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.