Enterprise AI: Build or Buy?
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Most enterprises run dozens of systems that each hold part of the performance picture. ERP platforms manage financials and operations. CRM systems track customers, opportunities, and pipeline. Industry-specific applications handle specialized workflows that horizontal software can't address. Data warehouses and lakes accumulate historical records that grow more valuable, and more unwieldy, over time. Each system works reasonably well within its domain. The challenge is seeing across all of them coherently.
Traditional integration approaches promise unified visibility but consistently underdeliver. Requirements gathering stretches into months. Custom ETL development creates brittle connections that break when source systems change.
By the time integration projects reach production, business priorities have shifted and the backlog of new integration requests has grown longer. This isn't a resource problem that bigger teams can solve. It's an architectural mismatch between how integration traditionally works and what enterprise observability actually requires.
This is why we’re providing a practical perspective for enterprise leaders evaluating how to achieve genuine cross-system observability without the traditional integration tax.
Unified observability proves difficult despite decades of integration tooling investment for reasons that are structural rather than technical. Understanding these root causes helps distinguish solutions that address them from those that simply relocate the problem.
Data model divergence creates the first obstacle. ERP systems structure financial data around accounting periods, cost centers, and general ledger hierarchies. CRM platforms organize information around accounts, contacts, opportunities, and sales stages. Operational systems track transactions, events, and workflow states using whatever schema made sense when they were implemented.
These aren't just different databases. They represent fundamentally different ways of modeling business reality. Reconciling them requires semantic translation that generic integration tools don't provide. Connecting the systems is straightforward; making the data coherent is not.
Velocity mismatch compounds the challenge. Finance closes monthly or quarterly, with data that stabilizes after period-end adjustments. Sales pipelines update continuously as opportunities progress. Operational metrics stream in real-time, with meaning that depends on freshness.
Traditional integration approaches designed for batch synchronization struggle when different systems operate at fundamentally different cadences. Forcing artificial synchronization either delays fast-moving data or creates false precision in slow-moving data.
And as if that wasn’t enough, ownership fragmentation adds organizational complexity to the technical challenges we just discussed. ERP belongs to finance. CRM belongs to sales operations. Operational systems belong to the business units that run them. You can argue that IT owns integration infrastructure, but every connection requires coordination across organizational boundaries.
The cumulative result is that most enterprises settle for system-specific dashboards rather than unified observability. Each function sees its own metrics clearly. Cross-functional visibility remains aspirational. And the dream utopia discussed in strategy sessions never quite reaches the top of the priority stack.
Timeline is where traditional enterprise integration most obviously fails. Requirements gathering and stakeholder alignment consume weeks before technical work begins. Data mapping and transformation logic require iterative development as edge cases surface. Testing across systems with different change management cycles creates scheduling dependencies. Deployment and stabilization add more weeks before the integration delivers production value.
For straightforward connections between modern cloud applications, timelines might compress to a quarter. For complex source systems like SAP, Oracle, or legacy platforms with decades of customization, six months to a year isn't unusual. And that's for a single integration. Keep in mind enterprises typically have dozens of systems that need connection.
This creates a structural mismatch between integration capability and business need. The dashboard that would have driven better Q2 decisions arrives in Q4. The cross-functional visibility that would have caught a customer churn pattern before it accelerated reaches production after the customers have already left. Integration backlogs grow faster than delivery capacity, and prioritization becomes a political exercise rather than a business optimization.
The underlying issue is architectural. Point-to-point integration requires custom development for each connection. Schema changes in source systems break existing integrations, diverting maintenance effort from new capabilities. The integration team becomes a bottleneck not because they're insufficiently skilled but because the approach doesn't scale.
AI-native observability platforms address this through pre-built connectors, adaptive schema handling, and configuration-driven integration that reduces connection timelines from months to days. This isn't incremental efficiency improvement. It's a structural shift in what's achievable within planning horizons that business leaders actually work with.
Unified observability requires translation layers that preserve meaning across contexts, not just pipelines that move data between databases. For example, "customer" means something different in ERP than in CRM. And revenue recognized in the general ledger doesn't map directly to pipeline value in the sales system.
Semantic unification goes beyond connecting systems to reconciling different data models into coherent business entities. When revenue anomalies appear, observability should automatically surface correlated signals. Any CRM pipeline changes, operational throughput shifts, or financial accrual adjustments should surface without requiring manual investigation across separate dashboards.
Then you need multi-velocity ingestion to accommodate batch data from financial systems alongside streaming data from operational sources without forcing artificial synchronization. The observability platform must handle monthly close data and real-time transaction streams within the same analytical framework, respecting the natural cadence of each source rather than distorting it.
Governance preservation ensures that integration doesn't bypass the access controls established in source systems. Unified observability must respect role-based permissions while still enabling cross-functional insight for authorized users. Sales leadership seeing customer data shouldn't automatically gain access to margin details that finance restricts. The unified view must be appropriately filtered for each user's authorization level.
These requirements determine whether integration delivers genuine business value or simply creates another data silo.
The fragmented observability that characterizes most enterprises isn't a permanent condition. It's a consequence of integration approaches that couldn't keep pace with business system proliferation.
AI-native observability platforms change the economics fundamentally. Believe it or not, the organizations achieving genuine cross-functional visibility aren't necessarily those with the largest integration budgets or the most sophisticated data engineering teams. They're organizations that have adopted architectures designed for the integration challenge as it actually exists today.
The dashboard proliferation era doesn't have to persist. Unified enterprise observability is an architecture decision, not a resource constraint. The practical question for enterprise leaders isn't whether unified visibility would be valuable, that's obvious. It's whether the integration approach under consideration can actually deliver it within timeframes that matter.
Unframe can. Book a meeting with us to discuss how to adopt AI-native observability without slowing down your business.
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