Mastering SAP S/4HANA Migrations

Strategies for Overcoming Data, Customisation, and Organisational Challenges

I remember a time when I was working on a high-stakes SAP implementation and the team felt so confident heading into user acceptance testing—until one glaring error halted our progress. Some customers’ addresses were completely incorrect, leading to a postponed go-live and frantic days spent investigating the cause. It turned out that relying on Excel for data preparation within the “Migrate Your Data” Fiori app left us with no change log. This meant that when errors appeared, no one could pinpoint who made the mistake or when it happened, and our confidence in the entire data migration process began to waver.

This kind of ordeal underscores the complexities of SAP S/4HANA conversions. According to The Hackett Group, organisations face a series of challenges when converting from SAP ERP (ECC) to S/4HANA, including lack of organisational planning, complex data migration, and insufficient testing. Given the 2027 deadline for moving to S/4HANA, project managers must navigate these challenges with clear strategies and proper guidance.

In this blog post, I want to highlight the lessons I’ve learned from these experiences and the insights gleaned from The Hackett Group’s findings. For SAP project managers, the problems are real, pressing, and multi-faceted. By focusing on data migration complexities, balancing custom code with standardisation, and managing organisational change effectively, we can help ensure smoother SAP S/4HANA transitions—and avoid the kind of setbacks I once faced.

Why SAP S/4HANA Conversion Matters

SAP S/4HANA is not just a newer version of SAP ERP (ECC); it introduces a more streamlined, real-time data processing architecture that promises faster analytics, simpler data models, and an array of capabilities designed to meet modern business demands. However, as we race towards the 2027 deadline, many organisations still find themselves grappling with how to effectively switch from ECC to S/4HANA. The Hackett Group identifies five major challenges—organisational disorganisation, source system preparation, data transfer complexity, custom code management, and insufficient testing—all of which can derail even the most well-intentioned project.

My own anecdote about the wrong address migration during user acceptance testing highlights just how critical it is to have robust processes. Data oversights or incomplete testing can seriously affect stakeholder trust, derail your timeline, and add unnecessary costs to an already resource-intensive project.

In the sections that follow, I will explore three pressing issues for SAP project managers—data migration, customisation vs. standardisation, and organisational change management—each woven into the larger tapestry of S/4HANA conversion. Drawing upon the article’s insights and my own experience, I’ll offer practical approaches for tackling these challenges head-on.

Navigating Data Migration Complexities in SAP S/4HANA Implementations

Data migration sits at the heart of any ERP transformation. For SAP S/4HANA, it’s particularly challenging because of the structural and architectural differences between SAP ECC and S/4HANA. Millions of records often need to be moved or transformed, and as The Hackett Group notes, “Moving data from SAP ERP (ECC) to S/4HANA is challenging if you don’t properly prepare for conversion.” When data is incorrectly mapped or accidentally altered during extraction, transformation, or loading (ETL), you risk major project delays and a breakdown of trust among users.

Practical Strategies

  • Early Data Assessment: Begin with a thorough data profiling exercise. Identify data quality issues (duplicates, incomplete records, outdated information) and address them long before migration starts.
  • Use SAP’s Recommended Tools: The SAP S/4HANA Migration Cockpit and the “Migrate Your Data” Fiori app streamline processes, but they also highlight how crucial version control and logs are. My own experience taught me the pitfalls of Excel-based preparation without a proper audit trail. Where possible, rely on built-in SAP tools that log activities.
  • Set Up a Governance Framework: Establish a data migration governance team to oversee roles, responsibilities, and decision-making. This includes naming who is responsible for approvals, who oversees data cleansing, and how issues are escalated.
  • Phased Migrations & Pilot Projects: Rather than migrating all data at once, consider phased approaches that allow you to learn from a pilot group of data sets. This helps you correct mistakes early and refine processes for subsequent waves.
  • Automated Testing & Validation Checks: Implement automated scripts to validate that data moves correctly. For instance, if you migrate customer addresses, run targeted checks to ensure data fields, postcodes, and city names match expected values. Automated comparison reports can be immensely helpful.

