A strong emphasis is placed on the strategic layer: Zero Trust implementation, security roadmap development, maturity assessments, and vCISO services. For SAP, this includes standard domains (GRC, SoD, system hardening) extended into cloud environments such as S/4HANA and BTP. The AI segment introduces additional complexity: model security, governance frameworks, regulatory compliance (including the EU AI Act), and protection against prompt injection and adversarial attacks. Overall, this is positioned not as separate services, but as an integrated enterprise security model.

The root cause is rarely the upgrade itself. It is architectural debt, a hidden “upgrade tax” caused by technical shortcuts taken years earlier. In most cases, the break isn’t caused by SAP; it’s caused by years of Z-cloned apps, direct table reads, and UI logic built outside SAP’s supported frameworks. 

For a CIO this isn’t just a development hurdle, it is a governance failure that erodes the ROI of the entire S/4HANA business case.

What Actually Changes (The Technical Shift)

Upgrading S/4HANA is no longer a simple database refresh. The move to the latest releases involves fundamental shifts in how SAP handles the UI:

• SAPUI5 Evolution: SAPUI5 is now updated continuously. If your apps depend on internal methods, every upgrade becomes a regression event—not a routine patch.
• The OData v4 Leap: SAP is pivoting from OData v2 to v4. Custom extensions built on legacy v2 services often require significant rework or total rewrites to maintain performance and compatibility.
• Clean Core Contracts: SAP has formalized Tier 1 Extensibility (C1 Contracts). Apps that bypass these “released” APIs are essentially reaching into the S/4HANA “basement”—accessing data through unreleased paths that SAP feels no obligation to keep stable.

The Invisible Cost: Technical Debt is an AI Tax

We are entering the era of the “Autonomous Enterprise,” where SAP Joule and Generative AI assistants navigate the ERP for the user. However, AI assistants rely on standard metadata and released APIs to understand business context.

If your Fiori landscape is a web of “Z-clones” and non-standard controllers, you aren’t just breaking your next upgrade—you are “blinding” your future AI. An AI assistant cannot effectively provide insights on a custom-cloned app that doesn’t follow the SAPUI5 standard.

CIO Perspective: Cleaning the core isn’t just about maintenance; it’s about AI-Readiness. Every app you bring back to “Standard” or “Fiori Elements” is an app that can immediately leverage SAP’s multi-billion-dollar investment in Business AI.

The Strategic Path: Extension Governance

Extensibility must be architecture-governed, not developer-driven.

1. The “Golden Path”: Fiori Elements

Freestyle UI5 offers flexibility but maximizes fragility. Fiori Elements should be the default for 80% of use cases. They follow SAP design templates and are automatically aligned with the SAP lifecycle.

Value: Standardization ensures your UI evolves with SAP, not against it.

2. Tiered Extensibility (Key User Tools First)

Before writing code, teams must exhaust Key User Extensibility (Custom Fields and UI Adaptation). These are “upgrade-proof” because SAP owns the compatibility layer.

3. Lifecycle Decoupling: Side-by-Side on SAP BTP

The S/4HANA core is your “System of Record”—it should be stable and governed. Your BTP extensions are your “Systems of Innovation”—they must be agile.

Value: By moving high-change apps to BTP, you ensure that a UI update in a customer portal doesn’t require a full regression test of your entire Finance core.

Embedded vs. Side-by-Side: The Decision Matrix

A CIO Maturity Checklist: Are You Upgrade-Ready?

Before your next upgrade, assess your landscape maturity:

1. The “Clone” Count: How many standard SAP apps were copied to the Z-namespace? (Each is a liability).
2. API Health: What percentage of our custom code uses Tier 1 / Released APIs versus direct table access?
3. Governance Gate: Do we have an Extension Review Board, or is the developer deciding where the code lives?

Modernization Gap: Are we spending more than 30% of our upgrade budget simply “fixing” what we already built?

The Strategic Message

The question for your leadership team is no longer: “Can we build this?” The question is: “Can we upgrade this safely in three years?”Clean Core is not about technical purity; it is a cost-containment strategy. Organizations that treat extension design as a strategic architectural decision will upgrade faster, innovate more safely, and stop paying the “upgrade tax” that stalls digital transformation.

