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AI decisioning use cases across industries | Braze
2026-03-07 23:17 Diff
https://www.braze.com/resources/articles/ai-decisioning-use-cases
Every team already makes hundreds of small decisions a day, like who to contact, which offer to show, how to route a claim, or which order to fulfill first. The hard part is doing this consistently, at scale, without adding too much manual work.
AI decisioning turns those choices into repeatable processes that link data, guardrails, and outcomes. Instead of debating where to start in the abstract, teams can point to specific triggers, take specific actions, then improve them one by one.
In this guide you’ll see AI decisioning use cases in practical terms, across a number of industries and find out how you too can use Braze and AI decisioning into your customer engagement strategy.
Customer engagement and marketing use cases

AI decisioning is used in customer engagement and marketing to optimize a defined goal, such as purchase rate, activation, or retention, based on how each individual customer responds over time. The use cases below show how common lifecycle moments can be mapped to decision options and which metrics can be used to measure impact.
Purchase & revenue optimization:

How can AI decisioning maximize purchase rates?

AI decisioning can maximize purchase rates by selecting the best mix of offer, creative, channel, timing, and frequency for each customer, tied to a purchase KPI.
To set it up, build a small “action bank” of viable offers and messages, then let decisioning learn which combinations drive purchases across different customer patterns.
Best for: Any industry with a strong customer lifecycle strategy and high message volume, such as retail or QSR
What AI decisioning chooses: offer, message/creative, channel, timing, frequency
Signals to use: recent purchases, category affinity, engagement recency, typical buy day/time, incentive sensitivity
How to measure lift: purchase rate and revenue per user vs. holdout (business-as-usual messaging)
How can AI decisioning drive upsell?

AI decisioning can drive upsell by choosing the right upgrade path, value message, and offer level for each customer, aligned to revenue or margin goals.
This is a strong fit when a flat discount strategy over-incentivizes customers who would have upgraded anyway.
Best for: premium tiers, bundles, and higher-margin categories
What AI decisioning chooses: upsell path, offer type and depth, message angle, timing, cadence
Signals to use: spend level, product mix, engagement depth, discount responsiveness, lifecycle stage
How to measure lift: incremental revenue per user and incremental margin vs. holdout
How can AI decisioning personalize cross-sell campaigns?

AI decisioning can personalize cross-sell by selecting which add-on to promote, whether to include an incentive, and how to sequence follow-ups by customer.
It’s especially useful when add-ons vary by margin, and customers adopt them for different reasons.
Best for: subscription bundles, marketplaces, retail add-ons, fintech product adoption
What AI decisioning chooses: cross-sell item, incentive inclusion, message/creative, channel, timing, cadence
Signals to use: current product owned, usage patterns, complementary interest, tenure, recent purchases
How to measure lift: incremental attach rate and incremental profit per user vs. holdout
Lifecycle & retention:

How can AI decisioning improve abandoned cart conversions?

AI decisioning can improve abandoned cart conversions by choosing follow-up timing and channel, reminder count, and creative that best fits each shopper’s intent.
This works best when you offer multiple reminder strategies and keep incentive use inside clear guardrails.
Best for: ecommerce and travel bookings with clear cart intent signals
What AI decisioning chooses: delay to first message, number of reminders, creative/CTA, incentive type (optional), channel mix
Signals to use: cart value, category, time since abandon, return history, loyalty status
How to measure lift: cart conversion rate and revenue per abandonment vs. holdout
How can AI decisioning drive repeat purchases?

AI decisioning can drive repeat purchases by learning the best reorder timing, channel, and message type for each buyer.
This use case is easier to launch when you can define a “reorder window” and provide a few message and offer variants.
Best for: consumables, beauty, wellness, pet, and replenishment cycles
What AI decisioning chooses: reorder window, product focus, offer inclusion, channel, cadence
Signals to use: time since purchase, typical reorder interval, basket size, category, engagement recency
How to measure lift: repeat purchase rate and revenue per buyer vs. holdout
How can AI decisioning optimize winback campaigns?

AI decisioning can optimize winback by selecting the best reactivation message, channel, timing, and offer for each lapsed customer.
This is a good fit when lapsed customers vary widely in why they stopped engaging.
Best for: retail reactivation, app re-engagement, and subscription winback
What AI decisioning chooses: winback angle, offer inclusion, channel, timing, cadence
Signals to use: time since last activity, last category engaged, churn proxy, prior discount response, tenure
How to measure lift: reactivation rate and revenue per lapsed user vs. holdout
How can AI decisioning reduce subscription cancellations?

AI decisioning can reduce cancellations by selecting the best save action for each customer at cancel intent, while controlling incentive costs.
This is strongest when your action set includes non-discount options, such as pauses, downgrades, or benefit reminders.
Best for: subscription services with churn volume and multiple save paths
What AI decisioning chooses: save path, offer depth (optional), channel, timing, cadence
Signals to use: tenure, usage decline, prior save response, plan type, support history
How to measure lift: churn rate, retained revenue, and incentive cost per save vs. holdout
How can AI decisioning deliver individual recommendations?

AI decisioning can deliver individual recommendations by selecting the item most likely to drive the target outcome for each person, then learning from downstream actions.
Treat recommendations as a decision set (what you can show), tied to one primary KPI (what success means), so measurement stays clean.
Best for: streaming, media, retail, travel, and apps with multiple “next best” items
What AI decisioning chooses: recommended item, message angle, channel, timing, frequency
Signals to use: recent browsing, affinity categories, session recency, prior conversions, price sensitivity proxy
How to measure lift: primary KPI (conversion, watch time, revenue per user) vs. holdout
Advocacy & loyalty:

How can AI decisioning drive points redemption?

AI decisioning can drive points redemption by selecting the best redemption prompt, benefit framing, and timing for each loyalty member.
It also helps manage loyalty economics by learning when a reminder is enough versus when a stronger nudge is needed.
Best for: loyalty programs where redemption links to retention and repeat buying
What AI decisioning chooses: prompt type, benefit framing, channel, timing, cadence
Signals to use: points balance, time-to-expiry, earn rate, category interest, prior redemption behavior
How to measure lift: redemption rate and downstream repeat purchase vs. holdout
How can AI decisioning improve referral performance?

AI decisioning can improve referrals by choosing what to offer, who to ask, and when to ask, based on likelihood to refer and downstream value.
This works best when you have multiple referral prompts and reward structures available.
Best for: subscriptions, marketplaces, fintech, and apps with clear sharing moments
What AI decisioning chooses: ask timing, reward type, message angle, channel, follow-up cadence
Signals to use: satisfaction proxy, engagement depth, tenure, referral history, sharing behavior
How to measure lift: referral rate and referred conversion rate vs. holdout
How can AI decisioning optimize activations?

AI decisioning can optimize activations by selecting onboarding message variants, CTAs, cadence, and timing that move each user to the activation event faster.
Keep the scope tight by defining one activation milestone, then offering a small set of treatment options.
Best for: apps, fintech, SaaS trials, and subscriptions with a clear “activation” event
What AI decisioning chooses: onboarding message variant, channel, timing, cadence
Signals to use: setup progress, early feature usage, session recency, intent events, signup source
How to measure lift: activation rate and time-to-activation vs. holdout
Growth & monetization:

How can AI decisioning convert free to paid subscriptions?

AI decisioning can convert free users to paid by choosing the right upgrade prompt, plan emphasis, and timing based on product engagement and conversion likelihood.
This is a strong subscription motion because the most useful signals come directly from usage behavior.
Best for: freemium apps, media subscriptions, and SaaS trials
What AI decisioning chooses: upgrade angle, plan emphasis, offer inclusion, channel, timing, cadence
Signals to use: feature adoption, usage frequency, paywall hits, content depth, tenure
How to measure lift: free-to-paid conversion rate vs. holdout
How can AI decisioning optimize plan upgrades?

AI decisioning can optimize plan upgrades by selecting which tier to recommend, how to frame value, and whether an incentive is needed for each subscriber.
It’s useful when different cohorts upgrade for different reasons, such as features, savings, or convenience.
Best for: tiered subscriptions, telecom plans, fintech products, and bundled services
What AI decisioning chooses: upgrade tier, value framing, offer inclusion, channel, timing, cadence
Signals to use: usage thresholds, constraints hit, preferences, support signals, price sensitivity proxy
How to measure lift: upgrade rate and incremental ARR per subscriber vs. holdout
How can AI decisioning improve contract renewals?

AI decisioning can improve contract renewals by choosing renewal messaging, timing, and value framing based on each customer’s usage and renewal risk.
It works best when renewal outreach starts early enough to adapt before the decision date.
Best for: annual contracts, memberships, and renewal windows
What AI decisioning chooses: message angle, channel mix, timing, cadence, offer inclusion (where allowed)
Signals to use: usage trends, health proxy, engagement recency, support burden, renewal stage
How to measure lift: renewal rate and retained revenue vs. holdout
How can AI decisioning personalize offers?

AI decisioning can personalize offers by selecting the right offer type and depth for each customer, aligned to conversion goals and value protection.
In practice, this often runs inside lifecycle moments, such as browse follow-up, cart, renewals, and winback.
Best for: brands that run multiple offer types and want to reduce blanket discounting
What AI decisioning chooses: offer type, offer depth, message angle, channel, timing, cadence
Signals to use: promo response, lifecycle stage, intent signals, loyalty status, margin sensitivity proxy
How to measure lift: conversion and profit per user vs. holdout
How can AI decisioning support a digital ads bidding strategy?

AI decisioning can support a digital ads bidding strategy by tailoring bid approaches using first-party signals, tied to downstream revenue outcomes.
This is typically framed as acquisition efficiency, with measurement focused on incremental value, not platform-only metrics.
Best for: brands with strong first-party data and clear post-click outcomes
What AI decisioning chooses: bid strategy variant by cohort, plus downstream nurture alignment
Signals to use: value proxies, intent events, category interest, recency, prior conversion paths
How to measure lift: revenue per acquired customer and CAC efficiency vs. control campaigns
How can AI decisioning personalize search lead acquisition follow-up?

AI decisioning can improve lead acquisition by selecting the best follow-up path after a search-driven lead, based on intent and likelihood to convert.
This keeps the focus on what you control in owned channels: response speed, framing, and nurture sequencing.
Best for: high-intent lead gen where follow-up quality drives conversion
What AI decisioning chooses: follow-up channel, timing, nurture cadence, offer or content angle
Signals to use: intent proxy, landing behavior, form quality, geo, prior engagement
How to measure lift: lead-to-qualified rate and lead-to-sale conversion vs. holdout
Kayo sports puts 1:1 fandom to work

Kayo Sports is Australia’s sports streaming service, built to give fans fast access to live and on-demand coverage across dozens of sports. Its team has invested in personalization across the subscriber journey, from sign-up through ongoing engagement.
The problem

Kayo needed a scalable way to engage a wide range of sports fans with more relevant content and recommendations across channels and devices. Its existing approach limited how far it could take personalization using its available data.
The solution

Kayo built “Customer Cortex,” a personalization engine that uses Braze and BrazeAI Decisioning Studio™ to support a 1:1 approach. AI decisioning selects the optimal mix of message, creative, channel, timing, frequency, and promotions for each subscriber, and expanded the number of potential actions from 300 to 1.2 million variations.
The results

14% increase in subscriptions in FY24
8% increase in average annual occupancy
105% increase in cross-sells
Results delivered while average subscription prices increased by 20%
Supply chain and logistics use cases

AI decisioning can support supply chain and logistics teams by optimizing decisions that have clear inputs, multiple options, and measurable outcomes. Common use cases include:
Demand forecasting: Analyze historical sales, market trends, and external factors (such as weather or economic indicators) to predict demand, optimize inventory levels, and reduce stockouts or overstock.
Route optimization: Use traffic patterns, weather conditions, and delivery schedules to identify efficient routes, reducing fuel costs and improving delivery times.
Inventory management: Monitor inventory in real time and predict reorder timing based on usage patterns and lead times to control carrying costs.
Supplier selection and risk management: Evaluate suppliers using performance history, financial stability, and geopolitical risk signals to support supplier decisions and reduce disruption risk.
Warehouse automation: Optimize warehouse operations using AI-powered robotics and automation for inventory handling, picking, packing, and shipping to improve efficiency and reduce errors.
Predictive maintenance: Use equipment and vehicle data to predict maintenance needs, reduce downtime, and extend asset lifespan.
Fraud detection and risk management use cases