Short Case Study
In one project, our team decided on a phased approach for sales data. We first migrated a small subset of historical transactions to the development environment. Automated validation scripts flagged inconsistencies in currency codes that had been incorrectly formatted during the transformation step. Because of this early detection, the broader migration to the quality environment was significantly more accurate. This early pilot helped us avoid the kind of go-live setback I once faced with address data errors.

Balancing Customisation and Standardisation in SAP S/4HANA Projects

Custom code is the lifeblood of many SAP implementations, shaping functionalities that cater to unique business processes. However, too much custom code can slow system performance, increase maintenance costs, and complicate upgrades. On the other hand, if you choose to rely solely on out-of-the-box SAP solutions, you may be forced to bend your existing processes, possibly losing valuable unique differentiators. A PwC statement reads, “Custom code developed in previous systems should be thoroughly tested in the new environment,” emphasising the delicate balance that project teams must strike.

Practical Strategies

  • Custom Code Inventory: Begin by creating an inventory of all custom developments in your ECC environment. Classify them based on business criticality and frequency of use. If a custom solution isn’t used regularly or is no longer aligned with current business strategies, consider retiring it.
  • Adopt SAP Standard Where Possible: SAP S/4HANA includes new standard functionalities that might replace your current custom solutions. Evaluate these carefully. Migrating to a standard solution can often reduce technical debt, cut maintenance costs, and align you with SAP’s innovation roadmap.
  • Refactor vs. Re-Implement: For codes that need to remain, assess whether a simple refactoring is enough to align with S/4HANA’s simplified data structures, or if a re-implementation is required to harness new functionalities.
  • Leverage Extensions and BAdIs: SAP S/4HANA provides extension mechanisms (In-App and Side-by-Side Extensions) that help you tailor your system without modifying the core. Business Add-Ins (BAdIs) and the SAP Cloud Platform Extension Factory are powerful tools that keep your customisations modular, easier to upgrade, and better integrated with SAP’s code lifecycle.
  • Collaborate with Key Stakeholders: The decision to retain or discard a custom feature isn’t purely a technical one; it’s a strategic business decision. Engage functional managers, business process owners, and IT teams in a collaborative workshop to weigh costs, benefits, and future implications.

Short Case Study
A manufacturing company with highly specialised production processes found themselves over-reliant on custom code that had been built up over a decade. When preparing for the S/4HANA conversion, they discovered that half of their legacy code was no longer necessary due to S/4HANA’s expanded standard features. By pruning unnecessary custom objects and refactoring the truly critical ones, they dramatically reduced future technical debt, saving both development and support costs in the long term.

Overcoming Organisational Change Management Challenges in SAP S/4HANA Transitions

Technology migrations often fail or stall for reasons unrelated to technology. Resistance to new processes, inadequate training, or poor communication can send even the best S/4HANA project off track. According to Oxford Global Resources, “Transitioning to SAP S/4HANA brings forth potential disruptions attributed to inadequate change management.” Because S/4HANA transitions often affect core business processes, ensuring stakeholder alignment and user readiness is as critical as the technical migration itself.

Practical Strategies

  • Stakeholder Engagement: Identify all relevant stakeholders—executives, department heads, end-users, and IT teams—and keep them informed about changes, timelines, and potential impacts. Early buy-in can preempt pushback and build excitement around new possibilities.
  • Structured Communication Plan: Develop a clear communication roadmap. Outline what messages will be shared, when, and through which channels (e.g., newsletters, lunch-and-learns, or online forums). Transparency about project milestones and challenges builds trust.
  • Comprehensive Training Programmes: Many users resist change simply because they lack confidence in new systems. Provide hands-on training sessions, e-learning modules, and sandbox environments. For instance, if a finance team is used to T-codes, help them understand how transactions are streamlined in Fiori-based apps.
  • Define New Roles and Responsibilities: S/4HANA’s new modules or functionalities might change the nature of certain roles. Clearly define who owns what processes, and update performance metrics to reflect new responsibilities.
  • Celebrate Quick Wins: Identify smaller milestones where you can demonstrate the benefits of S/4HANA (e.g., faster reporting times, simpler user interfaces). Spotlighting these successes encourages further user adoption.

