AI is reshaping the business landscape, but behind every headline about breakthrough models and transformative automation lies a simple truth: AI is only as strong as the data that fuels it. The real challenge for enterprises today isn’t just building AI, but preparing and orchestrating the data supply chain that makes AI useful, trustworthy, and actionable.

The hidden bottleneck in AI

When organizations rush into AI without addressing their data foundation, manual wrangling quickly becomes the bottleneck. Teams spend more time reconciling records and fixing quality issues than innovating. The pathway to value starts with data that is not just technically usable, but reliable, contextualized, and compliant from the beginning.

This is where the concept of data labeling comes in. Just as labeled datasets are critical for training and fine-tuning machine learning models, operational AI also depends on real-time context. A semantic data layer, which is where attributes function as dynamic, machine-readable “labels,” adds this context at the point of collection. Instead of raw, ambiguous signals, AI systems receive structured, labeled data streams that are easier to train on, faster to infer from, and more aligned with business meaning.

By embedding labeling and enrichment into the data pipeline, enterprises transform workflows that once required months of manual preparation into milliseconds of automated orchestration. The result: faster model deployment, lower operational overhead, and more trustworthy outputs. With governance and privacy built into the data foundation, teams can focus less on cleanup and more on high-value AI use cases.

Composability as the missing link

This is where composable architectures come in. For years, marketers and IT teams tried to build agility by layering tools on top of one another. But connecting tools is not the same as interoperability. Vendor-centric integrations often reinforce silos, creating more complexity rather than less.

A truly composable data architecture is neutral and open by design. It’s built on standardized data layers and open APIs, enabling enterprises to freely connect best-of-breed solutions, swap out tools without disruption, and evolve their stack as business needs change. This kind of interoperability ensures:

• Agility: Rapid adaptation to new use cases or technologies
• Centralization: Identity-resolved data accessible across every team
• Efficiency: Campaign velocity maintained even while onboarding new tools

Composability isn’t about dismantling existing systems; it’s about future-proofing them. By harmonizing data clouds for historical analysis with real-time orchestration platforms, organizations gain both reliability and speed without compromise.

AI as the orchestration engine

AI is now stepping into a new role — orchestrating the customer journey itself. Paired with a composable data foundation, AI enables:

• Automated data unification: Cleaning, matching, and contextualizing disparate records at scale
• Real-time personalization: Building single customer views and triggering hyper-relevant engagements in milliseconds
• Embedded governance: Enforcing consent and compliance automatically, reducing risk without slowing innovation

Consider how Tealium and Databricks partner to make this possible. Tealium streams behavioral data to Databricks in real time, where AI models generate insights such as “propensity to buy.” Those insights flow instantly back into Tealium, where they can trigger personalized push notifications, emails, or ads before the customer drops off. What used to be a disconnected, manual process is now continuous, automated, and adaptive.

Responsible data as the foundation

The promise of AI doesn’t rest on models alone; it depends on whether enterprises manage their data responsibly. “AI-ready” must mean more than technically clean. It must be governed, consent-aware, and identity-unified, ensuring that every customer interaction is transparent and trusted.

Leaders who succeed in this next era will embrace three priorities:

1. Data integrity as a non-negotiable foundation
2. Composable architectures that maximize interoperability
3. AI-powered orchestration that accelerates outcomes without compromising trust

The companies that thrive will not be those with the most tools or even the most sophisticated models, but those that have harmonized their data supply chain end-to-end. AI may be the accelerant, but it is responsible, real-time, composable data that determines how quickly and responsibly enterprises realize its promise.

About the Author:

With over 20 years of MarTech experience, Jay Calavas has been instrumental in scaling Tealium over the last 14 years. He has held various go-to-market and leadership roles and today leads the Vertical Product Strategy at Tealium. Prior to Tealium, Jay held strategic roles at Salesforce, Adobe, and Nuance.