AI decisioning can support fraud and risk teams by improving detection, prioritization, and response speed across high-volume workflows. Common use cases include:
Transaction monitoring: Analyze transaction patterns in real time to flag anomalies that may indicate fraud.
User behavior analytics: Establish a baseline of normal behavior, then alert on deviations like unusual login locations or sudden changes in spend.
Identity verification: Strengthen verification by analyzing biometrics (facial or voice recognition) and cross-referencing trusted databases to reduce identity theft.
Predictive risk scoring: Assign risk scores to users or transactions based on factors like history, size, and frequency to prioritize manual review or step-up verification.
Network analysis: Identify suspicious relationships and interaction patterns that suggest collusion or organized fraud.
Automated claims processing: Flag claims with high-risk characteristics based on historical fraud patterns for faster, more accurate review.
Patient care use cases

AI decisioning can help healthcare organizations support clinical decisions, personalize care, and improve operational performance. Common use cases include:
Predictive analytics for patient outcomes: Use historical patient data to predict treatment outcomes, improving care planning and reducing readmissions.
Personalized treatment plans: Tailor therapies using signals such as genetics, lifestyle, and medical history.
Clinical decision support systems (CDSS): Provide evidence-based recommendations during consultations, including test suggestions and drug interaction flags.
Remote patient monitoring: Analyze wearable and device data to detect early warning signs and prompt timely interventions.
Operational efficiency: Predict admissions, optimize staffing schedules, and streamline supply chain logistics to reduce wait times.
NLP for patient communication: Use chatbots and virtual assistants for questions, scheduling, and medication reminders, and analyze patient sentiment to improve care delivery.
Cross-industry benefits

AI decisioning supports a wide range of teams by improving how decisions are made, executed, and refined over time. Common benefits include:
Enhanced decision-making: Data-driven insights support faster, more accurate decisions.
Increased efficiency: Automation reduces time spent on routine work.
Cost reduction: Optimized resource allocation can reduce waste and operating costs.
Improved customer experience: More relevant, timely interactions can increase satisfaction and loyalty.
Predictive analytics: Forecasting supports proactive planning and risk management.
Scalability: Systems can handle more data and decisions without linear cost increases.
Enhanced accuracy: Reduced human error improves reliability in analysis and execution.
Real-time insights: Decisions can be informed by current data as conditions change.
Competitive advantage: Faster, smarter decisioning can support differentiation.
Continuous learning and improvement: Outcomes feed back into future decisions, improving performance over time.
Final thoughts on AI decisioning use cases

