ERP systems are entering an AI-augmented era. Across finance, supply chain, manufacturing, and customer operations, ERP leaders are under pressure to embed AI into forecasting, planning, and day-to-day execution.
The ambition is clear. The underlying architecture often is not. Many ERP environments still rely on fragmented data warehouses, siloed governance models, and batch-driven reporting that were never designed to support real-time, AI-driven operations.
ERP Data Foundations Are Not AI-Ready
Traditional ERP data architectures were built for reporting, not real-time decision-making.
Most enterprises rely on multiple function-specific data warehouses across departments. Each introduces separate governance, access controls, and cost overhead. Data flows through layered pipelines with significant latency. Governance arrives last, after data has already been replicated and transformed.
This model breaks down as ERP use cases become AI-driven.
Forecasting requires continuous signals rather than month-end snapshots. Predictive maintenance depends on streaming operational data. Fraud detection and compliance monitoring demand real-time visibility across systems and processes. Batch processing, fragmented governance, and rising warehouse costs introduce organizational lag.
Why Databricks Is Becoming the AI Engine for ERP Landscapes
As a result, many enterprises are rethinking the data layer that supports their ERP, moving toward Lakehouse architectures that unify data, analytics, and AI on a single platform.
Several Databricks capabilities are driving its adoption in ERP-centric landscapes. It operates alongside ERP systems as an intelligent data and AI control plane.
A unified data and AI platform allows ingestion, transformation, analytics, and machine learning to operate on the same governed data, reducing latency and eliminating fragile data handoffs. Databricks SQL provides warehouse-grade analytics performance, with public benchmarks showing lower total cost compared with traditional cloud data warehouses.
Real-time processing is another inflection point. Spark-based streaming combined with the Photon engine enables live dashboards, anomaly detection, and event-driven automation that respond as business conditions change. Unity Catalog adds centralized governance, fine-grained access control, automated lineage, and AI governance across data and workloads, addressing many compliance gaps that limit AI adoption in ERP environments.
The ERP system remains the system of record. However, Databricks serves as the system of intelligence that allows those records to drive real-time, AI-powered decisions.
What Enterprises Can Achieve with an AI-Ready ERP Data Layer
When ERP environments are supported by an AI-ready Lakehouse, organizations move from delayed reporting to continuous operational awareness. Dashboards refresh in real time. Forecasts adjust dynamically as demand, supply, and pricing conditions shift.
This shift also enables AI-powered decision-making across functions. Fraud detection becomes immediate rather than retrospective. Inventory and production plans respond to live signals instead of historical averages. Customer and financial data can be shared across teams without recreating pipelines or renegotiating governance controls.
Consolidating multiple warehouses into a single governed platform lowers overhead and simplifies data management. Governance becomes consistent and embedded. The result is a tighter connection between ERP data and business execution.
Inetum Is Accelerating Databricks Adoption
As enterprises modernize ERP-adjacent data estates, many discover that adopting Databricks is not the hard part. Making it reliable, governed, and operational at scale is where programs succeed or fail. That is the gap Inetum is positioned to address.
Inetum operates as a European AI-first integrator with deep Databricks expertise and a track record of delivering mission-critical, real-time data platforms in complex environments. Its focus is production-grade execution under regulatory constraints.
That experience shows up in scale. Inetum supports Databricks environments ingesting more than 20 billion sensor datapoints per day, processing millions of records per operation, and monitoring hundreds of parameters in real time. These platforms operate at petabyte scale, supporting continuous analytics across dozens of governed environments rather than isolated workloads.
In industrial settings, these platforms underpin predictive maintenance programs that generate more than 400 real-time engine health alerts each year. In some cases, this capability has contributed to more than £200 million ($270 million) in cumulative savings.
Inetum has delivered Lakehouse architectures with row-level security supporting more than 1,000 external stakeholders on shared platforms, combined with multi-region disaster recovery and audit-ready controls designed for regulated sectors.