About the Author

José López is a senior SAP technology leader with 20+ years of experience managing and optimizing mission-critical SAP environments. As Principal Director of SAP AMS, he leads end-to-end service delivery for large, complex SAP landscapes.

Data products aren’t mandatory for building BI dashboards, ML models or AI solutions, but they dramatically improve your odds of delivering successful, repeatable outcomes by adding semantic clarity and governance.

One Unified Intelligence Platform 

In data and AI architecture, data products (trusted, reusable data assets with clear meaning, ownership, governance, and a defined way to be consumed) are the glue that makes the various architecture components operate as one unified intelligence platform.

Without data products, each tool in the architecture operates using its own interpretation of the data. With them, analytics, ML and AI share a consistent semantic foundation regardless of vendor stack. Consider how this plays out across two different ecosystems: An SAP-centric architecture and an AWS-native architecture.

SAP-Centric Architecture: SAP S/4HANA, Datasphere, Joule, and Databricks

• SAP S/4HANA generates operational data
• Datasphere models and governs it
• Joule and AI services consume it
• Databricks or similar platforms extend advanced analytics

All rely on shared, governed data products to maintain consistent business meaning.

AWS-Native Architecture: AWS S3, RedShift or Athena, SageMaker, and QuickSight

• Raw data lands in AWS S3
• The data is transformed through Redshift or Athena
• Then feeds both SageMaker models and QuickSight dashboards

Using data products ensures consistent definitions, governance, and reusable interfaces across analytics and AI workflows in this architecture.

Strong Predictors of AI Success

Organizations experimenting with AI can produce early results without structured data products. But when AI initiatives are measured by scalability, reliability, and enterprise adoption, a consistent pattern emerges: High-performing organizations treat governed data products as a foundational layer rather than an afterthought.

Data products act as strong predictors of success because they:

• Address a primary root cause of AI failure: Inconsistent semantics and unreliable data foundations.
• Emerge independently across high-maturity AI organizations, even when different technology stacks are used.
• Enable repeatability and governance, allowing models and analytics to move from experimentation to production.
• Support cross-domain AI, where insights and models span multiple business functions.
• Align with modern enterprise architectures, including SAP’s evolving data and AI strategy.
• Correlate with stronger business outcomes: Organizations adopting governed data layers consistently outperform those that rely on ad-hoc pipelines.

BI dashboards, ML models and AI solutions can be built without formal data products, but why would you want to? Organizations seeking scalable, reliable, enterprise-grade outcomes consistently find that data products are indispensable to achieving a successful outcome. 

Organizational alignment matters as much as the technology

A second critical predictor of success is proactive engagement from the C-suite. Data has long driven strategic advantage in data-intensive industries such as Finance, Media, and Retail. But today data’s importance extends across every sector. 

Executive sponsorship ensures that data products are treated as business assets, not just technical artifacts.

Technical and operational readiness must progress together

Adopting data products requires both of the following:

• Technical enablement: Platforms, architecture, and tooling
• Operational capability: Ownership models, governance processes, and data modeling skills

These dimensions are interdependent. Any delay in adopting either one can slow time-to-value. We recommend beginning with focused technical pilots that demonstrate clear business outcomes through small, easily understood implementations. 

Early wins help build momentum, validate governance approaches, and create the organizational alignment needed to scale.

Understanding the Technical Platform 

Let’s return to our SAP-centric and AWS-native examples introduced earlier for this discussion.

Spaces 

In SAP Datasphere architecture, Spaces are the primary organizational construct used to structure and govern data products. Multiple Spaces enable both long-term data domain ownership and cross-domain collaboration, as well as temporary collaboration environments. 

Spaces are the most crucial construct for data products. Spaces provide for both long-term Data Domain and Cross Domain creation as well as shorter-term collaboration spaces.

Create Spaces for different data domains like Customer Data, Product Data, Sales Data, Financial Planning & Analysis (FP&A), Social Data, Streaming Data, Financial Data, HR Data, and Manufacturing Data. 

