Preface
Modern enterprises increasingly expect their integration platforms to do more than reliably move data between systems. SAP Integration Suite, combined with SAP BTP AI services, provides a foundation to embed intelligence directly into integration flows. This post highlights how combining integration with AI can unlock new capabilities and drive smarter, more adaptive processes across the enterprise.
Introduction
Enterprise integrations traditionally rely on deterministic, structured data—a design paradigm that has supported SAP business processes for decades. This determinism is what allows ERP systems to automatically and reliably create documents, update statuses, or trigger follow-on actions.
When a payload arrives in a predefined, standard format—via interfaces such as APIs, IDocs, or RFCs—the ERP system can create a business document and drive it forward to the furthest possible system-determined state, guided entirely by the incoming structured fields and the process logic configured within SAP ERP.
For example, an incoming ORDERS05 IDoc carries fields such as BSART (document type), VKORG (sales organization), KUNNR (sold-to), MATNR, MENGE, and PSTYV (item category). Each of these fields is restricted to predefined, allowed values and mapped to controlled domains in SAP—document types, organizational structures, master data, and item categories. Because of this, the system can deterministically derive pricing, partner functions, credit checks, availability checks, and subsequent status movements without any human involvement.
However, many real-world processes reach a designed stopping point—not due to technical limitations, but because a critical decision input exists only as free-text narrative that requires interpretation. At this point, the system can no longer progress deterministically.
One scenario where this consistently occurs is when free-text inputs determine the next business action. High-value examples include:
customer return comments in RMAs (Return Merchandise Authorization), capturing defect descriptions, expectations, or reason narratives;exception remarks on delivery notes, such as damage details, refusal reasons, or access issues; andjustification text in travel expense claims, explaining non-compliant or unusual charges.
These narrative fields often dictate the downstream outcome—whether a return is approved, a delivery is blocked, or an expense is reimbursed—but only after a human reads and interprets them.
GenAI as the Semantic Bridge
Recent advances in Generative AI — powered by large language models (LLMs) — make it possible to automate this traditionally human-only interpretation step. These models can read the free-form comments contained in incoming messages, infer intent and context, and return structured, process-ready attributes in a fast and consistent manner, which the integration layer can pass directly into ERP.
To realize this, the integration flow could simply call the LLM and instruct it to convert narrative comments into ERP-compatible attributes, codes, or flags. The model applies reasoning to questions such as:
Is the customer asking for a refund or a replacement?Is the delivery refusal due to damage or customer unavailability?Is the expense violation justified or not justified?
Based on this, the model produces clean ERP-friendly outputs, for example:
ReturnActionCode = “REFUND” or “REPLACEMENT”DeliveryBlockReason = “DAMAGED_GOODS” or “CUSTOMER_NOT_AVAILABLE”ExpenseExceptionStatus = “APPROVED_JUSTIFIED” or “REJECTED_UNJUSTIFIED”
Instead of receiving verbose, unstructured text that a human must interpret, the backend now receives clear, deterministic signals it can immediately act on — allowing the business process to progress further downstream rather than stopping at the point where human intervention was previously required.
Deep Dive Example: Customer Returns (As-Is vs. AI-Enhanced Flow)
Let us consider a model company named Smart Sense Technologies (SST), a global manufacturer of smart security devices. Like many enterprises today, SST’s IT organization faces both challenges and opportunities driven by internal and external factors such as a rapidly evolving technology landscape, shifting customer expectations, and increasing business demand for speed and agility. To respond effectively and deliver optimal outcomes for its stakeholders, SST embarked on a digital transformation journey supported by SAP Business Technology Platform (BTP). The organization leverages BTP to innovate and extend business capabilities, enabling greater process efficiency, faster time-to-market, and increased operational agility. To stay ahead of the competition, SST continuously evaluates and optimizes its end-to-end business processes.
In the As-Is process, SST’s customer returns initiated in SAP Commerce Cloud are integrated with SAP S/4HANA using the standard SAP Commerce Cloud Integration with SAP S/4HANA. This package provides a ready-made integration flow that transmits return requests from Commerce Cloud to S/4HANA as ORDERS05 IDocs, establishing a reliable and widely adopted baseline for returns processing.
This iflow sends the transactional data required to create the returns order in S/4HANA. Along with structured order attributes, it also forwards free-text return comments entered by the customer. These narrative descriptions are embedded in the IDoc and successfully persisted in the returns document, but they are not interpreted or acted upon automatically by the system.
