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The overlaps between ERP and AI implementation projects
While examining the differences between ERP and AI projects in the earlier blog post, it also makes sense to discuss what they have in common.
While ERP and AI implementation projects differ in many ways, they also share several overlapping areas, especially when it comes to project governance, stakeholder management, and data foundations. Here’s a breakdown of the key overlaps:
- Data Readiness – Both require clean, structured, and integrated data. Poor data translates into project risk.
- Change Management – Both projects involve significant process or behaviour change across teams.
- Cross-Functional Teams – Collaboration between IT, business, operations, and end-users is essential.
- Governance and Compliance – Need for strong project governance, documentation, and adherence to regulations (e.g., GDPR, SOX).
- Integration with Legacy – Both must connect to enterprise architecture (e.g., APIs, legacy systems).
- Project Management – Structured methodologies (e.g., agile, hybrid, or waterfall) are key to both.
- Executive Sponsorship – Strategic alignment and buy-in from leadership are critical to success.
- User Training & Adoption – Success depends on user understanding, trust, and ongoing enablement.
- Post-Go-Live Monitoring – Requires continuous monitoring, support, and optimization after rollout.
Q: Why does that matter?
A: Even though ERP is more process-centric and AI is data/model-centric, both:
• Introduce new ways of working
• Rely on shared enterprise data
• Require strategic vision and operational discipline
In fact, many organizations now treat AI as a complementary layer to ERP, automating decisions or adding intelligence on top of transactional systems.
The difference between ERP and AI implementation, a generic approach.
This blog entry is about a generic approach to differentiate between an ERP implementation project and a typical AI implementation project.
In short, an ERP implementation project and an AI implementation project differ significantly in scope, approach, and success criteria, though they can intersect. Here’s a clear comparison:
| ERP | AI | |
|---|---|---|
| Scope & Objectives | Focuses on standardizing and integrating business processes (Finance, HR, etc.) across the enterprise. Efficiency, compliance, and transparency. | Focuses on enhancing decision-making, automation, and predictive capabilities. Intelligence, adaptability, and optimization. |
| Ownership | Zentralized ownership, extensive timelines, and substantial IT involvement. | Service Matter Experts (SMEs) transition from requestors to builders and owners, enabling rapid and cost-effective software development. |
| Solution Category | Transactional system (rule-based, deterministic). Predefined process logic and workflows. | Probabilistic system (data-driven, learns patterns). Adaptive models, often with black-box behavior. |
| Implementation | Follows a structured waterfall or hybrid method (e.g., Blueprint, Build, Test, Deploy). Heavy on configuration, data migration, and integration. | Often follows an agile, experimental approach (PoC, Pilot, Scale). Heavy on data engineering, model training, and iteration. |
| Dependency | Dependent on process definitions, master data, and change management. Requires strong alignment with business processes. | Dependent on data quality, availability, and business context. Requires cross-functional collaboration (IT, Data Science, Business Experts). |
| Metrics | On-time, on-budget delivery, user adoption, process compliance. Binary outcome (Go-live success or "maybe" not). | Accuracy, precision/recall, business value from predictions/recommendations. Continuous improvement over time. |
| Change Management | Organizationally disruptive, involving extensive training and process redefinition. | Often localized but can raise ethical, trust, and transparency concerns. |
ERP systems increasingly integrate AI (e.g., SAP, Business Central, QAD, Infor, Oracle, etc. with embedded AI for invoice matching or predictive analytics, posting review. In such cases, AI projects augment ERP systems post-implementation.
Q: Why are the differences essential to consider?
A: Organizations can rapidly and efficiently automate smaller tasks in increments. This approach involves initiating automation with a single, manual task and progressively extending it to neighboring tasks within the end-to-end process. As value is demonstrated, automation can be expanded to neighboring processes.
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