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Technology / SaaS
April 20, 2026
Technology / SaaS

Enterprise ERP Transformation Insights: Navigating the 2027 SAP Support Deadline, Legacy Migration Realities, and Strategic Role of GCCs in Capturing OPEX Savings

Analyzes ERP transformation, highlighting slow cloud migration, hybrid architectures, data complexity, and AI-led automation driving efficiency gains and long-term value realization.

60 Mins
Former Head of Department
India
Public
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Companies Discussed
Accenture (ACN), Capgemini (CAP), Microsoft (MSFT), Oracle (ORCL), SAP (SAP)
Executive Summary
Topics Covered
Methodology
Free Preview — Executive Summary

This transcript examines ERP transformation, where most large enterprises still operate on legacy systems, with only 30–35% fully on cloud ERP, driving a phased hybrid migration approach. Transformation is led by vendor deadlines and demand for AI capabilities, but data migration and cleanup remain the biggest bottlenecks. Value realization typically occurs one to two years post-implementation, with efficiency gains such as reduced cycle times and 20–30% OPEX savings. Successful programs require strong governance, phased rollouts, and integration of AI layers to enhance automation and decision-making.

Topics Covered
  • ERP modernization drivers including cloud migration and vendor deadlines
  • Current split between legacy ERP and cloud ERP adoption
  • Economic value capture across implementation, services, and optimization
  • Hybrid ERP architectures vs full system replacement trade-offs
  • Data migration, integration, and process redesign as core complexity drivers
  • Timeline from ERP implementation to value realization
  • Role of system integrators vs in-house teams in execution
  • AI impact across automation, forecasting, and decision support
  • Standardization vs customization in ERP deployments
  • Role of GCCs in global ERP delivery and ownership shifts
  • Talent constraints, attrition, and cost inflation in ERP programs
  • ROI measurement across cost savings, efficiency, and revenue impact
  • Common failure points including governance, scope creep, and data issues
  • Investment outlook across cloud ERP, AI layers, and services
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Q: Can you walk us through the current GPU allocation framework at your organisation? How are you deciding between internal AI workloads and enterprise customer commitments? A: Sure. So the fundamental tension right now is that our internal AI teams — the ones building our own foundation models and inference services — are consuming GPUs at a rate that nobody anticipated even 18 months ago. We're talking about 3-4x the original projections. And that creates a real squeeze on what's available for enterprise customers. The allocation committee meets weekly now, which tells you everything. It used to be quarterly. We have a scoring matrix that weighs revenue potential, strategic importance, and internal capability gaps. But honestly, internal teams almost always win because the economics of our own AI services are so compelling compared to renting compute to enterprises...

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Expert Profile
Former Head of Department at Birlasoft
Duration
60 Mins
Call Date
April 20, 2026
Geography
India
Transcript Tier
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