Read: For more background on the Sales & Return process integration using IDOC.
At the IDoc level, two fields are particularly relevant in this context. E1EDP01-ABGRT carries the free-text return or cancellation description provided upstream, while E1EDP01-ABGRU represents the structured rejection or decision reason that drives subsequent processing in Sales and Returns Management. In real-world configurations, ABGRT is commonly populated by the inbound integration, whereas ABGRU is often left empty until a human has reviewed the return and made a decision.
It is also important to note that approval responsibilities may vary by landscape. In some scenarios, return approval may already occur in an upstream portal such as SAP Commerce Cloud in our example, while in others it is intentionally deferred to SAP S/4HANA. For the purpose of this example, we assume that the approval step is handled in S/4HANA. As a result, when the ORDERS05 IDoc arrives from Commerce Cloud, the return order is created with descriptive context (ABGRT) but without a decision code (ABGRU), causing the process to pause for human review.
As part of SST’s continuous improvement efforts, the IT organization is tasked with the responsibility to help the business improve its processes – with specific metrics such as cutting sales / return processing lead-time, improving accuracy, offering consistent customer experience. Considering this, SST’s integration architecture team proposes a solution leveraging BTP AI within Integration Suite iflow.
In this scenario, SST extends the standard integration flow with a focused AI-based classification step executed in a post-exit iflow in SAP Integration Suite. The free-text return description (ABGRT) received from SAP Commerce Cloud is passed to an LLM using a tightly constrained prompt that maps narrative customer input to a single, predefined SAP rejection reason code.
The following figure shows the Post-exit iflow that is called from the main flow. This iflow uses AI adapter to call an LLM to convert the free-text customer rejection comments into a deterministic return code.
Here, we pass the prompt directly from the processing tab of the AI Adapter, with the rejection text as parameter, that is fetched from the incoming message from Commerce cloud.
After deploying the post-exit iflow, next we proceed to test using a HTTP client (to mimic a message from SAP Commerce cloud). Here, rejection text is parameterized and fed from a variable.
The rejection text is passed as “Item arrived much later than agreed”.
This is the actual prompt that is sent to the LLM – after substitution of the rejection text, passed from the HTTP call.
The following is the LLM response, with S4_RejectionReasonCode being populated, strictly adhering to the prompting made to the LLM.
Next, we use this LLM derived RejectionReasonCode to enrich the IDOC mapping, to populate E1EDP01-ABGRU.
As demonstrated in the preceding steps, the extended integration flow now enriches the inbound ORDERS05 IDoc with a populated rejection reason code (ABGRU), derived from the customer’s free-text input (ABGRT) using AI-based reasoning and interpretation. With the decision code present at creation time, the returns document no longer stalls in an indeterminate state and can proceed through the standard S/4HANA returns process without waiting for manual interpretation.
Depending on landscape design and process ownership, this semantic enrichment can also be taken a step further. Instead of—or in addition to—populating the IDoc, the same inferred outcome can be used to directly invoke standard SAP S/4HANA APIs such as the Advanced Returns Management approval service (API_CUSTOMER_RETURN_SRV) to explicitly release or reject the return order. This represents the post–human-action document state that would traditionally be reached only after a manual approval step.
Together, these patterns illustrate how introducing AI-driven interpretation at the integration layer allows unstructured customer intent to be converted into structured, actionable business signals at the point of entry.
AI in Integration: Why It Fits and How to Govern It
1) Reasserted: The Integration platform’s timeless role as the enabler of Process Integration
In many enterprises, the integration layer, such as Integration Suite, serves as the natural ingestion point and middleware platform that facilitates transactions and orchestrates message flows between applications across the ERP landscape. Embedding GenAI-driven interpretation here fits naturally into CPI’s existing responsibilities: it allows the business meaning hidden inside narrative text to be converted into actionable signals before the request reaches the core ERP process. For integration architects and developers, this aligns perfectly with the integration layer’s role as the control point for routing, orchestration, and decisioning.
When free-text descriptions are converted into deterministic attributes—such as return_reason_code, refund_flag, inspection_required, or damage_classification—CPI can now call the exact API operation that corresponds to the post-review document state. In other words, CPI can directly invoke the relevant API to post the document in the status it would have reached only after a human had read, approved, or actioned the text. This removes the need to always invoke a “pre-review” API and wait for downstream human review.
