AI in ERP: value, hype, and unrealistic expectations 

Artificial intelligence is rapidly becoming a standard feature in modern ERP platforms. Vendors promise smarter forecasting, automated processes, and real time insights embedded directly into daily operations. For many organizations, AI in ERP represents the next leap in efficiency and decision making. 

At the same time, expectations are often set too high. AI is frequently positioned as a shortcut to better processes, cleaner data, and faster transformation. AI in ERP delivers value but only when it is grounded in solid foundations and realistic assumptions. 

Understanding where AI genuinely helps, and where hype tends to dominate, is critical for organizations looking to invest wisely. 

Where AI creates real value in ERP 

When applied correctly, AI can enhance ERP systems in practical and measurable ways. Most value comes from focused, well defined use cases rather than broad, autonomous intelligence. 

Common areas where AI already delivers benefits include: 

• Demand forecasting and inventory optimization based on historical and real time data 

• Identifying irregularities in financial transactions and operational processes 

• Automation of routine tasks such as invoice matching or expense classification 

• Predictive maintenance and asset performance insights 

In these scenarios, AI acts as an assistant rather than a replacement for human judgment. It augments existing processes and helps users make better decisions faster, provided the underlying data and processes are stable. 

The hype and where expectations go wrong 

Problems arise when AI is treated as a solution to structural issues. Many organizations expect AI to compensate for weak processes, inconsistent data, or unclear ownership. This is where disappointment often follows. 

Typical unrealistic expectations include: 

• AI will fix poor data quality automatically 

• AI will replace the need for process design and governance 

• AI will significantly reduce the need for business involvement 

• AI driven insights will be meaningful without context or validation 

ERP systems reflect how an organization operates. AI does not change that reality; it amplifies it. If processes are fragmented or data is unreliable, AI will at best deliver limited value, and at worst reinforce bad decisions at scale. 

AI in ERP requires fundamentals and governance 

AI in ERP is only as strong as the foundation it is built on. While AI does not require perfectly standardized processes, it does depend on process clarity, reliable data, and clear ownership. Without these fundamentals, AI could become an advanced layer on top of an unstable base, often leading to low adoption, mistrust in insights, and limited return on investment. 

Before scaling AI capabilities, organizations should ensure: 

• Clear and consistently applied core processes where it matters 

• Reliable data structures and master data governance 

• Users who understand and trust system outputs 

At the same time, AI initiatives require active leadership. Decisions about where and how AI is applied should be driven by business priorities, not vendor roadmaps. Leadership and governance help keep AI focused on real value, ensure transparency and compliance, and maintain clear accountability for outcomes. 

A balanced view going forward 

AI is a powerful capability that delivers tangible value when applied thoughtfully. Organizations that succeed are those that separate opportunities from hype and build solid ERP foundations. The key is to invest in data, processes, and governance first, then start with clear, limited AI use cases and scale only when value is proven.