Summary: Incorta solves one of the most common blockers to enterprise AI: getting clean, complete, AI-ready data out of complex ERP systems like Oracle, SAP, Workday, and NetSuite. Where custom-built pipelines typically take 18 to 24 months to deliver reliable data and absorb 75 to 80% of ongoing maintenance costs, Incorta compresses time-to-value from years to weeks using prebuilt ERP blueprints and automated data modeling. Incorta preserves full transaction-level granularity, native relational context across ERP objects, and supports near real-time ingestion, giving AI agents the data quality they need to reason, automate, and act. For data and AI leaders evaluating build vs. buy for ERP-driven AI strategies, Incorta eliminates the engineering burden of building and maintaining custom ERP pipelines and converts unpredictable infrastructure spend into a predictable subscription model.
Your AI Strategy has a data problem - your ERP is at the center of it
AI initiatives stall for plenty of reasons. Budgets get approved, use cases get mapped, stakeholder buy-in gets secured - then the data isn't ready. That's the conversation happening in data and IT leadership teams across every industry right now, and it almost always traces back to ERP systems.
Why ERP data is so hard to work with
ERP platforms hold the most valuable operational data in the enterprise: purchase orders, invoices, vendor records, general ledger entries, inventory movements. For AI to reason and automate, it needs this data granular, current, and contextually intact.
ERP systems were built to run operations, not to feed AI. Their schemas are sprawling (often tens of thousands of tables), relationships are complex and frequently undocumented, and many platforms include structural quirks like tables without primary keys that complicate replication and modeling. Getting clean, AI-ready data out of an ERP system is a multi-year undertaking for most organizations, and that timeline is incompatible with most AI roadmaps.
Why custom ERP pipeline builds fail
Time to value is longer than expected. Internal ERP data builds typically require 18 to 24 months before the business has reliable, AI-ready data. Every quarter spent building infrastructure is a quarter without AI value.
Total cost of ownership escalates. Custom builds absorb 75 to 80% of ongoing maintenance burden, including patches, updates, and accumulated technical debt. As AI scales, demands on the data layer grow: more volume, faster refresh cycles, more governance requirements. What starts as a bounded engineering project becomes an OPEX-heavy liability.
ERP pipelines require domain expertise, not just engineering. Understanding which tables matter, how business rules are embedded, and how cross-module dependencies behave takes years to develop. Pipelines built without that knowledge look correct and break in production.
Technical debt accumulates into AI risk. McKinsey estimates a 10 to 20% tech debt tax on engineering projects, diverting innovation budgets to remediation. Gartner puts the average annual cost of poor data quality at $12.9 million. Unstable pipelines produce unreliable AI outputs, and unreliable outputs kill adoption.
What AI actually needs from your ERP data
AI has three non-negotiable data requirements.
Granularity. AI models need full transaction-level detail. Pre-aggregated data reduces the signal AI needs to find patterns, anomalies, and causal relationships.
Timeliness. Agentic AI requires near real-time data. Batch pipelines with multi-hour latency make alert-triggering, approval automation, and exception flagging impossible.
Context. ERP data only makes sense relationally. An invoice needs its purchase order, receipt record, vendor history, and GL posting to be meaningful. AI needs that relational structure (PO to Receipt to Invoice to Vendor to GL) preserved, not flattened.
Custom builds routinely compromise on at least one of these. Data gets summarized to reduce complexity. Refresh cycles get extended to manage engineering load. Relationships get lost in transformation.
What a purpose-built platform can change
Purpose-built ERP data platforms address all three requirements by design. They ship with prebuilt blueprints for major ERP systems including Oracle, SAP, Workday, and NetSuite, encoding domain expertise that would otherwise take years to develop internally. They preserve native relationships and business logic without manual reconstruction and support near real-time ingestion.
On the cost side, buying converts unpredictable engineering spend into a predictable subscription. ERP schema changes, performance tuning, security patches, and infrastructure evolution become the vendor's responsibility.
The data on this is clear. IDC reports that 80% of data professional time is still spent on discovery and preparation rather than analytics. IDC Qlik research finds 81% of companies struggle with AI data quality in ways that directly undermine ROI. Gartner predicts over 40% of agentic AI projects will be canceled by 2027 because operational and data foundations were not ready.
Data leaders: build vs buy?
Build vs. buy is no longer a tooling conversation. It is a strategic decision with real timing consequences. AI value does not accrue while you are still building infrastructure. Organizations moving fastest on AI are not the ones with the most sophisticated in-house data engineering. They are the ones operating on a data foundation built for this moment.
Download our whitepaper, The AI Data Foundation Dilemma: Build vs. Buy for ERP-Driven AI Strategies, for the complete five-dimension analysis.
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