This shift eliminates traditional bottlenecks: manual interpretation steps, workflow inbox approvals, delays from human availability, and intermediate document states that halt progression. Because decisioning happens at the moment the request enters the landscape, CPI sends a clean, enriched, decision-ready payload to S/4HANA (or any backend), enabling the system to execute the correct business logic immediately.
Rather than replacing human judgment, this pattern formalizes and codifies human insight into a governed, automated, and fully auditable integration layer. This is why, in this scenario, AI belongs in the integration layer: This architectural capability empowers CPI to transform narrative inputs into deterministic, actionable signals, enabling downstream process automation while keeping the ERP core clean.
2) Clean Core by Design
Placing the intelligence in SAP BTP ensures that ERP systems remain standard, maintainable, and free from any text-interpretation logic. This approach avoids enhancing S/4HANA to parse unstructured text or extending it to process AI-derived attributes—both of which introduce long-term complexity and technical debt.
Instead, ERP systems receive clean, structured, enriched payloads through the REST/OData APIs designed for standard CRUD operations. This keeps backend applications stable and upgrade-friendly, while positioning the intelligence in a layer that is flexible, governed, and purpose-built to support innovation. By keeping the semantics outside the ERP and feeding it deterministic attributes through established integration patterns, the core remains clean by design.
3) A Perfect Problem–Solution Fit for AI
Interpretation of natural-language free text is inherently a language-understanding problem — a task that humans have traditionally handled because it requires context, nuance, and inference. GenAI models excel exactly in this domain: extracting meaning, identifying intent, and classifying the actions implied by narrative input.
Once the integration layer converts this text into deterministic attributes, every subsequent step becomes standard ERP automation — pricing behaviour, blocking, approvals, follow-on documents, status progression, and more.
4) High Business Value When Applied to High-Impact Scenarios
A clear business value emerges when Generative AI is applied to the right type of problem—specifically, scenarios where narrative text shapes important decisions. While many processes contain free-text fields, the highest value appears when unstructured descriptions create friction, ambiguity, or costly delays that materially affect customer satisfaction, compliance, or financial outcomes. The architectural decision to integrate an LLM must be strictly justified by a measurable Return on Investment (ROI), as these models operate on a metered consumption model. Consequently, the value delivered must always outweigh the cost of the service call, meaning GenAI should be applied only to high-impact, high-volume scenarios where the operational expense of manual intervention significantly exceeds the cost of an LLM call.
Typical examples of processes (as previously mentioned) that run at scale, carry high consequence, and often suffer from a high cost of delay include: Customer Returns (RMAs), delivery exceptions, and expense justifications. In these situations, shifting the interpretation logic to the point of ingestion (within the Integration Suite) fundamentally reshapes the flow. Processing accelerates because items no longer wait for manual review; accuracy and consistency improve through uniform, model-driven interpretation; and downstream data quality is strengthened. This leads to tangible improvements in efficiency and decision reliability, with human attention reserved for genuine edge cases. Scenarios that combine high repetition, high consequence, and high ambiguity are the strongest candidates for this GenAI-driven interpretation, whereas traditional deterministic approaches remain the appropriate and cost-effective choice when these characteristics are absent.
5) Auditability and Control Built In
While in our deep-dive example, we directly applied the prompt at the AI Adapter, it is also possible to build the request body, incorporating orchestration configurations provided out of the box with BTP AI Core (GenAI Hub), in the form of LLM guard-railing steps such as prompt templating, data masking, model configuration, and so on. Also, it is a best practice to make use of prompt templates, to make use of reusability. With such controls in place, the LLM response can stay relevant, reliable, and responsible (3 “R”s that constitute the key pillars of SAP’s AI strategy) ensuring that business process outcomes are explainable and auditable.
These details about Orchestration workflow can be found in this SAP help portal page.
I’d also recommend that you read this blog by my colleague Daniil, where Orchestration workflow in Generative AI Hub is used to invoke an LLM in a secure, GDPR-compliant way, from within an integration flow.
Conclusion
By leveraging SAP BTP AI services within SAP Integration Suite, organizations can elevate integration from a transport layer to a semantic processing layer—converting human intent into deterministic business signals at the point of entry. This approach enables Straight-Through Processing, automating decision-making while preserving governance, auditability, and maintainability.
Combining Integration and AI opens up new possibilities for business process efficiency. Are you examining how this could apply within your own landscape?