Short Case Study
In one multinational implementation, the team discovered that a significant percentage of users found the new Fiori apps daunting. A well-structured combination of e-learning courses, live workshops, and a ‘buddy system’ where experienced users guided newcomers helped boost confidence. Within three months, user adoption skyrocketed, and the project overcame early negativity.

Weaving It All Together: My Personal Experience

My anecdote about go-live being postponed reminds me of the interconnected nature of these challenges. A data issue (wrong address migration) also became a matter of organisational trust: once users saw incorrect data, they began doubting the entire system. The setback could have been mitigated by:

  • Robust data governance and version control (to see who made the changes and when).
  • Adequate testing on each iteration of data loads, rather than waiting until the end.
  • Clear communication so end-users understood the process and had a way to raise red flags early.

Despite the frustration and delay, the experience led to a more disciplined approach. We refined our data migration checks and invited more user feedback loops. By the time we went live, users felt assured that our data validation was airtight. This underlines how each of the three major challenges—data migration, custom code decisions, and change management—must be addressed holistically.

The path to a successful SAP S/4HANA conversion is undeniably complex, but it’s a worthwhile journey that delivers long-term benefits. By 2027, organisations still on SAP ECC will either have to confront these challenges head-on or risk falling behind in a marketplace that demands agility, real-time insights, and streamlined processes.

From my own experience, the greatest lessons revolve around thorough planning, consistent testing, and transparent communication. The transition from ECC to S/4HANA is more than a technical upgrade—it’s a pivotal shift in how businesses operate. As we’ve explored, data migration can be an Achilles’ heel if overlooked, custom code can become a burden if it’s not properly evaluated, and your people can make or break the project if they’re not guided through the change.

Addressing the three core challenges—navigating data migration complexities, balancing customisation with standardisation, and overcoming organisational resistance—requires a blend of solid technical expertise and empathetic human leadership. Leveraging standard features and cutting-edge tools is half the battle; the other half is building a culture that embraces continuous improvement and collaboration.

With these strategies in mind, SAP project managers are well-equipped to deliver a smooth, timely, and effective S/4HANA conversion. Success is possible if you approach it systematically, engage stakeholders, and champion proactive measures every step of the way.

I’d love to hear your experiences! Share your thoughts or questions in the comments, and let’s keep this conversation going.

Unleashing the Power of AI in SAP Build

Transforming Application Development Through Prompt Engineering and Data Governance

I recently stumbled upon an article on the SAP Community site titled “AI in SAP Build – Overview and Best Practices”. As a dedicated SAP consultant, I am constantly on the lookout for technological advancements that can streamline project deliverables and offer better value to clients. This particular article captivated my attention because it underscores how Artificial Intelligence (AI) capabilities within SAP Build are reshaping the manner in which we create, customise, and maintain business applications. It also sheds light on the concept of prompt engineering and emphasises how integral it is to maintain high data standards when working with AI tools.

In my own consulting practice, I’ve discovered the power of a Universal AI Prompt—one streamlined prompt that I can adapt to generate project documents such as business blueprints, functional specifications, meeting minutes, test scripts, and training materials. Not only has this unified prompt saved me a significant amount of time, but it has also noticeably improved the quality and consistency of my deliverables. Reading this SAP Community article resonated with my own experience and affirmed that methodical strategies in AI, data governance, and prompt engineering can lead to extraordinary outcomes.

