Enterprise generative AI: Transforming data, decisions, and customer experiences

Customers expect more and more relevant communication. Yet according to the Braze Global Customer Engagement Review 2025, nearly half of marketing leaders aren’t confident they understand what their users want. Many still make engagement decisions based on instinct, not insight.

Enterprise generative AI marketing helps change that. It can automate testing, accelerate content creation, and learn from customer interactions to make personalization scalable, measurable, and adaptable.

What is enterprise generative AI?

Enterprise generative AI refers to large-scale AI systems built for organizations that need control and measurable outcomes. These systems use models such as large language models (LLMs) and diffusion models to create text, images, and code—but unlike public tools, they’re trained on governed data and embedded within enterprise environments.

An enterprise environment is a controlled technology ecosystem—one that brings together customer data platforms, analytics systems, APIs, and engagement tools. It’s where AI models can safely access real-time data, apply company-specific rules, and generate outputs that align with business goals and brand guidelines.

Enterprise AI now operates as part of a continuous learning system. Generative models work alongside predictive and agentic capabilities, creating an ecosystem where AI supports marketing activity and improves with every interaction.

From automation to intelligence: The enterprise AI evolution

Early customer engagement automation focused on rules and triggers—efficient, but rigid. Messages were delivered on time, but not always at the right time. Enterprise AI changes that dynamic by introducing systems that can interpret context, predict outcomes, and adjust decisions continuously.

Generative AI adds a creative layer to this evolution. It helps teams test faster, create campaign assets, and personalize messages at scale without adding complexity to workflows. Beyond generative AI, predictive models power segmentation and targeting, while agentic systems like BrazeAI Decisioning Studio™ learn which message, channel, and moment perform best for each individual.

These capabilities move brands from task automation to adaptive intelligence—a state where every decision refines the next.

Building an enterprise AI strategy for adaptive decisioning

An effective enterprise AI strategy connects three things: data, decisioning, and delivery. When these layers are unified, marketers can move from reactive to responsive, making every customer interaction a learning opportunity.

This progression mirrors what the Braze Customer Engagement Index (CEI) describes as Activate → Accelerate → Ace:

• Activate: Foundational automation and channel-based messaging.
• Accelerate: AI-assisted orchestration and predictive personalization.
• Ace: Autonomous, agentic engagement that learns in real time.

According to the Braze Global Customer Engagement Review 2025, high-maturity brands are 15% more likely to use generative AI for image content creation and 30% more likely to use predictive analytics in campaign planning. This connection between creativity and intelligence is what enables personalization to scale responsibly.

Why is enterprise generative AI different from consumer AI?

Enterprise generative AI is designed for reliability, accountability, and scale. For large organizations, this balance—speed with control—is what defines enterprise readiness.

Scalability and reliability at enterprise scale

Enterprise systems manage millions of interactions and decisions each day. These high-volume decision systems are designed for accuracy and responsiveness even under heavy demand. BrazeAI Decisioning Studio™ applies reinforcement learning and real-time orchestration to make individualized engagement decisions efficiently.

Integration with enterprise infrastructure and APIs

Enterprise AI only delivers value when it’s connected to the rest of the stack. Braze integrates directly with customer data platforms, analytics tools, and APIs so intelligence can flow across channels in real time. This connected architecture links decisioning, content generation, and delivery in one continuous process.

Research indicates that total cost of ownership can exceed expectations if governance, retraining, and oversight aren’t planned early. Enterprises that establish ethical guardrails, invest in high-volume decision systems, and build AI literacy across teams achieve stronger ROI and more sustainable long-term performance.

How Braze operationalizes different kinds of AI

Braze brings enterprise AI into daily marketing operations. It connects data, decisioning, and delivery, enabling teams to test, learn, and personalize experiences in real time through a single platform.

Generative AI

AI copywriting assistant

An AI Copywriting Assistant natural language generation tool makes it easy to whip up copy for use across channels, including push notifications, SMS, emails, and both in-browser and in-app messages. Furthermore, with Tone Control, you can decide the exact tone of the message that you are looking to generate. The BrazeAI™ Copywriting Assistant also allows you to make the most of customizable brand guidelines, while referencing your past campaign copy.

AI Image generator

The BrazeAI™ Image Generator, can give marketers a competitive advantage by empowering them to quickly advance from ideation to action without relying on (or waiting for) overburdened creative teams.

Liquid assistant

With the AI Liquid Assistant, you can easily personalize messages at scale. By providing the idea using natural language prompts, BrazeAI™ will turn it into working Liquid code, making it faster to create, improve, and enhance customer experiences across channels and touchpoints.

AI Decisioning

BrazeAI Decisioning Studio™ adapts engagement logic based on context and outcomes. Built on reinforcement learning, it automates experimentation and identifies the best message, channel, and timing for each individual. This replaces slow, manual testing with continuous optimization that compounds results over time.

Intelligent Selection

Intelligent timing

Solutions like Braze Intelligent Timing help send customers messages that reach customers when they want to hear from your brand. So, if your customer is active in the mornings or prefers to engage at night, you can rest easy knowing your campaign will go out when they are most likely to see it and take action.

Intelligent channel

Intelligent Channel sends messages to customers via their preferred engagement channel, as determined by their past engagement behavior.

