Preface

17

Target Audience

18

SAP Practitioners

18

Implementation Leads and Enterprise Architects

18

Business Process Owners and Finance Transformation Leaders

19

Objectives of This Book

20

A Finance-Centric View of the AI Landscape in SAP

20

Design Patterns for Custom AI Without Becoming a Coding Manual

21

Preparing for the Next Frontier

21

How to Read This Book

22

Closing Thoughts

23

1 Introduction to AI with SAP

25

1.1 Types of AI in Your SAP Landscape

26

1.1.1 Standard AI and Machine Learning

27

1.1.2 Agentic AI

34

1.1.3 Custom AI

38

1.2 SAP Business AI

41

1.2.1 Core Principles

42

1.2.2 Embedded AI

43

1.2.3 Generative and Agentic AI

44

1.3 Solutions in SAP BTP

47

1.3.1 SAP AI Core and SAP AI Launchpad

47

1.3.2 SAP Business Data Cloud

50

1.3.3 SAP Build

54

1.4 Third-Party Solutions

58

1.4.1 Amazon

58

1.4.2 Microsoft

59

1.4.3 Google

61

1.4.4 OpenAI

61

1.4.5 UiPath

62

1.5 Summary

63

2 Accounts Receivable

67

2.1 Master Data

68

2.1.1 Master Data Workflow

68

2.1.2 Mass Creation of Business Partners

73

2.2 Customer Invoices

79

2.2.1 Invoicing with SAP Build Process Automation

79

2.2.2 Sales Order Automation

80

2.3 Payment Advice and Bank Statements

89

2.4 Dispute Management

91

2.5 Cash Application

94

2.6 Cash Forecasting

98

2.7 Summary

99

3 Accounts Payable

101

3.1 Master Data

101

3.2 Supplier Invoices

102

3.2.1 Automating Supplier Invoices Without a Purchase Order

103

3.2.2 Invoicing with Joule

105

3.3 Supplier Downpayment Requests

111

3.4 GR/IR Reconciliation

113

3.5 Invoice Automation with OpenText

114

3.6 Summary

118

4 Intelligent Asset Accounting

121

4.1 Master Data

122

4.1.1 Fixed Asset Creation with Joule

122

4.1.2 Mass Creation of Fixed Assets with SAP Build Process Automation

123

4.2 Asset Acquisitions

127

4.3 Asset Transfers

130

4.3.1 Fixed Asset Transfer Bots

130

4.3.2 Fixed Asset Transfer Approval Workflow

132

4.4 Asset Retirements

136

4.4.1 Asset Retirement Bots

136

4.4.2 Manage Asset Retirement Workflow

138

4.5 Asset Scrapping

142

4.6 Summary

144

5 General Ledger Accounting

147

5.1 Journal Entries

148

5.1.1 Journal Entry Bots

148

5.1.2 Mass Journal Entry Upload

149

5.2 AI-Assisted Journal Upload

154

5.2.1 General Ledger Journal Entry Posting

155

5.2.2 Joule-Orchestrated Journal Entries

157

5.3 Accruals

160

5.3.1 Accrual Object Creation Approval

161

5.3.2 Joule-Orchestrated Accruals

162

5.4 Reclassification

164

5.5 Exchange Rates

167

5.6 Intelligent Intercompany Matching and Reporting

171

5.6.1 ICMR Transaction Orchestration

171

5.6.2 Intelligent Intercompany Reconciliation Service

174

5.7 Summary

177

6 Predictive Accounting

179

6.1 Prediction Ledger

179

6.2 Sales Order

181

6.3 Data Quality Management

183

6.4 Profit Center Reorganization

186

6.5 Travel and Expense Management

193

6.6 Summary

198

7 Predictive Planning and Analytics

201

7.1 Just Ask

202

7.1.1 Data and Architecture

202

7.1.2 Features and Process Flow

203

7.1.3 Supported Scenarios

207

7.1.4 Key Considerations

208

7.2 Smart Insights

210

7.2.1 Data and Architecture

211

7.2.2 Features and Process Flow

212

7.2.3 Supported Scenarios

216

7.2.4 Key Considerations

216

7.3 Time Series Forecasting

217

7.3.1 Data and Architecture