In this blog post, I explore the key insights from the article—particularly focusing on three problem statements around AI integration, prompt engineering, and data governance—while weaving in my personal experience as an SAP consultant. My hope is that other consultants and SAP professionals can harness these insights to expedite project timelines, minimise errors, and enhance overall efficiency in their own client engagements.

The Evolution of AI in SAP Build

Artificial Intelligence (AI) is revolutionising the way enterprises operate, from high-level strategic planning down to hands-on system implementation. As a consultant, I’ve seen first-hand how AI-driven functionalities are entering virtually every sphere of SAP, from embedded analytics to intelligent process automation. According to the article, “Artificial Intelligence (AI) is revolutionising the way businesses operate, and SAP Build is at the forefront of this transformation.” This statement rings particularly true to those of us who spend our days configuring, optimising, and customising SAP solutions to help clients derive tangible value.

SAP Build in a Nutshell

SAP Build is a low-code/no-code environment that streamlines how consultants, developers, and business users create custom applications. By integrating AI features, SAP Build empowers us to deploy intelligent applications with minimal coding. The article states, “SAP Build integrates AI capabilities to enhance application development, process automation, and overall business efficiency.” For me, the significant takeaway is that these AI-enhanced features help speed up development cycles without compromising on functionality. Instead of orchestrating complex manual coding sessions, I can leverage AI to auto-generate or refine project documents, enabling me to dedicate more of my time to higher-level consultancy tasks—like advising on strategy, re-engineering processes, or identifying cost savings for my clients.

The Role of Prompt Engineering

A key theme in the article is prompt engineering, described as “the art of crafting effective prompts to guide AI models in generating accurate and relevant outputs.” In my experience, the prompt itself is both an art and a science. If your question or instruction is too vague, you’ll receive generic or incomplete outputs from the AI. If you’re too restrictive, you might stifle the model’s capacity to explore creative solutions. Developing a universal, reusable prompt for SAP tasks took me several rounds of trial and error. Once I found a sweet spot, however, I noticed how effectively the AI could standardise my project documents, reduce overhead, and ensure a uniform level of quality that impresses clients.

Enhancing AI Integration in SAP Build for Streamlined Application Development

Why This Matters to SAP Consultants

The first major challenge is how to integrate AI smoothly into SAP Build to simplify the creation of applications, documentation, and processes. Even though AI technologies are readily available, a common obstacle is determining where and how to incorporate them within a client’s environment. As consultants, we must juggle multiple elements: stakeholder expectations, project constraints, data landscapes, and existing solution architectures. The promise is straightforward—AI can substantially cut down on manual effort and errors—but bridging the gap between theory and practice sometimes proves challenging.

Insights from the Article

The article highlights that AI is not just a trendy buzzword. Instead, it’s becoming an indispensable asset for businesses aiming to stay ahead of the curve. From a consultant’s viewpoint, it underlines the potential for:

  • Reduced Development Time: Auto-generated test scripts, functional specs, or business process flows speed up the initial phases of configuration.
  • Better Resource Allocation: Time saved on manual drafting or coding can be reinvested in strategic consulting tasks, like solution optimisation.
  • Enhanced Collaboration: AI-based collaboration tools can unify cross-functional teams, ensuring consistent outputs and methodologies.

Practical Strategies for Integration

  1. Start with Targeted Use Cases
    Before rolling out AI-driven functionalities across multiple processes, I recommend focusing on high-impact, low-risk scenarios. For instance, if a client frequently needs training materials, begin by using AI to create these. Once you’ve validated the benefits, you can expand to more complex scenarios such as automating defect-tracking or user acceptance testing scripts.
  2. Leverage SAP Build’s Existing AI Templates
    SAP Build often comes packaged with sample templates for AI-enabled processes. These resources provide an excellent baseline and best practices, especially for those new to AI. By customising the templates to your client’s needs, you can deploy solutions more rapidly.
  3. Collaborate Across Disciplines
    AI integration is not purely technical; it also involves business and organisational change. Collaborate closely with process owners, IT departments, and end-users to identify the right tasks for automation. This collaborative approach helps ensure buy-in and a smoother adoption curve.
  4. Provide Ample Training
    AI systems are only as good as the people using them. Offer hands-on workshops or micro-learning sessions for the client’s internal teams, so they understand how to maintain, troubleshoot, and improve AI capabilities once your project is completed.

