As more enterprises shift from piloting AI to deploying it in production, developers and data science teams are being asked to design and maintain increasingly complex data flows between customer data, machine learning and AI models, and the systems that activate insights in real time. But what does that look like in practice?
In my time leading AI solutions for Samsung’s eCommerce business, I saw how impactful well-integrated machine learning can be when applied to customer data at scale. We deployed both real-time and batch recommendation systems across multiple funnels, personalizing experiences for over 200+ million users. From building predictive affinity models to leading platform-level AI innovations, the challenge was always the same: connecting rich, timely data to intelligent real-time decision-making systems in production.
Now, working closely with Tealium customers, I see the same challenge and familiar patterns emerging across industries. This post is a practical breakdown of the major enterprise AI use cases I’ve encountered, focused on what developers need to know to build scalable, real-time, and personalized AI workflows.
1. Product Recommendations and Personalization Engines
Teams It Benefits: Marketing, eCommerce
Technical Focus: Real-time data pipelines → ML inference endpoints → activation via personalization channels
Enterprises are building recommendation engines that personalize offers, products, or content using live behavioral data. These often combine in-house trained ML or tuned LLM models (hosted on platforms like AWS SageMaker, Google Cloud Vertex AI or Snowflake) with real-time scoring pipelines triggered by Tealium.
Example pattern: A global retailer collects behavior data through Tealium iQ and AudienceStream, then streams enriched profiles into a cloud ML service for inference. The model outputs product rankings, which are sent to an email for post-visit personalization or on-site personalization engine in real-time. In one case, AWS Lambda functions trigger SageMaker to personalize a relevant product for each user based on in-session propensity score or product affinity score.
Developer takeaway: This use case highlights the importance of low-latency, event-driven architectures. You’ll be managing ML/AI model development, event streams, API integrations, and the logic for triggering model scoring in near-real time.
2. Predictive Scoring and Next Best Action
Teams It Benefits: Marketing, Sales, Customer Retention
Technical Focus: Event data streaming → scoring models → decisioning systems (e.g. Pega, custom engines)
Another dominant use case is real-time propensity scoring and “Next Best Action” (NBA) decisioning. Here, customer behavior and profile data is sent to a model that predicts the best action to take: send a promotion, recommend a call, or offer a service upgrade.
Example pattern: Customers use Tealium to collect session data, stream it into Snowflake, and run models to determine upgrade propensities. From there, they can trigger targeted offers through various channels such as on-site banners or email. One company shrank scoring latency from two days to under one minute using this architecture.
Another customer in financial services feeds AudienceStream data into Pega’s NBA engine, which recommends personalized outreach (e.g. send a quote reminder) based on user behavior and AI model outputs.
Developer takeaway: Using Tealium’s intelligent data platform to feed customer profile information in real time to a propensity model can help improve customer targeting. By handling the data wrangling for developers, Tealium is able to save development effort when connecting streaming data pipelines to scoring endpoints and business rules engines.
3. Chatbots and Conversational AI Enrichment
Team It Benefits: Customer Support, Sales Enablement
Technical Focus: LLM orchestration → real-time data enrichment → API-based chatbot platforms
Chatbots aren’t new, but the new wave of AI-powered assistants—often backed by large language models (LLMs)—are far more powerful. The real breakthrough for developers is context enrichment: using Tealium data to personalize chatbot (or voice AI) behavior.
Example pattern: A major bank uses Tealium to stream consented user traits and behavior into their LLM-driven chatbot. The bot adapts its tone and responses based on each user profile (e.g., casual tone for younger users) and references recent activity (e.g., “I see you were looking at mortgage calculators”). Other companies feed session data into call center AI agents to generate call summaries, suggest responses, or route calls intelligently.
Developer takeaway: You’ll be integrating APIs between CDPs, LLMs, and messaging platforms. Consider allowing the chatbot / agent to access Tealium’s APIs via MCP to enable open and easy integration of context. Pay attention to latency to ensure your chatbot or agent can respond ‘in the moment’ and consider the issues of data minimization, since sometimes there can be such things as too much context.
4. Fraud Detection and Risk Signals
Teams It Benefits: Operations, Security, Financial Services
Technical Focus: High-velocity data ingestion → ML model scoring → alerting or workflow triggers
In industries like banking and eCommerce, AI is used to flag suspicious behaviors—impossible travel logins, bot traffic, or anomalous transactions.
Example pattern: A financial institution uses Tealium AudienceStream to stream behavioral events (e.g., device, location, frequency) into a fraud detection model. The model is scored on the fly and, if risk is high, signals are passed to the security stack for flagging or escalation.
In another example, a telecom scores incoming orders with a predictive model to assess rejection likelihood due to fraud.
Developer takeaway: These systems are performance-sensitive and require high-throughput, low-latency pipelines. Expect to work closely with ML teams on model interfaces and with security teams on alert routing.
5. Sentiment Analysis and Voice of Customer
Teams It Benefits: Customer Experience, Product, Marketing
Technical Focus: Text ingestion → NLP/LLM services → dashboards or segmentation
Companies are increasingly analyzing unstructured feedback—support transcripts, product reviews, surveys—to uncover customer sentiment.
Example pattern: A mortgage company feeds customer call transcripts into a large language model for sentiment tagging and topic extraction. These signals feed back into AudienceStream to power re-engagement campaigns (e.g., “follow up with frustrated users”).
Another example involves summarizing product reviews using a custom model deployed in Databricks, then surfacing highlights in customer-facing pages (“Most users loved the battery life”).
Developer takeaway: You’ll often be building pipelines from raw text sources into NLP or LLM services, then integrating structured outputs back into the customer data layer. Watch for privacy concerns (e.g., PII in call logs), and ensure feedback loops are tight.
6. Generative AI for Content and Search
Teams It Benefits: Marketing, Digital Experience, Commerce
Technical Focus: Prompt engineering → context injection → content generation or search result ranking
Some enterprises are testing generative AI to enhance internal tools (like search) or customer-facing outputs (like email campaigns).
Example pattern: A major airline built an AI destination search using a generative model. Tealium’s Moments API provides real-time user data (past trips, preferences) that is injected into the AI’s prompt, allowing for more tailored travel suggestions. Similarly, a car marketplace combines lead classification models with LLMs to generate email follow-ups.
Developer takeaway: These use cases often involve combining real-time user context with carefully structured prompts to control the behavior of LLMs. Expect to work on prompt templating, latency reduction, and fallback logic.
Final Thoughts for Developers
If you’re a developer working with Tealium or exploring AI use cases in your stack, these patterns show where the industry is heading:
- AI in production means real-time orchestration: the days of batch-only pipelines are fading fast.
- Tealium is often used as the real-time context layer, feeding live behavior and profiles into models, decisioning engines, and downstream apps.
- Success depends on connectivity: clean APIs, flexible data models, and strong observability into where and how AI insights are used.
Whether you’re building a proof-of-concept chatbot or optimizing an enterprise-scale recommendation engine, the blueprint is already out there. You don’t have to start from scratch—just build on what’s working.
