Key Takeaways

• Zero compliance violations are achievable: Leading shapewear brands using structured AI safety frameworks achieve flawless performance across thousands of customer conversations while driving measurable conversion gains—proof that safety and performance reinforce each other
• The $50 billion brand safety crisis: Fashion loses over $50 billion annually to counterfeiting while 60% of sustainability claims prove misleading—AI amplifies these risks exponentially in body-sensitive categories without proper evaluation
• Regulatory enforcement is here, not coming: EU AI Act transparency obligations phase in over 24 months (many by 2026), with heightened requirements for virtual try-on using biometric identification
• Human oversight is non-negotiable: Despite AI advancement, leading platforms maintain that human judgment remains essential for brand safety in sensitive categories where pure automation carries unacceptable reputational risk
• Three-pronged safety architecture works: Shapewear brands combining tailored compliance models, red teaming protocols, and consumer-grade guardrails prevent the body-shaming language, sizing bias, and privacy violations that destroy customer trust

The shapewear industry faces a paradox: AI promises to solve the category's biggest friction points—sizing anxiety, body image concerns, and product discovery challenges—while simultaneously creating unprecedented brand safety risks. When your AI makes a mistake recommending automotive parts, customers get frustrated. When your AI makes a mistake in shapewear conversations about bodies, sizing, or compression benefits, you face lawsuits, regulatory violations, and permanent reputational damage.

This isn't hypothetical. Traffic from GenAI browsers to retail sites increased 4,700% year-over-year, with over half of consumers anticipating AI assistant usage for shopping by year-end. Your customers are already using AI to shop for shapewear—the only question is whether you control that conversation through brand-safe AI agents or leave your reputation to chance.

The brands winning this transition aren't avoiding AI—they're implementing rigorous evaluation frameworks that make safety invisible to shoppers while maintaining absolute compliance. Here's how they do it.

Why brand safety in shapewear demands different standards than general fashion

Generic fashion AI evaluation simply doesn't account for the unique risks in body-shaping products. While 69% of marketers have integrated AI into operations, most are using evaluation frameworks designed for apparel, electronics, or home goods—categories where the stakes are fundamentally different.

Consider what's at risk when AI conversations touch on shapewear:

• FTC compliance landmines: General AI models trained on internet data routinely confuse acceptable compression garment descriptions with illegal medical device claims or prohibited weight-loss promises
• Body image sensitivity: One algorithmic misstep suggesting a customer needs a different size can trigger lasting emotional harm and viral social media backlash
• Privacy regulations: Virtual try-on and body measurement AI collect sensitive biometric data subject to heightened scrutiny under emerging regulations
• Size inclusivity requirements: AI trained on limited datasets perpetuates sizing bias that excludes entire customer segments while creating discrimination liability

The regulatory landscape is tightening specifically around these risks. The EU AI Act introduces risk-based frameworks requiring fashion brands to disclose AI-generated content, with virtual try-on tools using facial recognition or body scans facing heightened compliance requirements.

Meanwhile, 60% of sustainability claims have been classified as unsubstantiated or misleading. In shapewear, the equivalent risks involve body-transformation claims, compression benefit promises, and postpartum product positioning—all areas where generic AI frequently generates non-compliant language.

The business case for rigorous evaluation extends beyond avoiding fines. AI agent customers are 10% more engaged and reach retailers further down the sales funnel—but only when AI interactions build confidence rather than create anxiety. Shapewear shopping already involves heightened purchase hesitation; AI that fails to navigate sensitivity appropriately converts browsers into permanent exits.

Red teaming for shapewear: Testing AI against real-world failure scenarios

Red teaming—systematically testing AI responses against adversarial scenarios—is where evaluation frameworks prove their worth. Think of it as stress-testing your AI's judgment before customers encounter its mistakes.

For shapewear brands, red teaming must address category-specific failure modes:

Body-shaming language detection: Test whether AI responds appropriately when customers ask questions like "Will this hide my belly?" or "I need something for my problem areas." Generic models often reinforce negative body talk rather than redirecting toward body-positive product benefits.

Weight-loss claim boundaries: Probe AI responses to questions about "slimming effects" or "losing inches." The line between acceptable "smoothing silhouette" language and illegal weight-loss claims requires nuanced understanding that general models lack.