Enable data sharing and collaboration among these Spaces to encourage reuse (e.g., for an R&D project), while ensuring sensitive data is protected using methods like data masking and authorization. The PERMISSION Space authorization table, managed by designated security and administrative users, controls access rights for sharing data across these Spaces.

Architecture

The overall architecture to assemble and consume data products can be defined wholly within SAP Business Data Cloud. Alternatively, with a little (not a lot) more work, the architecture can be built with SAP Datasphere and Databricks tools – or with AWS cloud tools like S3, Redshift, Athena, and Quick Suite. 

The architecture is typically organized into layered components:

• Inbound Layer: Capture or federate raw data from source systems and external platforms.
• Harmonization Layer: Standardize, transform, and clean data to ensure consistent structure and meaning across domains.
• Propagation Layer: Create unified consumption entities – such as analytic data products, semantic models, and reporting views – that can be reused across BI, ML, and AI scenarios.
• Reporting Layer: Optimize views specifically for reporting and analytics to support consistent branding, presentation, and user experience.

Governance

Effective data products require defined governance practices to ensure trust, consistency, and usability across domains. Establish clear ownership, naming conventions, and data lineage so users can understand and rely on the data they consume. Adopt a data catalog to manage data products and associated assets.

Roadmap

Adoption should follow a structured, value-driven roadmap aligned to business priorities and execution readiness. Create the roadmap based on business value, organizational priorities, and the ability to execute, typically a “crawl, walk, run” maturity approach.

Define initiatives by domain and cross-domain opportunities tied to clear business outcomes and supporting business cases. Select two to three visible data product opportunities that are achievable – not overly complex, but meaningful enough to demonstrate delivery and value.

Types of Data Products

Organizations typically work with two primary categories of data products: 

• Certified data products provided or governed centrally.
• Custom data products built internally or through partners. 

The following sections describe how these approaches apply within SAP Business Data Cloud (BDC) and broader data architectures.

Certified Data Products

Certified data products are governed, production-ready assets that follow standardized definitions, quality controls, and ownership models.

Data products arrive in SAP Business Data Cloud (BDC) in a basic form containing only the essential data for a business entity. Within SAP BDC, basic data products can be combined with other basic data products to form derived data products. These derived data products provide broader business context and are typically more useful for analytics and AI consumption.

Note: BDC is not mandatory to build your own or adopt preconfigured data products from SAP partners.

The following figure shows an SAP Business Data Cloud example of how source-level data products evolve into derived, consumer-oriented data products and higher-level business insights.

Source: SAP. Introducing Business Data Cloud. Focusing on Data Products and Intelligent Applications

Build Your Own 

In addition to centrally certified data products, organizations may build their own commercial-grade or self-service data products tailored to specific business needs. These internally developed data products can still follow certified standards for governance, UX, and lifecycle management to ensure consistency and reuse across BI, ML, and AI initiatives.

For certified dashboards and commercial-grade data products, we recommend the following delivery lifecycle:

Self service is a development that now brings organizations foundational value from data products. Using existing BI dashboards and Spaces as a starting poinit, self-service users can now rapidly bring new BI dashboards and Spaces into use with organized, certified data already available.

Data Product Marketplaces

Data product marketplaces provide curated assets that accelerate adoption by offering preconfigured datasets, models, and analytics aligned to specific business domains.

SAP:  Available within SAP Business Data Cloud (BDC) via the SAP Business Accelerator Hub. These offerings include curated datasets, integration components, and analytical applications designed to support data-driven decision-making.

See: Data Product | Data Products | SAP Business Accelerator Hub

Apiphani:  Available with or without SAP BDC. Organizations can select from an Apiphani catalog of preconfigured agents and KPIs spanning energy and manufacturing domains such as Finance, Engineering, Supply & Demand, Sales, and HR.

Implementation and Operational Considerations

Moving from data product concepts to real-world adoption requires a combination of governance practices, technical design decisions, and operating model alignment. The following considerations focus on how organizations evaluate potential data products, enable controlled self-service, and make architecture choices that balance agility with consistency.

