Breaking Down Silos: Why AI Shopping Success in 2026 Demands Retail Agility

Retailers have been hearing about “the future of AI” for years, but 2026 is the year it will become operational. Artificial intelligence is quickly moving from being a novelty that consumers test out with curiosity to a tool they turn to for comparisons, deal-finding, and full purchase-path orchestration. In PYMNTS’ 2025 Black Friday survey, 50.3 percent of the respondents used generative AI at least once during holiday shopping. This signals a significant shift in how consumers shop and points to AI’s bigger role as a shopping advisor, decision-making filter, and even a buyer.

Yet even as retailers allocate budgets toward AI pilots, more foundational issues are coming to light and holding many brands back: organizational and technical silos that make agility difficult.

The Barrier to AI Optimization is the Org Chart

Most retailers have built teams around legacy channels, including e-commerce, digital marketing, CRM, affiliate, loyalty and paid media. Those teams often use their own vendors, operate on separate datasets, maintain separate key performance indicators, and follow separate road maps. This structure was workable when e-commerce was dominated by search-driven discovery, website UX, and paid media. However, AI collapses those boundaries. A shopping agent won’t distinguish between an affiliate link, a loyalty perk, a price match, or a PDP feature. It can evaluate product data points and the entire value ecosystem around a product, analyzing availability, incentives, historical pricing, shipping speed, trust factors, consumer sentiment, etc. Then, it can choose the optimal product for the user.

If internal teams aren’t aligned, the AI surface that consumers see becomes fragmented, outdated or incomplete, and inconsistencies become liabilities in an AI-assisted shopping world. Retailers whose internal systems produce coherent, unified, real-time signals for large language models (LLMs) and agents will outperform those that don’t.

When Systems Slow Down Strategy

Many retailers still operate on complex, aging e-commerce backends: monolithic platforms layered with middleware, legacy tracking scripts, and years of custom integrations. Even small changes could require coordination across engineering, legal, marketing, and analytics teams, making execution slow.

ITP compliance illustrates the challenge. Apple introduced Intelligent Tracking Prevention (ITP) in 2017 to limit cross-site tracking, disrupting cookie-based attribution. The issue has been discussed for years, but anecdotal evidence suggests it remains unresolved for some retailers. Not due to lack of awareness, but because these types of changes can take quarters, not weeks.

All this matters even more now. AI-driven shopping requires clean product data, real-time pricing and incentives, reliable server-side tracking, and architecture ready for instant payments and account-to-account payment rails. Retailers that struggle to modernize infrastructure will in turn struggle to compete in AI-assisted commerce.

AI Will Reward Agility, Not Ad Spend

An overlooked implication of AI shopping agents is how they redistribute competitive advantage. In the search era, retailers with the biggest paid media budgets often won visibility. However, AI agents do not prioritize spend. They prioritize structured data quality, consumer value, and trustworthiness.

A slow-moving organization with messy data could effectively be invisible to AI shopping systems. More agile retailers will be better equipped to make the updates necessary for AI tools to access clean, real-time data and become preferred by default.

How Retailers Can Break Down Silos and Accelerate Change

The path forward requires both organizational restructuring and system modernization. Three strategies to drive change include:

1. Establish a cross-functional AI commerce council. Bring together the relevant teams that need to be part of the conversation: e-commerce, CRM, affiliate, loyalty, engineering, data analysis, compliance, etc. Empower this group with decision authority over AI-related road map items. McKinsey notes that cross-functional operating models like this dramatically improve speed to market for digital initiatives.
2. Prioritize infrastructure, not pilots. To optimize e-commerce sites for AI agents or LLM referrals, modernize the system “plumbing.” These foundational upgrades actually improve all retail channels.

• Consolidate product feeds and enrich them with the unique, descriptive attributes that AI tools look for, and keep them updated.
• Clean up your site and your product pages by using org markup to ensure LLM bots can access it.
• Build API/MCP accessibility for models and agents.

3. Move toward modular architecture. Gartner emphasizes composable commerce as a core capability for e-commerce sites seeking more agility. Breaking monolithic legacy platforms into flexible, deployable component services improves organizations’ ability to move faster and reduces dependency bottlenecks.

AI-Assisted Shopping Will Reward the Most Adaptable Companies

Organizations that break down silos, modernize their infrastructure, and accelerate operational agility will be ready as consumers shift more and more to AI-assisted shopping. Those who prepare now are building a durable competitive advantage for what’s next.

Tristan Barnum is chief marketing officer and head of AI innovation at Wildfire Systems, a white-label loyalty platform.