My Consulting Experience with AI Integration

My journey integrating AI into SAP Build projects has taught me that small wins can lead to big transformations. For example, I once worked on a supply chain optimisation project where the client spent countless hours drafting functional specs for each iteration. By employing AI to create the first draft of these specs—guided by a carefully tailored prompt—we slashed documentation time by over 50%. Best of all, the project team could then refine the AI-generated documentation with their own domain expertise, ensuring both speed and accuracy. This success paved the way for further AI-driven enhancements, like auto-generating user guides and test scripts.

Bridging the Skill Gap in Prompt Engineering for Effective AI Utilisation

The Consultant’s Perspective on Skill Gaps

While sophisticated AI tools are readily available, the real challenge is often skill-based: do the teams involved know how to frame requests or instructions so that the AI can respond meaningfully? This is where prompt engineering comes into play. If we, as consultants, don’t invest time in honing our ability to interact with AI effectively, we risk underutilising powerful tools and delivering less-than-stellar outputs to our clients.

Article Highlights

The article underscores that mastering prompt engineering is essential to unlock the full potential of AI. A well-crafted prompt should include context, constraints, examples, and even the desired format of the output. If you fail to specify these parameters, the AI model might produce vague or irrelevant information. Conversely, overloading your prompt with excessive detail can confuse the model or hamper its ability to innovate solutions.

  1. Train and Upskill
    To bridge the gap, consistent and targeted training is vital. Encourage your colleagues and clients to take short courses on AI basics, focusing on how language models interpret input text. Comprehensive understanding of AI fundamentals greatly enhances how effectively people craft prompts.
  2. Develop Prompt Playbooks
    From my experience, it’s incredibly useful to build a ‘prompt playbook’—a repository of tried-and-tested prompts tailored for SAP-related tasks. This could include prompts for writing functional specs, generating meeting minutes, or creating data migration strategies. Keeping these prompts in one place for easy reference helps maintain standards and fosters continuous improvement.
  3. Iterate and Experiment
    Prompt engineering thrives on iteration. Simple tweaks in language—such as specifying a target audience or adding an example of the desired output—can drastically change the AI response. Regular experimentation with prompts is the fastest way to learn and adapt your approach.

My Universal AI Prompt Success

My personal breakthrough came after several rounds of trial and error, during which I crafted a Universal AI Prompt that guides the AI to produce consistent, high-quality drafts of various SAP project deliverables. Initially, I tested generic prompts like: “Write a functional specification for a retail module.” The outputs were serviceable but often required extensive edits. By refining my prompt to include exact details—target environment, business requirements, compliance considerations, and the document structure—I ended up with a single, adaptable template. Now, I can simply adjust minor variables within this core prompt to generate well-structured documents for multiple project scenarios, significantly cutting down on drafting and review time.

Establishing Robust Data Governance for Optimal AI Performance in SAP Build

Why Data Governance is Critical

Good data is the foundation on which AI relies. In an SAP environment, where data drives everything from transactional processes to predictive analytics, ensuring data integrity is non-negotiable. If your data contains inaccuracies, duplications, or inconsistencies, the AI model will struggle to produce useful or accurate outputs—no matter how well you craft your prompts.

Alignment with the Article

The article pinpoints data governance as a decisive factor in successful AI deployments. Ensuring that data is consistently structured, free from errors, and accessible to the AI model can be a tall order. Yet, as a consultant, I’ve seen how effective data governance can elevate the performance of AI-driven functionalities in SAP Build. With robust data frameworks in place, clients are better able to trust AI-generated insights, leading to more confident decision-making and smoother project execution.