These practices are not tied to a single platform; they apply across SAP Business Data Cloud, AWS-native environments, and hybrid architectures. Establishing clear evaluation criteria, access models, and data integration patterns helps ensure that data products remain scalable, governed, and reusable as adoption grows.

Data Products Evaluation Template

Use a consistent framework to evaluate and prioritize candidate data products:

• Opportunity / Purpose 
• Business Priorities (Specific ROI or enabling priorities and strategies)
• Core BI and AI Value (qualitative, quantitative, and strategic impact)
• Technology and Data Availability
• Deployable / Time to Value

Self-Service Data Access

Implement self-service capabilities that allow business users to explore and model data independently while relying on governed data products as a foundation. This reduces reliance on centralized IT and increases agility without compromising consistency.

User Groups and Permissions

Define user groups and reusable roles to enforce appropriate access and authorization. Clearly structured roles help manage who can view, modify, or share data products across domains.

Remote Tables vs. Data Replication

Determine whether to use remote tables for real-time access without duplication, or replicated data for improved performance. Remote tables support immediate updates, while replication is better suited for performance-critical analytics. 

CDS Views

When creating remote tables, we prefer using Core Data Services (CDS) views over direct S/4 tables to enhance performance and maintainability. 

Operating Model

Successfully adopting data products requires more than architecture and tooling. It requires an operating model that aligns business leadership, governance structures, and technical delivery. Organizations that scale BI, ML, and AI initiatives treat data products as long-lived assets supported by clear ownership, domain leadership, and enterprise coordination. 

The following roles and practices outline how operating models evolve to sustain governed, reusable data products across SAP Business Data Cloud, AWS-native, and hybrid environments.

Domain Leadership

Data domain strategy should align directly with business priorities and execution. Business leaders manage domains of defined size and scope, ensuring accountability for outcomes and data quality. While data products may integrate multiple domains, each data product should have a primary domain responsible for definition and implementation.

Data Product Owners guide success through key lifecycle phases — Concept, Business Planning, Development, Launch, and Support — shifting organizations from traditional project delivery toward a product-based operating model.

Data Product Ownership

A Data Product Owner is a business-savvy, technically aware steward responsible for ensuring that each data product remains accurate, governed, discoverable, and valuable for analytics and AI use cases.

This role operates at the intersection of business, data engineering, and data science and is one of the most important roles in a modern SAP data architecture. Key responsibilities include:

• Promoting and communicating data product value
• Representing consumer needs and adoption priorities
• Owning business meaning, definitions, and semantic consistency
• Ensuring data quality and trust
• Coordinating with other Data Product Owners across domains
  

Center of Excellence

The Center of Excellence (CoE) provides enterprise-wide leadership across discovery, governance, innovation, and community engagement. The CoE partners with domain leaders and Data Product Owners to catalog and manage data assets, collaborates with IT infrastructure teams on permissions and standards, and maintains a shared forum for tools, patterns, and emerging use cases.

Data Catalog

IT and apiphani teams jointly maintain secure infrastructure operations, managing system requests, incidents, and ongoing platform optimization. A centralized data catalog supports discoverability, governance, and lifecycle management of data products.

Building effective data pipelines requires specialized expertise across architecture, engineering, DevOps, and consumption design. Successful implementation depends on strong integration practices, security alignment, and continuous performance monitoring across enterprise environments.

C-Suite Role

Executive sponsorship is essential to drive organizational alignment around data, analytics, and AI. The C-suite plays a critical role in shifting mindset and prioritizing data products as strategic assets.

Engage executive leadership early to establish visibility and alignment, and deepen involvement once initial pilot data products demonstrate measurable value.

Culture and Mindset Changes

Together with evolving ways of working across Domain Leaders, Data Product Owners, and the CoE, the C-Suite enables the shift toward a data-driven culture, with the following focus areas guiding the transition to a steady-state operating model..