218

7.3.2 Features and Process Flow

219

7.3.3 Key Considerations

224

7.4 Smart Grouping

225

7.4.1 Data and Architecture

226

7.4.2 Features and Process Flow

227

7.4.3 Key Considerations

231

7.5 Predictive Planning

232

7.5.1 Data and Architecture

233

7.5.2 Features and Process Flow

234

7.5.3 Supported Scenarios

236

7.5.4 Key Considerations

237

7.6 Regression and Classification via Smart Predict

238

7.6.1 Modeling Flow

239

7.6.2 Key Considerations

242

7.7 Summary

244

8 Designing, Deploying, and Scaling Custom AI Solutions for Finance

249

8.1 Identify Information Need and Finance Stakeholders

250

8.1.1 Start with the Decision, Not the Model

251

8.1.2 Map Business, Technical, and Assurance Stakeholders

251

8.1.3 Define the Information Needs Brief

253

8.2 Assess Out-of-the-Box Functionality

256

8.2.1 Test Standard Capabilities First

256

8.2.2 Configuration, Extension, or Net-New Build?

257

8.2.3 Produce a Documented Design Decision

259

8.3 Define Use Cases

259

8.3.1 Convert Opportunity Statements into Use Case Cards

259

8.3.2 Choose the Dominant Pattern

260

8.3.3 Set Scope, Metrics, and Exclusions

262

8.4 Prioritize for Impact

263

8.4.1 Balance Value with Feasibility

263

8.4.2 Sequence the Portfolio

265

8.5 Review Available Tools

266

8.5.1 SAP Build Process Automation

267

8.5.2 SAP Build Code

269

8.5.3 SAP Build Apps

270

8.5.4 Joule

271

8.5.5 Generative AI Hub

274

8.5.6 Third-Party Solutions

275

8.6 Assess Development Cost

278

8.6.1 See the Whole Cost Stack

278

8.6.2 Recognize Hidden Cost Drivers

279

8.7 Define Implementation Steps

281

8.8 Understand Audit and Compliance Concerns

284

8.8.1 Design Controls into the Solution

284

8.8.2 Protect Data and Authorization Boundaries

285

8.8.3 Preserve Evidence and Human Accountability

286

8.8.4 Govern Prompts, Models, and Change

286

8.8.5 Plan for Resilience and Oversight

287

8.9 Summary

288

9 Summarizing and Synthesizing Financial Data

291

9.1 Automated Variance Commentary

292

9.1.1 Smart Insights via SAP Analytics Cloud

293

9.1.2 SAP Datasphere with Joule and SAP Knowledge Graph

295

9.1.3 Finance Agents in Microsoft 365

298

9.2 Executive-Ready Flash Reporting

300

9.2.1 Predictive Planning with SAP Analytics Cloud

301

9.2.2 SAP Databricks with AI Foundation

303

9.2.3 Joule

304

9.3 Earnings Call Preparation

307

9.3.1 SAP Analytics Cloud

308

9.3.2 Joule with the Generative AI Hub

310

9.3.3 Azure OpenAI

314

9.4 Cross-LOB Performance Synthesis

317

9.4.1 SAP Knowledge Graph

318

9.4.2 Joule

320

9.4.3 SAP Datasphere and SAP Analytics Cloud

321

9.5 Summary

323

10 Retrieving Financial Data

325

10.1 Natural Language Assistants

325

10.1.1 Joule

326

10.1.2 Gemini Enterprise Agent Platform

328

10.2 Automated KPI Lookups

331

10.2.1 Embedded AI in SAP Analytics Cloud and SAP Datasphere

331

10.2.2 SAP Databricks

337

10.3 Contract and Transaction Data Retrieval

341

10.3.1 RAG Integrations with PDFs and Structured Data

342

10.3.2 Amazon Bedrock

345

10.4 Dynamic Drilldowns into Reports

349

10.4.1 Joule

350

10.4.2 SAP Databricks

354

10.5 Summary

358

11 Transforming Content

359

11.1 Raw Transaction Logs

360

11.1.1 SAP Datasphere

361

11.1.2 SAP Databricks

363

11.1.3 Log Transformation Workflow