AI decisioning use cases give teams a concrete way to turn “use more AI” into specific, testable changes. Each one connects a clear decision, the data behind it, and the outcome that matters—whether that’s higher conversion, lower fraud, or faster time to care.
The most effective programs start small, with a single journey, a defined risk decision, or one part of the supply chain, then expand as models prove their value and governance practices mature. Over time, those individual decisions add up to a more responsive, more consistent experience for customers and internal teams alike.
If you’re exploring how to bring these AI decisioning use cases into your own engagement strategy, this is a good moment to look at where Braze can help with orchestration, testing, and real-time decisioning.
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class="flex-none flex items-center justify-center text-white w-12 h-12 bg-purple-400 rounded-full"><svg width="24" height="24" role="img" aria-label="lightning-02"><use href="/icons/sprites.svg#lightning-02"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Braze Data Platform</span><span class="text-sm font-normal text-gray-600">Unify, activate, and distribute your data</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/cross-channel-messaging"><div class="flex-none flex items-center justify-center text-white w-12 h-12 bg-purple-400 rounded-full"><svg width="24" height="24" role="img" aria-label="grid-01"><use href="/icons/sprites.svg#grid-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Cross-Channel</span><span class="text-sm font-normal text-gray-600">Send all your messages from one place</span></div></a></li></ul></div><div class="nav-list relative z-10 grow flex flex-col gap-4"><div class="flex flex-col gap-1 text-sm text-gray-500"><span class="font-semibold uppercase">FEATURED CAPABILITIES</span></div><ul class="flex flex-col gap-y-4"><li><a class="nav-item flex gap-3" href="/product/brazeai-decisioning-studio"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="route"><use href="/icons/sprites.svg#route"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">BrazeAI Decisioning Studio™<span class="inline-block rounded text-xs text-white font-normal leading-none p-1" style="background:#3608D7">NEW</span></span><span class="text-sm font-normal text-gray-600">Deliver 1:1 personalization, at scale</span></div></a></li><li><a class="nav-item flex gap-3" href="/product/journey-orchestration"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="skew"><use href="/icons/sprites.svg#skew"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Journey Orchestration</span><span class="text-sm font-normal text-gray-600">Create multi-step, cross-channel experiences</span></div></a></li><li><a class="nav-item flex gap-3" href="/product/ai-agents"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="cube-02"><use href="/icons/sprites.svg#cube-02"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">BrazeAI™ Agents<span class="inline-block rounded text-xs text-white font-normal leading-none p-1" style="background:#3608D7">NEW</span></span><span class="text-sm font-normal text-gray-600">Scale smarter engagement with always-on AI agents</span></div></a></li><li><a class="nav-item flex gap-3" href="/product/reporting-analytics"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="presentation-chart-01"><use href="/icons/sprites.svg#presentation-chart-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Reporting &amp; Analytics</span><span class="text-sm font-normal text-gray-600">Analyze performance &amp; uncover insights</span></div></a></li></ul></div></div></div><div class="nav-dropdown__right-section relative py-8 xl:pl-8 xl:w-3/5 before:bg-gray-50 before:absolute before:z-0 before:top-0 before:w-screen before:h-full before:left-1/2 before:-translate-x-1/2 xl:before:left-0 xl:before:transform-none"><div class="nav-list relative z-10 grow flex flex-col gap-4"><div class="flex flex-col gap-1 text-sm text-gray-500"><span class="font-semibold uppercase">FEATURED CHANNELS</span></div><ul class="flex flex-col gap-y-4"><li><a class="nav-item flex gap-3 items-center" href="/product/email"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="mail-01"><use href="/icons/sprites.svg#mail-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Email</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/mobile-app-messaging"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="phone-02"><use href="/icons/sprites.svg#phone-02"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Mobile App Messaging</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/web-messaging"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="browser"><use href="/icons/sprites.svg#browser"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Web Messaging</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/sms"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="message-text-square-02"><use href="/icons/sprites.svg#message-text-square-02"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">SMS/RCS</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/whatsapp"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="phone-call-01"><use href="/icons/sprites.svg#phone-call-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">WhatsApp</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/cross-channel-messaging"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="dots-grid"><use href="/icons/sprites.svg#dots-grid"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md text-purple-700">View all channels</span></div></a></li></ul></div><div class="nav-dropdown__section-footer flex flex-wrap sm:flex-nowrap p-2 sm:p-3 bg-[#C9C4FF80] relative z-10 gap-3 items-end sm:justify-between xl:justify-start rounded-lg mt-4"><div class="flex gap-2 sm:gap-3"><span class="flex items-center justify-center flex-none size-8 text-purple-700 mr-2 xl:mr-0"><svg width="20" height="20" role="img" aria-label="star-02"><use href="/icons/sprites.svg#star-02"></use></svg></span><div class="flex flex-col justify-center gap-1"><span class="font-semibold text-gray-900 text-md">How It Works</span><p class="font-normal text-gray-600 text-sm">Get a breakdown of our vertically- integrated technology</p></div></div><a class="inline-flex items-center justify-center text-center rounded-full transition-colors sm:w-fit whitespace-nowrap relative z-10 gap-1 font-aribauGrotesk font-bold px-[14px] py-2 text-sm bg-white border border-transparent text-gray-900 shadow-xs hover:bg-[#F9FAFB] active:bg-white active:border-[#D0D5DD] active:border disabled:bg-white disabled:border-[#EAECF0] disabled:border disabled:text-[#98A2B3] w-full" href="/product">Learn More</a></div></div></div></div></div></li><li class="nav-dropdown"><button type="button" class="relative flex items-center w-full h-full xl:w-auto font-semibold text-md ja:text-xs hover:text-primary-600 text-black py-6 xl:py-[10px] gap-[6px] px-0" aria-expanded="false">Solutions<svg width="20" height="20" role="img" aria-label="chevron-down" class="transition-transform !duration-150"><use href="/icons/sprites.svg#chevron-down"></use></svg></button><div id="nav-dropdown-Solutions" class="nav-dropdown__content absolute top-0 xl:top-full left-0 w-full pb-10 xl:pb-0 h-[calc(100vh-72px)] xl:h-auto flex flex-col overflow-y-auto overflow-x-hidden xl:overflow-hidden bg-white xl:border xl:border-gray-200 xl:shadow-lg transition !duration-300 transform origin-top pointer-events-none -z-1 opacity-0 translate-x-full xl:translate-x-0" aria-hidden="true" inert="true"><div class="container transition delay-150 !duration-300 origin-center xl:opacity-0 xl:translate-y-[5px]"><div class="mt-10 mb-4 xl:hidden"><button type="button" class="items-center justify-center text-center transition-colors sm:w-fit whitespace-nowrap relative z-10 font-aribauGrotesk font-bold text-sm flex gap-2 rounded-none border-none p-0 text-gray-600 shadow-none w-fit hover:text-gray-700 active:text-gray-600 disabled:text-gray-300" aria-controls="nav-dropdown-Solutions"><svg width="20" height="20" role="img" aria-label="arrow-left" class="w-5 h-5" aria-hidden="true"><use href="/icons/sprites.svg#arrow-left"></use></svg>Back</button></div><div class="xl:flex gap-8"><div class="nav-dropdown__left-section w-full py-8"><div class="nav-list-container flex flex-col xl:flex-row gap-6"><div class="nav-list relative z-10 grow flex flex-col gap-4"><div class="flex flex-col gap-1 text-sm text-gray-500"><span class="font-semibold uppercase">Industries</span></div><ul class="flex flex-col gap-y-4"><li><a class="nav-item flex gap-3 items-center" href="/solutions/retail-and-ecommerce"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="globe-01"><use href="/icons/sprites.svg#globe-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center 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type="application/ld+json">{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Resources","item":"https://www.braze.com/resources"},{"@type":"ListItem","position":2,"name":"Blog","item":"https://www.braze.com/resources/articles"},{"@type":"ListItem","position":3,"name":"AI decisioning use c...","item":"https://www.braze.com/resources/articles/ai-decisioning-use-cases"}]}</script><div class="mt-8 flex flex-col gap-6 sm:mt-12 md:flex-row md:pb-16"><div class="flex shrink-0 grow basis-0 flex-col gap-8"><h1 class="text-display-md font-bold text-white">AI decisioning use cases: Real examples in 2026</h1><p class="flex gap-3 text-xs font-extrabold text-white"><span>Published on January 16, 2026</span><span>/</span><span>Last edited on January 16, 2026</span><span>/</span><span>14<!-- --> min read</span></p></div><div class="shrink-0 grow basis-0"><img alt="AI decisioning use cases: Real examples in 2026" loading="lazy" width="600" height="400" decoding="async" data-nimg="1" class="h-auto max-h-[352px] w-full rounded-xl object-cover" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F0b4bf1ef585e87c9fb466df3e4d5f1b5b410f459-1920x1080.jpg&amp;w=640&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F0b4bf1ef585e87c9fb466df3e4d5f1b5b410f459-1920x1080.jpg&amp;w=1200&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F0b4bf1ef585e87c9fb466df3e4d5f1b5b410f459-1920x1080.jpg&amp;w=1200&amp;q=75"/></div></div></div></div><div class="container py-[50px] md:py-24"><div class="flex flex-col gap-8 md:flex-row"><div class="w-full shrink-0 grow-0 basis-full md:w-[72px] md:basis-[72px]"><div class="flex gap-4 text-gray-500 md:sticky md:top-20 md:flex-col"><div class="w-fit cursor-pointer text-gray-500 hover:text-primary-500"><svg width="24" height="24" 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bg-gray-100"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" class="size-full object-cover" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa871ef5dc9e894d320555710664c2e67f03dad70-1000x1000.png&amp;w=256&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa871ef5dc9e894d320555710664c2e67f03dad70-1000x1000.png&amp;w=384&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa871ef5dc9e894d320555710664c2e67f03dad70-1000x1000.png&amp;w=384&amp;q=75"/></span><div class="flex flex-col items-start justify-center gap-4 sm:flex-row md:items-center"><h6 class="text-md font-semibold text-gray-900 sm:text-lg">Team Braze</h6><div class="hidden min-h-[30px] border-r-2 border-gray-300 bg-gray-300 sm:block "></div><span class="text-md text-gray-900">Braze</span></div></div></div><div class="pl-4 md:pl-6 border-l-2 border-gray-300 space-y-4"><h2 class="text-md uppercase tracking-wider text-gray-900">Contents</h2><nav><ul class="flex flex-col gap-2.5"><li class="flex items-center gap-2"><svg width="18" height="18" role="img" aria-label="arrow-down" class="text-gray-600"><use href="/icons/sprites.svg#arrow-down"></use></svg><a href="#h-4d84c3ad3510" class="text-gray-900 hover:text-primary-700 transition-colors leading-relaxed underline decoration-primary-700 underline-offset-2">Customer engagement and marketing use cases</a></li><li class="flex items-center gap-2"><svg width="18" height="18" role="img" aria-label="arrow-down" class="text-gray-600"><use href="/icons/sprites.svg#arrow-down"></use></svg><a href="#h-ebdbc5d1d58b" class="text-gray-900 hover:text-primary-700 transition-colors leading-relaxed underline decoration-primary-700 underline-offset-2">Supply chain and logistics use cases</a></li><li class="flex items-center gap-2"><svg width="18" height="18" role="img" aria-label="arrow-down" class="text-gray-600"><use href="/icons/sprites.svg#arrow-down"></use></svg><a href="#h-2a117be15d17" class="text-gray-900 hover:text-primary-700 transition-colors leading-relaxed underline decoration-primary-700 underline-offset-2">Fraud detection and risk management use cases</a></li><li class="flex items-center gap-2"><svg width="18" height="18" role="img" aria-label="arrow-down" class="text-gray-600"><use href="/icons/sprites.svg#arrow-down"></use></svg><a href="#h-ea771eb4be75" class="text-gray-900 hover:text-primary-700 transition-colors leading-relaxed underline decoration-primary-700 underline-offset-2">Patient care use cases</a></li><li class="flex items-center gap-2"><svg width="18" height="18" role="img" aria-label="arrow-down" class="text-gray-600"><use href="/icons/sprites.svg#arrow-down"></use></svg><a href="#h-02232c67880c" class="text-gray-900 hover:text-primary-700 transition-colors leading-relaxed underline decoration-primary-700 underline-offset-2">Cross-industry benefits</a></li><li class="flex items-center gap-2"><svg width="18" height="18" role="img" aria-label="arrow-down" class="text-gray-600"><use href="/icons/sprites.svg#arrow-down"></use></svg><a href="#h-d07dbd75fc38" class="text-gray-900 hover:text-primary-700 transition-colors leading-relaxed underline decoration-primary-700 underline-offset-2">Final thoughts on AI decisioning use cases</a></li><li class="flex items-center gap-2"><svg width="18" height="18" role="img" aria-label="arrow-down" class="text-gray-600" aria-hidden="true"><use href="/icons/sprites.svg#arrow-down"></use></svg><a href="#faq-accordion" class="text-gray-900 hover:text-primary-700 transition-colors leading-relaxed underline decoration-primary-700 underline-offset-2">Frequently Asked Questions</a></li></ul></nav></div><div class="flex flex-col gap-6"><p>Every team already makes hundreds of small decisions a day, like who to contact, which offer to show, how to route a claim, or which order to fulfill first. The hard part is doing this consistently, at scale, without adding too much manual work.</p><p>AI decisioning turns those choices into repeatable processes that link data, guardrails, and outcomes. Instead of debating where to start in the abstract, teams can point to specific triggers, take specific actions, then improve them one by one.</p><p>In this guide you’ll see AI decisioning use cases in practical terms, across a number of industries and find out how you too can use Braze and AI decisioning into your customer engagement strategy.</p><h2 id="h-4d84c3ad3510" class="my-4 text-gray-900 lg:text-display-lg">Customer engagement and marketing use cases</h2><p>AI decisioning is used in customer engagement and marketing to optimize a defined goal, such as purchase rate, activation, or retention, based on how each individual customer responds over time. The use cases below show how common lifecycle moments can be mapped to decision options and which metrics can be used to measure impact.</p><h3 id="h-7a616d4cd029" class="my-4 text-gray-900"><strong>Purchase &amp; revenue optimization:</strong></h3><h3 id="h-3b06a03100e3" class="my-4 text-gray-900"><strong>How can AI decisioning maximize purchase rates?</strong></h3><p>AI decisioning can maximize purchase rates by selecting the best mix of offer, creative, channel, timing, and frequency for each customer, tied to a purchase KPI.</p><p>To set it up, build a small “action bank” of viable offers and messages, then let decisioning learn which combinations drive purchases across different customer patterns.</p><p><strong>Best for: </strong>Any industry with a strong customer lifecycle strategy and high message volume, such as retail or QSR</p><p><strong>What AI decisioning chooses:</strong> offer, message/creative, channel, timing, frequency</p><p><strong>Signals to use:</strong> recent purchases, category affinity, engagement recency, typical buy day/time, incentive sensitivity</p><p><strong>How to measure lift:</strong> purchase rate and revenue per user vs. holdout (business-as-usual messaging)</p><img alt="A diagram outlining an AI framework to maximize purchase rates, using customer signals to optimize offers, messages, and timing, measured by purchase rate and revenue against a baseline." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F494ccfaad7abca5f3308a9847ba969c4269bed8f-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F494ccfaad7abca5f3308a9847ba969c4269bed8f-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F494ccfaad7abca5f3308a9847ba969c4269bed8f-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-46312682e477" class="my-4 text-gray-900"><strong>How can AI decisioning drive upsell?</strong></h3><p>AI decisioning can drive upsell by choosing the right upgrade path, value message, and offer level for each customer, aligned to revenue or margin goals.</p><p>This is a strong fit when a flat discount strategy over-incentivizes customers who would have upgraded anyway.</p><p><strong>Best for:</strong> premium tiers, bundles, and higher-margin categories</p><p><strong>What AI decisioning chooses:</strong> upsell path, offer type and depth, message angle, timing, cadence</p><p><strong>Signals to use:</strong> spend level, product mix, engagement depth, discount responsiveness, lifecycle stage</p><p><strong>How to measure lift:</strong> incremental revenue per user and incremental margin vs. holdout</p><img alt="Diagram of an AI-powered upsell strategy, showing input signals, AI decisioning, outcomes, and measurement." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F04a8a1b7ecf0aaaa07e434cf382af18a9c1952f9-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F04a8a1b7ecf0aaaa07e434cf382af18a9c1952f9-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F04a8a1b7ecf0aaaa07e434cf382af18a9c1952f9-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-72da2fc81276" class="my-4 text-gray-900"><strong>How can AI decisioning personalize cross-sell campaigns?</strong></h3><p>AI decisioning can personalize cross-sell by selecting which add-on to promote, whether to include an incentive, and how to sequence follow-ups by customer.</p><p>It’s especially useful when add-ons vary by margin, and customers adopt them for different reasons.</p><p><strong>Best for:</strong> subscription bundles, marketplaces, retail add-ons, fintech product adoption</p><p><strong>What AI decisioning chooses:</strong> cross-sell item, incentive inclusion, message/creative, channel, timing, cadence</p><p><strong>Signals to use:</strong> current product owned, usage patterns, complementary interest, tenure, recent purchases</p><p><strong>How to measure lift:</strong> incremental attach rate and incremental profit per user vs. holdout</p><img alt="Diagram showing an AI-driven framework to personalize cross-sell campaigns, detailing input signals, AI decisioning choices, desired outcomes, and measurement metrics." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F84f71dd6332e120b5436690a2d98f056ab9be03b-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F84f71dd6332e120b5436690a2d98f056ab9be03b-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F84f71dd6332e120b5436690a2d98f056ab9be03b-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-547303f0bb44" class="my-4 text-gray-900"><strong>Lifecycle &amp; retention:</strong></h3><h3 id="h-8ccc52d23d94" class="my-4 text-gray-900"><strong>How can AI decisioning improve abandoned cart conversions?</strong></h3><p>AI decisioning can improve abandoned cart conversions by choosing follow-up timing and channel, reminder count, and creative that best fits each shopper’s intent.</p><p>This works best when you offer multiple reminder strategies and keep incentive use inside clear guardrails.</p><p><strong>Best for:</strong> ecommerce and travel bookings with clear cart intent signals</p><p><strong>What AI decisioning chooses:</strong> delay to first message, number of reminders, creative/CTA, incentive type (optional), channel mix</p><p><strong>Signals to use:</strong> cart value, category, time since abandon, return history, loyalty status</p><p><strong>How to measure lift:</strong> cart conversion rate and revenue per abandonment vs. holdout</p><img alt="Diagram outlining an AI strategy to improve abandoned cart conversions, using signals like cart value and loyalty status to determine actions like delay and creative, optimizing cart conversion rate, and measured by a randomized holdout." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F26d7a79fc7652871ba50621b9fdd5ca09e48cdbe-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F26d7a79fc7652871ba50621b9fdd5ca09e48cdbe-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F26d7a79fc7652871ba50621b9fdd5ca09e48cdbe-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-fe11b52292f8" class="my-4 text-gray-900"><strong>How can AI decisioning drive repeat purchases?</strong></h3><p>AI decisioning can drive repeat purchases by learning the best reorder timing, channel, and message type for each buyer.</p><p>This use case is easier to launch when you can define a “reorder window” and provide a few message and offer variants.</p><p><strong>Best for:</strong> consumables, beauty, wellness, pet, and replenishment cycles</p><p><strong>What AI decisioning chooses:</strong> reorder window, product focus, offer inclusion, channel, cadence</p><p><strong>Signals to use:</strong> time since purchase, typical reorder interval, basket size, category, engagement recency</p><p><strong>How to measure lift:</strong> repeat purchase rate and revenue per buyer vs. holdout</p><img alt="Diagram showing an AI use case to drive repeat purchases, detailing signals like purchase time, AI decisions on reorder window, an outcome of optimized repeat purchase rate, and measurement of repeat purchase rate and revenue per buyer over 30 days." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fac5c5d3ed53a00f832f6d2c00b80d723776f4fe5-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fac5c5d3ed53a00f832f6d2c00b80d723776f4fe5-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fac5c5d3ed53a00f832f6d2c00b80d723776f4fe5-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-8b10fd22547f" class="my-4 text-gray-900"><strong>How can AI decisioning optimize winback campaigns?</strong></h3><p>AI decisioning can optimize winback by selecting the best reactivation message, channel, timing, and offer for each lapsed customer.</p><p>This is a good fit when lapsed customers vary widely in why they stopped engaging.</p><p><strong>Best for:</strong> retail reactivation, app re-engagement, and subscription winback</p><p><strong>What AI decisioning chooses:</strong> winback angle, offer inclusion, channel, timing, cadence</p><p><strong>Signals to use:</strong> time since last activity, last category engaged, churn proxy, prior discount response, tenure</p><p><strong>How to measure lift:</strong> reactivation rate and revenue per lapsed user vs. holdout</p><img alt="A diagram detailing how AI optimizes winback campaigns using signals to make winback decisions for an improved reactivation rate." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F6912b37abcd2d2ddbaef0cd4e925f210128e289d-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F6912b37abcd2d2ddbaef0cd4e925f210128e289d-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F6912b37abcd2d2ddbaef0cd4e925f210128e289d-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-cf7bfac627b6" class="my-4 text-gray-900"><strong>How can AI decisioning reduce subscription cancellations?</strong></h3><p>AI decisioning can reduce cancellations by selecting the best save action for each customer at cancel intent, while controlling incentive costs.</p><p>This is strongest when your action set includes non-discount options, such as pauses, downgrades, or benefit reminders.</p><p><strong>Best for:</strong> subscription services with churn volume and multiple save paths</p><p><strong>What AI decisioning chooses:</strong> save path, offer depth (optional), channel, timing, cadence</p><p><strong>Signals to use:</strong> tenure, usage decline, prior save response, plan type, support history</p><p><strong>How to measure lift:</strong> churn rate, retained revenue, and incentive cost per save vs. holdout</p><img alt="AI process diagram for reducing subscription cancellations, showing input signals, AI choices, churn reduction outcome, and measurement." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fc0b0411cff7f581a0f0823c5d7c83ac89ab5c4bf-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fc0b0411cff7f581a0f0823c5d7c83ac89ab5c4bf-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fc0b0411cff7f581a0f0823c5d7c83ac89ab5c4bf-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-d89e91a283a3" class="my-4 text-gray-900"><strong>How can AI decisioning deliver individual recommendations?</strong></h3><p>AI decisioning can deliver individual recommendations by selecting the item most likely to drive the target outcome for each person, then learning from downstream actions.</p><p>Treat recommendations as a decision set (what you can show), tied to one primary KPI (what success means), so measurement stays clean.</p><p><strong>Best for:</strong> streaming, media, retail, travel, and apps with multiple “next best” items</p><p><strong>What AI decisioning chooses:</strong> recommended item, message angle, channel, timing, frequency</p><p><strong>Signals to use:</strong> recent browsing, affinity categories, session recency, prior conversions, price sensitivity proxy</p><p><strong>How to measure lift:</strong> primary KPI (conversion, watch time, revenue per user) vs. holdout</p><img alt="Diagram titled &quot;Individual recommendations&quot; detailing signals in, AI decisioning choices, desired outcome (conversion or engagement), and measurement strategy." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F10a1641da42a79871e612efea4d8cce5ca89550e-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F10a1641da42a79871e612efea4d8cce5ca89550e-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F10a1641da42a79871e612efea4d8cce5ca89550e-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-05f682258346" class="my-4 text-gray-900"><strong>Advocacy &amp; loyalty:</strong></h3><h3 id="h-e694c02ee289" class="my-4 text-gray-900"><strong>How can AI decisioning drive points redemption?</strong></h3><p>AI decisioning can drive points redemption by selecting the best redemption prompt, benefit framing, and timing for each loyalty member.</p><p>It also helps manage loyalty economics by learning when a reminder is enough versus when a stronger nudge is needed.</p><p><strong>Best for:</strong> loyalty programs where redemption links to retention and repeat buying</p><p><strong>What AI decisioning chooses:</strong> prompt type, benefit framing, channel, timing, cadence</p><p><strong>Signals to use:</strong> points balance, time-to-expiry, earn rate, category interest, prior redemption behavior</p><p><strong>How to measure lift:</strong> redemption rate and downstream repeat purchase vs. holdout</p><img alt="A diagram outlining an AI strategy for driving points redemption, detailing signals like points balance and time-to-expiry, AI decisions on prompt type, channel, and timing, an optimized redemption rate outcome, and measurement via randomized holdout." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F6854669afbbbe5bf54a0c4e74a7f2b13775fdc25-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F6854669afbbbe5bf54a0c4e74a7f2b13775fdc25-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F6854669afbbbe5bf54a0c4e74a7f2b13775fdc25-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-3fccd6ebed22" class="my-4 text-gray-900"><strong>How can AI decisioning improve referral performance?</strong></h3><p>AI decisioning can improve referrals by choosing what to offer, who to ask, and when to ask, based on likelihood to refer and downstream value.</p><p>This works best when you have multiple referral prompts and reward structures available.</p><p><strong>Best for:</strong> subscriptions, marketplaces, fintech, and apps with clear sharing moments</p><p><strong>What AI decisioning chooses:</strong> ask timing, reward type, message angle, channel, follow-up cadence</p><p><strong>Signals to use:</strong> satisfaction proxy, engagement depth, tenure, referral history, sharing behavior</p><p><strong>How to measure lift:</strong> referral rate and referred conversion rate vs. holdout</p><img alt="Diagram titled &quot;Referrals&quot; outlining an AI optimization process, detailing input signals, AI decisioning on ask parameters, outcome to optimize referral rate, and measurement strategy." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa386c7ab6826fcba47fc3852e99aeb8d3c5632c1-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa386c7ab6826fcba47fc3852e99aeb8d3c5632c1-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa386c7ab6826fcba47fc3852e99aeb8d3c5632c1-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-46a6667d1627" class="my-4 text-gray-900"><strong>How can AI decisioning optimize activations?</strong></h3><p>AI decisioning can optimize activations by selecting onboarding message variants, CTAs, cadence, and timing that move each user to the activation event faster.</p><p>Keep the scope tight by defining one activation milestone, then offering a small set of treatment options.</p><p><strong>Best for:</strong> apps, fintech, SaaS trials, and subscriptions with a clear “activation” event</p><p><strong>What AI decisioning chooses:</strong> onboarding message variant, channel, timing, cadence</p><p><strong>Signals to use:</strong> setup progress, early feature usage, session recency, intent events, signup source</p><p><strong>How to measure lift:</strong> activation rate and time-to-activation vs. holdout</p><img alt="Diagram titled &quot;Optimize activations&quot; outlining signals (setup progress, usage, source), AI choices (message, channel, timing, frequency), and outcome (optimizing activation rate), measured via randomized holdout." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F85d5c6c1d0fc6d7824cef1ae4235c8bb72380a95-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F85d5c6c1d0fc6d7824cef1ae4235c8bb72380a95-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F85d5c6c1d0fc6d7824cef1ae4235c8bb72380a95-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-bcc636d4c5f5" class="my-4 text-gray-900"><strong>Growth &amp; monetization:</strong></h3><h3 id="h-ec72a85cf0bb" class="my-4 text-gray-900"><strong>How can AI decisioning convert free to paid subscriptions?</strong></h3><p>AI decisioning can convert free users to paid by choosing the right upgrade prompt, plan emphasis, and timing based on product engagement and conversion likelihood.</p><p>This is a strong subscription motion because the most useful signals come directly from usage behavior.</p><p><strong>Best for:</strong> freemium apps, media subscriptions, and SaaS trials</p><p><strong>What AI decisioning chooses:</strong> upgrade angle, plan emphasis, offer inclusion, channel, timing, cadence</p><p><strong>Signals to use:</strong> feature adoption, usage frequency, paywall hits, content depth, tenure</p><p><strong>How to measure lift:</strong> free-to-paid conversion rate vs. holdout</p><img alt="AI-driven framework for converting free to paid subscriptions, detailing signals, AI choices, outcome, and measurement." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F2bee47eb481145fd25d8f56f8479a597ab7ff284-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F2bee47eb481145fd25d8f56f8479a597ab7ff284-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F2bee47eb481145fd25d8f56f8479a597ab7ff284-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-9193a7716c6b" class="my-4 text-gray-900"><strong>How can AI decisioning optimize plan upgrades?</strong></h3><p>AI decisioning can optimize plan upgrades by selecting which tier to recommend, how to frame value, and whether an incentive is needed for each subscriber.</p><p>It’s useful when different cohorts upgrade for different reasons, such as features, savings, or convenience.</p><p><strong>Best for:</strong> tiered subscriptions, telecom plans, fintech products, and bundled services</p><p><strong>What AI decisioning chooses:</strong> upgrade tier, value framing, offer inclusion, channel, timing, cadence</p><p><strong>Signals to use:</strong> usage thresholds, constraints hit, preferences, support signals, price sensitivity proxy</p><p><strong>How to measure lift:</strong> upgrade rate and incremental ARR per subscriber vs. holdout</p><img alt="Diagram outlining AI-driven plan upgrade optimization, detailing input signals, AI&#x27;s upgrade choices (tier, framing, timing), desired outcomes (upgrade rate or ARR), and measurement via randomized holdout." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Ffe755eab5ad40ede86ccfdefbf550360eb918a78-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Ffe755eab5ad40ede86ccfdefbf550360eb918a78-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Ffe755eab5ad40ede86ccfdefbf550360eb918a78-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-eb8f66d4fa67" class="my-4 text-gray-900"><strong>How can AI decisioning improve contract renewals?</strong></h3><p>AI decisioning can improve contract renewals by choosing renewal messaging, timing, and value framing based on each customer’s usage and renewal risk.</p><p>It works best when renewal outreach starts early enough to adapt before the decision date.</p><p><strong>Best for:</strong> annual contracts, memberships, and renewal windows</p><p><strong>What AI decisioning chooses:</strong> message angle, channel mix, timing, cadence, offer inclusion (where allowed)</p><p><strong>Signals to use:</strong> usage trends, health proxy, engagement recency, support burden, renewal stage</p><p><strong>How to measure lift:</strong> renewal rate and retained revenue vs. holdout</p><img alt="Diagram for AI-driven contract renewals, showing signals (usage, health, engagement), AI decision factors (message, channel, timing), desired outcome (optimize renewal rate), and measurement (randomized holdout)." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F806aff181730e392da6122773a0c0bc8544b2afc-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F806aff181730e392da6122773a0c0bc8544b2afc-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F806aff181730e392da6122773a0c0bc8544b2afc-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-fe2a586130ba" class="my-4 text-gray-900"><strong>How can AI decisioning personalize offers?</strong></h3><p>AI decisioning can personalize offers by selecting the right offer type and depth for each customer, aligned to conversion goals and value protection.</p><p>In practice, this often runs inside lifecycle moments, such as browse follow-up, cart, renewals, and winback.</p><p><strong>Best for:</strong> brands that run multiple offer types and want to reduce blanket discounting</p><p><strong>What AI decisioning chooses:</strong> offer type, offer depth, message angle, channel, timing, cadence</p><p><strong>Signals to use:</strong> promo response, lifecycle stage, intent signals, loyalty status, margin sensitivity proxy</p><p><strong>How to measure lift:</strong> conversion and profit per user vs. holdout</p><img alt="Diagram outlining AI-driven offer personalization, detailing inputs like promo response and loyalty status, AI choices for offer type and timing, outcome of optimized conversion or profit, and measurement via randomized holdout." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F79de4223aba2a467f7577ae28f0cb540d6e658ca-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F79de4223aba2a467f7577ae28f0cb540d6e658ca-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F79de4223aba2a467f7577ae28f0cb540d6e658ca-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-2aacef3012b1" class="my-4 text-gray-900"><strong>How can AI decisioning support a digital ads bidding strategy?</strong></h3><p>AI decisioning can support a digital ads bidding strategy by tailoring bid approaches using first-party signals, tied to downstream revenue outcomes.</p><p>This is typically framed as acquisition efficiency, with measurement focused on incremental value, not platform-only metrics.</p><p><strong>Best for:</strong> brands with strong first-party data and clear post-click outcomes</p><p><strong>What AI decisioning chooses:</strong> bid strategy variant by cohort, plus downstream nurture alignment</p><p><strong>Signals to use:</strong> value proxies, intent events, category interest, recency, prior conversion paths</p><p><strong>How to measure lift:</strong> revenue per acquired customer and CAC efficiency vs. control campaigns</p><img alt="A diagram outlining a digital ads bidding strategy, detailing signals in, AI decisioning choices, the outcome of optimizing for acquisition efficiency, and measurement methods." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa2abf717e33c6be8c6395db850e7f1d2a742ac71-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa2abf717e33c6be8c6395db850e7f1d2a742ac71-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa2abf717e33c6be8c6395db850e7f1d2a742ac71-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-f2ed76b8d064" class="my-4 text-gray-900"><strong>How can AI decisioning personalize search lead acquisition follow-up?</strong></h3><p>AI decisioning can improve lead acquisition by selecting the best follow-up path after a search-driven lead, based on intent and likelihood to convert.</p><p>This keeps the focus on what you control in owned channels: response speed, framing, and nurture sequencing.</p><p><strong>Best for:</strong> high-intent lead gen where follow-up quality drives conversion</p><p><strong>What AI decisioning chooses:</strong> follow-up channel, timing, nurture cadence, offer or content angle</p><p><strong>Signals to use:</strong> intent proxy, landing behavior, form quality, geo, prior engagement</p><p><strong>How to measure lift:</strong> lead-to-qualified rate and lead-to-sale conversion vs. holdout</p><img alt="Diagram for &quot;Search lead acquisition follow-up&quot; detailing signals, AI choices, conversion outcome, and measurement strategy." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa46d0ad4c3da069914fc9aeae646c3e6a5dc4603-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa46d0ad4c3da069914fc9aeae646c3e6a5dc4603-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa46d0ad4c3da069914fc9aeae646c3e6a5dc4603-1200x600.jpg&amp;w=1920&amp;q=75"/><h3 id="h-53975d855da8" class="my-4 text-gray-900"><strong>Kayo sports puts 1:1 fandom to work</strong></h3><p><a href="https://www.braze.com/customers/kayo-sports-case-study" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">Kayo Sports</a> is Australia’s sports streaming service, built to give fans fast access to live and on-demand coverage across dozens of sports. Its team has invested in personalization across the subscriber journey, from sign-up through ongoing engagement.</p><h4 id="h-f9291496889d" class="my-4 text-gray-900"><strong>The problem</strong></h4><p>Kayo needed a scalable way to engage a wide range of sports fans with more relevant content and recommendations across channels and devices. Its existing approach limited how far it could take personalization using its available data.</p><img alt="An Outlook email client displaying a list of emails and an open Kayo email offering a discount on a sports streaming subscription." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F58383a2936148b351d3fbbec86e5849daac979c7-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F58383a2936148b351d3fbbec86e5849daac979c7-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F58383a2936148b351d3fbbec86e5849daac979c7-1200x600.jpg&amp;w=1920&amp;q=75"/><h4 id="h-536aa577bf48" class="my-4 text-gray-900"><strong>The solution</strong></h4><p>Kayo built “Customer Cortex,” a personalization engine that uses Braze and BrazeAI Decisioning Studio™ to support a 1:1 approach. AI decisioning selects the optimal mix of message, creative, channel, timing, frequency, and promotions for each subscriber, and expanded the number of potential actions from 300 to 1.2 million variations.</p><img alt="Two iPhones display promotional offers: a text message for BINGE and a lock screen notification for Kayo." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F7536c7a1c2ce964cc12a6da35db92f8256b07527-1200x600.jpg&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F7536c7a1c2ce964cc12a6da35db92f8256b07527-1200x600.jpg&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F7536c7a1c2ce964cc12a6da35db92f8256b07527-1200x600.jpg&amp;w=1920&amp;q=75"/><h4 id="h-208c8c71f9d7" class="my-4 text-gray-900"><strong>The results</strong></h4><ul><li class="mb-2 ml-10 list-disc">14% increase in subscriptions in FY24</li><li class="mb-2 ml-10 list-disc">8% increase in average annual occupancy</li><li class="mb-2 ml-10 list-disc">105% increase in cross-sells</li><li class="mb-2 ml-10 list-disc">Results delivered while average subscription prices increased by 20%</li></ul><h2 id="h-ebdbc5d1d58b" class="my-4 text-gray-900 lg:text-display-lg">Supply chain and logistics use cases</h2><p>AI decisioning can support supply chain and logistics teams by optimizing decisions that have clear inputs, multiple options, and measurable outcomes. Common use cases include:</p><ul><li class="mb-2 ml-10 list-disc"><strong>Demand forecasting:</strong> Analyze historical sales, market trends, and external factors (such as weather or economic indicators) to predict demand, optimize inventory levels, and reduce stockouts or overstock.</li><li class="mb-2 ml-10 list-disc"><strong>Route optimization:</strong> Use traffic patterns, weather conditions, and delivery schedules to identify efficient routes, reducing fuel costs and improving delivery times.</li><li class="mb-2 ml-10 list-disc"><strong>Inventory management:</strong> Monitor inventory in real time and predict reorder timing based on usage patterns and lead times to control carrying costs.</li><li class="mb-2 ml-10 list-disc"><strong>Supplier selection and risk management:</strong> Evaluate suppliers using performance history, financial stability, and geopolitical risk signals to support supplier decisions and reduce disruption risk.</li><li class="mb-2 ml-10 list-disc"><strong>Warehouse automation:</strong> Optimize warehouse operations using AI-powered robotics and automation for inventory handling, picking, packing, and shipping to improve efficiency and reduce errors.</li><li class="mb-2 ml-10 list-disc"><strong>Predictive maintenance:</strong> Use equipment and vehicle data to predict maintenance needs, reduce downtime, and extend asset lifespan.</li></ul><h2 id="h-2a117be15d17" class="my-4 text-gray-900 lg:text-display-lg">Fraud detection and risk management use cases</h2><p>AI decisioning can support fraud and risk teams by improving detection, prioritization, and response speed across high-volume workflows. Common use cases include:</p><ul><li class="mb-2 ml-10 list-disc"><strong>Transaction monitoring:</strong> Analyze transaction patterns in real time to flag anomalies that may indicate fraud.</li><li class="mb-2 ml-10 list-disc"><strong>User behavior analytics:</strong> Establish a baseline of normal behavior, then alert on deviations like unusual login locations or sudden changes in spend.</li><li class="mb-2 ml-10 list-disc"><strong>Identity verification:</strong> Strengthen verification by analyzing biometrics (facial or voice recognition) and cross-referencing trusted databases to reduce identity theft.</li><li class="mb-2 ml-10 list-disc"><strong>Predictive risk scoring:</strong> Assign risk scores to users or transactions based on factors like history, size, and frequency to prioritize manual review or step-up verification.</li><li class="mb-2 ml-10 list-disc"><strong>Network analysis:</strong> Identify suspicious relationships and interaction patterns that suggest collusion or organized fraud.</li><li class="mb-2 ml-10 list-disc"><strong>Automated claims processing:</strong> Flag claims with high-risk characteristics based on historical fraud patterns for faster, more accurate review.</li></ul><h2 id="h-ea771eb4be75" class="my-4 text-gray-900 lg:text-display-lg">Patient care use cases</h2><p>AI decisioning can help healthcare organizations support clinical decisions, personalize care, and improve operational performance. Common use cases include:</p><ul><li class="mb-2 ml-10 list-disc"><strong>Predictive analytics for patient outcomes:</strong> Use historical patient data to predict treatment outcomes, improving care planning and reducing readmissions.</li><li class="mb-2 ml-10 list-disc"><strong>Personalized treatment plans:</strong> Tailor therapies using signals such as genetics, lifestyle, and medical history.</li><li class="mb-2 ml-10 list-disc"><strong>Clinical decision support systems (CDSS):</strong> Provide evidence-based recommendations during consultations, including test suggestions and drug interaction flags.</li><li class="mb-2 ml-10 list-disc"><strong>Remote patient monitoring:</strong> Analyze wearable and device data to detect early warning signs and prompt timely interventions.</li><li class="mb-2 ml-10 list-disc"><strong>Operational efficiency:</strong> Predict admissions, optimize staffing schedules, and streamline supply chain logistics to reduce wait times.</li><li class="mb-2 ml-10 list-disc"><strong>NLP for patient communication:</strong> Use chatbots and virtual assistants for questions, scheduling, and medication reminders, and analyze patient sentiment to improve care delivery.</li></ul><h2 id="h-02232c67880c" class="my-4 text-gray-900 lg:text-display-lg">Cross-industry benefits</h2><p>AI decisioning supports a wide range of teams by improving how decisions are made, executed, and refined over time. Common benefits include:</p><ul><li class="mb-2 ml-10 list-disc"><strong>Enhanced decision-making:</strong> Data-driven insights support faster, more accurate decisions.</li><li class="mb-2 ml-10 list-disc"><strong>Increased efficiency:</strong> Automation reduces time spent on routine work.</li><li class="mb-2 ml-10 list-disc"><strong>Cost reduction:</strong> Optimized resource allocation can reduce waste and operating costs.</li><li class="mb-2 ml-10 list-disc"><strong>Improved customer experience:</strong> More relevant, timely interactions can increase satisfaction and loyalty.</li><li class="mb-2 ml-10 list-disc"><strong>Predictive analytics:</strong> Forecasting supports proactive planning and risk management.</li><li class="mb-2 ml-10 list-disc"><strong>Scalability:</strong> Systems can handle more data and decisions without linear cost increases.</li><li class="mb-2 ml-10 list-disc"><strong>Enhanced accuracy:</strong> Reduced human error improves reliability in analysis and execution.</li><li class="mb-2 ml-10 list-disc"><strong>Real-time insights:</strong> Decisions can be informed by current data as conditions change.</li><li class="mb-2 ml-10 list-disc"><strong>Competitive advantage:</strong> Faster, smarter decisioning can support differentiation.</li><li class="mb-2 ml-10 list-disc"><strong>Continuous learning and improvement:</strong> Outcomes feed back into future decisions, improving performance over time.</li></ul><h2 id="h-d07dbd75fc38" class="my-4 text-gray-900 lg:text-display-lg">Final thoughts on AI decisioning use cases</h2><p>AI decisioning use cases give teams a concrete way to turn “use more AI” into specific, testable changes. Each one connects a clear decision, the data behind it, and the outcome that matters—whether that’s higher conversion, lower fraud, or faster time to care.</p><p>The most effective programs start small, with a single journey, a defined risk decision, or one part of the supply chain, then expand as models prove their value and governance practices mature. Over time, those individual decisions add up to a more responsive, more consistent experience for customers and internal teams alike.</p><p>If you’re exploring how to bring these AI decisioning use cases into your own engagement strategy, this is a good moment to look at where Braze can help with orchestration, testing, and real-time decisioning.</p><div class="p-8 bg-primary-400 rounded-xl gap-8 flex flex-col text-white"><div class="flex flex-col gap-4"><div class="font-bold text-display-sm">Ready to get started?</div></div><div class="flex flex-wrap gap-4"><a class="inline-flex items-center justify-center text-center rounded-full transition-colors whitespace-nowrap relative z-10 gap-1 font-aribauGrotesk font-bold px-[18px] py-[10px] text-md border border-[#4411D6] bg-[#4411D6] text-white shadow-normal hover:border-[#1505C9] hover:bg-[#1505C9] hover:shadow-normal active:border-[#4411D6] active:bg-[#4411D6] active:shadow-focused disabled:border disabled:border-gray-200 disabled:bg-gray-200 disabled:text-gray-400 disabled:shadow-xs w-full sm:w-fit false" href="/get-started">Connect with sales</a></div></div><p><br/></p></div><div class="space-y-6"><h2 id="faq-accordion" class="mb-0 text-display-xs sm:text-display-sm font-bold">Frequently Asked Questions</h2><section class="bg-white rounded-xl shadow-xl shadow-purple-500/5 border border-purple-200 overflow-hidden" aria-labelledby="faq-accordion"><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="What are the most common AI decisioning use cases?