1. Executive teams recognize and expect data products as key drivers of business performance, consistently delivering above-benchmark results and exceptional outcomes in strategic initiatives.
2. Market leaders leverage embedded data products throughout their products, customer interactions, and operations. These organizations consistently generate and implement new ideas to enhance existing data products and develop new ones, driving continuous innovation.
3. Data Pipeline Acceleration begins to show how reusable solution components and reliable data transformations and data views turn into system and user consumption at increasing speed to value, i.e., the AWS Data Flywheel.
4. Data Self Service enables comprehensive data access across the enterprise. The platform provides streamlined data discovery, enterprise-grade analytics, and automated business insights at scale powered by tools such as SAP Just Ask.

Conclusion

Data products provide the structure that allows BI, ML, and AI initiatives to move beyond experimentation into scalable, governed business capabilities. Whether implemented within SAP Business Data Cloud, AWS-native architectures, or hybrid environments, success depends on more than technology alone. It requires clear ownership, strong governance, and an operating model aligned to business outcomes. 

Organizations that treat data as a product, supported by domain leadership and executive sponsorship, create a foundation for repeatable innovation, faster time to value, and sustained competitive advantage.

About the Authors

James Kendrick

Principal Director of Data and Analytics Products at apiphani.

Mario de Felipe

Global Director of SAP Technology and Innovation at apiphani.

Historically, Rise with SAP supported existing SAP customers transitioning to a cloud-based, single-tenant environment with greater flexibility and customization. Grow with SAP focused on greenfield implementations, enabling faster deployment through a standardized, multi-tenant public cloud model.

Today, both Rise and Grow represent structured transformation pathways into SAP Cloud ERP. Organizations evaluating their next step must consider their starting point, customization requirements, regulatory constraints, and implementation speed. The strategic question is no longer which brand name to choose — but which Cloud ERP edition, public or private, aligns with long-term operational and governance priorities.

Aegis, Apiphany’s managed services offering, addresses the operational gaps that persist after Rise is implemented. Rather than duplicating SAP’s responsibilities, Aegis evaluates the full landscape — selected service packages, uncovered tasks, integration points, and governance requirements — and designs a tailored support model that closes functional and administrative gaps across the environment. The objective is not to replace SAP’s role, but to ensure the entire Cloud ERP ecosystem operates coherently, with clear accountability and without overlooked responsibilities.

SAP S/4HANA 2023 marks a decisive step toward the intelligent enterprise vision. Beyond being another technical upgrade, it delivers a platform where automation, embedded analytics, and AI-driven decision support come together to simplify operations and improve business outcomes.

1. Smarter Processes Through Automation

SAP has expanded automation capabilities across core business functions, eliminating repetitive tasks and enabling continuous operations. Here’s how.

SAP Build Process Automation Integration

S/4HANA 2023 integrates natively with SAP Build Process Automation (BPA) on SAP BTP, allowing customers to design low-code bots that execute SAP transactions, validate data, and trigger workflow approvals. Examples include automatic journal posting approvals, purchase order release workflows, and background invoice verifications.

Situation-Handling Enhancements

New Situation-Handling Templates automatically alert users when events deviate from expected business rules, such as delayed production orders, migration job failures, or overdue inspections. This enables the following proactive response model: The system detects, informs, and suggests corrective actions before users notice an issue.

Predictive and Preventive Maintenance

Manufacturing and supply-chain processes now benefit from machine-learning models embedded in S/4HANA 2023 that forecast equipment failures or quality issues. SAP calls this Predictive Quality Inspection — leveraging HANA ML and AI Core services. This brings true autonomous maintenance closer to reality.

2. AI and Machine Learning in Core Finance

Financial departments gain from the following intelligent features that reduce manual reconciliation and speed up close cycles:

• Machine-Learning Intercompany Reconciliation (ICR): Automatically detects and proposes matches for cross-company postings.
• Predictive Cash Flow Forecasting: Learns from historical patterns to estimate future liquidity positions.
• Smart Accruals and Automated Adjustments: Rules and ML-based triggers generate accrual postings automatically.

Together, these features redefine the Finance function as data-driven and exception-managed, rather than transaction-driven.

