As enterprises approach the 2027 ECC sunset, the S/4HANA Brownfield vs. Greenfield debate continues to evolve as a new factor dominates strategic conversations: AI.

Historically, Brownfield was the go-to for its lower cost, faster timeline, and minimal disruption, especially in highly customized environments. But in an AI-first world, your transformation strategy can no longer be evaluated purely by technical lift or project duration.

The conversation must now include when and how your enterprise will start extracting tangible value from AI. The result? Inaction or legacy-first thinking risks locking you out of AI’s competitive advantage. It’s no longer just about how you migrate. It’s about what future you’re enabling. Rethinking your approach now may be essential to staying competitive tomorrow.

The GenAI Shift: Where Brownfield Falls Short

Brownfield may still offer short-term savings, but that advantage is shrinking. In the end, this ignores the long-term risks of short-term thinking.

SAP has concentrated its AI investments on operational AI, with capabilities designed to boost productivity after go-live. Tools like Joule and Business Technology Platform (BTP) allow users to resolve invoice disputes, generate change orders via prompts instead of transaction codes, and automate invoice matching using the Document Information Extraction service.

At Sapphire, SAP also highlighted the introduction of 40 pre-built AI agents that will be rolled out over the next year as well as Business Data Cloud (BDC), which unifies SAP and non-SAP data. BDC is currently only available on AWS.

However, these AI capabilities depend on something many Brownfield projects often skip: clean, structured data and simplified processes. If your organization skips Business Process Redesign and carries over technical debt, outdated data, or legacy custom code, you might reach S/4HANA, but you’ll be locked out of the future AI can deliver.

From Fast Lift to Smart Foundation

It’s tempting to treat your S/4HANA migration as a simple lift-and-shift exercise, which is exactly what Brownfield promises: speed, continuity, and reuse. But AI isn’t just a destination, it’s a capability that must be architected for in the present.

To unlock real value from tools like Joule or BDC, you need a clean core, modular architecture, and streamlined process layers. These aren’t just technical preferences, they’re prerequisites for AI.

This is why Greenfield transformations are more than technical resets. They create space to retire obsolete customizations, rationalize master data, and align with SAP’s clean core vision. And that matters because every AI use case, whether it’s smart invoice routing or real-time exception handling, relies on clean data, simplified workflows, and scalable infrastructure.

In this new AI-first world, the cost to future-proof your enterprise through Greenfield may be far less than you think.

Greenfield Is Not the Cost Sink It Once Was

Ten years ago, Greenfield meant blank-sheet rebuilds, multi-year timelines, and bloated budgets. But AI is rewriting that equation.

SAP has started embedding GenAI into its implementation offerings. Tools like Joule can now generate code snippets, assist with unit testing, and mock dependencies. And with Business Technology Platform (BTP), new functionality can be built outside the core ERP to help maintain a clean core, the foundation upon which operational AI thrives.

But it’s not just SAP. System Integrators are also leaning into AI. At Sapphire, SAP highlighted partnerships with firms like Accenture and Deloitte, whose delivery models are now infused with proprietary AI accelerators. These tools can automate major chunks of the implementation lifecycle, from blueprinting and WRICEF development to test case generation, automated testing, deployment, and documentation.

Some tools even extend to change management, helping generate training materials and job aids tailored to new processes.

The result? Implementation timelines are shrinking, documentation is improving, and the historical cost gap between Greenfield and Brownfield is narrowing. With so much AI-driven efficiency focused on enabling clean-slate transformations, it’s worth asking: Is Brownfield still the cheaper option or just the more familiar one?

Brownfield vs Greenfield: The Strategic Tradeoff

AI toolsets are making Greenfield more attractive, but they don’t eliminate the need for organizational change management. To truly rearchitect business processes for an AI-first future, enterprises must still invest in rethinking how work gets done.

For smaller or more agile organizations, that level of change may be achievable, and with AI-accelerated tools, Greenfield could reach parity with Brownfield on both cost and timeline.

For larger enterprises, however, the change management burden may feel prohibitive. But before defaulting to Brownfield, leaders should ask themselves:

• Do we have valid AI use cases in the roadmap, and is our current data and code base clean enough to support them?
• What is our timeline for realizing AI value, and what decisions must be made now to stay on track?
• How does the true cost of Greenfield (including AI accelerators and change management) compare to Brownfield? Does this math change when we factor in the long-term cost of technical debt, bad data, or delayed AI adoption?

In other words: Is Brownfield truly cheaper, or is it just kicking the can down the road and making our next upgrade far more expensive?

Bluefield: A Middle-Ground with a Lot to Offer

Brownfield may put you further behind, but Greenfield could still be a bridge too far for complex organizations. For organizations that require an alternative between these two extremes, Bluefield provides a more focused approach, letting you modernize only the business processes where it matters most without overhauling everything.

To evaluate whether Bluefield is right for your transformation, consider:

• Which modules or processes concentrate the majority of your AI use cases?
• Where is your data clean enough (or cleanable enough) to enable AI?
• Are there specific high-impact workflows where redesigning to standard could unlock measurable ROI?
• Do you have the governance structure to manage scope tightly and prevent your Bluefield project from creeping into the realm of Greenfield?
• Could AI tooling accelerate your selective transformation enough to justify the additional change management effort?

Bluefield doesn’t just split the difference; it focuses investment where AI and process modernization offer the highest return.

How a Third-Party Advisor Can Help

GenAI is changing the game and raising the stakes. With SAP and System Integrators making bold claims about productivity gains, cost savings, and AI-readiness, it’s hard to separate hype from reality, and even harder to determine what’s worth paying for.

That’s where UpperEdge comes in. We help organizations cut through the noise and make commercially sound decisions by:

• Determining whether a Phase-0 is truly necessary.
• Shaping an RFP that forces vendors to show, not tell, when it comes to AI capabilities.
• Pressuring SAP and your SI to provide transparency on pricing, value delivered, and roadmap stability when it comes to AI elements.
• Using over a decade of benchmarks to quantify the impact of AI on staffing models and project scope.
• Embedding protections in your commercial agreements to guard against change orders tied to evolving AI features.
• Advising on when and how to strategically introduce GenAI into your transformation.

If you’re not sold on Greenfield or Bluefield, but worry Brownfield could limit your AI potential, it may be time to step back and reassess your overall strategy.

A strong third-party advisor brings more than technical insight. UpperEdge helps you align AI with business outcomes, mitigate sourcing risk, and negotiate from a position of strength, ensuring AI becomes a competitive asset, not an expensive science project. Ready to learn more? Reach out today to see how we can help with your unique needs.