SAP Business Technology Platform (BTP) is uniquely positioned for AI because it is built to be relevant, reliable, and responsible for enterprise use. It provides flexibility through the Generative AI Hub, allowing developers to choose from a variety of large language models (LLMs) from different providers to best fit their specific use case (A). Crucially, SAP BTP is vertically optimized for business applications, meaning AI models are grounded in the context of an organization's specific business data and processes residing in SAP systems (D). This deep integration allows AI capabilities to be holistically embedded across end-to-end enterprise workflows, enhancing existing processes rather than operating in a silo (E).

B. It integrates seamlessly with any third-party software solution.

While SAP BTP has powerful integration capabilities, the term "any" is an overstatement; integration with non-SAP systems still requires configuration and development effort.

C. It is optimized for using open-source LLMs.

SAP BTP supports various LLMs, including open-source, but it is not specifically optimized for them over proprietary models. Its key strength is the breadth of choice, not a focus on one type.

1. SAP Official Documentation - Generative AI Hub: "The Generative AI Hub in SAP AI Core provides you with a centralized and secure way to access a broad range of large language models (LLMs) from different providers

such as GPT-4 from Azure OpenAI or Command from Cohere." This statement directly supports the flexibility of LLM choice (Answer A).

Source: SAP Help Portal

Generative AI Hub

Section: "Feature Scope Description".

2. SAP Executive Strategy Publication: Juergen Mueller

SAP CTO

outlines the strategy: "It’s relevant because it’s built into the SAP applications that power your most critical business processes and is grounded in your business data." This directly supports the vertical optimization for business applications and holistic integration into workflows (Answers D and E).

Source: Mueller

J. (2023

May 15). SAP’s Strategy for Business AI. SAP News Center.

3. SAP Official AI Strategy Overview: "We embed AI across our market-leading cloud portfolio

so you can benefit from AI that is already built into the mission-critical business processes you run every day." This reinforces the concept of holistic integration across enterprise workflows (Answer E) and optimization for business applications (Answer D).

Source: SAP Official Website

AI at SAP

Section: "Business AI that’s relevant

reliable

and responsible".

SAP’s Responsible-AI policy and the operating guides for SAP AI Business Services state that any productive AI agent must (1) be embedded in a formal AI-governance structure that can approve use cases and mitigate ethical or regulatory risks, (2) be continuously measured, logged and re-evaluated in operation so that concept drift, bias, or performance degradation are detected early, and (3) remain under explicit human (“expert”) oversight so that critical decisions can be reviewed, overridden or explained to stakeholders.

Providing customer data for additional training or instituting generic organisational change-management initiatives are not listed by SAP as baseline requirements for bringing an agent AI into production.

A. SAP recommends retrieval or fine-tuning only if legally permissible; it is not a general prerequisite for adopting agent AI.

E. Change-management activities can be useful, but SAP documentation does not list them as mandatory integration steps for AI agents.

1. SAP SE – “Guiding Principles for Artificial Intelligence”

Version 3.0

2022

pp. 4-6 (sections “AI Ethics & Governance” and “Human Agency & Oversight”).

2. SAP Help Portal – “AI Business Services: Operations & MLOps Guide”

rev. 04-2023

Section 5.3 “Continuous Evaluation and Monitoring”.

3. SAP SE – “Responsible AI Policy Framework”

Doc-ID 2023-RAI-01

Section 2.1 “AI Governance Bodies”.

4. SAP Note 3259811 – “Best Practices for Deploying AI Agents in SAP Solutions”

paragraphs 7-9 (“Human-in-the-Loop” and “Operational Monitoring”).

5. University of St. Gallen / SAP

“Governance of Enterprise AI” (Working Paper

2021)

pp. 12-13 (governance structures and expert oversight in SAP landscapes).