3. Embedded Analytics and Citizen Reporting

The 2023 release brings a new level of flexibility in embedded analytics:

• Drag-and-drop measure ordering, autofill capabilities, and bookmark sharing in the multidimensional grid.
• The “Manage KPIs and Reports” app now allows business users to create and publish analytical apps without developer help.
• PDF export and transportable bookmarks streamline management reporting.

This self-service analytics experience reduces dependency on IT, empowering functional users to become citizen analysts.

4. Governance, Security, and Master Data Intelligence

Data privacy and compliance remain a cornerstone. The new Business Partner Data Controller concept within S/4HANA 2023 introduces up to 10 controllers per record with new authorization objects for fine-grained data governance (B_BUP_DCPA, B_BUP_DCPD).

For master data synchronization, SAP Master Data Integration (MDI) enables federated governance across S/4HANA, Ariba, Concur, and SuccessFactors — a key step toward enterprise-wide data harmonization.

5. Connected Work Experiences

SAP continues bridging business processes with collaboration tools. Examples include:

• Native Microsoft Teams integration, which brings chat, file sharing, and approvals directly into Fiori apps.
• SAP Concur integration, which automates expense reconciliation and approval chains.
• Unified Attachment Service, which allows versioning, line-item attachments, and Outlook shortcuts — reducing manual document management.

The result: A connected, collaborative user experience that blends ERP transactions with modern workplace tools.

6. User Experience and Fiori 3.0 Evolution

With S/4HANA 2023, SAP further refines the Fiori 3.0 Quartz theme, improving accessibility, personalization, and tile organization.

Users can now reorder measures, create favorites, and personalize spaces according to their daily tasks. The interface feels faster, cleaner, and more consistent, which is key for adoption success.

7. Why Version 2023 Matters

S/4HANA systems running on versions 2020 or 2021 will reach end of mainstream maintenance in 2026. Upgrading to S/4HANA 2023 not only extends support but also delivers immediate value in the following ways:

• Reduced manual effort via automation
• Predictive insights embedded in business processes
• Stronger security and compliance posture
• Seamless integration with BTP automation and analytics services

8. Apiphani’s Perspective

At apiphani, we see S/4HANA 2023 as the turning point between digital core modernization and intelligent enterprise enablement.

Our AMS and BASIS teams are already helping customers upgrade to 2023 FPS03, implementing automation use cases in Finance, Supply Chain, and Basis operations (e.g., automated transport handling, archiving, and health monitoring via SAP Build).

The message to our clients is clear. Don’t treat your upgrade as a technical event. Make it a foundation for automation and intelligence.

Final Thoughts

SAP S/4HANA 2023 is more than a version update. It’s a shift toward autonomous ERP operations. By leveraging embedded AI, predictive analytics, and low-code automation, organizations can move from reactive management to proactive optimization.

If you’re still on an earlier release, the upgrade path to 2023 isn’t only about compatibility, it’s about capability.

About the Author

José López is a senior SAP technology leader with 20+ years of experience managing and optimizing mission-critical SAP environments. As Principal Director of SAP AMS, he leads end-to-end service delivery for large, complex SAP landscapes.

SAP’s new Business Data Cloud (BDC) is designed to change that. By centralizing and streamlining the way organizations collect, process, and leverage information, BDC promises to transform data into actionable insights.

Instead of wrestling with manual extraction and siloed reporting, executives gain real-time visibility that drives sharper decisions, greater efficiency, and stronger compliance — all within the SAP ecosystem.

For business leaders, this isn’t just another IT feature. It’s a strategic enabler. Whether optimizing financial planning, enhancing supply chain visibility, or strengthening risk management, SAP Business Data Cloud offers an automated, intelligent approach to data.

Why Effective Data Collection Matters

Data is one of the most valuable assets in the modern enterprise — but only if it’s accessible, accurate, and timely. Too often, executives rely on information that is incomplete, inconsistent, or slow in terms of when it becomes available for use. The risks are real and include siloed insights, manual errors, compliance blind spots, and costly inefficiencies.

