Q&A: AI Fuels ERP Modernization Surge in the Channel

ERP modernization is no longer a discretionary IT refresh; it has become a strategic inflection point. 

As organizations confront end-of-maintenance deadlines, mounting security exposure, talent attrition, AI-driven competitive pressure, and rising expectations for real-time performance, the question is shifting from whether to modernize to how, and how fast. 

In this Q&A, Henrik Wagner, the chief partner officer at AI and cloud consultancy Lemongrass, breaks down the forces reshaping ERP in 2026 and beyond, and what they mean for customers and the partners guiding them.

Why ERP modernization has become urgent

What’s really driving customers to modernize legacy systems now: cost, risk, AI readiness, performance limits, etc.? How is that shaping the types of projects partners are winning?

Risk and obsolescence of existing systems: ECC end-of-maintenance pressure, aging infrastructure, and unsupported integrations, security exposure, and loss as legacy experts retire with knowledge about systems. The projects that partners are winning are more in Selective Transformations and shorter advisory engagements rather than “Big Bang” long-term projects.

Are customers more often replacing legacy systems to enable AI and analytics, or trying to layer AI on top of what they already have? Where do partners add the most value in that decision?

AI readiness and data foundation are increasingly becoming top priorities for both customers and partners in their recommendations to clients. We are seeing both clients wanting to take advantage of incremental innovation of AI use cases against their existing SAP platforms as well as clients using the topic of AI to help create the business case for modernizations of platforms.

The rise of pre-transformation advisory and clean core

Cloud migration was the headline story for years. Is the bigger opportunity now in optimization, refactoring, and performance tuning of existing environments?

Pre-transformation advisory is now a bigger opportunity, such as projects around helping clients de-risk modernization journeys, including laying down a foundation for BTP, license optimization, and looking at moving to a Clean Core by using AI to analyze where clients currently are and what can be done to start moving to a “cleaner core.”

How are partners reframing application performance and observability in terms of business outcomes like user experience, revenue impact, and productivity rather than just technical KPIs?

We are seeing recommendations to shift from technical KPIs to business-process outcomes, such as improving order-to-cash cycle time, invoice processing time, and close cycle duration. 

Reactive support is moving to predictive business continuity to avoid revenue disruption, quarter-end reporting risk, and customer dissatisfaction—all of which have a direct revenue impact. 

User experience and interactions with systems has to be frictionless and these are key areas that partners can be instrumental in helping achieve success for their customers.

How big a barrier is poor data quality or fragmented data environments for customers pursuing AI? How are channel firms building services around data readiness and governance?

Poor data quality and fragmented data silos are definitely one of the barriers to the adoption of AI. We are hearing that “data” (not technology) is the gating factor for being able to adopt enterprise AI use cases faster. 

ERP silos, inconsistent master data across systems, heavy customization with duplicate logic, and the sprawl of data lakes need to be modernized and optimized in order to be able to drive outcomes faster with AI. 

Partners are responding by packaging “AI Readiness” assessments and Clean Core scanning using AI and data assessment. 

We are also building Data Governance as a Service offerings to help clients solve these problems around data quality and fragmented data silos. We also see adoption around data foundation programs, including BTP & BDC “Starter Pack” services as a way to get off the ground.

Consolidation and platform simplification

Customers often accumulate many tools for monitoring, analytics, and modernization. Do you see demand shifting toward consolidated platforms, and how does that affect partner strategy?

Yes. Consolidation of tools and processes, including a stronger technical governance model, are key. The demand is shifting toward integrated platforms and architectural simplification. 

Partners that lead with a consolidation strategy, not just tool deployment, are winning higher-value, longer-term engagements.

How partner revenue models are evolving

For partners focused on modernization and performance, is the revenue mix moving more toward recurring managed services, IP-based offerings, or consulting-led engagements?

We are definitely delivering more advisory engagement to start engagements with clients, using IP-accelerated implementation to reduce cost and risk by shortening projects. Scaling through technology and automation is key here, rather than trying to scale with people. 

Consulting-led advisory combined with modern toolsets is many times the strategic entry point for modernization roadmaps, data + AI readiness, BTP strategy foundations, strategy tool rationalization, and building the right operation model and skills set for clients to deliver successful projects.

Which capabilities most clearly differentiate modernization-focused partners today: deep legacy expertise, cloud-native development, AI/data skills, or industry-specific knowledge?

The most differentiated modernization partners offer architecture discipline, bringing the right skills and data/AI experience combined with deep industry context.