In the digital transformation era, ERP systems play a crucial role in modern businesses. Artificial intelligence is reshaping industries. AI promises smarter, faster, and more agile ERP systems, but the reality is something else.

The challenge? AI models can only perform well when they’re fed with the right context. Without the right business processes, domain rules, and data, AI might deliver fast but irrelevant results.  

This is where Context Engineering emerges as a game changer. It’s not just about telling AI what to do – it’s about providing AI model with the right knowledge in the right structure at the right moment. 

Beyond Prompt Engineering: Bringing business context to ERP transformations

How many of us have been sold on the benefits of upgrading our ERP systems, whether it’s enabling new capabilities, streamlining processes, simplifying integrations, or ensuring ongoing vendor support?  

Yet, ERP transformations are not easy to achieve and often result in certain problems. It includes budget overruns, lengthy delays, reliance on costly consultants, and the diversion of critical internal resources from their primary roles. This becomes problematic for established enterprises, whose success is built on decades of specialized knowledge across various factors such as regions, departments, and business units.  

Currently, the average S&P 500 company has over 20 years of operational experience. This indicates a clear sign of how deep and complex their business context is. 

In recent years, AI has become a key solution to this problem. However, the idea that off-the-shelf AI solutions, guided only by clever instructions (prompt engineering), can truly understand decades of accumulated business context is unrealistic. The reality is that, in ERP implementations, context is king. This is why a more sophisticated approach is essential – Context Engineering.

AI’s promise and the reality of context 

As business leaders, we continuously seek innovative ways to improve profitability while reducing risks. AI is frequently hailed as a transformative solution that can deliver streamlined processes, shorter timelines, and lower costs. Imagine expecting a generalist to instantly adopt your decades-old business nuances based only on brief, superficial instructions. AI solutions need far more depth to achieve meaningful results. 

Prompt Engineering vs. Context Engineering 

To appreciate why Context Engineering is essential, let’s first contrast it with traditional Prompt Engineering: 

Prompt Engineering 

A basic method of interacting with AI, which is quickly becoming outdated because it: 

  • Focuses only on wording and syntax. 
  • Depends heavily on narrowly defined, explicit instructions.
  • Is like handing an intern a rigid playbook and expecting expert performance. 

Consider an intern given one-time instructions to complete the task “update a variety of financial reports.” Without detailed context such as specific accounting rules, historical adjustments, or company-specific guidelines, they are unlikely to produce accurate, repeatable, meaningful results. 

Context Engineering 

In contrast, Context Engineering deeply integrates your company’s comprehensive knowledge by: 

  • Embedding documentation, (TDDs, PDDs), RICEFW, technical/product design documents, process flows, historical data, and robust validation rules. 
  • Acting like an experienced Subject Matter Expert (SME), familiar with company-specific logic. 
  • Ensuring AI-generated outputs are consistent, accurate, and highly relevant. 

Common challenges in ERP transformations and why Context Engineering matters 

Organizations often face significant problems during ERP implementations. This includes: 

  • Limited stakeholder availability: Key business users and decision-makers are frequently constrained by operational commitments and pressing revenue targets. 
  • End-to-end traceability and audit compliance: Regulatory and compliance requirements demand comprehensive documentation and clear visibility. 
  • Fragmented business data: Critical information spread across project management tools (e.g., ADO, Jira), document repositories (ALM, SharePoint), varied formats (Excel, PDFs, Word), or trapped in individual employees’ expertise. 
  • Generic testing scenarios: Off-the-shelf test cases often fail to align with unique business validation needs. 

Context Engineering directly addresses these challenges by embedding extensive, company-specific context into AI solutions. This approach ensures comprehensive stakeholder alignment, regulatory compliance, seamless data consolidation, and tailored test scenarios. 

Our approach: Accelerating ERP Testing through Context Engineering 

Our Context Engineering solution consolidates diverse, essential business assets, think Technical & Product Design Documents (TDDs, PDDs), RICEFW, business rules, process flows, and historical examples, into a unified, context-rich secured AI environment. Within hours, this sophisticated AI-driven approach evaluates ERP requirements against your specific business rules, generating precise, customized testing scopes for System Integration Testing (SIT), User Acceptance Testing (UAT), and regression testing, complete with detailed manual test steps tailored explicitly to your organization’s unique processes and data points. 

This reduces test scope and script generation from weeks of resource-intensive manual workshops to mere hours. Thus, delivering greater accuracy, immediate usability, and continuous improvement through human-in-the-loop validation. By deploying a context rich environment as detailed above, you can mitigate the traditional pitfalls of prompt engineering AI: 

  1. Reducing AI failures: Most AI inaccuracies or hallucinations stem from insufficient conceptual understanding and over-dependency on generic AI training data. Context Engineering resolves this by embedding rich, company-specific context directly into AI solutions.  
  2. Ensuring consistency: Context-rich AI adheres to established business practices, maintains traceability, and meets compliance standards, critical in ERP environments. 
  3. Handling complex scenarios: Context-aware AI effectively manages multi-step ERP workflows, automating early phases of the Software Development Life Cycle (SDLC), such as requirement validation, scope and test script generation. 
  4. Facilitating self-correction: Integrated validation loops enable AI to proactively identify and correct errors, significantly reducing downstream costs and timeline delays. Each defect identified later in the SDLC can conservatively increase remediation costs by 10x per phase. 

Impact and ROI 

By adopting Context Engineering, our clients experience tangible, executive-level benefits, such as: 

  • Accelerated ERP delivery milestones: Secure your programs against delays and budget overruns.
  • Enhanced employee productivity: Redeploy valuable talent to strategic tasks rather than tedious manual processes. 
  • Reduced business fatigue: Minimize burnout by simplifying time-consuming, repetitive activities. 
  • Increased confidence in testing coverage: Achieve thorough and precise testing without unnecessary expense. 

Embracing Context Engineering 

As reliance on ERP systems grows, contextually intelligent AI is no longer merely beneficial, it’s a necessary resource! Organizations transitioning from Prompt Engineering to Context Engineering can realize unparalleled accuracy, efficiency, and agility, transforming daunting ERP upgrades into strategic business advantages ensure the sold dream of ROI is turned into a reality.