Digital Transformation Stories and Insights

Retail loyalty has long been built around a simple assumption: customers will open your app, browse the catalog, respond to offers, and complete their purchase within your brand’s digital channels. AI assistants are beginning to change that behavior. Customers are aggressively delegating the digital legwork of shopping—from discovering products to comparing prices—to AI. The shift is already measurable: according to Adobe, AI-driven traffic to US retail sites surged 393% year-over-year in Q1 2026, converting 42% better than traditional channels. We saw the early warning signs last summer when ChatGPT quietly absorbed 16% of Zara’s inbound web traffic and 8% of H&M’s. What’s in this article: How real-time APIs that expose loyalty perks to AI may prevent algorithmic cart abandonment in today's hybrid retail era Why human-centric experiences remain your strongest defense against AI-driven price commoditization How portable loyalty credentials might eventually replace walled-garden apps to power decentralized commerce Ways proprietary data and machine-targeted incentives could help brands win algorithmic auctions in future AI discovery engines This trend has fueled the specter of "invisible commerce"—the anxiety that customers will blindly click "buy" based on an AI summary, or let algorithms auto-replenish their wardrobes, severing the brand relationship entirely. But a recent DOSS survey puts this in perspective. While 77% of consumers eagerly use AI to research products, a mere 6% actually trust the machine to execute the final transaction autonomously. The shopper is still human, but the visitor increasingly is not. How do you build loyalty in this changed retail landscape? Navigating this transformation demands a two-phased roadmap. First, brands must master today's hybrid reality by engineering systems for both AI agents and humans. Second, they must prepare for the future, when loyalty outgrows the walled-garden app to become a portable credential. Phase 1: The Hybrid Reality: One Program, Two Customers To serve both human shoppers and AI agents, brands must adapt their infrastructure in three ways. Make Loyalty Perks Algorithmically Visible Agents optimize on quantifiable value. Brands spend millions designing gamified VIP portals and splashy banners, but if member pricing or free-shipping perks exist only in the frontend UI, an AI agent assumes the customer needs to pay full retail price (because it can only parse the data layer). There is a valid fear among revenue leaders here: Doesn't exposing promotions via API invite bots to ruthlessly scrape our margins? It is a fair concern, as standardizing this data will accelerate cross-brand comparability. The alternative, however, is algorithmic invisibility. In a split-second matrix evaluation, an AI will bypass your brand in favor of a competitor whose loyalty data is structured for the machine. The plumbing required to bridge this is already standardizing rapidly. Talon.One’s Unified Incentives Protocol (UIP), released this January, laid the groundwork for exposing loyalty benefits natively to machines. More critically, Google’s Universal Commerce Protocol (UCP) allows an AI agent to authenticate a shopper’s profile and securely apply their rewards during a transaction. If your retail infrastructure isn't conversant in these protocols today, your brand simply won't appear in the AI's optimized cart. Make Data Quality a Loyalty Feature Even with the right APIs in place, the entire premise of agentic discovery collapses without quality, real-time data. This was evident when OpenAI pulled its Instant Checkout pilot from ChatGPT in March. The initiative, which had roughly 30 merchants live, failed because the AI relied on scraped, lagging inventory and pricing data. Many assume that slight data latency is acceptable for top-of-funnel product discovery. But that assumes a traditional shopping journey. In an agent-assisted flow, the dynamic is different. Consider the reality of how this works: a customer asks their AI to find a specific jacket and apply their VIP tier discount. The bot parses the retailer's available data and presents a definitive price. The customer then clicks through to execute the purchase. If they arrive at the checkout and the price has jumped (or the item is suddenly out of stock) because the retailer's data feed was lagging, the trust is instantly broken. The customer will not blame the bot for hallucinating the price, but they will definitely blame the brand for breaking a promise. Real-time data synchronization is thus a frontline defense against algorithmic cart abandonment. Protect the Human Relationships As retailers rebuild systems for AI, there is also the danger of going too far. If your loyalty program is redesigned solely to satisfy a bot’s mathematical hunt for the lowest price, you risk reducing your fashion brand to a mere commodity. The best defense against a race to the bottom is the human experience. A bot cannot feel the thrill of an early product drop, the exclusivity of a private community, or the personal touch of a styling event. But humans absolutely do. This is why programs like Levi’s Red Tab and Nike Membership continue to capture up to half of direct-to-consumer revenue and triple customer spend. They achieve this by