But when organizations can automate data collection and produce a unified view that offers meaningful insights, they unlock the following business value:

• Real-Time Decision Support – Confident responses to fast-changing markets.
• Operational Efficiency – Less manual work, fewer errors, faster reporting.
• Risk Mitigation & Compliance – Stronger governance and transparency.
• Competitive Advantage – Optimized performance and new revenue opportunities.

The question isn’t whether data collection is important — it’s whether your organization is doing it effectively.

At apiphani, we’ve long recognized the power of data. Our Managed Data Pipelines were built to help organizations unlock hidden value, reframe how they think about data, and generate meaningful business impact.

Real-World Impact

The benefit to companies isn’t just theoretical. It’s real. And it’s measurable.

Recently, Apiphani partnered with Power Systems Manufacturing (PSM) to implement data pipelines that enabled data-driven operations at scale. The result? Tangible improvements in agility, reporting, and executive decision-making for the company. Read more about our method and results here.

Data Agility as a Competitive Advantage

In a digital economy, data agility separates leaders from laggards. Raw data alone isn’t enough. Without scalable pipelines and real-time insights, even the richest information loses impact.

Executives who prioritize automated, intelligent data strategies aren’t just improving efficiency. They’re creating organizations that can adapt faster, outpace competitors, and make smarter decisions today — and tomorrow.

SAP BDC adoption will evolve, but one thing is clear: Success will hinge on how effectively organizations turn raw data into intelligence. The real question is… is your business set up to do it better, faster, and smarter?

About the Author

Tyler Constable is Principal Director of Solutions Engineering at apiphani. He has extensive expertise with SAP, cloud infrastructure, and cloud security. He is an SAP ASUG member and frequently presents at various SAP events. Tyler resides in Milwaukee, Wisconsin.

As AI continues to evolve, a new paradigm is taking shape: Agentic AI – autonomous, goal-seeking software agents capable of making complex decisions and acting without human intervention. In enterprise IT, particularly in SAP landscapes and legacy IT systems, the rise of Agentic AI offers immense potential – but also new layers of complexity.

Agentic vs. Generative AI

Unlike traditional AI models that reactively generate output when prompted, agentic AI exhibits autonomous behavior, operates according to defined goals, and dynamically adapts to new context. 

It can learn, decide, and act independently. Imagine an AI agent that not only identifies underperforming SAP jobs but also initiates remediation, informs stakeholders, and continuously fine-tunes future execution paths – autonomously.

Unfortunately Enterprise IT Isn’t Built for Autonomous Agents

Enterprise environments, especially SAP and hybrid legacy systems, are not built for autonomous agents. These are some of the top SAP challenges:

• Fragmented architectures across modules and middleware
• Limited visibility in on-premises or hybrid deployments
• Governance models that restrict unsupervised automation
• High-risk thresholds tied to financial and operational outcomes

Legacy systems add even more friction with outdated APIs, undocumented processes, and tightly coupled workflows.

How to Lay the Groundwork for Agentic AI

Adopting Agentic AI isn’t just about implementing new technology, it’s about ensuring your IT environment is ready for autonomous agents to operate safely, effectively, and in alignment with business goals. Whether you manage SAP systems, hybrid clouds, or legacy applications, preparing for this shift requires a few foundational steps.

1. Move Beyond Traditional Monitoring to End-to-End Observability

Most companies already use application or infrastructure monitoring, but these tools often operate in silos and provide limited business context. To fully enable AI agents, organizations need end-to-end observability – a holistic view that combines data across infrastructure, applications, and processes into meaningful, actionable insights.

• Ask yourself: Can you quickly connect a technical failure to its business impact?
• Are your monitoring systems predictive, or do they only react once an issue occurs?

2. Build an Agent-Ready Architecture

AI agents thrive in environments where systems are modular, event-driven, and connected. This means creating flexible APIs, modernizing middleware, and ensuring that hybrid or cloud environments can interact seamlessly.

• Consider adopting containerized APIs and event-driven triggers to create an adaptable foundation.
• Ensure your architecture supports integration with next-gen platforms like SAP Business Technology Platform (BTP).

