Enterprise AI: Build or Buy?
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Your organization did everything right. You invested in Snowflake or Databricks. You hired the data engineering team to build the pipelines. You migrated workloads to the cloud and consolidated your analytics environment. The dashboards work beautifully. The reports run on time. Finance finally trusts the numbers.
And yet, your AI projects keep stalling.
This pattern is more common than anyone wants to admit. Enterprises have poured billions into unified data platforms over the past decade. Snowflake and Databricks alone represent over $200 billion in combined market value, built on the promise that centralizing data would unlock its potential.
For analytics, that promise was delivered. For AI, it's proving insufficient. The platform that solved your reporting problem isn't solving your AI problem. And understanding why requires recognizing that AI for siloed data demands something fundamentally different than what these platforms were designed to provide.
The rise of unified data platforms solved a genuine enterprise pain point. Organizations struggled with inconsistent metrics across business units, and unified platforms fixed this by centralizing data into a single governed environment.
One definition of "customer." One calculation for "revenue." One source of truth that everyone could query. The platform delivers exactly what it promised for its intended use case. The problem is that AI has different requirements.
An AI agent doesn't query historical data to build a report. It assembles context in real time to make decisions. A workflow automation doesn't need last night's batch extract. It needs to know what happened five minutes ago. The architecture that makes platforms excellent for analytics makes them structurally unsuited for production AI.
Data platforms ingest information on schedules. This cadence works fine when the consumer is a dashboard that refreshes periodically or an analyst running a query against historical data. AI systems operate on a different timeline.
When a customer service agent asks your AI for help with a frustrated customer, the system needs to see the support ticket created ten minutes ago, the email that arrived this morning, and the Slack conversation happening right now. If any of those haven't synced to the platform yet, the AI responds with incomplete context. The customer gets a worse answer.
The latency problem compounds with scale. More data sources mean more pipelines to maintain. And bolting real-time ingestion onto batch-oriented architecture creates its own complexity. The fundamental design assumed periodic updates, not continuous synchronization. Making it work for AI means fighting the architecture rather than leveraging it.
Unified data platforms excel at structured data. Rows and columns. Defined schemas. Clean dimensional models that support fast analytical queries. This strength becomes a limitation when AI needs to reason over context that doesn't fit neatly into tables.
Consider what an AI system actually needs to answer a question about a customer relationship. The CRM record provides account details. But the full picture includes the contract stored in SharePoint, the negotiation history in email threads, the feature requests logged in Jira, and the recent conversation in Slack where the customer mentioned they're evaluating competitors. Each piece lives in a different system with a different data model.
Business meaning lives in these connections. Platforms store data. They don't preserve the semantic layer that AI needs to reason correctly. The consolidation-first approach creates a single source of truth that's simultaneously complete and contextually impoverished.
Platforms operate on the core assumption that you’ll model your business once and query it many ways. Define the schema, build the relationships, then let analysts explore. This works because analytical queries share common patterns. Revenue by region. Customers by segment. Trends over time.
AI use cases don't share common patterns. Each application needs different context assembled in different ways. No universal schema captures all of these well. Attempting to build one creates a model so abstract that it loses the specificity each use case requires, or so complex that it becomes unmaintainable.
Effective AI data management takes a different approach. Rather than forcing all use cases through a single schema, it defines context models specific to each application. The contract analysis system gets exactly the entities and relationships it needs. The customer service system gets a different model optimized for its requirements. Each AI application receives purpose-built context rather than generic data.
This is the foundation of modular AI architecture. Building blocks that assemble the right context for each use case rather than forcing every use case through the same data model.
You can learn more about AI-native data management in this video:
Production AI requires an approach that inverts the platform model. Instead of centralizing data and then querying it, the system connects to data where it lives and assembles context on demand.
The system maintains live connections to source systems without requiring data to move. Your Salesforce data stays in Salesforce. Your documents stay in SharePoint. Your operational data stays in your ERP. The AI layer can access all of them without waiting for synchronization.
Semantic organization replaces rigid schemas. Runtime assembly replaces batch extraction. When an AI system needs to answer a question or make a decision, the data foundation assembles the required context at that moment. Not from a cached snapshot. Not from last night's load. From the live systems where the data currently resides, with latency measured in seconds rather than hours.
Inherited governance replaces rebuilt permissions. The access controls defined in your source systems flow through to the AI layer automatically. You don't recreate your security model in a separate environment. The governance architecture you've already invested in carries forward.
This approach powers effective enterprise search and AI-driven knowledge retrieval. The AI can synthesize information across fragmented systems because the data foundation handles the complexity of connecting, organizing, and serving context from wherever it lives.
Your investment in centralized analytics infrastructure will continue to deliver value for the workloads it was designed to support. BI dashboards, historical analysis, regulatory reporting, and executive metrics don't disappear when
you add AI.
The mistake is expecting the platform to also serve AI requirements it was never built for. Asking your data warehouse to power real-time AI is like asking your reporting database to serve your production application. Different workloads require different architectures. AI needs a complementary layer, not a platform replacement.
The runtime data foundation works alongside your existing infrastructure. Data stays where it performs best. Analytics workloads continue running on the platform optimized for analytics. AI workloads run on infrastructure designed for real-time context assembly. Each architecture serves its intended purpose.
Unified data platforms solved the analytics fragmentation problem. AI for siloed data requires solving a different problem. Specifically, you need semantic context delivered at runtime, assembled from distributed sources, organized for specific use cases.
The platforms built for batch analytics don't solve this problem because they weren't designed to. Expecting them to is the source of countless stalled AI initiatives.
The enterprises succeeding with AI aren't the ones with the most sophisticated data platforms. They're the ones that recognized AI needs purpose-built infrastructure and stopped waiting for their analytics stack to evolve into something it was never meant to become.
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