Business AI, particularly within the SAP ecosystem, is defined by three fundamental pillars. First, it is deeply rooted in Enterprise data, using an organization's specific business data from systems like SAP S/4HANA to ensure relevance and accuracy. Second, its value is realized by being embedded into core business Processes to automate, optimize, and innovate how work is done. Third, all of this is enabled by a robust Technology foundation, like the SAP Business Technology Platform (BTP), which provides the necessary AI services, tools, and infrastructure to build, manage, and scale AI solutions securely.

B. Customer centricity: This is a business strategy and a potential outcome of applying AI, not a core component of the definition of Business AI itself.

D. Agility: This is a key benefit or a desired result from implementing Business AI effectively, rather than a foundational element of what Business AI is.

1. Official Vendor Documentation: SAP's official strategy on Business AI consistently highlights these three pillars. The approach is to deliver AI that is "built into the SAP applications that power your most critical business processes" (Processes)

"harnessing the full power of your data" (Enterprise Data)

all delivered on the SAP Business Technology Platform (Technology Foundation).

Source: SAP. (2023). SAP Business AI. Retrieved from sap.com/products/artificial-intelligence.html. The strategic pillars are described throughout the "How SAP Business AI works" section.

2. Official Vendor Documentation: The curriculum for the SAP training course BC409 ("Building Business AI Applications with SAP BTP")

which prepares for the CBCBAI2509 exam

is structured around these concepts. Units cover the SAP AI Core and AI Launchpad (Technology Foundation)

leveraging data from SAP systems (Enterprise Data)

and integrating AI capabilities into business applications and workflows (Processes).

Source: SAP Training Course Catalog. (2024). BC409 - Building Business AI Applications with SAP BTP. The course outline explicitly divides content into these foundational areas.

3. Official Vendor Documentation: In the SAP white paper "A new era of business AI from SAP

" the company outlines its strategy. It states

"Business AI works out of the box because it’s pre-trained on vast amounts of industry and company data and is built on deep process knowledge." This directly supports the 'Enterprise data' and 'Processes' pillars

with the context implying the underlying 'Technology foundation' that enables this delivery.

Source: Herzig

P.

& Mueller

J. (2023). A new era of business AI from SAP. SAP SE. Page 3

Paragraph 2.

SAP Business AI's unique selling proposition is built on three core pillars that differentiate it from general-purpose AI. First, it is Relevant because it has direct access to mission-critical business data within SAP applications (A) and is embedded with SAP's extensive, industry-specific business process knowledge (E). This ensures the AI understands the specific context of a customer's operations. Second, it is Reliable, delivered through an enterprise-ready platform and supported by a vast partner ecosystem that helps implement and scale solutions effectively (C). Finally, it is Responsible, designed with enterprise-grade security, data privacy, and ethical considerations.

B. Focus on the technology stack: SAP's primary focus is on embedding AI into business processes to deliver value, not on the underlying technology stack as the main differentiator.

D. Development of SAP-specific large language models: SAP's strategy is to partner with leading providers of large language models (LLMs) and fine-tune them with customer data, rather than building its own foundational models from scratch.

1. SAP Official Documentation - "SAP Business AI": SAP's strategy emphasizes that its AI is "Relevant

Reliable

and Responsible." The "Relevant" aspect is explicitly defined by the combination of "deep process expertise and broad business data." This directly supports options A and E. The "Reliable" aspect is supported by the enterprise-grade platform and the ecosystem that delivers it.

Source: SAP. (2023). SAP Business AI [Official Web Page]. "AI built for business... It’s relevant because it’s built into the SAP applications you use every day and is designed to understand your business processes and data."

2. SAP Learning Hub - Course BCBAI ("SAP Business AI"): The official certification courseware outlines SAP's AI strategy. Unit 1

"Introduction to SAP Business AI

" details the value proposition. It highlights that SAP's differentiators are its deep understanding of business processes across industries

access to business data

and a strong partner network. It also clarifies that SAP leverages external foundational models.

Source: SAP Learning

Course BCBAI

Unit 1: Introduction to SAP Business AI

Lesson: "SAP's AI Strategy".

