With the ECC 2027 sunset approaching and extended support incurring significant costs through 2030, many enterprises are choosing to migrate sooner rather than later. Security risks, compliance exposure, and integration challenges will only increase as ECC continues to age.

For many organizations, Brownfield remains a compelling path forward due to:

• Cost Efficiency: Reuses existing infrastructure, configurations, and custom code, potentially saving time and money compared to a full rebuild.
• Reduced Disruption: Preserves business continuity, especially critical in highly regulated or operationally sensitive environments.
• Faster Time to Value: Typically enables shorter project timelines, accelerating access to S/4HANA improvements such as deeper analytics and Fiori UX.

However, the next phase of transformation is no longer just about reaching S/4HANA, it’s about enabling AI. And that’s where the Brownfield conversation gets more complex. Not all paths to S/4HANA are equal when it comes to unlocking AI. The question isn’t just how you migrate, but what future you’re enabling once you get there.

Brownfield Gets you off ECC, but It’s Not AI-Ready

Brownfield will get you to S/4HANA, but without deliberate planning, it won’t get you any closer to an AI future.

SAP’s GenAI tools like Joule, Document Information Extraction, and AI-driven process automation are not native to S/4. They are delivered through Business Technology Platform (BTP), SAP’s cloud services layer that enables clean extensibility, modular integration, and intelligent automation. Tools like Joule can assist with generating code snippets, mocking dependencies, and streamlining unit tests, but only if BTP is in place.

And here’s the catch: Brownfield projects rarely include BTP by default. Without BTP, most of SAP’s operational AI offerings are out of reach, but choosing to include BTP may be cost prohibitive without a strong use case. The notion that you can migrate via Brownfield and “turn on” AI later is misleading, because you may be carrying forward the very technical debt and legacy complexity that makes AI difficult to implement in the first place.

That doesn’t mean AI has no role in a Brownfield implementation. It means the value isn’t typically coming from SAP. At Sapphire, SAP emphasized partnerships with SIs like Accenture and Deloitte, who are embedding AI into their delivery toolkits. These SI tools can help accelerate certain Brownfield phases, but they don’t persist post go-live.

Before assuming AI will simply “show up” in your Brownfield program, it’s worth asking:

• Where can AI materially reduce cost or schedule in our Brownfield implementation?
• Is it worth the extra cost to include BTP services that would allow us to activate SAP’s operational AI post go-live?
• What use cases are we targeting, and are those realistic within a Brownfield context?
• How are we validating our SI’s AI claims, and can we see real examples of cost or schedule impact?

These are not questions most implementation playbooks answer by default. But failing to ask them could mean missing the AI wave, or paying for AI services that don’t fit your context.

Making Brownfield AI-Enabled: What It Takes

SAP has embedded much of its GenAI capability, including Joule, into BTP. During implementation, Joule can assist with code generation, unit testing, and mocking dependencies. In production, AI agents also powered by BTP can help users resolve invoice disputes, create change orders via natural language, and automate exception handling.

But here’s the catch: If you’re going Brownfield without rewriting for a clean core, cleansing your data, or adopting BTP services, most of SAP’s GenAI portfolio will remain out of reach.

That doesn’t mean Brownfield and AI are incompatible, but it does mean you need to engineer for AI rather than assume it will “just work.” To make Brownfield truly AI-enabled, you’ll need to:

• Selectively modernize processes: Identify areas where AI could drive business value, and redesign just those processes (this is where Brownfield begins leaning into Bluefield strategy).
• Adopt strategic licensing: License only the services you need and tie them directly to targeted use cases.
• Invest in clean core discipline: Even if you’re not starting from scratch, move custom logic and extensions out of the ERP and into side-by-side apps via BTP where possible.
• Vet your SI’s AI tooling: Some SIs claim AI benefits regardless of implementation methodology, but can their accelerators function effectively in a Brownfield scope with legacy constraints?

If your strategy is “Brownfield + no BTP,” then you’ll be relying on your SI for AI tooling, and likely not taking advantage of any SAP production related AI. Before moving forward, consider asking SAP:

• Which AI offerings can we realistically use without BTP?
• Does your roadmap include any production or implementation-focused AI that runs natively in S/4HANA?
• Can we see a working demo of your AI capabilities deployed in a Brownfield scenario?

If the answers are vague or if they depend heavily on future roadmap delivery, you may want to rethink whether SAP’s AI plays a meaningful role in your transformation.