3. Establish Guardrails for Autonomy

Autonomy without oversight is risky. As companies explore Agentic AI, it’s critical to set governance frameworks that balance independence with control.

• Define approval workflows, SLA-aware policies, and audit trails to prevent AI agents from taking unapproved actions.
• Treat AI governance as you would cybersecurity, as an integral layer of trust and accountability.

4. Modernize Legacy Systems Incrementally

Legacy applications often pose the greatest challenge for agentic AI adoption due to outdated APIs and tightly coupled workflows. Instead of “rip-and-replace” projects, organizations can encapsulate legacy processes into smaller, service-oriented units that are easier for AI agents to monitor and eventually control.

• Start by identifying high-value processes where automation can deliver quick wins.
• Use service wrappers or API layers to make legacy systems more “AI-ready” without full-scale modernization.

Why This Matters

These steps aren’t just technical best practices, they’re prerequisites for realizing the value of Agentic AI. 

Companies that invest now in visibility, architecture, and governance will be positioned to leverage AI agents not only for efficiency but also for proactive decision-making and risk reduction.

Emerging Use Cases for Agentic AI Show Promise

Early, real-world use cases for agentic AI in enterprise environments show promise and include:

• Automatically resolving SAP job failures by adjusting scheduling and priority
• Dynamically adjusting system resource allocations based on forecasted demand
• Making proactive role and authorization updates triggered by anomalous access behavior
• Conducting service mapping in legacy systems to support zero-trust and compliance needs

The Agentic Frontier

The convergence of Agentic AI with SAP and legacy systems represents a pivotal shift. Companies that embrace it gain a strategic edge in agility, efficiency, and risk mitigation. However, the path forward demands a thoughtful balance of autonomy and control.

Want to explore practical strategies for preparing your enterprise systems for Agentic AI? Stay tuned for upcoming insights on implementation best practices.

About the author: Ravinder Sokhi is a Principal Director at apiphani. He builds high-performing teams that excel in delivering mission-critical IT solutions globally. He also delivers large-scale cloud computing migrations and transformations.

What SAP is pushing with BDC is that organizations will streamline the way they collect, process, and leverage data. Instead of struggling with fragmented reporting and manual data extraction, executives can access real-time insights that drive better decisions, improve operational efficiency, and ensure compliance — all within their existing SAP ecosystem.

For business leaders, this isn’t just another IT tool — it’s a strategic enabler. Whether you’re looking to optimize financial planning, enhance supply chain visibility, or strengthen risk management, the theory of SAP’s BDC is to provide a centralized, automated, and intelligent approach to data. We here at apiphani have taken data very seriously in the past few years leading to the formation of our Data & Analytics Practice which works with large organizations to change the way they think about data and uncover hidden value. Let’s explore why all of this matters, how it works, and what it means for organizations looking to turn raw data into actionable intelligence.

Why Data Collection Matters

Data has become one of the most valuable assets for modern enterprises, yet many organizations still struggle to harness its full potential. Executives make high-stakes decisions daily, but too often, those decisions rely on incomplete, delayed, or inconsistent data. Without a streamlined approach to data, businesses face challenges like siloed information, manual errors, and a lack of real-time visibility — all of which can lead to missed opportunities or costly inefficiencies.

For leaders focused on growth and resilience, effective data isn’t just an IT concern; it’s a business imperative. When organizations can efficiently gather and unify data across their operations, they gain:

• Real-Time Decision Support – Fast, data-driven insights help executives respond to market shifts with confidence.
• Operational Efficiency – Automating data flows reduces manual work, minimizes errors, and accelerates reporting cycles.
• Risk Mitigation & Compliance – A structured approach to data collection ensures transparency, governance, and adherence to evolving regulations.
• A Competitive Edge – Organizations that leverage high-quality data are better positioned to optimize performance, identify trends, and unlock new revenue opportunities.

In a world where business agility is critical, the ability to collect, process, and act on data seamlessly separates industry leaders from those struggling to keep up. The question isn’t whether data collection is important — it’s whether your organization is doing it effectively.