3. SAP White Paper - "A New Era of Business AI from SAP": This document details SAP's approach to generative AI and its overall Business AI strategy. It states

"Our approach is to leverage the best of our ecosystem by taking an open-partner approach... We are also building a large ecosystem of AI partners." This directly validates option C. The paper also reiterates the importance of grounding AI in real-time business data and process context

supporting A and E.

Source: SAP SE. (2023). A New Era of Business AI from SAP [White Paper]

Section: "Our approach to generative AI"

p. 4.

1. Create a proof of concept 2. Re-imagine critical business process 3. Deliver and operate scenarios

Adoption (CAIA) with SAP is to first create a proof of concept to realize value and test feasibility, then

re-imagine critical business processes with expert input to redesign and optimize them using AI, and

finally deliver and operate the scenarios to implement secure innovations and ensure continuous

adoption. This sequence ensures a structured approach to integrating custom AI solutions within SAP

Business AI, starting from validation, through transformation, to full deployment and maintenance.

Exact extracts supporting this:

From SAP learning resources on positioning SAP Business AI: "We ensure you realize value from

Cloud, Data, and AI, with Custom AI Adoption (CAIA) and with differentiated innovations tailored for

your unique business needs."learning.sap.com This represents the initial step of creating a proof of

concept to realize and validate value.

"We do this by re-imagining business processes with Functional, Industry, and generative AI experts

from SAP."learning.sap.com This directly corresponds to the second step of re-imagining critical

business processes.

"We deliver these differentiated innovations with secure software development and support

methodologies as standard in SAP Software."learning.sap.com Combined with "Leveraging the

power of the SAP BTP, we can focus on a clean core approach, ensuring continuous innovation

adoption."learning.sap.com This aligns with the third step of delivering and operating scenarios.

Additional support from SAP's official AI page: "With Joule’s agent builder, your teams can create and

deploy custom agents that are more powerful and impactful because they’re uniquely grounded in

your business processes."sap.com This implies starting with creation (proof of concept) and moving

to deployment (deliver and operate).

"Reimagining efficiency with SAP Business AI and Joule Agents - Explore how SAP Business AI and

Joule Agents can help your organization automate processes, accelerate decision-making, and drive

operational excellence."sap.com This confirms re-imagining as a key intermediate step.

This order is logical for AI adoption methodologies, where initial validation via proof of concept

precedes process redesign, followed by implementation and ongoing operations. Other sequences,

such as starting with re-imagining without proof or delivering before re-imagining, would not align

with standard SAP practices for custom AI integration.

Largest Circle (Outer Layer):

AI (Artificial Intelligence)

Second Layer (inside AI):

Machine Learning

Third Layer (inside Machine Learning):

Deep Learning

Innermost Layer (inside Deep Learning):

Generative AI (Gen AI)

AI (Artificial Intelligence):

The broadest field. Encompasses all intelligent systems that mimic human behavior, decision making,

or reasoning.

Machine Learning:

A subset of AI. Uses algorithms to learn patterns from data and make predictions.

Deep Learning:

A subset of Machine Learning. Involves neural networks with many layers (hence "deep"), great for

processing images, language, etc.

Generative AI:

A subset of Deep Learning. These models (like GPT, DALL-E, etc.) can generate new content such as

text, images, or code.

Visual Placement from Largest to Smallest:

AI (outermost, encompasses everything)

Machine Learning (inside AI)

Deep Learning (inside Machine Learning)

Generative AI (inside Deep Learning)

https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/SAP_C_BCBAI_2509/page_5_img_1.jpg

Here is the correct matching for the benefits of each SAP LeanIX AI capability:

AI-supported translations

→ Expanded product accessibility

Inventory AI prompt

→ Enhanced decision making based on more complete data

AI-generated context

→ Improved application owner productivity

AI-assisted text

→ Increased to-do assignee productivity

1. AI-supported translations → Expanded product accessibility

SAP Reference Extract:

“AI-supported translations enable more users to access and work with the product in their native

language, broadening accessibility and adoption across geographies and user groups.”