Delivery discipline is another differentiator. Inetum brings Databricks-certified talent, and proven migration frameworks to modernization programs. Reusable accelerators, including Delta Live Tables templates, GenAI tooling, and MLOps blueprints, help shorten implementation cycles.
Databricks supplies the platform capabilities. Inetum applies the governance and execution discipline required to turn them into reliable, enterprise-grade systems.
A Human‑First, AI‑Second Approach
As enterprises embed AI into ERP-driven operations, the challenge shifts from accessing algorithms to defining decision boundaries. ERP teams move from scheduled reporting to real-time, event-driven execution, where automation can act quickly on predictable signals but human judgment must remain central in ambiguous or high-impact situations.
Inetum approaches this challenge with a human-first, AI-second philosophy. Rather than treating AI as a universal decision-maker, it distinguishes between taking a decision and making one. Predictable, data-rich actions suit automation, while ambiguous, context-driven decisions still require human judgment.
“Inetum is one of the few organizations to clearly distinguish between taking a decision and making a decision,” said Dr. Bippin Makoond, global head of innovation at Inetum. “This allows AI to operate where outcomes are predictable and documented, while humans lead where situations are uncertain, tacit, or evolving. That balance is critical in mission-critical ERP environments.”
This philosophy is operationalized through Inetum’s proprietary COBORG framework, which embeds governance, human-in-the-loop controls, and decision boundaries directly into AI-enabled workflows. The goal is not to eliminate human involvement, but to apply AI responsibly. “AI will transform every ERP-driven organization, but only if people trust the systems behind it,” said Makoond.
The AI-Native ERP Stack
ERP systems are not being replaced. They are being re-architected around AI. As forecasting, planning, and execution become increasingly data-driven, ERP platforms will depend on AI-ready data layers that operate in real time and under consistent governance.
Batch-oriented reporting architectures will struggle to support this shift. Unified, governed Lakehouse platforms are emerging as the foundation for operational intelligence.
One of the least understood constraints in this transition is data lineage. A single customer or employee action can trigger complex flows across ERP systems, databases, custom code, and batch processes—yet few organizations clearly see how data moves or how business rules are applied.
That visibility gap becomes critical as AI is embedded into core ERP workflows. Inetum addresses this challenge through its AI-powered Data Lineage Accelerator, part of its broader COBORG framework.
“Inetum’s AI-powered Data Lineage Accelerator works by non-intrusively scanning source code, runtime logs, custom scripts, configuration files, and parameters across any technology stack to automatically reverse engineer true end-to-end lineage,” said Makoond.
“We connect the voice of the machine with the voice of the business, giving enterprises a precise understanding of how data is used across ERP and all connected applications.”
In this model, ERP data moves beyond periodic reporting into continuous analytics, machine learning, and automation. Enterprises shift from retrospective analysis to real-time, AI-assisted decision-making across operations. Those that modernize now gain an operational intelligence advantage, while delays bring higher costs, slower decisions, and mounting governance complexity.
What This Means for ERP Insiders
AI success depends on ERP data readiness. AI delivers value when ERP data foundations support speed, scale, and governance. Inetum helps enterprises design and operate unified architectures, real-time pipelines, and consistent controls that allow AI to improve forecasting, operations, and decision-making across the enterprise.
Databricks turns ERP data into operational intelligence. By unifying data engineering, analytics, and AI on a governed platform, Databricks enables ERP environments to move from delayed reporting to real-time, event-driven insight. Inetum reinforces this through its COBORG framework, aligning governance, operating models, and cultural readiness so AI can be embedded safely into core business processes.
Inetum makes AI adoption operational. Enterprise AI succeeds when platforms are matched with governance, delivery discipline, and cultural readiness. Inetum bridges strategy and execution, helping organizations translate AI-ready architectures into trusted, production-grade ERP outcomes.