making the shopper feel like an insider. Retailers must continue to invest in the high-touch experiences that an LLM cannot replicate. When an AI assistant reduces the discovery phase to pure math, experiential perks are the only safeguard preventing humans from overriding their loyalty and reaching for the cheapest option. Phase 2: The Post-Channel Era: Loyalty as a Portable Credential Merkle forecasts that by 2030, roughly $2 trillion in digital commerce will flow entirely through autonomous AI agents. Surviving this shift means navigating four new realities: No More Walled Gardens Retail’s reflexive instinct is to hoard first-party data by keeping customers inside an owned ecosystem, forcing them to download an app just to access rewards. But when discovery and checkout happen inside an AI chat prompt, that walled garden could become a barrier to revenue. The payments industry has already accepted this shift. Visa’s Intelligent Commerce and Mastercard’s Agent Pay are already issuing secure, delegable tokens bound to specific AI agents. Loyalty identities can also adopt this framework. A VIP customer need not log into your site; their AI assistant can carry a revocable "loyalty passport" containing their tier status, historical preferences, and entitlements, and present it at digital surfaces across the open web. Algorithmic Arbitrage and the "Fit" Defense Portability introduces a new problem: ruthless comparability. Accenture data shows that 37% of consumers will authorize their agent to break brand loyalty if it finds a mathematically superior deal. In this algorithmic auction, legacy loyalty mechanics will fail. If your program relies on complex mental arithmetic (“Spend $150 to earn 500 points toward a future 10% discount"), an agent optimizing for immediate value will simply route the purchase to a competitor offering a flat 12% off today. To prevent this race to the bottom, retail brands must inject non-price utility into their loyalty credentials. For apparel, that unfair advantage is fit. Online apparel returns currently hover around a margin-crushing 24.4%, with up to 53% of those attributed to fit/sizing issues. Retailers like ASOS are already clawing back margins here, cutting return rates by 160 basis points through a mix of virtual try-on technology and targeted policy changes. Imagine a loyalty program that stores a dynamically verified body profile as part of its portable token. If a brand issues a mathematical promise—"When your agent buys our recommended size, it fits, or we absorb 100% of the return cost"—it creates a calculable value that no generic promotional code can beat. That definitive, zero-risk utility wins the AI's auction every single time. Agent-Facing Incentives Because AI agents will act as the gatekeepers of discovery, brands must learn to market directly to the machine. We are moving past SEO and into Agentic Engine Optimization (AEO). AI optimizes for two things: the absolute lowest price and the highest probability of task completion. To rank on third-party discovery surfaces, your incentive design must be agent-facing. This means offering "machine-only" utility. For example, a retailer might offer an exclusive 2% discount specifically for agent-routed transactions, simply because the bot bypasses the retailer's frontend UI and lowers compute costs. Or, the brand guarantees a 99.9% inventory accuracy via API, ensuring the bot never fails its checkout task. You must mathematically incentivize the algorithm to present your cart over a competitor’s. Security at the Speed of Autonomy Finally, retail CxOs must address the dark side of agentic commerce. Autonomous agents are already turning loyalty point balances into soft targets for automated fraud. If a bot can browse without friction, bad actors can program an agent to easily drain unmonitored reward points across thousands of accounts in seconds. Security must therefore scale with autonomy. Scoped permissions (allowing an agent to apply a discount but forbidding it from redeeming cash-value points), algorithmic spend limits, and per-agent token revocation will be baseline requirements for operating in the agentic era. The Engineering Mandate For retail CxOs, the leap from today’s walled-garden apps to the portable, tokenized loyalty of 2030 can seem daunting. The roadmap, however, is quite practical. It begins by laying the precise data foundations that AI demands, while simultaneously designing the high-touch, visceral experiences that humans crave. Our retail and luxury retail services operate at this intersection. While the fully autonomous agentic passports of the future are still being defined, the architecture required to survive the transition must be built today. Leveraging our High AI-Q™ engineering approach, we help brands bridge the technology gap. We build the real-time data layers that satisfy the agent’s rigorous math, design the experiential loyalty programs that defend your emotional moat, and seamlessly wire the two together. Ultimately, agentic commerce is not about bypassing the human; it is about serving a shopper who now navigates the market armed with a powerful proxy. So the question for your next executive meeting may not be how to get more people to download your app. It may be: When an AI agent searches on behalf of our best customer, what does it see?

