95% of retailers report AI implementation actively decreases annual operating costs, with 20% to 30% reductions in inventory levels through predictive demand modeling.

What this means for CX and Marketing Leaders:

The shift to agentic commerce means brands must optimize for AI discoverability. This involves transitioning from traditional Search Engine Optimization (SEO) to Answer Engine Optimization (AEO), ensuring product information is machine-readable, structured, and verifiable for AI agents. Brand storytelling must balance emotional resonance with semantic clarity, using precise data points for materials, weights, and global trade identifiers.

What to do:

• Invest in AEO: Prioritize structured data and machine-readable product descriptions. Ensure product attributes are precise and comprehensive, including certifications and trade codes, to facilitate AI agent discovery and recommendation.

• Dual-track Marketing Strategy: Develop marketing strategies that appeal to both AI agents (data integrity, structured facts) as well as human customers (brand narrative, emotional connection).

• Measure AI-driven Conversion: Track conversion rates specifically from AI-recommended products or stores to quantify impact and refine AI integration strategies.

• Address Generational Trust: Implement transparent AI policies and clear opt-in mechanisms for data usage, allowing consumers to review transactions before completion (for the 21% open to it). This is critical for segments wary of AI.

What to avoid:

• Relying solely on traditional SEO: Keyword stuffing and backlinks are becoming less effective as AI agents provide direct answers.

• Ignoring data integrity: AI agents require verified, factual data. Manipulative tactics to force recommendations will lead to penalties and erode trust.

• Treating AI as a siloed tool: Isolated AI pilots will not yield the enterprise-wide benefits seen by leaders. Deep integration across marketing, product, and operations is essential.

Operationalizing AI: From Backend to Hyper-Personalization

The evolution of AI in e-commerce is marked by a progression from early recommendation systems and chatbots in the mid-2010s to today’s generative AI-driven hyper-personalization and agentic commerce. This trajectory is driven by advances in machine learning, cloud computing, and data availability, allowing AI systems to process complex, heterogeneous datasets in real time. This capability enables proactive decision-making, continuous optimization, and adaptive responses beyond what traditional batch processing could achieve.

Agentic commerce fundamentally compresses the shopping journey. A conventional 5-step process of discovery, research, checkout, and post-purchase management can be reduced to a 2-step “prompt-and-select” interaction. For example, an AI agent can analyze a prompt, curate product options with detailed breakdowns (specifications, price, reviews), complete the transaction, and manage post-purchase actions like order tracking and returns. This reduces cognitive load for consumers and eliminates friction points. The Stord report notes that two-thirds of consumers expect checkout in under 4 minutes, and 24% will abandon carts if the process is too complex. AI agents address this directly.

Hyper-personalization, powered by AI, moves beyond static segments and historical purchases to tailor experiences based on real-time intent, lifecycle stage, location, and external context (e.g., weather, trends). This enhances product discovery, ensuring recommendations are relevant and accurate. For instance, AI-driven sizing and fit engines reduce return rates. & Other Stories reported a 32% reduction in knitwear returns after deploying AI-powered fit recommendations. This also extends to the physical experience, with AI enabling hyper-personalized branded inserts in unboxing.

On the backend, AI is transforming operations from reactive reporting to prescriptive action. This includes:

• Prescriptive Inventory Management: AI agents ingest external signals (weather, geopolitical risks, social sentiment) to predict demand with high accuracy. This triggers autonomous replenishment, moving stock to distributed warehouses closest to anticipated demand, reducing inventory levels by 20% to 30%.
• Automated Tax Compliance: AI-driven platforms like Taxually process 40,000 tax calculations per minute, validate transaction data, and ensure cross-border compliance, reducing manual workloads by up to 70% in FTEs and saving 20-30 hours per month in VAT preparation for clients.
• Intelligent Logistics and Smart Routing: AI dynamically selects optimal carriers and routes, anticipating and pre-empting disruptions. This has resulted in 65% better service levels and 15% lower logistics costs, especially crucial as the last mile accounts for over 53% of total shipping expenditure. AI-powered Estimated Delivery Dates (EDDs) provide transparency, reducing customer inquiries and building trust.
• Collapsed Click-to-Ship Window: AI transforms warehouses into software-defined spaces, guiding operations from layout to worker routing with dynamic pathing and augmented precision (e.g., pick-to-light systems), eliminating legacy fulfillment delays and achieving elastic execution for high-velocity micro-drops.

What this means for Operations and IT Leaders:

Operationalizing AI effectively requires a unified, real-time data foundation and a shift away from legacy, fragmented systems. IT budgets consumed by “keeping the lights on” for legacy infrastructure (up to 31%) must be reallocated towards agentic execution.

Immediate priorities (first 90 days):

• Data Readiness Audit: Assess existing data architecture for fragmentation, quality, and real-time streaming capabilities. Prioritize moving from batch processing to real-time data streaming to enable accurate AI decision-making.