Intelligent selection

Braze Intelligent Selection uses a reinforcement learning algorithm to determine which campaign each customer in a given segment receives based on campaign performance over time. This automates the shared campaign variant based on which one performs most effectively, allowing you to automatically send users the campaign variant that they are more likely to engage with.

Predictive AI

Predictive events

Braze Predictive Events is designed to interpret and predict a user’s likelihood to perform any custom event, such as purchasing a product, renewing a subscription, or even something like trying out a new feature. Using these insights, marketers can send relevant messaging prompts to encourage those users to perform a specific action.

From tempting previously unlikely shoppers to make their first purchase with a personalized promo code, or nudging those most likely to purchase to increase their order size, this feature can provide nearly endless possibilities for more effective engagement.

Predictive churn

With AI marketing tools like Braze Predictive Churn, brands can get ahead of potential retention issues before they become a problem. This solution identifies which customers are at risk of churning and flags the behaviors associated with churn so you can spring into action while you still have time. With this tool, you can use segments and filters within the Braze Audience Builder feature to create highly-targeted win-back campaigns to serve enticing promotions and incentives.

Personalized Pathing

Personalized variant

Braze Personalized Variant is designed to determine the message for each individual based on their unique behaviors, preferences, and attributes. This automates personalized messages from an A/B test to each individual that help increase engagement and conversions for each campaign.

Personalized paths

Personalized Paths help facilitate true journey personalization at scale. This automates journeys that make sure each customer gets the message copy, creative, channel, offer, and more they’re most likely to engage with at every stage of a journey—all with a simple toggle from an a/b test.

Kayo Sports leverages AI to create 1:1 messaging at scale, driving customer loyalty and lifetime value

Kayo Sports, Australia’s largest sports streaming service, needed a way to personalize communications for a broad audience of sports fans across multiple devices and channels. With Braze and BrazeAI Decisioning Studio™, the team built its Customer Cortex—an AI-powered personalization engine that determines the best message, creative, channel, timing, frequency, and promotion for each subscriber.

Manual workflows and segmentation limited how deeply Kayo could personalize. They needed a decisioning layer that could analyze individual behavior, preferences, and engagement patterns, and trigger the right action automatically.

The strategy

Using reinforcement learning and first-party data, Kayo trained ten purpose-built AI models to understand subscribers on a granular level.

• Data: Customer data flows into Braze via APIs, SDKs, and Cloud Data Ingestion.
• Decisioning: BrazeAI Decisioning Studio™ analyzes each subscriber’s behavior and recommends the optimal content, timing, and channel.
• Delivery: Recommendations are sent through Canvas, triggering API-based journeys for email, SMS, push notifications, and in-app messages.

This setup makes it possible to run millions of real-time variations automatically. For instance, when the system predicts that one subscriber will engage best with an SMS at 9:37 a.m., that message is sent with the correct copy and offer. Another subscriber might receive an email later that day with different creative, timing, and offer logic—all handled within the same connected infrastructure.

The results

• 14% increase in subscriptions in FY24
• 8% increase in average annual occupancy
• 105% increase in cross-sell to sister service BINGE
• Achieved while average subscription prices rose 20%

Kayo’s Customer Cortex now delivers more than 1.2 million daily variations of personalized messages—up from just 300—showing how BrazeAI™ and Canvas combine to drive engagement, loyalty, and lifetime value at enterprise scale.

How do enterprises ensure generative AI is used responsibly?

In any enterprise setting, AI should work in partnership with people. It needs to be transparent enough for brands to understand how recommendations are made and flexible enough for teams to apply their own judgment. AI should simplify complex work—testing, learning, personalizing—without losing creative control or context.

BrazeAI™ is designed around those same principles. Predictive, generative, and agentic tools help brands move faster while staying fully involved in the process. Every recommendation, piece of content, and campaign path can be reviewed, edited, and approved before activation, with a clear view of how AI reached each decision.

All of this happens inside secure, first-party data environments. Customer information remains protected, and data is not shared externally or used to train public models. This gives brands the confidence to personalize at scale while keeping their data, and their decision-making, in their own hands.

The future of enterprise AI: From intelligence to autonomy

Enterprise AI is growing up. Predictive tools help brands see what’s ahead, generative features make it easier to create, test, and adapt and Agentic AI takes the next step, acting on insight safely and learning from every outcome.

In large organizations, these systems connect teams and data. Insights move freely across marketing, product, and analytics, so decisions happen faster and in sync. Agentic workflows keep learning in motion—adjusting campaigns, refining journeys, and improving results automatically.

People still guide the process though. AI should make work clearer, not more complex. Teams need to see how recommendations are formed and stay in control of what’s approved, edited, or sent. Enterprise AI helps brands act with speed and confidence at scale.

Many enterprises are already setting up continuous learning environments where teams, tools, and data evolve together. That culture of adaptation is what will keep organizations relevant and responsive as the next wave of AI takes shape.

Key takeaways for enterprise generative AI

• Generative AI is redefining how data becomes decision and how decision becomes engagement—turning customer insight into meaningful action across every channel.
• Enterprise-scale AI connects teams, systems, and data so organizations can personalize responsibly and learn continuously.
• Reinforcement learning and agentic workflows keep adaptation ongoing, combining automation with clear human oversight.
• Secure, first-party data environments support brand-safe personalization across global operations.
• Braze bridges the gap between strategy and execution through responsible, adaptive AI—helping enterprises activate insight and deliver 1:1 experiences at scale.