365

11.2 Regulatory Filings

368

11.2.1 Joule

369

11.2.2 Azure OpenAI

371

11.2.3 Regulatory Filing Transformation Workflow

374

11.3 Multientity Reports

378

11.3.1 SAP Databricks

379

11.3.2 SAP Analytics Cloud

381

11.3.3 SAP Snowflake

383

11.3.4 Large Language Models

384

11.3.5 Multientity Report Transformation Workflow

386

11.4 Historical Data

388

11.4.1 SAP Knowledge Graph

389

11.4.2 Gemini Enterprise Agent Platform

392

11.4.3 Graph-Enhanced Feature Engineering Workflow

394

11.5 Summary

396

12 Turning Static Outputs into Dynamic Stakeholder Narratives

399

12.1 Additional Context

401

12.1.1 SAP Analytics Cloud

402

12.1.2 SAP Databricks

403

12.2 Market Intelligence

407

12.2.1 SAP Knowledge Graph

407

12.2.2 Amazon Bedrock

408

12.3 Real-Time KPIs

412

12.3.1 SAP Snowflake

414

12.3.2 Azure OpenAI

414

12.3.3 SAP Analytics Cloud

414

12.4 Sentiment and Peer Analysis

418

12.4.1 Joule with Retrieval-Augmented Generation

419

12.4.2 AlphaSense

419

12.5 Summary

424

13 Leveraging Assistants

427

13.1 Financial Analyst Copilots

428

13.1.1 Joule

429

13.1.2 SAP Databricks

431

13.1.3 OpenAI

433

13.1.4 Financial Analyst Assistant Workflow

436

13.2 Investor Relations Assistants

439

13.2.1 Joule and Retrieval-Augmented Generation

439

13.2.2 AlphaSense

441

13.2.3 Investor Relations Assistant Workflow

443

13.3 Procurement Bots

448

13.3.1 SAP Fieldglass

449

13.3.2 Amazon Bedrock

450

13.3.3 SAP Databricks

452

13.3.4 Procurement Bot Workflow

453

13.4 Controllership Agents

458

13.4.1 Joule

458

13.4.2 Azure OpenAI

460

13.4.3 Controllership Agent Workflow

461

13.5 Summary

467

14 Creating Content

469

14.1 Commentary

470

14.1.1 SAP Analytics Cloud

471

14.1.2 SAP Databricks

472

14.1.3 OpenAI

474

14.2 Investor Relations Scripts

476

14.2.1 SAP Knowledge Graph

477

14.2.2 Gemini

478

14.3 Financial Summaries

480

14.3.1 SAP Analytics Cloud

480

14.3.2 SAP Snowflake

482

14.3.3 Azure OpenAI

483

14.4 Policies

485

14.4.1 SAP Signavio

486

14.4.2 Natural Language Generation Solutions

487

14.5 Summary

489

15 Nine Elements of Future ERP AI Automation

493

15.1 The Four Pillars of the Intelligent Enterprise

494

15.2 The Nine Elements

495

15.2.1 Simplified, Future-Proof Business Data Model

498

15.2.2 Real-Time Transaction Engine on a High-Performance Database

499

15.2.3 Clean Core Application Architecture

500

15.2.4 Digitally Mapped Processes and Controls

501

15.2.5 Transaction-Level APIs and Services

503

15.2.6 Governed Integration with Cloud Services

504

15.2.7 Application Extensibility with Low-Code and No-Code Tools

505

15.2.8 Persona-Based Workbenches

506

15.2.9 Data Flows That Thread Processes Across Applications

507

15.3 Implications for Finance Leaders

508

15.4 Technology Impacts and Operating Model Shifts

510

15.5 Common Misconceptions and Failure Patterns

512

15.6 Putting the Nine Elements into Motion

513

15.6.1 Sequencing with Dependencies

513

15.6.2 Anonymized Scenarios

514

15.6.3 Governance and Measurement

516

15.7 From Principles to Roadmap: Building the Automation Portfolio

518

15.8 Measured Outcomes and Value Realization

520

15.8.1 A Practical Value Model

521

15.8.2 Sustaining Momentum

522

15.9 Summary

522

The Author

525

The Contributors

525