As Tealium’s CMO, I walked out of Digital Velocity New York City with a simple conclusion: the next decade belongs to teams that can turn trusted, real-time customer data into machine-consumable context for both humans and AI agents.
DV NYC was a room full of people who own data layers, CDPs, warehouses, and models. This recap is for you: a technical view of where the market is going and five concrete moves you can execute now.
The Moment: AI Agents as a New Channel
We’ve all lived through channel shifts: IVR, web, mobile, apps. AI agents are the next one—and they’re different:
- Agents are decision-makers, not just referrers. They select products, flows, and even vendors on behalf of customers.
- Agentic traffic is high-intent by default. When an agent hits your site or APIs, it’s usually mid–late funnel.
- By 2030, agent-originated transactions will be material. If your stack can’t serve rich, policy-aware context, you’re effectively invisible.
The pattern from every prior shift holds: if you miss the new channel, you lose the next generation of customers.
At DV NYC, we framed the question this way:
Are you building the context supply chain that both humans and AI agents will depend on?
Tealium as the Independent Context Layer for AI
Tealium’s role is straightforward: we sit between your digital properties, data clouds, AI platforms, and engagement systems as the real-time context layer.
Concretely, that means:
- Collection at the edge:
Client-side and server-side capture across web, mobile, call center, brick & mortar, and more, with low-latency streaming. - Identity + profiles in motion:
Unified profiles and audiences that recompute as events arrive, not hours later. - Data labeling, enrichment, and consent at collection time:
PII classification, purpose flags, region/policy tags, and business semantics applied before data leaves your environment, so it’s AI-ready and policy-aware. - Real-time APIs for humans and agents:
Services like MomentsAPI, server-side connectors, and Functions expose the full customer signal in a single call—identity, traits, predictions, entitlements, and history. - Independence by design:
We integrate deeply with AWS, Snowflake, Databricks, and major marketing clouds without tying you to any one of them. In an AI world where models and platforms will change, vendor independence is a feature, not a bug.
Think of Tealium as the trusted context bus your LLMs, agents, and apps can plug into.
How Tealium Works with AWS, Snowflake, and Databricks
As you think about next steps after Digital Velocity, we’re here to help you make the most of the investments you’ve already made in your data stack. Tealium is designed to work natively with the platforms you rely on most, including AWS, Snowflake, and Databricks.
- Tealium + AWS: Capture rich, real-time customer data from web, mobile, and offline channels and stream it directly into AWS services (like Amazon S3, Redshift, Kinesis, and EventBridge) to power analytics, AI/ML, and personalization across your AWS environment.
- Tealium + Snowflake (including the new Native App): Create a bi-directional bridge between your behavioral data and Snowflake. Tealium can feed high-quality, consented customer data into Snowflake and activate Snowflake audiences in real time across channels. With the new Tealium Native App in Snowflake, you can manage key CDP capabilities natively inside Snowflake—reducing data movement while improving governance and time-to-value.
- Tealium + Databricks: Unite real-time customer behavior with your lakehouse data in Databricks to build more accurate features and models, then push high-value segments and predictions back through Tealium for activation across marketing, product, and customer experience use cases.
From Slides to Systems: What Leading Brands Are Doing
Patterns we showcased in NYC weren’t prototypes—they’re live:
- Operationalizing ML and traditional models in real time
Models trained in AWS/Snowflake/Databricks; scores land back in Tealium; downstream actions (offers, journeys, suppressions) fire within seconds, not after a batch cycle. - Feeding LLMs and RAG pipelines with governed customer data
Tealium’s labeling and consent controls determine which attributes are legal and appropriate for LLM context and retrieval. - Equipping agents with full customer state
Not just “who is this?” but value tier, churn risk, current intent, sentiment, and recent actions—all available in a single context call. - Closing the loop on agent outcomes
Every agent decision (offer, answer, route) and outcome (accept, escalate, churn) is written back into Tealium, so your next prediction and next interaction—human or AI—is smarter.
This is what “from collection to action” actually looks like in 2026.
Five Actions to Take Post–Digital Velocity NYC
You don’t need a 12‑month transformation plan to start. Here are five moves you can make now.