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">What are the most common AI decisioning use cases?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>The most common AI decisioning use cases include send-time optimization, channel selection, next-best-action, personalization, fraud detection, and credit or risk scoring. These AI decisioning use cases show up in marketing, finance, operations, and customer support.</p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="How is AI decisioning used in marketing?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">How is AI decisioning used in marketing?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>AI decisioning in marketing is used to decide who to contact, when to reach them, which channel to use, and what content or offer to send.</p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="How does AI decisioning improve personalization?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">How does AI decisioning improve personalization?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>AI decisioning improves personalization by using customer behavior, preferences, and context to decide which message, product, or experience to show next.</p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="What data does AI decisioning require?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">What data does AI decisioning require?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>AI decisioning requires data such as events (clicks, purchases, logins), customer attributes (location, device, preferences), and contextual signals (time, channel, risk scores).</p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="Which industries benefit most?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">Which industries benefit most?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>Industries that make frequent, repeatable decisions—like retail, eCommerce, financial services, insurance, media, entertainment, gaming, sports, travel and hospitality, telecommunications, utilities, and healthcare—benefit most from AI decisioning use cases. These sectors can tie decision quality directly to revenue, risk, and customer satisfaction.</p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="What’s the difference between AI decisioning and automation?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">What’s the difference between AI decisioning and automation?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>The difference between AI decisioning and automation is that AI decisioning chooses between options based on data and models, while automation executes predefined steps. Many AI decisioning use cases combine both, with AI picking the action and automation carrying it out across systems and channels.</p></div></div></details></section><script type="application/ld+json">{"@context":"https://schema.org","@type":"FAQPage","mainEntity":[{"@type":"Question","name":"What are the most common AI decisioning use cases?","acceptedAnswer":{"@type":"Answer","text":"The most common AI decisioning use cases include send-time optimization, channel selection, next-best-action, personalization, fraud detection, and credit or risk scoring. These AI decisioning use cases show up in marketing, finance, operations, and customer support."}},{"@type":"Question","name":"How is AI decisioning used in marketing?","acceptedAnswer":{"@type":"Answer","text":"AI decisioning in marketing is used to decide who to contact, when to reach them, which channel to use, and what content or offer to send."}},{"@type":"Question","name":"How does AI decisioning improve personalization?","acceptedAnswer":{"@type":"Answer","text":"AI decisioning improves personalization by using customer behavior, preferences, and context to decide which message, product, or experience to show next."}},{"@type":"Question","name":"What data does AI decisioning require?","acceptedAnswer":{"@type":"Answer","text":"AI decisioning requires data such as events (clicks, purchases, logins), customer attributes (location, device, preferences), and contextual signals (time, channel, risk scores)."}},{"@type":"Question","name":"Which industries benefit most?","acceptedAnswer":{"@type":"Answer","text":"Industries that make frequent, repeatable decisions—like retail, eCommerce, financial services, insurance, media, entertainment, gaming, sports, travel and hospitality, telecommunications, utilities, and healthcare—benefit most from AI decisioning use cases. These sectors can tie decision quality directly to revenue, risk, and customer satisfaction."}},{"@type":"Question","name":"What’s the difference between AI decisioning and automation?","acceptedAnswer":{"@type":"Answer","text":"The difference between AI decisioning and automation is that AI decisioning chooses between options based on data and models, while automation executes predefined steps. Many AI decisioning use cases combine both, with AI picking the action and automation carrying it out across systems and channels."}}]}</script></div></div><div class="flex w-full shrink-0 grow-0 basis-full flex-col gap-8 md:w-[280px] md:basis-[280px]"><div class="hidden flex-col gap-8 bg-white  md:sticky md:top-20 md:flex"><div class="rounded-xl bg-primary-500 shadow-xl p-6 flex flex-col gap-6 items-start"><div class="w-full flex flex-col gap-4 flex-1"><span class="text-sm text-primary-100 font-bold">SUBSCRIBE</span><h4 class="!text-white text-display-xs font-extrabold">Be Absolutely Engaging.<!-- -->™</h4><p class="text-lg text-primary-100">Sign up for regular updates from Braze.</p></div><span class="hidden" id="formInvalidOptinValue">Please select one option only and then submit your preference.</span><span class="hidden" id="formInvalidBusinessEmail">Please enter your business email address.</span><div class="is-loading font-semibold text-center">Loading...</div><form 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risk management use cases\"}]]}],[\"$\",\"li\",\"ea771eb4be75\",{\"className\":\"flex items-center gap-2\",\"children\":[[\"$\",\"svg\",null,{\"width\":18,\"height\":18,\"role\":\"img\",\"aria-label\":\"arrow-down\",\"className\":\"text-gray-600\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#arrow-down\"}]}],[\"$\",\"a\",null,{\"href\":\"#h-ea771eb4be75\",\"className\":\"text-gray-900 hover:text-primary-700 transition-colors leading-relaxed underline decoration-primary-700 underline-offset-2\",\"children\":\"Patient care use cases\"}]]}],[\"$\",\"li\",\"02232c67880c\",{\"className\":\"flex items-center gap-2\",\"children\":[[\"$\",\"svg\",null,{\"width\":18,\"height\":18,\"role\":\"img\",\"aria-label\":\"arrow-down\",\"className\":\"text-gray-600\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#arrow-down\"}]}],[\"$\",\"a\",null,{\"href\":\"#h-02232c67880c\",\"className\":\"text-gray-900 hover:text-primary-700 transition-colors leading-relaxed 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gap-2\",\"children\":[[\"$\",\"svg\",null,{\"width\":18,\"height\":18,\"role\":\"img\",\"aria-label\":\"arrow-down\",\"className\":\"text-gray-600\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#arrow-down\"}]}],[\"$\",\"a\",null,{\"href\":\"#faq-accordion\",\"className\":\"text-gray-900 hover:text-primary-700 transition-colors leading-relaxed underline decoration-primary-700 underline-offset-2\",\"children\":\"Frequently Asked Questions\"}]]}]]}]}]]}],[\"$\",\"div\",null,{\"className\":\"flex flex-col gap-6\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"Every team already makes hundreds of small decisions a day, like who to contact, which offer to show, how to route a claim, or which order to fulfill first. The hard part is doing this consistently, at scale, without adding too much manual work.\"]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning turns those choices into repeatable processes that link data, guardrails, and outcomes. Instead of debating where to start in the abstract, teams can point to specific triggers, take specific actions, then improve them one by one.\"]}],[\"$\",\"p\",null,{\"children\":[\"In this guide you’ll see AI decisioning use cases in practical terms, across a number of industries and find out how you too can use Braze and AI decisioning into your customer engagement strategy.\"]}],[\"$\",\"h2\",null,{\"id\":\"h-4d84c3ad3510\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Customer engagement and marketing use cases\"]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning is used in customer engagement and marketing to optimize a defined goal, such as purchase rate, activation, or retention, based on how each individual customer responds over time. The use cases below show how common lifecycle moments can be mapped to decision options and which metrics can be used to measure impact.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-7a616d4cd029\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Purchase \u0026 revenue optimization:\"]}]]}],[\"$\",\"h3\",null,{\"id\":\"h-3b06a03100e3\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning maximize purchase rates?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can maximize purchase rates by selecting the best mix of offer, creative, channel, timing, and frequency for each customer, tied to a purchase KPI.\"]}],[\"$\",\"p\",null,{\"children\":[\"To set it up, build a small “action bank” of viable offers and messages, then let decisioning learn which combinations drive purchases across different customer patterns.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for: \"]}],\"Any industry with a strong customer lifecycle strategy and high message volume, such as retail or QSR\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" offer, message/creative, channel, timing, frequency\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" recent purchases, category affinity, engagement recency, typical buy day/time, incentive sensitivity\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" purchase rate and revenue per user vs. holdout (business-as-usual messaging)\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/494ccfaad7abca5f3308a9847ba969c4269bed8f-1200x600.jpg\",\"alt\":\"A diagram outlining an AI framework to maximize purchase rates, using customer signals to optimize offers, messages, and timing, measured by purchase rate and revenue against a baseline.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-46312682e477\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning drive upsell?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can drive upsell by choosing the right upgrade path, value message, and offer level for each customer, aligned to revenue or margin goals.\"]}],[\"$\",\"p\",null,{\"children\":[\"This is a strong fit when a flat discount strategy over-incentivizes customers who would have upgraded anyway.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" premium tiers, bundles, and higher-margin categories\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" upsell path, offer type and depth, message angle, timing, cadence\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" spend level, product mix, engagement depth, discount responsiveness, lifecycle stage\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" incremental revenue per user and incremental margin vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/04a8a1b7ecf0aaaa07e434cf382af18a9c1952f9-1200x600.jpg\",\"alt\":\"Diagram of an AI-powered upsell strategy, showing input signals, AI decisioning, outcomes, and measurement.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-72da2fc81276\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning personalize cross-sell campaigns?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can personalize cross-sell by selecting which add-on to promote, whether to include an incentive, and how to sequence follow-ups by customer.\"]}],[\"$\",\"p\",null,{\"children\":[\"It’s especially useful when add-ons vary by margin, and customers adopt them for different reasons.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" subscription bundles, marketplaces, retail add-ons, fintech product adoption\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" cross-sell item, incentive inclusion, message/creative, channel, timing, cadence\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" current product owned, usage patterns, complementary interest, tenure, recent purchases\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" incremental attach rate and incremental profit per user vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/84f71dd6332e120b5436690a2d98f056ab9be03b-1200x600.jpg\",\"alt\":\"Diagram showing an AI-driven framework to personalize cross-sell campaigns, detailing input signals, AI decisioning choices, desired outcomes, and measurement metrics.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-547303f0bb44\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Lifecycle \u0026 retention:\"]}]]}],[\"$\",\"h3\",null,{\"id\":\"h-8ccc52d23d94\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning improve abandoned cart conversions?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can improve abandoned cart conversions by choosing follow-up timing and channel, reminder count, and creative that best fits each shopper’s intent.\"]}],[\"$\",\"p\",null,{\"children\":[\"This works best when you offer multiple reminder strategies and keep incentive use inside clear guardrails.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" ecommerce and travel bookings with clear cart intent signals\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" delay to first message, number of reminders, creative/CTA, incentive type (optional), channel mix\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" cart value, category, time since abandon, return history, loyalty status\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" cart conversion rate and revenue per abandonment vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/26d7a79fc7652871ba50621b9fdd5ca09e48cdbe-1200x600.jpg\",\"alt\":\"Diagram outlining an AI strategy to improve abandoned cart conversions, using signals like cart value and loyalty status to determine actions like delay and creative, optimizing cart conversion rate, and measured by a randomized holdout.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-fe11b52292f8\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning drive repeat purchases?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can drive repeat purchases by learning the best reorder timing, channel, and message type for each buyer.\"]}],[\"$\",\"p\",null,{\"children\":[\"This use case is easier to launch when you can define a “reorder window” and provide a few message and offer variants.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" consumables, beauty, wellness, pet, and replenishment cycles\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" reorder window, product focus, offer inclusion, channel, cadence\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" time since purchase, typical reorder interval, basket size, category, engagement recency\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" repeat purchase rate and revenue per buyer vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/ac5c5d3ed53a00f832f6d2c00b80d723776f4fe5-1200x600.jpg\",\"alt\":\"Diagram showing an AI use case to drive repeat purchases, detailing signals like purchase time, AI decisions on reorder window, an outcome of optimized repeat purchase rate, and measurement of repeat purchase rate and revenue per buyer over 30 days.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-8b10fd22547f\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning optimize winback campaigns?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can optimize winback by selecting the best reactivation message, channel, timing, and offer for each lapsed customer.\"]}],[\"$\",\"p\",null,{\"children\":[\"This is a good fit when lapsed customers vary widely in why they stopped engaging.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" retail reactivation, app re-engagement, and subscription winback\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" winback angle, offer inclusion, channel, timing, cadence\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" time since last activity, last category engaged, churn proxy, prior discount response, tenure\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" reactivation rate and revenue per lapsed user vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/6912b37abcd2d2ddbaef0cd4e925f210128e289d-1200x600.jpg\",\"alt\":\"A diagram detailing how AI optimizes winback campaigns using signals to make winback decisions for an improved reactivation rate.