Medical device categorization: Test AI handling of compression garment questions that border on medical applications—postpartum recovery, lymphedema support, or post-surgical use. One wrong claim transforms shapewear into regulated medical devices with entirely different compliance requirements.

Size recommendation bias: Challenge AI with diverse body types, non-standard proportions, and edge cases. Does the AI default to assuming certain body shapes? Does it handle plus-size inquiries with the same confidence as straight-size requests?

The proprietary 3-pronged approach used by leading implementations combines red teaming with tailored models and consumer-grade guardrails. This isn't academic—brands implementing comprehensive red teaming achieve zero compliance violations across thousands of conversations while competitors accumulate regulatory risk with every customer interaction.

Modern red teaming for shapewear should include:

• Adversarial prompt injection: Deliberately attempting to trick AI into making prohibited claims
• Edge case body types: Testing AI responses across full size ranges and non-standard measurements
• Cultural sensitivity scenarios: Evaluating AI handling of religious modesty requirements, cultural body image norms, and regional sizing standards
• Privacy boundary testing: Verifying AI never requests or stores unnecessary body measurement data
• Escalation trigger validation: Confirming AI appropriately routes complex body-related questions to human specialists

The evaluation isn't one-time. As your catalog evolves, new products introduce new risks. Continuous red teaming catches these before customers do.

Tailormade models: Training AI that understands compliance in intimate apparel

Generic AI models trained on internet data are fundamentally unsuitable for shapewear conversations. They've learned from Reddit threads, blog posts, and social media where accuracy, compliance, and sensitivity are optional. Custom model training rewrites these patterns with domain-specific intelligence.

Here's what tailormade model training accomplishes for shapewear brands:

Compliance corpus integration: Training datasets incorporate FTC guidelines for advertising, approved structure/function claims for compression garments, prohibited disease claims, and brand-specific legal requirements. The AI learns compliance as native language rather than an imposed filter.

Brand voice alignment for sensitivity: Generic models default to clinical language or overly casual tone—both inappropriate for intimate apparel. Custom training embeds your brand's specific approach to body positivity, size inclusivity, and customer empowerment into every response.

Product-specific knowledge depth: Instead of hallucinating features or benefits, AI trained on your actual product specifications, install guides, customer reviews, and order data provides accurate, helpful guidance. Leading implementations learn from catalogs and customer interactions to deliver personalized shopping journeys.

Prohibited terminology enforcement: Custom models can be trained to never use specific phrases that violate compliance—"weight loss," "medical grade" without substantiation, "eliminates cellulite," or other high-risk claims that generic models might generate.

The AI in fashion market reached $1.77 billion in 2025, up from $1.26 billion in 2024—a 40.4% year-over-year increase. But most of that investment focuses on trend forecasting and content generation, not the compliance-critical conversational AI that directly touches customers. Shapewear brands investing in properly trained models gain structural advantages competitors cannot quickly replicate.

Custom training also enables nuanced handling of sensitive topics. When customers ask about postpartum shapewear, AI needs to understand the difference between supporting recovery (acceptable) and making medical claims about healing (prohibited). Generic models lack this precision; tailormade models make it second nature.

Consumer-grade AI: Making safety invisible to shoppers

The best brand safety is the kind customers never notice. When AI guardrails break conversational flow with robotic disclaimers or excessive caution, they erode the trust they're meant to protect. Consumer-grade AI makes safety seamless.

This concept matters intensely for shapewear, where AI creates safe spaces for personal questions customers feel uncomfortable asking human sales associates. Shoppers want to ask about sizing for specific body types, styling for particular occasions, or product suitability for sensitive needs—without judgment. Poorly implemented safety measures that flag these normal questions as "sensitive" destroy the psychological safety that drives engagement.

Consumer-grade AI safety includes:

Graceful boundaries around body image topics: When conversations approach areas requiring human sensitivity, AI should redirect naturally rather than shutting down. Instead of "I can't help with that," effective AI says "Let me connect you with a specialist who can provide personalized guidance for your situation."

Transparent limitations that build trust: Consumer-grade safety includes clear communication about AI capabilities and human handoff points without making customers feel like they're interrogating a machine.

Natural language compliance: Safety controls should generate responses that sound helpful, not legal. "This style provides smoothing support under fitted clothing" works better than "This garment is not intended to treat, cure, or prevent any medical condition"—even though both maintain compliance.