GenAI is no longer an experiment in a single innovation lab. Network telemetry shows enterprise GenAI traffic surged nearly 900% in 2024. Organizations are interacting with dozens of unsanctioned GenAI apps, causing data loss incidents linked to AI to double. Today, they make up 14% of all SaaS Data Loss Prevention (DLP) events. Behind these numbers is a familiar pattern: when vetted AI tools are slowed by pay-per-token budgets or compliance controls that affect output quality, employees turn to personal subscriptions that offer fewer constraints. This is "shadow AI" in practice, and it has rapidly evolved into one of the most expensive drivers of data breaches. IBM’s 2025 Cost of a Data Breach report shows incidents involving shadow AI cost an average of $670,000 more than typical breaches, accounting for roughly 20% of all breaches today. This article outlines the active governance-first blueprint we deploy today, leveraging modern Security Service Edge (SSE) and Identity platforms, to enable rapid AI adoption while strictly containing data leakage and regulatory risk. What's in this article: How unvetted AI tools bypass standard security and inflate data breach costs Why legacy security, such as blocking domains, fails against shadow AI A four-pillar governance framework to map AI usage, enforce risk-based policies, and apply data guardrails without stifling innovation A 90-day execution blueprint to move to a secure, observable AI environment Shadow AI: How We Got Here “Shadow AI” describes AI tools and services used without IT or security teams’ approval. It can be browser plugins, SaaS copilots, GenAI APIs integrated directly by teams, and personal accounts of popular AI platforms. The intent is mostly benign: employees are trying to draft emails faster, summarize documents, debug code, or explore ideas. The problem is what goes into the prompt. Recent studies highlight the scale of this behaviour: Widespread Adoption: The average enterprise interacts with around 66 different GenAI applications, roughly 10% of which are rated high-risk. High-volume Policy violations: Organizations see an average of 223 monthly attempts to paste regulated data, source code, Intellectual Property (IP), and credentials into GenAI prompts. Inadequate Tooling: Only about half of organizations have DLP controls capable of preventing sensitive data from leaking via GenAI apps in real time. In other words, GenAI usage is exploding, sensitive data is flowing through prompts, and technical guardrails are lagging far behind. Training and awareness alone cannot close this gap. How Unvetted AI Creates Costly Data Breaches Traditional shadow IT created isolated islands of unapproved tools. Shadow AI goes further: it creates direct channels for high-value data to leave your organization entirely. Here are the key risk patterns driving this leakage: Prompt-based Exfiltration: Employees paste customer records, strategy decks, or proprietary code into prompts to "get better answers." Today, regulated data and IP make up the largest share of GenAI policy violations. For instance, an engineer pasting proprietary authentication logic into a public AI tool to debug a production outage inadvertently exposes critical IP to the vendor's training dataset. The Long Tail of GenAI Apps: Enterprises often see traffic flowing to dozens of unvetted GenAI tools. Many of these lack mature security, contractual protections, or data handling guarantees. Opaque Data Flows: Over 80% of organizations cannot see where their AI-related data ends up. They are running hundreds of unofficial apps with zero visibility. Expanded Attack Surface: OAuth integrations and AI browser extensions frequently request broad access to mailboxes, files, and code repositories. Left unchecked, they become easy pivot points for attackers. When a breach involves shadow AI, remediation is costlier and slower. Security teams are left completely blind, unsure of which tools were used, what specific data was leaked, or how long the exposure has been occurring. Why Legacy Governance Models Fail Against GenAI Most enterprises today respond to GenAI with one of three approaches: Block Everything: Outbound traffic to popular GenAI domains is blocked. This may reduce leakage in the short term, but employees often route around it via personal devices, mobile hotspots, or lesser-known tools. Awareness-first: Organizations run training, issue AI usage guidelines, and rely on user judgment. Without technical enforcement, this tends to drift back toward convenience under pressure. Tool-centric: Security teams deploy a new “AI security” platform without aligning it to existing data governance, identity, and risk processes. The underlying issue is architectural: AI is treated as a new silo instead of a channel that cuts across data, identity, cloud, and SaaS. Leading guidance from regulators and industry bodies—such as NIST’s AI Risk Management Framework (RMF) and emerging regional AI acts—stresses that AI risk must be integrated into existing governance and assurance structures, not managed in a vacuum. The Four Pillars of Governance-First GenAI Security A governance-first model for GenAI adoption can be framed around four core principles. 1. Inventory Before Innovation You cannot govern what you cannot see. Discover AI Usage: Utilize AI-powered Security Service Edge (SSE) platforms, combining Cloud Access Security Broker (CASB), endpoint DLP, and proxy capabilities, to maintain full visibility into GenAI traffic across both on-premises and remote environments. Map Data Flows: Understand which repositories (CRM, ERP, source code, file shares) are feeding prompts directly or via connectors. Catalog Tools: Establish a living register of approved AI services and explicitly track unsanctioned tools with associated risk levels. 