But SAP isn’t the only player promising AI-driven efficiencies. Your SI is likely offering its own proprietary AI accelerators as part of your implementation, or bringing 3^rd party tools with AI embedded. Just don’t assume those benefits are turn-key.

That’s where your next round of due diligence begins.

You’re Paying for GenAI, but Are You Capturing the Value?

In a Brownfield scenario, SI-developed tools may be the only real AI value you unlock during your transformation. That makes it critical to understand what you’re actually getting and whether the tools work effectively in your environment.

Many SIs now claim their AI-infused toolkits can support:

• Automated documentation for specifications, training materials, and status reports
• AI-assisted WRICEF code generation or configuration logic
• Automated test set creation and validation
• Data profiling and anomaly detection to support migration efforts

These capabilities sound promising. But how well do they really work in a Brownfield context, especially if your environment includes legacy code, inconsistent config, or incomplete documentation?

Before assuming these accelerators will save time and cost, ask your SI:

• Which AI use cases are feasible in a Brownfield context and which ones aren’t?
• Which of your proprietary tools use AI, and can we see their output in a demo or deliverable?
• Which of your proposed 3^rd party tools use AI?
• How do you measure the cost or schedule impact of these tools, and what benchmarks do you have for Brownfield?
• What is your visibility into SAP’s AI roadmap? How do your tools evolve alongside SAP’s AI offerings?
• What is your AI roadmap? Are there any tools coming available during the implementation and how might we get use out of them?

The SI will position these tools as differentiators, but without proof points, governance, or commercial guardrails, they may be more slideware than substance. In addition, some SI’s may be vague around assumptions or the data availability/cleanliness required to unlock value with their AI toolsets. Make sure you’re not paying AI premiums for deliverables that still require significant manual effort or fall apart under legacy complexity.

The GenAI Ceiling: What Brownfield Leaves Behind

Brownfield gets you to S/4HANA, and you may be able to accelerate toward that end with some SI AI toolsets, but it doesn’t necessarily position you to take advantage of what comes next. That’s the AI ceiling of a Brownfield implementation.

Unless you make targeted investments during your migration, Brownfield:

• Doesn’t clean your data; it carries forward what you already have, flaws and all.
• Doesn’t rationalize your codebase; it preserves customizations that may conflict with AI tools or block extensibility.
• Doesn’t modernize your process layer; this limits your ability to apply AI to real-time exceptions, user prompts, and smart automation.
• Doesn’t include BTP by default; core AI capabilities like Joule, Document Information Extraction, and Process Automation remain inaccessible.

This ceiling matters because SAP’s AI roadmap assumes a clean, modular, BTP-enabled core. Without that foundation, you may go live on S/4HANA only to realize your architecture can’t support the very innovations you expected to unlock without yet another upgrade.

You don’t need to scrap Brownfield to avoid this outcome, but you do need to go into the migration with eyes open, and with a roadmap that explicitly addresses the AI gap.

How a Third-Party Advisor Can Help

In a Brownfield transformation, AI doesn’t just show up. You have to go looking for it. SAP’s offerings are layered behind BTP. SI claims are often generic or roadmap-dependent, and most Brownfield programs aren’t scoped or staffed to pressure-test these claims.

That’s where UpperEdge comes in.

We help you build a realistic, commercially grounded GenAI strategy that works within the constraints of Brownfield while avoiding the traps of unverified claims and post-go-live regret. Specifically, we help you:

• Interrogate AI claims from SAP and your SI and demand demos, not just decks.
• Quantify GenAI feasibility within Brownfield constraints based on benchmarks, not promises.
• Pressure-test GenAI staffing models so you’re not paying AI premiums for manual delivery.
• Examine your SAP contract to ensure any BTP spend is tied to specific, achievable use cases so you don’t overpay for shelfware.
• Negotiate protections against GenAI scope creep to formalize in the SOW, especially in change orders and licensing terms.

If your path forward is Brownfield, it doesn’t mean your AI ambitions are dead, but they do need discipline. UpperEdge brings clarity, negotiation leverage, and delivery realism to AI adoption, so your upgrade doesn’t become a missed opportunity or worse, a costly re-do.

Ready to close the AI gap in your Brownfield transformation? Our Project Execution Advisory Services help you validate SI claims, align AI investments to real use cases, and avoid costly mistakes. Learn how UpperEdge can support your transformation.