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-cf7bfac627b6\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning reduce subscription cancellations?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can reduce cancellations by selecting the best save action for each customer at cancel intent, while controlling incentive costs.\"]}],[\"$\",\"p\",null,{\"children\":[\"This is strongest when your action set includes non-discount options, such as pauses, downgrades, or benefit reminders.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" subscription services with churn volume and multiple save paths\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" save path, offer depth (optional), channel, timing, cadence\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" tenure, usage decline, prior save response, plan type, support history\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" churn rate, retained revenue, and incentive cost per save vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/c0b0411cff7f581a0f0823c5d7c83ac89ab5c4bf-1200x600.jpg\",\"alt\":\"AI process diagram for reducing subscription cancellations, showing input signals, AI choices, churn reduction outcome, and measurement.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-d89e91a283a3\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning deliver individual recommendations?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can deliver individual recommendations by selecting the item most likely to drive the target outcome for each person, then learning from downstream actions.\"]}],[\"$\",\"p\",null,{\"children\":[\"Treat recommendations as a decision set (what you can show), tied to one primary KPI (what success means), so measurement stays clean.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" streaming, media, retail, travel, and apps with multiple “next best” items\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" recommended item, message angle, channel, timing, frequency\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" recent browsing, affinity categories, session recency, prior conversions, price sensitivity proxy\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" primary KPI (conversion, watch time, revenue per user) vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/10a1641da42a79871e612efea4d8cce5ca89550e-1200x600.jpg\",\"alt\":\"Diagram titled \\\"Individual recommendations\\\" detailing signals in, AI decisioning choices, desired outcome (conversion or engagement), and measurement strategy.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-05f682258346\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Advocacy \u0026 loyalty:\"]}]]}],[\"$\",\"h3\",null,{\"id\":\"h-e694c02ee289\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning drive points redemption?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can drive points redemption by selecting the best redemption prompt, benefit framing, and timing for each loyalty member.\"]}],[\"$\",\"p\",null,{\"children\":[\"It also helps manage loyalty economics by learning when a reminder is enough versus when a stronger nudge is needed.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" loyalty programs where redemption links to retention and repeat buying\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" prompt type, benefit framing, channel, timing, cadence\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" points balance, time-to-expiry, earn rate, category interest, prior redemption behavior\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" redemption rate and downstream repeat purchase vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/6854669afbbbe5bf54a0c4e74a7f2b13775fdc25-1200x600.jpg\",\"alt\":\"A diagram outlining an AI strategy for driving points redemption, detailing signals like points balance and time-to-expiry, AI decisions on prompt type, channel, and timing, an optimized redemption rate outcome, and measurement via randomized holdout.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-3fccd6ebed22\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning improve referral performance?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can improve referrals by choosing what to offer, who to ask, and when to ask, based on likelihood to refer and downstream value.\"]}],[\"$\",\"p\",null,{\"children\":[\"This works best when you have multiple referral prompts and reward structures available.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" subscriptions, marketplaces, fintech, and apps with clear sharing moments\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" ask timing, reward type, message angle, channel, follow-up cadence\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" satisfaction proxy, engagement depth, tenure, referral history, sharing behavior\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" referral rate and referred conversion rate vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/a386c7ab6826fcba47fc3852e99aeb8d3c5632c1-1200x600.jpg\",\"alt\":\"Diagram titled \\\"Referrals\\\" outlining an AI optimization process, detailing input signals, AI decisioning on ask parameters, outcome to optimize referral rate, and measurement strategy.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-46a6667d1627\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning optimize activations?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can optimize activations by selecting onboarding message variants, CTAs, cadence, and timing that move each user to the activation event faster.\"]}],[\"$\",\"p\",null,{\"children\":[\"Keep the scope tight by defining one activation milestone, then offering a small set of treatment options.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" apps, fintech, SaaS trials, and subscriptions with a clear “activation” event\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" onboarding message variant, channel, timing, cadence\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" setup progress, early feature usage, session recency, intent events, signup source\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" activation rate and time-to-activation vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/85d5c6c1d0fc6d7824cef1ae4235c8bb72380a95-1200x600.jpg\",\"alt\":\"Diagram titled \\\"Optimize activations\\\" outlining signals (setup progress, usage, source), AI choices (message, channel, timing, frequency), and outcome (optimizing activation rate), measured via randomized holdout.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-bcc636d4c5f5\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Growth \u0026 monetization:\"]}]]}],[\"$\",\"h3\",null,{\"id\":\"h-ec72a85cf0bb\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning convert free to paid subscriptions?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can convert free users to paid by choosing the right upgrade prompt, plan emphasis, and timing based on product engagement and conversion likelihood.\"]}],[\"$\",\"p\",null,{\"children\":[\"This is a strong subscription motion because the most useful signals come directly from usage behavior.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" freemium apps, media subscriptions, and SaaS trials\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" upgrade angle, plan emphasis, offer inclusion, channel, timing, cadence\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" feature adoption, usage frequency, paywall hits, content depth, tenure\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" free-to-paid conversion rate vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/2bee47eb481145fd25d8f56f8479a597ab7ff284-1200x600.jpg\",\"alt\":\"AI-driven framework for converting free to paid subscriptions, detailing signals, AI choices, outcome, and measurement.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-9193a7716c6b\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning optimize plan upgrades?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can optimize plan upgrades by selecting which tier to recommend, how to frame value, and whether an incentive is needed for each subscriber.\"]}],[\"$\",\"p\",null,{\"children\":[\"It’s useful when different cohorts upgrade for different reasons, such as features, savings, or convenience.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" tiered subscriptions, telecom plans, fintech products, and bundled services\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" upgrade tier, value framing, offer inclusion, channel, timing, cadence\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" usage thresholds, constraints hit, preferences, support signals, price sensitivity proxy\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" upgrade rate and incremental ARR per subscriber vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/fe755eab5ad40ede86ccfdefbf550360eb918a78-1200x600.jpg\",\"alt\":\"Diagram outlining AI-driven plan upgrade optimization, detailing input signals, AI's upgrade choices (tier, framing, timing), desired outcomes (upgrade rate or ARR), and measurement via randomized holdout.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-eb8f66d4fa67\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning improve contract renewals?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can improve contract renewals by choosing renewal messaging, timing, and value framing based on each customer’s usage and renewal risk.\"]}],[\"$\",\"p\",null,{\"children\":[\"It works best when renewal outreach starts early enough to adapt before the decision date.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" annual contracts, memberships, and renewal windows\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" message angle, channel mix, timing, cadence, offer inclusion (where allowed)\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" usage trends, health proxy, engagement recency, support burden, renewal stage\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" renewal rate and retained revenue vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/806aff181730e392da6122773a0c0bc8544b2afc-1200x600.jpg\",\"alt\":\"Diagram for AI-driven contract renewals, showing signals (usage, health, engagement), AI decision factors (message, channel, timing), desired outcome (optimize renewal rate), and measurement (randomized holdout).\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-fe2a586130ba\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning personalize offers?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can personalize offers by selecting the right offer type and depth for each customer, aligned to conversion goals and value protection.\"]}],[\"$\",\"p\",null,{\"children\":[\"In practice, this often runs inside lifecycle moments, such as browse follow-up, cart, renewals, and winback.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" brands that run multiple offer types and want to reduce blanket discounting\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" offer type, offer depth, message angle, channel, timing, cadence\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" promo response, lifecycle stage, intent signals, loyalty status, margin sensitivity proxy\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" conversion and profit per user vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/79de4223aba2a467f7577ae28f0cb540d6e658ca-1200x600.jpg\",\"alt\":\"Diagram outlining AI-driven offer personalization, detailing inputs like promo response and loyalty status, AI choices for offer type and timing, outcome of optimized conversion or profit, and measurement via randomized holdout.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-2aacef3012b1\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning support a digital ads bidding strategy?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can support a digital ads bidding strategy by tailoring bid approaches using first-party signals, tied to downstream revenue outcomes.\"]}],[\"$\",\"p\",null,{\"children\":[\"This is typically framed as acquisition efficiency, with measurement focused on incremental value, not platform-only metrics.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" brands with strong first-party data and clear post-click outcomes\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" bid strategy variant by cohort, plus downstream nurture alignment\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" value proxies, intent events, category interest, recency, prior conversion paths\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" revenue per acquired customer and CAC efficiency vs. control campaigns\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/a2abf717e33c6be8c6395db850e7f1d2a742ac71-1200x600.jpg\",\"alt\":\"A diagram outlining a digital ads bidding strategy, detailing signals in, AI decisioning choices, the outcome of optimizing for acquisition efficiency, and measurement methods.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-f2ed76b8d064\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How can AI decisioning personalize search lead acquisition follow-up?\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can improve lead acquisition by selecting the best follow-up path after a search-driven lead, based on intent and likelihood to convert.\"]}],[\"$\",\"p\",null,{\"children\":[\"This keeps the focus on what you control in owned channels: response speed, framing, and nurture sequencing.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Best for:\"]}],\" high-intent lead gen where follow-up quality drives conversion\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"What AI decisioning chooses:\"]}],\" follow-up channel, timing, nurture cadence, offer or content angle\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Signals to use:\"]}],\" intent proxy, landing behavior, form quality, geo, prior engagement\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"How to measure lift:\"]}],\" lead-to-qualified rate and lead-to-sale conversion vs. holdout\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/a46d0ad4c3da069914fc9aeae646c3e6a5dc4603-1200x600.jpg\",\"alt\":\"Diagram for \\\"Search lead acquisition follow-up\\\" detailing signals, AI choices, conversion outcome, and measurement strategy.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h3\",null,{\"id\":\"h-53975d855da8\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Kayo sports puts 1:1 fandom to work\"]}]]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"href\":\"https://www.braze.com/customers/kayo-sports-case-study\",\"rel\":\"noreferrer noopener\",\"target\":\"$undefined\",\"className\":\"font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black\",\"aria-label\":\"External link\",\"children\":[\"Kayo Sports\"]}],\" is Australia’s sports streaming service, built to give fans fast access to live and on-demand coverage across dozens of sports. Its team has invested in personalization across the subscriber journey, from sign-up through ongoing engagement.\"]}],[\"$\",\"h4\",null,{\"id\":\"h-f9291496889d\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"The problem\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"Kayo needed a scalable way to engage a wide range of sports fans with more relevant content and recommendations across channels and devices. Its existing approach limited how far it could take personalization using its available data.\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/58383a2936148b351d3fbbec86e5849daac979c7-1200x600.jpg\",\"alt\":\"An Outlook email client displaying a list of emails and an open Kayo email offering a discount on a sports streaming subscription.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h4\",null,{\"id\":\"h-536aa577bf48\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"The solution\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"Kayo built “Customer Cortex,” a personalization engine that uses Braze and BrazeAI Decisioning Studio™ to support a 1:1 approach. AI decisioning selects the optimal mix of message, creative, channel, timing, frequency, and promotions for each subscriber, and expanded the number of potential actions from 300 to 1.2 million variations.