The Shift from Transactional ERP to Operational Intelligence
The enterprise platform landscape is undergoing one of its most significant structural shifts.
Most large organisations continue to rely on ERP systems designed for a transactional era; optimised for data entry, process compliance, and financial control. While these foundations remain essential, they are increasingly misaligned with the demands of modern operations.
Today, assets are more connected. Services are more contracted. Workforces are more widely distributed. Customers expect outcomes, not processes. At the same time, disruption driven by climate pressures, supply chain volatility, workforce constraints, and inflation has become a constant rather than an exceptional occurrence.
As organisations look toward 2026 and beyond, enterprise platforms must evolve from systems that primarily record activity into operationally intelligent foundations, where industrial AI is applied directly within asset, service, and planning workflows to anticipate issues, optimise decisions, and orchestrate execution in real time. This shift underpins the emergence of a new paradigm. The Operational Digital Core.
Why Traditional ERP Models Are Under Pressure
Macroeconomic and operational pressures are accelerating this transition, exposing the limitations of traditional ERP models.
Persistent inflation, ongoing supply chain fragility, geopolitical uncertainty, and labour shortages are forcing organisations to extract more value from existing assets while managing risk in increasingly fast-paced and dynamic operating environments. At the same time, digital expectations continue to rise, particularly in asset-intensive and service-driven industries, as leaders increasingly expect enterprise platforms to support real-time decision-making, continuous optimisation, and coordinated execution rather than only reporting and control.
Industry analysts are pointing to structural change in enterprise platforms. Gartner predicts that “by the end of 2026, 40% of enterprise applications will include integrated task-specific AI agents, up from less than 5% today,”signaling how intelligence is becoming embedded directly into enterprise systems. Against this backdrop, industrial AI is being increasingly adopted by industrial companies to ease these pressures by improving foresight, responsiveness, and operational performance. IDC warns that “legacy systems and siloed data remain some of the biggest barriers to AI effectiveness,” highlighting how traditional architectures can actively slow progress toward intelligent operations.
What is becoming clear is that transactional systems alone are no longer sufficient. Organisations need platforms capable of sensing change, responding in real time, and optimising performance across assets, services, and people, particularly in environments where disruption is the norm rather than the exception.
What Will Shape Enterprise Platforms in 2026?
1. Multi-Platform Enterprise Architecture Becomes the Default
Multi-Platform Enterprise Architecture Becomes the Default
The shift away from monolithic ERP architectures has been underway for more than a decade, but adoption is accelerating as organisations seek greater flexibility and speed. By 2026, how enterprise platforms are designed will look fundamentally different. Rather than relying on a single system to standardise every process, organisations are increasingly favouring:
- Redesigning processes from the ground up, eliminating non-value-adding steps before automation
- Modular capability assembly for ecosystem-based design
- Domain-driven data ownership and business-unit autonomy supported by API-first governance
In this landscape, organisations increasingly look for platforms that integrate deeply while still allowing choice at the edge, focusing operations on what is truly business-critical rather than extending blanket process automation across the enterprise.
For operational leaders, this architectural shift enables faster response to change. For finance leaders, it improves transparency without sacrificing control. For IT teams, it marks a move away from rigid platforms toward governed flexibility.
2. Operational Intelligence Overtakes Transactional Processing
The next phase of enterprise transformation is not process automation, but operational intelligence.
Organisations are increasingly prioritising sense-and-respond orchestration across assets, services, and workforces. Capabilities that were once considered advanced are becoming baseline requirements, including: anomaly detection, predictive maintenance, autonomous scheduling, and continuous optimisation.
This shift reflects a broader change in expectations. Leaders no longer want systems that simply execute predefined processes; they want platforms that can interpret operational signals, recommend actions, and increasingly act autonomously within defined parameters.
AI plays a central role here, but its value is realised only when intelligence is embedded directly into operational workflows. Platforms that treat AI as an external analytics layer struggle to influence real-world execution. Those that integrate intelligence into day-to-day operations enable faster decisions, reduced downtime, and improved outcomes.
3. Service and Asset Performance Become Core Value Drivers3.
Across asset-intensive industries, service and asset performance are no longer viewed purely as cost centres.
In 2026, organisations will increasingly link margin expansion and competitive differentiation to outcomes such as asset uptime, SLA performance, service profitability, and lifecycle cost
optimisation. This is particularly evident as service-based and outcome-based business models continue to grow.
Traditional ERP systems, designed primarily for financial reporting, lack the operational depth required to manage these requirements alone. As a result, organisations are adopting platforms that connect strategic investment planning, asset design, service execution, and performance management within a single operational context.
For functional leaders, this enables proactive performance management rather than reactive firefighting. For the C-Suite, it provides clearer visibility into the true cost and value of assets and services across their lifecycle.
4. Integration, Data Convergence, and Edge Execution Redefine Operations
While multi-platform architecture reshapes how enterprise systems are designed, operational execution realities are reshaping how those platforms must function on a day-to-day basis.
The historical separation between ERP, EAM, FSM, MES, and operational control systems is breaking down as organisations demand real-time processing and visibility across increasingly distributed environments. This includes central operations, field teams, and production or plant-level execution.
Organisations are therefore prioritising converged data models that support faster decision-making and more consistent execution across the value chain. By 2026, dependence on proprietary integration platforms and rigid back-office standardisation will increasingly give way to modular operational cores built on unified data models, reducing complexity while improving responsiveness where work actually happens.
5. AI-Native ERP to Drive Operational Richness.
Where earlier shifts focus on why operational intelligence matters, this trend reflects what that intelligence requires from enterprise platforms themselves.
AI and automation are evolving from decision-support tools into active participants in operational execution. Agentic AI refers to autonomous software systems that can perceive their environment, process information, make decisions, and take actions to achieve specific goals, operating independently within defined parameters and governance. Increasingly, these agents operate directly within enterprise workflows, supporting asset-level decision-making, workforce coordination, intelligent workflows, and predictive execution.
Importantly, this evolution does not remove the human element. Instead, it changes the nature of work, shifting human effort away from repetitive decision-making toward oversight, exception handling, and strategic planning.
Platforms that support AI-native processes, rather than retrofitting AI onto legacy transactional workflows, will be better positioned to deliver operational resilience, consistency, and scalability in complex environments.
How Technology Enables the Operational Digital Core
Technology is the enabler that makes this transformation possible.
The convergence of cloud-native architectures, edge computing, and AI agents allows intelligence to be embedded directly into operational workflows. GenAI chaining models support complex decision-making across multiple domains, while unified data models ensure context and consistency.
Crucially, these technologies only deliver value when aligned to real operational needs. Clean, operationally aligned data, industry-specific AI models, and rapid iteration cycles are key enablers of success.
Together, these capabilities form the foundation of the Operational Digital Core, supporting real-time execution, resilience, and continuous optimisation.
SOURCE: Federico Tondi (2026 January 29) The Rise of the Operational Digital Core. IFS Blog. https://blog.ifs.com/the-rise-of-the-operational-digital-core