PrefaceModern enterprises increasingly expect their integration platforms to do more than reliably move data between systems. SAP Integration Suite, combined with SAP BTP AI services, provides a foundation to embed intelligence directly into integration flows. This post highlights how combining integration with AI can unlock new capabilities and drive smarter, more adaptive processes across the enterprise.IntroductionEnterprise integrations traditionally rely on deterministic, structured data—a design paradigm that has supported SAP business processes for decades. This determinism is what allows ERP systems to automatically and reliably create documents, update statuses, or trigger follow-on actions.When a payload arrives in a predefined, standard format—via interfaces such as APIs, IDocs, or RFCs—the ERP system can create a business document and drive it forward to the furthest possible system-determined state, guided entirely by the incoming structured fields and the process logic configured within SAP ERP.For example, an incoming ORDERS05 IDoc carries fields such as BSART (document type), VKORG (sales organization), KUNNR (sold-to), MATNR, MENGE, and PSTYV (item category). Each of these fields is restricted to predefined, allowed values and mapped to controlled domains in SAP—document types, organizational structures, master data, and item categories. Because of this, the system can deterministically derive pricing, partner functions, credit checks, availability checks, and subsequent status movements without any human involvement.However, many real-world processes reach a designed stopping point—not due to technical limitations, but because a critical decision input exists only as free-text narrative that requires interpretation. At this point, the system can no longer progress deterministically.One scenario where this consistently occurs is when free-text inputs determine the next business action. High-value examples include:customer return comments in RMAs (Return Merchandise Authorization), capturing defect descriptions, expectations, or reason narratives;exception remarks on delivery notes, such as damage details, refusal reasons, or access issues; andjustification text in travel expense claims, explaining non-compliant or unusual charges.These narrative fields often dictate the downstream outcome—whether a return is approved, a delivery is blocked, or an expense is reimbursed—but only after a human reads and interprets them.GenAI as the Semantic BridgeRecent advances in Generative AI — powered by large language models (LLMs) — make it possible to automate this traditionally human-only interpretation step. These models can read the free-form comments contained in incoming messages, infer intent and context, and return structured, process-ready attributes in a fast and consistent manner, which the integration layer can pass directly into ERP.To realize this, the integration flow could simply call the LLM and instruct it to convert narrative comments into ERP-compatible attributes, codes, or flags. The model applies reasoning to questions such as:Is the customer asking for a refund or a replacement?Is the delivery refusal due to damage or customer unavailability?Is the expense violation justified or not justified?Based on this, the model produces clean ERP-friendly outputs, for example:ReturnActionCode = “REFUND” or “REPLACEMENT”DeliveryBlockReason = “DAMAGED_GOODS” or “CUSTOMER_NOT_AVAILABLE”ExpenseExceptionStatus = “APPROVED_JUSTIFIED” or “REJECTED_UNJUSTIFIED”Instead of receiving verbose, unstructured text that a human must interpret, the backend now receives clear, deterministic signals it can immediately act on — allowing the business process to progress further downstream rather than stopping at the point where human intervention was previously required.Deep Dive Example: Customer Returns (As-Is vs. AI-Enhanced Flow)Let us consider a model company named Smart Sense Technologies (SST), a global manufacturer of smart security devices. Like many enterprises today, SST’s IT organization faces both challenges and opportunities driven by internal and external factors such as a rapidly evolving technology landscape, shifting customer expectations, and increasing business demand for speed and agility. To respond effectively and deliver optimal outcomes for its stakeholders, SST embarked on a digital transformation journey supported by SAP Business Technology Platform (BTP). The organization leverages BTP to innovate and extend business capabilities, enabling greater process efficiency, faster time-to-market, and increased operational agility. To stay ahead of the competition, SST continuously evaluates and optimizes its end-to-end business processes.In the As-Is process, SST’s customer returns initiated in SAP Commerce Cloud are integrated with SAP S/4HANA using the standard SAP Commerce Cloud Integration with SAP S/4HANA. This package provides a ready-made integration flow that transmits return requests from Commerce Cloud to S/4HANA as ORDERS05 IDocs, establishing a reliable and widely adopted baseline for returns processing.This iflow sends the transactional data required to create the returns order in S/4HANA. Along with structured order attributes, it also forwards free-text return comments entered by the customer. These narrative descriptions are embedded in the IDoc and successfully persisted in the returns document, but they are not interpreted or acted upon automatically by the system.Read: For more background on the Sales & Return process integration using