• Legacy System Integration Strategy: Develop a roadmap for integrating or replacing siloed WMS, ERP, and storefront systems to create a single source of truth for inventory, orders, and customer data.

• Pilot Agent-Augmented Workflows: Identify high-impact, low-risk areas to deploy AI agents for tasks like dynamic pricing or basic customer service inquiries.

Operating model and roles:

• Agent-Augmented Workforce: Redefine roles where AI agents handle routine, high-volume tasks (e.g., inventory forecasting, routing, basic customer inquiries), freeing human operators to focus on strategic planning, exception management, and complex problem-solving.

• AI Governance Team: Establish a cross-functional team (IT, Operations, Marketing, Legal) responsible for AI policy, performance monitoring, and ethical guidelines.

• AI Upskilling Programs: Train existing staff to collaborate with AI tools, interpret AI outputs, and leverage AI for enhanced decision-making.

What ‘good’ looks like:

• Unified Visibility: Real-time, end-to-end visibility of inventory, orders, and customer interactions across digital and physical touchpoints.

• Proactive Operations: AI systems autonomously anticipate demand, optimize logistics, and resolve issues before they impact the customer.

• Measurable Outcomes: Consistent improvements in key metrics such as:

• Customer Service: Reduced time-to-resolution (TTR), higher First Contact Resolution (FCR), improved Customer Satisfaction (CSAT) and Net Promoter Score (NPS) due to proactive communication and autonomous issue resolution.

• Fulfillment: Achieved 99%+ on-time delivery rates, reduced mis-picks (target <0.1%), and optimized logistics costs.

• Inventory: Maintained optimal inventory levels (20-30% reduction) with minimal stockouts, while meeting demand spikes.

Strategic Implementation and Governance: Lessons from Early Adopters

The rapid adoption of AI has provided critical lessons in what works, and what to avoid, in large-scale enterprise integration. The core takeaway is that AI is only as powerful as the infrastructure and governance behind it. Intelligence cannot compensate for fragmented systems or a lack of real-world grounding.

Governance and Risk Controls:

Early adopters have highlighted several key areas for robust governance:

Authenticity Over Automation: The Air Canada chatbot incident in 2024, where a chatbot hallucinated a bereavement policy leading to legal liability, demonstrated that speed without accuracy damages loyalty. AI is perceived as the voice of the brand.

• What to do: Implement Retrieval-Augmented Generation (RAG) to ensure AI agents ground responses in verified, real-time databases. Conduct red-teaming exercises to identify and mitigate hallucination risks.
• What to avoid: Deploying AI agents without robust factual grounding or clear escalation paths for complex, nuanced queries.

Ethical Dynamic Pricing: Delta Airlines faced backlash for using AI to adjust ticket prices based on individual user data, leading to “surveillance pricing” concerns and FTC investigations.

• What to do: Adopt “Value-Based Dynamic Pricing” where AI adjusts prices based on transparent factors like real-time inventory levels, shipping distances, or market demand, rather than individual customer profiles. Ensure pricing algorithms are auditable and compliant with regulatory mandates.
• What to avoid: Implementing opaque, personalized pricing models that could be perceived as discriminatory or exploitative, eroding customer trust and inviting regulatory scrutiny.

Augmented Intelligence with Guardrails: Unmonitored AI can lead to catastrophic blind spots, as demonstrated by Zillow’s iBuying failure, where an algorithm, unable to account for a shifting housing market, caused $500 million in losses.

• What to do: Implement an augmented intelligence model. For instance, Klarna’s AI assistant handles two-thirds of customer service chats, escalating complex disputes to human agents. Similarly, Stitch Fix uses AI to suggest styles, but human stylists make the final selection. This “human-in-the-loop” approach ensures contextual judgment and integrity.
• What to avoid: Full autonomous execution in high-stakes, volatile, or sensitive areas without human oversight, leading to potential significant financial or reputational damage.

Integrated Data for Visibility: Target’s 2022 inventory glut and Nike’s struggles with inventory and sales despite AI investments were both linked to fragmented data. AI amplifies errors when foundational data and processes are misaligned.

• What to do: Prioritize a unified data layer across sales channels, CRMs, order management systems, and fulfillment networks. This provides AI agents with a real-time, end-to-end view of the commerce stack and logistics network, ensuring accuracy in decision-making.
• What to avoid: Implementing AI on top of fragmented architectures, which will lead to “garbage in, garbage out” scenarios, accelerating errors and hindering enterprise-wide impact.

By prioritizing these strategic shifts and embedding AI across their value chains, large enterprises can transform customer experiences, optimize operations, and secure a competitive edge in the evolving AI-commerce landscape. The brands that adopt an AI-first mentality, underpinned by robust governance and integrated infrastructure, will be best positioned to thrive in 2026 and beyond.

Report Source: Stord 2026 State of AI in E-Commerce Report.