Confidence-building, not hesitation-creating: Leading implementations build confidence and remove purchase hesitation. Safety measures that make customers doubt AI reliability defeat the purpose.

The Envive approach demonstrates consumer-grade safety in practice: complete control over agent responses crafted to foster customer loyalty while maintaining zero compliance violations. This isn't achieved through blunt content filtering but through sophisticated understanding of when and how to provide information.

For shapewear specifically, consumer-grade safety means AI that celebrates body diversity in natural language, redirects body-negative framing toward product benefits, and maintains empathy in tone even when enforcing strict compliance boundaries. Customers should feel supported, not censored.

Measuring what matters: AI safety metrics beyond basic accuracy

Traditional AI metrics—accuracy, response time, user engagement—miss the critical safety indicators that determine whether your shapewear AI creates value or liability. Effective evaluation requires measuring what actually matters for regulated, body-sensitive categories.

Essential safety metrics for shapewear AI:

• Compliance violation rate: Track any instance where AI generates claims outside approved guidelines. Leading implementations maintain zero violations across thousands of conversations—anything above zero indicates unacceptable risk.

• Escalation frequency and appropriateness: Monitor how often AI routes conversations to human specialists and whether escalation triggers fire correctly. Too few escalations suggest AI is handling complex body-image questions it shouldn't; too many suggest over-cautious guardrails that frustrate customers.

• Claim drift detection: Continuously validate that AI responses haven't drifted from approved messaging as the model learns from new conversations. Drift represents one of the highest risks in generative AI—responses gradually shifting toward non-compliant territory.

• Response consistency across demographics: Test whether AI provides equivalent quality and sensitivity across different customer segments, body types, and inquiry patterns. Inconsistency signals potential algorithmic bias.

• Prohibited phrase monitoring: Automated scanning for high-risk terminology that should never appear in customer-facing content regardless of context—"weight loss," "medical benefits," "eliminates," "cures," etc.

• Audit trail completeness: Verify that every AI interaction is logged with sufficient detail for regulatory review if needed. The global average cost of a data breach reached $4.88 million in 2024—comprehensive audit trails are defensive infrastructure.

Performance dashboards should balance conversion metrics with safety indicators. Leading implementations need real-time visibility into what percentage of AI interactions result in compliant, brand-safe recommendations.

Advanced monitoring includes semantic similarity scoring to detect when AI responses deviate from approved templates even when exact prohibited phrases don't appear. Hallucination detection algorithms flag responses that reference product features, benefits, or specifications not present in verified product data.

The measurement framework should answer: "If regulators reviewed every conversation our AI had this month, would we face any enforcement actions?" If you can't confidently answer "no," your evaluation framework has gaps.

FTC compliance: What shapewear brands must understand about AI recommendations

The Federal Trade Commission has announced aggressive enforcement against AI-generated misinformation. For shapewear brands, this means every AI-driven product recommendation, fit suggestion, or benefit claim must meet the same substantiation standards as traditional advertising.

Critical FTC compliance areas for shapewear AI:

Substantiation requirements for compression benefits: Any claims about compression levels, support benefits, or body-shaping effects require reasonable basis. Generic AI models frequently generate unsubstantiated superlatives—"maximum compression," "clinical-grade support," "immediate results"—without the testing data these claims demand.

Before-and-after visual disclaimers: If AI references transformation potential or body-shaping effects, FTC rules require clear "results not typical" language. AI must understand when comparative claims trigger disclosure requirements.

Material connections and endorsements: When AI references customer reviews, influencer mentions, or user-generated content, it must comply with endorsement disclosure rules. This is particularly complex when AI synthesizes patterns across multiple reviews rather than quoting specific sources.

Reasonable basis doctrine: The FTC requires advertisers to possess reasonable substantiation for objective product claims before making them. AI systems that generate claims based on language patterns rather than verified product specifications violate this foundational requirement.

Deceptive practices in sizing and fit: AI fit recommendations that systematically misdirect customers toward smaller sizes (to make them feel better about purchases) or larger sizes (to reduce returns) constitute deceptive practices. Algorithmic bias here creates regulatory exposure.

The legal precedent is already set: brands are responsible for AI-generated content. When Air Canada's chatbot provided incorrect bereavement fare information, courts held the company liable—establishing that "we didn't know what our AI would say" is not a defense.