2. Policy and Risk-Based Access Once an inventory exists, governance can move from generic warnings to concrete rules. Define AI Usage Policy: Codify what data classes (for example, payment data, health data) may never be sent to external GenAI, and under what conditions internal AI platforms may consume sensitive data. Risk-based Access Tiers: Establish rules for tools. For example, Tier 1 high-risk tools are blocked; Tier 2 moderate-risk tools allow read-only, anonymized, or synthetic data, and full usage is reserved for managed devices and within specific roles. Align with Regulations: Ensure policy explicitly references applicable frameworks and laws (NIST AI RMF, sectoral regulations, EU AI Act, etc.). 3. Guardrails in the Data Path Operationalize policy via guardrails where data flows. AI-aware DLP and CASB: Extend DLP policies to GenAI endpoints, inspecting prompts and file uploads in real time to block or redact sensitive data before it leaves the organization. Industry data shows that only around half of organizations have this in place today, despite rapidly increasing policy violations. Secure AI Gateways and Proxies: Route GenAI calls, both user and application integration traffic, through gateways that handle authentication, encryption, and logging. For example, in our architecture, integrating identity providers with SSE gateways ensures only authenticated users on compliant devices reach sanctioned GenAI tools. Pattern Libraries for Safe Prompts: Offer approved prompt templates, context windows, and integration patterns (for example, a retrieval-augmented pattern that keeps sensitive data in a private vector store). 4. Continuous Assurance and Monitoring GenAI systems evolve too quickly for a one-time risk assessment. Establish a cross-functional GenAI council to track metrics like blocked uploads and newly discovered apps. Apply risk assessment gates at each stage of the GenAI solution lifecycle, from pilot to production. Aligning AI Accountability: Three Lines of Defense Governance-first security is as much about operating model as it is about technology. A practical structure often mirrors the “three lines of defense” concept adapted for AI: Line of DefenseOwnerKey ResponsibilitiesFirst LineBusiness & Product TeamsIdentify use cases, classify data sent to AI systems, and apply approved templates for safe usage.Second LineCentral AI Governance & SecurityDefine policy/risk appetite, approve or deny GenAI tools, and operate AI gateways, DLP, and CASB controls.Third LineAudit & AssurancePeriodically review control effectiveness, policy adherence, and validate that reported usage aligns with reality. Clear Responsible, Accountable, Consulted, and Informed (RACI) Matrix definitions around GenAI use cases, from idea intake to decommissioning, are critical. Without this, shadow AI fills the vacuum as teams move faster than central functions can respond. A 90-Day Blueprint for Secure AI Adoption Enterprises often ask a simple question: Where do we start? Based on our own phased rollout and internal learnings, the following is a pragmatic 90-day plan to establish a minimum viable governance-first model. Days 1-30: Discovery Run discovery for GenAI traffic across proxies, firewalls, and CASB tools. Identify top GenAI applications in use (sanctioned and unsanctioned). Map which business units and roles are driving the highest usage. Draft an interim AI usage guideline to reduce risky behaviors while governance is being formalized. Days 31-60: Codify Policy and Implement Controls Formally sanction a dedicated GenAI governance working group. Define prohibited and restricted data categories for GenAI usage. Implement or extend DLP and CASB policies specifically for GenAI endpoints. Deploy an AI gateway or secure proxy for at least one sanctioned GenAI platform and route pilot traffic through it. Days 61-90: Operationalize and Scale Document approved GenAI usage patterns and publish them as reference architectures. Extend AI-aware DLP to additional high-risk channels (developer tools, collaboration platforms). Integrate GenAI risk checks into existing change management and solution review processes. Establish monthly metric reporting to the governance forum and quarterly summaries to executive leadership. This is not the final state, but it is sufficient to move to a controlled, observable environment where innovation and security can coexist. Characteristics of a Mature GenAI Governance Program A governance-first GenAI security model should be judged less by the number of blocked prompts and more by its ability to support safe, scalable adoption. Success indicators include: Reduced Surprise: Shadow AI tools and traffic become rare; new tools are surfaced via discovery and quickly triaged through governance. Fewer Risky Prompts: Policy violations tied to GenAI start to plateau and then decline as guardrails and training work together. Improved Breach Economics: Fewer incidents involve GenAI; when they do, logs, policies, and architectures allow faster containment. Regulatory Readiness: The organization can easily prove to auditors that its GenAI usage aligns with global AI risk management frameworks (such as the NIST AI RMF or ISO/IEC 42001) and emerging regulations (like the EU AI Act). Business Momentum: More business units confidently bring GenAI use cases to production through standardized, approved paths rather than going rogue. Ultimately, the aim is not to eliminate shadow AI by decree, but to make the sanctioned path to GenAI clearly safer, easier, and more valuable than the alternatives. Closing Thoughts Shadow AI is the predictable byproduct of powerful tools meeting unmet needs. The response cannot simply be more blocking or another point solution. Enterprises that succeed with GenAI will treat it as a first-class citizen in their governance and security architecture: visible, policy-driven, and controlled at the points where data moves. By starting with inventory, codifying risk-based policies, inserting guardrails across the data path, tracking the provenance of AI-generated assets, and anchoring everything in a clear operating model, organizations can turn GenAI from a leakage multiplier into a governed capability.