\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/7536c7a1c2ce964cc12a6da35db92f8256b07527-1200x600.jpg\",\"alt\":\"Two iPhones display promotional offers: a text message for BINGE and a lock screen notification for Kayo.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h4\",null,{\"id\":\"h-208c8c71f9d7\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"The results\"]}]]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[\"14% increase in subscriptions in FY24\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[\"8% increase in average annual occupancy\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[\"105% increase in cross-sells\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[\"Results delivered while average subscription prices increased by 20%\"]}]]}],[\"$\",\"h2\",null,{\"id\":\"h-ebdbc5d1d58b\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Supply chain and logistics use cases\"]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can support supply chain and logistics teams by optimizing decisions that have clear inputs, multiple options, and measurable outcomes. Common use cases include:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Demand forecasting:\"]}],\" Analyze historical sales, market trends, and external factors (such as weather or economic indicators) to predict demand, optimize inventory levels, and reduce stockouts or overstock.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Route optimization:\"]}],\" Use traffic patterns, weather conditions, and delivery schedules to identify efficient routes, reducing fuel costs and improving delivery times.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Inventory management:\"]}],\" Monitor inventory in real time and predict reorder timing based on usage patterns and lead times to control carrying costs.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Supplier selection and risk management:\"]}],\" Evaluate suppliers using performance history, financial stability, and geopolitical risk signals to support supplier decisions and reduce disruption risk.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Warehouse automation:\"]}],\" Optimize warehouse operations using AI-powered robotics and automation for inventory handling, picking, packing, and shipping to improve efficiency and reduce errors.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Predictive maintenance:\"]}],\" Use equipment and vehicle data to predict maintenance needs, reduce downtime, and extend asset lifespan.\"]}]]}],[\"$\",\"h2\",null,{\"id\":\"h-2a117be15d17\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Fraud detection and risk management use cases\"]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can support fraud and risk teams by improving detection, prioritization, and response speed across high-volume workflows. Common use cases include:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Transaction monitoring:\"]}],\" Analyze transaction patterns in real time to flag anomalies that may indicate fraud.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"User behavior analytics:\"]}],\" Establish a baseline of normal behavior, then alert on deviations like unusual login locations or sudden changes in spend.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Identity verification:\"]}],\" Strengthen verification by analyzing biometrics (facial or voice recognition) and cross-referencing trusted databases to reduce identity theft.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Predictive risk scoring:\"]}],\" Assign risk scores to users or transactions based on factors like history, size, and frequency to prioritize manual review or step-up verification.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Network analysis:\"]}],\" Identify suspicious relationships and interaction patterns that suggest collusion or organized fraud.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Automated claims processing:\"]}],\" Flag claims with high-risk characteristics based on historical fraud patterns for faster, more accurate review.\"]}]]}],[\"$\",\"h2\",null,{\"id\":\"h-ea771eb4be75\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Patient care use cases\"]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning can help healthcare organizations support clinical decisions, personalize care, and improve operational performance. Common use cases include:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Predictive analytics for patient outcomes:\"]}],\" Use historical patient data to predict treatment outcomes, improving care planning and reducing readmissions.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Personalized treatment plans:\"]}],\" Tailor therapies using signals such as genetics, lifestyle, and medical history.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Clinical decision support systems (CDSS):\"]}],\" Provide evidence-based recommendations during consultations, including test suggestions and drug interaction flags.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Remote patient monitoring:\"]}],\" Analyze wearable and device data to detect early warning signs and prompt timely interventions.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Operational efficiency:\"]}],\" Predict admissions, optimize staffing schedules, and streamline supply chain logistics to reduce wait times.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"NLP for patient communication:\"]}],\" Use chatbots and virtual assistants for questions, scheduling, and medication reminders, and analyze patient sentiment to improve care delivery.\"]}]]}],[\"$\",\"h2\",null,{\"id\":\"h-02232c67880c\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Cross-industry benefits\"]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning supports a wide range of teams by improving how decisions are made, executed, and refined over time. Common benefits include:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Enhanced decision-making:\"]}],\" Data-driven insights support faster, more accurate decisions.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Increased efficiency:\"]}],\" Automation reduces time spent on routine work.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Cost reduction:\"]}],\" Optimized resource allocation can reduce waste and operating costs.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Improved customer experience:\"]}],\" More relevant, timely interactions can increase satisfaction and loyalty.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Predictive analytics:\"]}],\" Forecasting supports proactive planning and risk management.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Scalability:\"]}],\" Systems can handle more data and decisions without linear cost increases.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Enhanced accuracy:\"]}],\" Reduced human error improves reliability in analysis and execution.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Real-time insights:\"]}],\" Decisions can be informed by current data as conditions change.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Competitive advantage:\"]}],\" Faster, smarter decisioning can support differentiation.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Continuous learning and improvement:\"]}],\" Outcomes feed back into future decisions, improving performance over time.\"]}]]}],[\"$\",\"h2\",null,{\"id\":\"h-d07dbd75fc38\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Final thoughts on AI decisioning use cases\"]}],[\"$\",\"p\",null,{\"children\":[\"AI decisioning use cases give teams a concrete way to turn “use more AI” into specific, testable changes. Each one connects a clear decision, the data behind it, and the outcome that matters—whether that’s higher conversion, lower fraud, or faster time to care.\"]}],[\"$\",\"p\",null,{\"children\":[\"The most effective programs start small, with a single journey, a defined risk decision, or one part of the supply chain, then expand as models prove their value and governance practices mature. Over time, those individual decisions add up to a more responsive, more consistent experience for customers and internal teams alike.\"]}],[\"$\",\"p\",null,{\"children\":[\"If you’re exploring how to bring these AI decisioning use cases into your own engagement strategy, this is a good moment to look at where Braze can help with orchestration, testing, and real-time decisioning.\"]}],[\"$\",\"$L36\",null,{\"_key\":\"e0633f0ea0ef\",\"_type\":\"ctaCard\",\"actions\":[{\"_key\":\"f13df895cb73\",\"_type\":\"ctaAction\",\"actionType\":\"link\",\"buttonSize\":\"lg\",\"buttonStyle\":\"purple-button\",\"buttonText\":\"Connect with sales\",\"iconPosition\":\"trailing\",\"internalLink\":{\"blank\":false},\"link\":{\"blank\":false,\"href\":\"https://www.braze.com/get-started\"}}],\"heading\":\"Ready to get started?\",\"markDefs\":null}],[\"$\",\"p\",null,{\"children\":[\"\",[\"$\",\"br\",null,{}],\"\"]}]]}],[\"$\",\"div\",null,{\"className\":\"space-y-6\",\"children\":[[\"$\",\"h2\",null,{\"id\":\"faq-accordion\",\"className\":\"mb-0 text-display-xs sm:text-display-sm font-bold\",\"children\":\"Frequently Asked Questions\"}],[\"$\",\"section\",null,{\"className\":\"bg-white rounded-xl shadow-xl shadow-purple-500/5 border border-purple-200 overflow-hidden\",\"aria-labelledby\":\"faq-accordion\",\"children\":[[\"$\",\"details\",null,{\"className\":\"group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50\",\"name\":\"faq-accordion\",\"children\":[[\"$\",\"summary\",null,{\"className\":\"flex items-center justify-between cursor-pointer list-none px-6 py-8 [\u0026::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2\",\"aria-label\":\"What are the most common AI decisioning use cases?\",\"children\":[[\"$\",\"span\",null,{\"className\":\"text-lg font-semibold text-gray-900 group-hover:text-purple-700\",\"children\":\"What are the most common AI decisioning use cases?\"}],[\"$\",\"svg\",null,{\"width\":20,\"height\":20,\"role\":\"img\",\"aria-label\":\"chevron-down\",\"stroke\":\"currentColor\",\"className\":\"shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#chevron-down\"}]}]]}],[\"$\",\"div\",null,{\"className\":\"px-6 pb-6\",\"children\":[\"$\",\"div\",null,{\"className\":\"text-base leading-relaxed text-gray-600\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"The most common AI decisioning use cases include send-time optimization, channel selection, next-best-action, personalization, fraud detection, and credit or risk scoring. These AI decisioning use cases show up in marketing, finance, operations, and customer support.\"]}]]}]}]]}],[\"$\",\"details\",null,{\"className\":\"group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50\",\"name\":\"faq-accordion\",\"children\":[[\"$\",\"summary\",null,{\"className\":\"flex items-center justify-between cursor-pointer list-none px-6 py-8 [\u0026::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2\",\"aria-label\":\"How is AI decisioning used in marketing?\",\"children\":[[\"$\",\"span\",null,{\"className\":\"text-lg font-semibold text-gray-900 group-hover:text-purple-700\",\"children\":\"How is AI decisioning used in marketing?\"}],[\"$\",\"svg\",null,{\"width\":20,\"height\":20,\"role\":\"img\",\"aria-label\":\"chevron-down\",\"stroke\":\"currentColor\",\"className\":\"shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#chevron-down\"}]}]]}],[\"$\",\"div\",null,{\"className\":\"px-6 pb-6\",\"children\":[\"$\",\"div\",null,{\"className\":\"text-base leading-relaxed text-gray-600\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"AI decisioning in marketing is used to decide who to contact, when to reach them, which channel to use, and what content or offer to send.\"]}]]}]}]]}],[\"$\",\"details\",null,{\"className\":\"group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50\",\"name\":\"faq-accordion\",\"children\":[[\"$\",\"summary\",null,{\"className\":\"flex items-center justify-between cursor-pointer list-none px-6 py-8 [\u0026::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2\",\"aria-label\":\"How does AI decisioning improve personalization?\",\"children\":[[\"$\",\"span\",null,{\"className\":\"text-lg font-semibold text-gray-900 group-hover:text-purple-700\",\"children\":\"How does AI decisioning improve personalization?\"}],[\"$\",\"svg\",null,{\"width\":20,\"height\":20,\"role\":\"img\",\"aria-label\":\"chevron-down\",\"stroke\":\"currentColor\",\"className\":\"shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#chevron-down\"}]}]]}],[\"$\",\"div\",null,{\"className\":\"px-6 pb-6\",\"children\":[\"$\",\"div\",null,{\"className\":\"text-base leading-relaxed text-gray-600\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"AI decisioning improves personalization by using customer behavior, preferences, and context to decide which message, product, or experience to show next.\"]}]]}]}]]}],[\"$\",\"details\",null,{\"className\":\"group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50\",\"name\":\"faq-accordion\",\"children\":[[\"$\",\"summary\",null,{\"className\":\"flex items-center justify-between cursor-pointer list-none px-6 py-8 [\u0026::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2\",\"aria-label\":\"What data does AI decisioning require?\",\"children\":[[\"$\",\"span\",null,{\"className\":\"text-lg font-semibold text-gray-900 group-hover:text-purple-700\",\"children\":\"What data does AI decisioning require?\"}],[\"$\",\"svg\",null,{\"width\":20,\"height\":20,\"role\":\"img\",\"aria-label\":\"chevron-down\",\"stroke\":\"currentColor\",\"className\":\"shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#chevron-down\"}]}]]}],[\"$\",\"div\",null,{\"className\":\"px-6 pb-6\",\"children\":[\"$\",\"div\",null,{\"className\":\"text-base leading-relaxed text-gray-600\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"AI decisioning requires data such as events (clicks, purchases, logins), customer attributes (location, device, preferences), and contextual signals (time, channel, risk scores).\"]}]]}]}]]}],[\"$\",\"details\",null,{\"className\":\"group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50\",\"name\":\"faq-accordion\",\"children\":[[\"$\",\"summary\",null,{\"className\":\"flex items-center justify-between cursor-pointer list-none px-6 py-8 [\u0026::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2\",\"aria-label\":\"Which industries benefit most?\",\"children\":[[\"$\",\"span\",null,{\"className\":\"text-lg font-semibold text-gray-900 group-hover:text-purple-700\",\"children\":\"Which industries benefit most?\"}],[\"$\",\"svg\",null,{\"width\":20,\"height\":20,\"role\":\"img\",\"aria-label\":\"chevron-down\",\"stroke\":\"currentColor\",\"className\":\"shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#chevron-down\"}]}]]}],[\"$\",\"div\",null,{\"className\":\"px-6 pb-6\",\"children\":[\"$\",\"div\",null,{\"className\":\"text-base leading-relaxed text-gray-600\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"Industries that make frequent, repeatable decisions—like retail, eCommerce, financial services, insurance, media, entertainment, gaming, sports, travel and hospitality, telecommunications, utilities, and healthcare—benefit most from AI decisioning use cases. These sectors can tie decision quality directly to revenue, risk, and customer satisfaction.\"]}]]}]}]]}],[\"$\",\"details\",null,{\"className\":\"group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50\",\"name\":\"faq-accordion\",\"children\":[[\"$\",\"summary\",null,{\"className\":\"flex items-center justify-between cursor-pointer list-none px-6 py-8 [\u0026::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2\",\"aria-label\":\"What’s the difference between AI decisioning and automation?\",\"children\":[[\"$\",\"span\",null,{\"className\":\"text-lg font-semibold text-gray-900 group-hover:text-purple-700\",\"children\":\"What’s the difference between AI decisioning and automation?\"}],[\"$\",\"svg\",null,{\"width\":20,\"height\":20,\"role\":\"img\",\"aria-label\":\"chevron-down\",\"stroke\":\"currentColor\",\"className\":\"shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#chevron-down\"}]}]]}],[\"$\",\"div\",null,{\"className\":\"px-6 pb-6\",\"children\":[\"$\",\"div\",null,{\"className\":\"text-base leading-relaxed text-gray-600\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"The difference between AI decisioning and automation is that AI decisioning chooses between options based on data and models, while automation executes predefined steps. 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