For shapewear specifically, complete control over responses tailored for FTC and brand-specific legal requirements is essential. This means:

• Pre-approved response templates for any claims about product performance
• Mandatory disclosure language integrated naturally into AI conversations
• Verification that AI never makes comparative claims without substantiation
• Size and fit recommendations based on actual measurement data, not aspirational sizing
• Clear boundaries preventing AI from venturing into medical benefit territory

Leading implementations demonstrate that FTC compliance and conversion performance are not in tension. Accurate, substantiated claims build more customer confidence than exaggerated promises that create returns and regulatory risk.

From development to deployment: Building evaluation into your AI workflow

AI safety isn't a pre-launch checklist—it's continuous quality assurance integrated throughout the development and operational lifecycle. Shapewear brands need evaluation pipelines that catch compliance issues before customers encounter them.

Pre-deployment evaluation stages:

1. Training data audit: Before models learn from your data, verify that source content (product descriptions, customer service transcripts, reviews) doesn't contain prohibited claims or problematic language. Garbage in, garbage out—but with legal consequences.

2. Red team testing against edge cases: Systematically probe AI with scenarios designed to trigger non-compliant responses: customers asking for weight-loss advice, postpartum medical questions, requests for before-after comparisons, body-negative self-descriptions requiring sensitive redirection.

3. Brand voice validation: Test response consistency against your brand guidelines across hundreds of sample queries. Does the AI maintain your specific approach to body positivity? Does it avoid the clinical language that alienates customers or the casual tone that undermines product quality perception?

4. Legal and compliance review: Before launch, have actual legal counsel review AI response samples across all major query categories.

5. Staged rollout with monitoring: Deploy to limited traffic initially while monitoring compliance metrics intensively. Leading implementations can be quick to train and compliant from day one, but continuous validation ensures systems remain safe as they learn.

Post-deployment continuous evaluation:

• Daily automated compliance scanning: Algorithms review every AI-generated response for prohibited phrases, claim drift, and tone consistency
• Weekly human quality sampling: Trained reviewers examine random conversation samples for nuanced compliance issues automation might miss
• Monthly model regression testing: Re-run original red team scenarios to verify AI hasn't degraded on known edge cases
• Quarterly legal compliance review: Formal assessment of any regulatory changes requiring prompt or model updates

The CI/CD integration ensures that product catalog updates, new regulatory guidance, or brand messaging changes trigger automatic model revalidation before deploying to production. You never want AI confidently explaining products that no longer exist or making claims that are no longer approved.

Real results: How shapewear leaders maintain safety at conversion-driving scale

The proof that rigorous AI evaluation drives business results rather than constraining them comes from actual implementation data. Spanx achieved a 100%+ conversion rate increase and $3.8M in annualized incremental revenue through AI sales agents while maintaining complete brand safety and compliance.

This wasn't luck—it was architecture. The implementation combined:

• Tailored compliance models understanding shapewear-specific regulatory boundaries and Spanx's brand positioning around body confidence
• Red teaming protocols that tested AI responses against thousands of potentially problematic body-image and sizing scenarios before customer exposure
• Consumer-grade guardrails that made safety invisible—customers experienced helpful, confidence-building conversations without robotic compliance disclaimers

The Coterie case study in baby products provides parallel proof: flawless performance handling thousands of conversations without a single compliance issue. Baby products and shapewear share similar regulatory complexity—both involve sensitive product categories where generic AI creates unacceptable risk.

What makes these results remarkable isn't just zero violations—it's that safety enabled rather than limited performance. When customers trust AI conversations won't make them feel judged, they engage more deeply. When AI accurately understands product specifications rather than hallucinating benefits, recommendations drive actual purchases rather than returns.

The conversion economics are compelling: leading implementations achieve 6x average conversion rate lift compared to traditional search, 6% increase in revenue per visitor, and 4x higher engagement rates. These metrics come from AI that shoppers trust because it's demonstrably safe, accurate, and brand-aligned.

Risk mitigation provides the other half of ROI. Avoiding just one significant compliance violation pays for years of rigorous evaluation infrastructure. Add the reputational damage from body-shaming AI gone viral on social media—incalculable but potentially brand-ending for shapewear companies—and the business case becomes overwhelming.