IDOC.At the IDoc level, two fields are particularly relevant in this context. E1EDP01-ABGRT carries the free-text return or cancellation description provided upstream, while E1EDP01-ABGRU represents the structured rejection or decision reason that drives subsequent processing in Sales and Returns Management. In real-world configurations, ABGRT is commonly populated by the inbound integration, whereas ABGRU is often left empty until a human has reviewed the return and made a decision.It is also important to note that approval responsibilities may vary by landscape. In some scenarios, return approval may already occur in an upstream portal such as SAP Commerce Cloud in our example, while in others it is intentionally deferred to SAP S/4HANA. For the purpose of this example, we assume that the approval step is handled in S/4HANA. As a result, when the ORDERS05 IDoc arrives from Commerce Cloud, the return order is created with descriptive context (ABGRT) but without a decision code (ABGRU), causing the process to pause for human review.As part of SST’s continuous improvement efforts, the IT organization is tasked with the responsibility to help the business improve its processes – with specific metrics such as cutting sales / return processing lead-time, improving accuracy, offering consistent customer experience. Considering this, SST’s integration architecture team proposes a solution leveraging BTP AI within Integration Suite iflow.In this scenario, SST extends the standard integration flow with a focused AI-based classification step executed in a post-exit iflow in SAP Integration Suite. The free-text return description (ABGRT) received from SAP Commerce Cloud is passed to an LLM using a tightly constrained prompt that maps narrative customer input to a single, predefined SAP rejection reason code.The following figure shows the Post-exit iflow that is called from the main flow. This iflow uses AI adapter to call an LLM to convert the free-text customer rejection comments into a deterministic return code.Here, we pass the prompt directly from the processing tab of the AI Adapter, with the rejection text as parameter, that is fetched from the incoming message from Commerce cloud.After deploying the post-exit iflow, next we proceed to test using a HTTP client (to mimic a message from SAP Commerce cloud). Here, rejection text is parameterized and fed from a variable.The rejection text is passed as “Item arrived much later than agreed”.This is the actual prompt that is sent to the LLM – after substitution of the rejection text, passed from the HTTP call.The following is the LLM response, with S4_RejectionReasonCode being populated, strictly adhering to the prompting made to the LLM.Next, we use this LLM derived RejectionReasonCode to enrich the IDOC mapping, to populate E1EDP01-ABGRU.As demonstrated in the preceding steps, the extended integration flow now enriches the inbound ORDERS05 IDoc with a populated rejection reason code (ABGRU), derived from the customer’s free-text input (ABGRT) using AI-based reasoning and interpretation. With the decision code present at creation time, the returns document no longer stalls in an indeterminate state and can proceed through the standard S/4HANA returns process without waiting for manual interpretation.Depending on landscape design and process ownership, this semantic enrichment can also be taken a step further. Instead of—or in addition to—populating the IDoc, the same inferred outcome can be used to directly invoke standard SAP S/4HANA APIs such as the Advanced Returns Management approval service (API_CUSTOMER_RETURN_SRV) to explicitly release or reject the return order. This represents the post–human-action document state that would traditionally be reached only after a manual approval step.Together, these patterns illustrate how introducing AI-driven interpretation at the integration layer allows unstructured customer intent to be converted into structured, actionable business signals at the point of entry.AI in Integration: Why It Fits and How to Govern It1) Reasserted: The Integration platform’s timeless role as the enabler of Process IntegrationIn many enterprises, the integration layer, such as Integration Suite, serves as the natural ingestion point and middleware platform that facilitates transactions and orchestrates message flows between applications across the ERP landscape. Embedding GenAI-driven interpretation here fits naturally into CPI’s existing responsibilities: it allows the business meaning hidden inside narrative text to be converted into actionable signals before the request reaches the core ERP process. For integration architects and developers, this aligns perfectly with the integration layer’s role as the control point for routing, orchestration, and decisioning.When free-text descriptions are converted into deterministic attributes—such as return_reason_code, refund_flag, inspection_required, or damage_classification—CPI can now call the exact API operation that corresponds to the post-review document state. In other words, CPI can directly invoke the relevant API to post the document in the status it would have reached only after a human had read, approved, or actioned the text. This removes the need to always invoke a “pre-review” API and wait for downstream human review.This shift eliminates traditional bottlenecks: manual interpretation steps, workflow inbox approvals, delays from human availability, and intermediate document states that halt progression. Because decisioning happens at the moment the request