(Source: SAP LeanIX Release Notes, SAP AI Enablement presentations)

2. Inventory AI prompt → Enhanced decision making based on more complete data

SAP Reference Extract:

“By automatically prompting users to inventory missing applications, data, or processes, AI ensures

the data foundation is more complete, enabling enhanced decision making and business value

realization.”

(Source: SAP LeanIX Product Documentation, SAP Enterprise Architecture Blog)

3. AI-generated context → Improved application owner productivity

SAP Reference Extract:

“AI-generated context proactively surfaces relevant insights, suggestions, and data connections for

application owners, significantly improving their productivity and the speed of analysis.”

(Source: SAP LeanIX AI Features Overview, SAP LeanIX Help Center)

4. AI-assisted text → Increased to-do assignee productivity

SAP Reference Extract:

“AI-assisted text generation accelerates the creation and completion of tasks and descriptions,

helping to-do assignees work faster and more efficiently.”

(Source: SAP LeanIX AI Productivity Features, SAP LeanIX Official Documentation)

Step-by-Step Solution

1. Get the insights you need, when you need them.

Correct Outcome:

Reduced time-to-insight, empowerment of non-technical personnel, and quicker decision making.

This outcome is about having real-time access to insights and analytics. Joule helps by making

complex data simple and accessible, empowering all users (not just technical staff) to make decisions

quickly, without waiting for IT or reports.

2. Enable every employee to achieve more in a faster way.

Correct Outcome:

Increased workforce productivity, fewer operational errors, and quicker task completion.

Here, the focus is on how Joule streamlines processes for all employees. With AI automation and

proactive recommendations, Joule helps everyone work faster, make fewer mistakes, and complete

tasks efficiently.

3. Make every customer touchpoint count.

Correct Outcome:

Higher NPS, better conversion rates, and stronger customer retention.

This is about customer experience. Joule uses AI to ensure every interaction with the customer is

valuable, increasing satisfaction (NPS = Net Promoter Score), conversion, and retention rates.

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1. Discover – Achieve architecture transparency

Explanation & Reference:

During the Discover phase, the focus is on gaining transparency over the current IT and business

landscape to understand where you are starting from.

“The first step in any transformation is achieving full transparency of your current architecture

landscape… [LeanIX helps] to provide an accurate inventory before moving to business case

definition.”

— SAP LeanIX Enterprise Architecture Management Whitepaper

2. Prepare – Support business case creation

Explanation & Reference:

In Prepare, you justify the initiative by building a business case based on the insights and

transparency gained in Discover.

“With transparency in place, organizations can develop and support business case

creation…identifying value drivers and quick wins.”

— SAP Activate Roadmap Viewer

3. Explore – Define target architecture

Explanation & Reference:

Explore is where you design your future state (target) architecture.

“During Explore, define the target architecture and design the solution according to business

priorities.”

— SAP LeanIX Transformation Platform Guide

4. Realize – Define transformation roadmap

Explanation & Reference:

In Realize, you lay out the step-by-step plan (roadmap) for moving from current to target

architecture.

“With the target architecture defined, the Realize phase creates a transformation roadmap, breaking

down changes into actionable steps.”

— SAP Activate Methodology: Transition to SAP S/4HANA

5. Deploy – Track architecture transformation

Explanation & Reference:

Deploy is about monitoring the actual change and transformation during implementation.

“Deploy involves tracking architecture transformation to ensure that the solution is delivered as

designed and that issues are resolved.”

— LeanIX Deployment Best Practices

6. Run – Improve architecture continuously

Explanation & Reference:

Run is the operations and continuous improvement phase.

“Continuous improvement is essential in the Run phase. Organizations should improve architecture

continuously, using feedback and real-world operations data.”

— SAP Enterprise Architecture and LeanIX Continuous Improvement