Key Strategies for Strong Data Governance

  1. Define Clear Data Ownership
    Identify who within the organisation is responsible for specific datasets. For instance, the finance team might own the general ledger data, while the logistics department manages inventory records. Assigning data owners fosters accountability and makes it easier to enforce consistency.
  2. Use Built-in Validation Rules
    SAP Build allows you to incorporate validation checks at the application level. These rules ensure that only data conforming to specific formats or ranges is entered into the system. By catching errors at the point of entry, you maintain cleaner datasets that AI can reliably process.
  3. Regular Data Audits
    Setting up a schedule for data audits helps unearth hidden issues. Whether quarterly or semi-annually, these reviews often reveal data anomalies, which can then be addressed through cleansing initiatives. The consistency of these efforts leads to a continuous improvement cycle for data quality.
  4. AI for Data Management
    Interestingly, AI can be employed to support data governance itself. Machine learning algorithms can analyse large datasets to detect anomalies or patterns that might indicate data corruption or duplication. By flagging these issues promptly, the overall data environment remains cleaner.

A Consultant’s Experience with Data Integrity

I remember one particular engagement where the client was eager to roll out AI-driven functionalities for predictive maintenance. However, their equipment master data was riddled with inconsistencies in naming conventions and incomplete entries. The AI model’s output was subpar at best. Working closely with the client, we enforced a strict data governance policy, standardised naming conventions, and cleansed the data records. Once the data reached a suitable level of accuracy, the AI model performed significantly better, delivering actionable insights that helped the client optimise their maintenance schedules and reduce unplanned downtime.

A Synergistic Approach

Connecting AI Integration, Prompt Engineering, and Data Governance

When I reflect on my experiences—both successes and lessons learned—it becomes obvious that AI integration, prompt engineering, and data governance are deeply interlinked. Effective AI integration is amplified by strong data governance, since AI algorithms derive maximum value from high-quality data. At the same time, well-crafted prompts can only go so far if the underlying data is flawed or mismanaged. Conversely, no matter how clean your data might be, if your team lacks the ability to frame the right prompts, your AI outputs will remain limited.

How the Universal AI Prompt Fits In

My journey with the Universal AI Prompt highlights the importance of synergy among these three elements. It was only after refining my prompt (prompt engineering), integrating AI seamlessly into my workflow (AI integration), and ensuring that my project data was reliable (data governance) that I saw a dramatic upswing in both the speed and quality of my deliverables. By standardising my approach, I also found that clients gained confidence in the outputs, enabling them to make faster, more informed decisions throughout the project lifecycle.

AI-powered transformations in SAP Build hold enormous promise for consultants and clients alike. The article “AI in SAP Build – Overview and Best Practices” captures the essence of this transformative potential by highlighting how AI optimises development processes, drives efficiency, and elevates overall business operations. Crucial to these benefits, however, are two cornerstones: prompt engineering—“the art of crafting effective prompts to guide AI models in generating accurate and relevant outputs”—and robust data governance frameworks.

In my consulting journey, I have witnessed the profound impact that a carefully structured approach to AI can bring, from cutting document preparation time to offering innovative ways to automate repetitive tasks. Yet these benefits are not automatic. They require thoughtful planning, iterative experimentation, and a commitment to impeccable data standards. My own Universal AI Prompt stands as proof that, with the right strategies in place, consultants can deliver measurably superior results while building deeper trust with clients.

Ultimately, every SAP consultancy project is unique, shaped by its own business requirements, technical landscapes, and organisational cultures. By addressing challenges around AI integration, refining the skill set in prompt engineering, and investing in data quality, we position ourselves—and our clients—to thrive in a marketplace increasingly driven by AI.

Have you explored AI functionalities in your SAP Build projects? Share your experiences and insights in the comments below—let’s continue learning from each other and growing together!