enters the landscape, CPI sends a clean, enriched, decision-ready payload to S/4HANA (or any backend), enabling the system to execute the correct business logic immediately.Rather than replacing human judgment, this pattern formalizes and codifies human insight into a governed, automated, and fully auditable integration layer. This is why, in this scenario, AI belongs in the integration layer: This architectural capability empowers CPI to transform narrative inputs into deterministic, actionable signals, enabling downstream process automation while keeping the ERP core clean.2) Clean Core by DesignPlacing the intelligence in SAP BTP ensures that ERP systems remain standard, maintainable, and free from any text-interpretation logic. This approach avoids enhancing S/4HANA to parse unstructured text or extending it to process AI-derived attributes—both of which introduce long-term complexity and technical debt.Instead, ERP systems receive clean, structured, enriched payloads through the REST/OData APIs designed for standard CRUD operations. This keeps backend applications stable and upgrade-friendly, while positioning the intelligence in a layer that is flexible, governed, and purpose-built to support innovation. By keeping the semantics outside the ERP and feeding it deterministic attributes through established integration patterns, the core remains clean by design.3) A Perfect Problem–Solution Fit for AIInterpretation of natural-language free text is inherently a language-understanding problem — a task that humans have traditionally handled because it requires context, nuance, and inference. GenAI models excel exactly in this domain: extracting meaning, identifying intent, and classifying the actions implied by narrative input.Once the integration layer converts this text into deterministic attributes, every subsequent step becomes standard ERP automation — pricing behaviour, blocking, approvals, follow-on documents, status progression, and more.4) High Business Value When Applied to High-Impact ScenariosA clear business value emerges when Generative AI is applied to the right type of problem—specifically, scenarios where narrative text shapes important decisions. While many processes contain free-text fields, the highest value appears when unstructured descriptions create friction, ambiguity, or costly delays that materially affect customer satisfaction, compliance, or financial outcomes. The architectural decision to integrate an LLM must be strictly justified by a measurable Return on Investment (ROI), as these models operate on a metered consumption model. Consequently, the value delivered must always outweigh the cost of the service call, meaning GenAI should be applied only to high-impact, high-volume scenarios where the operational expense of manual intervention significantly exceeds the cost of an LLM call.Typical examples of processes (as previously mentioned) that run at scale, carry high consequence, and often suffer from a high cost of delay include: Customer Returns (RMAs), delivery exceptions, and expense justifications. In these situations, shifting the interpretation logic to the point of ingestion (within the Integration Suite) fundamentally reshapes the flow. Processing accelerates because items no longer wait for manual review; accuracy and consistency improve through uniform, model-driven interpretation; and downstream data quality is strengthened. This leads to tangible improvements in efficiency and decision reliability, with human attention reserved for genuine edge cases. Scenarios that combine high repetition, high consequence, and high ambiguity are the strongest candidates for this GenAI-driven interpretation, whereas traditional deterministic approaches remain the appropriate and cost-effective choice when these characteristics are absent.5) Auditability and Control Built InWhile in our deep-dive example, we directly applied the prompt at the AI Adapter, it is also possible to build the request body, incorporating orchestration configurations provided out of the box with BTP AI Core (GenAI Hub), in the form of LLM guard-railing steps such as prompt templating, data masking, model configuration, and so on. Also, it is a best practice to make use of prompt templates, to make use of reusability. With such controls in place, the LLM response can stay relevant, reliable, and responsible (3 “R”s that constitute the key pillars of SAP’s AI strategy) ensuring that business process outcomes are explainable and auditable.These details about Orchestration workflow can be found in this SAP help portal page.I’d also recommend that you read this blog by my colleague Daniil, where Orchestration workflow in Generative AI Hub is used to invoke an LLM in a secure, GDPR-compliant way, from within an integration flow.ConclusionBy leveraging SAP BTP AI services within SAP Integration Suite, organizations can elevate integration from a transport layer to a semantic processing layer—converting human intent into deterministic business signals at the point of entry. This approach enables Straight-Through Processing, automating decision-making while preserving governance, auditability, and maintainability.Combining Integration and AI opens up new possibilities for business process efficiency. Are you examining how this could apply within your own landscape?Read our earlier article on how BTP’s AI Services can help in your Integration flow’s routing, decision-making steps. Read More Technology Blog Posts by SAP articles
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