Navigating sensitive conversations: Body image, sizing, and medical claim boundaries

Shapewear AI confronts uniquely sensitive territory where small missteps create large consequences. Effective evaluation frameworks must specifically address how AI handles the psychological and regulatory complexity of body-related conversations.

Body positivity without compromising product benefits:

Customers buy shapewear for body-shaping effects—denying this reality serves no one. But language matters intensely. Evaluation must verify AI:

• Frames benefits as "smoothing silhouette under clothing" rather than "hiding problem areas"
• Celebrates body diversity while acknowledging that different body types have different support needs
• Redirects body-negative customer language toward empowering product features
• Never reinforces shame or unrealistic beauty standards even when customers introduce these frames

Sizing complexity and measurement privacy:

Virtual try-on and AI fit prediction require sensitive body measurement data. Evaluation frameworks must ensure:

• AI requests only necessary measurements and explains why they're needed
• Clear privacy disclosures about data usage and storage
• Size recommendations based on actual fit algorithms, not aspirational sizing that creates returns
• Graceful handling of between-size scenarios without making customers feel like they're "difficult to fit"

Medical claim boundaries for compression garments:

This is where regulatory exposure becomes acute. Shapewear that provides compression can easily drift into medical device territory with wrong claims. Evaluation must rigorously test AI responses to:

• Postpartum recovery questions (support vs. healing claims)
• Lymphedema or circulation concerns (comfort vs. treatment)
• Post-surgical compression needs (when to redirect to medical providers)
• Pain relief or health benefit inquiries (strict boundaries required)

The rule: if it sounds like a medical claim, it probably is one. AI must be trained to recognize these boundaries and either provide acceptable alternative framing or escalate to human specialists who can navigate the complexity appropriately.

The human element: When AI should escalate and why it matters

Despite AI handling 90%+ of routine interactions independently, the 10% requiring human judgment determines whether your shapewear AI builds lasting customer relationships or creates incidents you're managing.

Escalation trigger design for shapewear:

Effective evaluation frameworks define precise escalation rules:

• Complex body image concerns: When customers express deep insecurity, dissatisfaction with their bodies, or emotional distress, human empathy is non-negotiable
• Medical questions beyond comfort: Any inquiry suggesting health conditions, medical treatments, or physician recommendations should route to humans who can appropriately decline medical advice
• Fit issues with high return likelihood: When AI detects measurement patterns suggesting high return probability, human specialists can provide nuanced guidance AI cannot
• VIP or high-value customer interactions: Strategic relationship management requires human judgment about customization, special requests, or white-glove service
• Complaints or service failures: Human recovery is essential when something went wrong

The human oversight model maintained by leading platforms uses AI to flag risks while humans make final decisions on controversial content. For shapewear, this hybrid approach means AI handles confident, routine sizing and product discovery while humans take over when conversations require judgment, empathy, or relationship nuance.

Evaluation must verify escalation mechanisms work both ways:

• AI correctly identifies when human handoff is needed
• Humans receive sufficient conversation context to continue seamlessly
• Customers experience escalation as helpful elevation, not AI failure
• Human specialists can provide feedback that improves AI handling of similar future scenarios

Training support teams on AI-generated conversation histories is critical. When humans take over mid-conversation, they need to understand what AI already communicated to avoid contradicting earlier recommendations or repeating information.

Future-proofing your AI safety framework as regulations evolve

The regulatory landscape for AI in ecommerce is shifting rapidly. 63% of executives agree companies not adopting AI agents risk falling behind within two years—but those adopting without compliance infrastructure face different risks entirely.

Regulatory horizon for shapewear AI:

• EU AI Act biometric data requirements: Virtual try-on tools using body scans face heightened transparency and consent requirements by 2026
• FTC algorithmic transparency initiatives: Expect increasing scrutiny of how AI recommendations work, particularly for bias in sizing and body-type assumptions
• State-level privacy laws: Growing patchwork of requirements for handling sensitive body measurement data across different jurisdictions

Evaluation frameworks built today must accommodate regulations not yet written. This means:

Modular compliance architecture: Design systems where regulatory controls can be updated independently without rebuilding entire models. When new disclosure requirements emerge, you should be able to integrate them within days, not months.

Comprehensive audit trails: Log not just what AI said but why—the product data, customer context, and decision logic behind each recommendation. This explainability becomes mandatory under various regulatory proposals.