1. Make Your Website Agent-Ready with Tealium iQ
LLM-based agents (ChatGPT, Gemini, Claude, Perplexity, etc.) already crawl your site. They’re not just ranking you—they’re deciding whether to recommend you.
Do this:
- Use Tealium iQ to inject JSON-LD / schema (products, pricing, inventory, FAQs, policies) via your existing data layer.
- Ship it as an iQ extension—no engineering sprint, no code deploy.
- Optionally, personalize schema per visitor (e.g., offers, availability) using MomentsAPI or Data Layer Enrichment, so agents see context that matches the actual user.
Why it matters:
Agents strongly prefer clean, machine-readable, trustworthy content. If your competitor is easier to parse and reason over, they will win the recommendation.
2. Treat AI-Referral Traffic as a First-Class Channel
Most teams are flying blind here. AI referrals show up today as “direct” or generic “referral,” even though they often convert far better.
Do this:
- In Tealium, define a single rule that classifies requests from known AI user agents, referrers, or tracking params into an “AI Referral” channel.
- Write that classification into events and profiles so it appears in your warehouse/BI, experimentation tools, and downstream activations.
- Build journeys and audiences specifically for AI-referred visitors (e.g., shorter education path, more aggressive offers, fewer basic explainer steps).
Why it matters:
You can’t optimize what you don’t measure. AI referral will be one of your highest-intent inbound sources; you should treat it with the same rigor as paid search.
3. Close the Loop with AWS, Snowflake, and Databricks
If you use any of these platforms, you’re probably sitting on models that aren’t fully operationalized.
Do this:
- Stream clean, consented events and profile traits from Tealium into your data cloud (S3/Redshift, Snowflake, Databricks Delta) as the canonical behavioral feed.
- Standardize one or two production models (e.g., churn, upsell propensity, risk score) and publish outputs back into Tealium as profile attributes.
- Use Tealium’s audiences and connectors to wire those predictions into email, mobile, web personalization, ad platforms, and AI agents.
Why it matters:
This is how you turn “we have a model in a notebook” into lift in conversion, retention, and CSAT, not just nicer dashboards.
4. Pilot an “Agentic Front Door” with Tealium as the Context Supply Chain
As agents move from browsing to transacting, you’ll need authenticated, consent-aware endpoints that:
- Accept calls from customer agents (MCP, A2A, A2C),
- Resolve identity,
- Enforce consent and policy,
- Serve the right context and actions back.
Do this:
- Pick one high-value use case (e.g., high-value returns, loan pre-qualification, complex B2B renewals).
- Use MomentsAPI (and Tealium’s emerging MCP flows) to expose a single, composable context payload: identity, traits, ML scores, recent events, and allowlist of permitted actions.
- Log every agent call and result back into Tealium as events, so you can score, QA, and iterate on prompts, policies, and journeys.
Why it matters:
Your “agentic front door” should be above any specific LLM or framework. Tealium supplies a stable, governed context contract while you experiment with different AI providers.
5. Double Down on Consent, Privacy, and On-Device AI
As you expose more data to more models and agents, trust becomes the hard constraint.
Do this:
- Make sure every new AI initiative (on AWS, Snowflake, Databricks, OpenAI, Vertex, Bedrock, etc.) is wired through Tealium’s consent and labeling model from day zero.
- Use Tealium’s Consent Manager and privacy tooling to:
- Classify attributes (PII, PHI, sensitive vs non-sensitive),
- Tag allowed purposes (analytics, personalization, ML training, agent context),
- Enforce filters on what can be sent where.
- Explore on-device AI (mobile SDKs, browser-side models) for use cases where you want personalization without exporting raw sensitive data to the cloud.
Why it matters:
Data quality and latency get you better AI; governance and consent keep you out of the news.
Looking Ahead
When every customer has an agent, every enterprise needs a context supply chain.
Tealium’s job is to be that trusted, real-time, independent context layer—one that works with your investments in AWS, Snowflake, Databricks, and the broader AI ecosystem rather than competing with them.
DV NYC was about ideas. The next 90 days should be about implementation:
- Make your site agent-ready.
- Start measuring AI referral as its own channel.
- Wire Tealium into your data clouds for closed-loop ML.
- Pilot an agentic front door.
- Tighten your consent and privacy posture for AI.
We’re building Tealium for exactly this moment. Now is the time to turn that architecture into durable growth.