Adaptive learning constraints: While AI should learn from customer interactions, evaluation must verify it doesn't learn prohibited patterns even when customers repeatedly request them. Popularity doesn't equal compliance.

Model documentation standards: Maintain detailed records of training data sources, bias testing methodologies, safety protocols, and performance benchmarks. Regulatory reviews will demand this transparency.

Brands allocating digital commerce budgets to AI initiatives in year one position themselves to adapt as requirements evolve. Those treating AI as a bolt-on feature will face expensive retrofitting when compliance gaps emerge.

The winning approach: build evaluation frameworks that exceed current requirements while remaining flexible for future ones. If your AI safety architecture only meets minimum compliance today, you're already behind where regulations are heading tomorrow.

FAQ

How do I know if my shapewear brand's AI is perpetuating sizing bias across different body types?

Test systematically across your full size range with real customer measurement data, not theoretical scenarios. Red team your AI with inquiries from plus-size customers, petite customers, tall customers, and those with non-standard proportions. Compare response quality, confidence levels, and product recommendation relevance across segments. If your AI provides detailed guidance for straight sizes but generic responses for extended sizes, you have algorithmic bias. Leading implementations conduct quarterly bias audits with diverse testing panels and adjust training data to ensure equivalent service quality regardless of body type. The technical solution involves validating that your AI training dataset represents your actual customer base, not industry-standard model measurements.

What's the actual difference between AI that's "brand safe" versus just compliant with FTC regulations for shapewear?

FTC compliance is the legal minimum—avoiding prohibited medical claims, substantiating compression benefits, and maintaining honest sizing guidance. Brand safety is the strategic imperative—protecting your reputation, customer trust, and market positioning. An AI can be technically FTC-compliant while still using body-negative language that alienates customers, recommending products poorly, or creating experiences inconsistent with your brand values. The distinction matters because while compliance violations trigger regulatory penalties, brand safety failures destroy customer relationships. Shapewear brands need evaluation frameworks that measure both: legal compliance through prohibited claim monitoring and brand safety through tone analysis, customer sentiment tracking, and alignment with your body positivity positioning.

Can I use general AI evaluation tools designed for other industries, or does shapewear require specialized frameworks?

General evaluation tools miss shapewear-specific risks entirely. Standard brand safety platforms scan for explicit content, hate speech, and generic misinformation—but they don't flag subtle body-shaming language, medical device claim drift, or sizing recommendation bias. The sensitivity required for body-related conversations demands category expertise. You need evaluation frameworks that understand the difference between acceptable "smoothing" claims and prohibited "weight loss" promises, that recognize when compression language crosses into medical territory, and that assess whether virtual try-on AI handles diverse body types equitably. While you can use general tools for baseline safety, shapewear demands additional layers specifically calibrated for intimate apparel compliance and body image sensitivity.

How frequently should I re-evaluate my AI as my shapewear product line expands or regulations change?

Continuous automated monitoring with formal comprehensive reviews quarterly at minimum. Every new product introduction requires validation that AI accurately understands features, benefits, and appropriate positioning without generating new compliance risks. Regulatory changes demand immediate model updates—when new requirements emerge, you'll need rapid deployment of updated protocols. The practical approach combines daily automated compliance scanning flagging potential violations, weekly human quality sampling reviewing conversation subtleties, monthly regression testing against known edge cases, and quarterly legal compliance audits assessing regulatory alignment. Leading implementations treat AI evaluation as continuous quality assurance, not periodic projects. If you're only evaluating when something goes wrong, you're already accumulating risk.

What metrics should I track to prove AI safety investment is delivering ROI beyond just avoiding compliance violations?

Quantify both risk mitigation and performance enablement. Track compliance violation rates (target: zero), legal incident costs avoided (calculate potential settlement values), brand reputation monitoring (social sentiment, review ratings mentioning AI), customer trust indicators (engagement rates with AI recommendations, return rates on AI-suggested products), operational efficiency gains (reduced prompt engineering costs versus wrapper solutions, support team time savings from AI handling routine inquiries), and conversion performance (AI-driven sales, average order value lift, revenue per visitor increases). The compelling ROI story combines preventing just one major compliance incident with measurable conversion improvements. For a shapewear brand achieving results similar to documented case studies—100%+ conversion rate increases and millions in incremental revenue—AI safety isn't a cost center but a revenue driver that happens to also eliminate legal risk.