Trade-offs of autonomous AI in SAP environments

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗵𝗮𝘀 𝘁𝗵𝗲 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝘁𝗼 𝗿𝗲��𝗲𝗳𝗶𝗻𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻, 𝗯𝘂𝘁 𝗶𝘁 𝗰𝗼𝗺𝗲𝘀 𝘄𝗶𝘁𝗵 𝗯𝗶𝗴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀. Like 𝗝𝗮𝗻𝘂𝘀, the Roman god with two faces, agentic AI presents a dual narrative: one of immense promise and one of significant caution. On one side, it offers a transformative leap in automating unstructured workflows, enabling enterprises to streamline operations in ways previously unimaginable. On the other, it demands we confront foundational challenges in integration, ethics, and ROI. ◆ 𝗖𝘆𝗰𝗹𝗶𝗰𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: The idea that automation evolves in predictable cycles assumes every leap is just a continuation of the past. But agentic AI isn’t simply the next step—it introduces fundamentally new risks. For instance, in high-stakes workflows like compliance reporting, the unpredictability of probabilistic systems could lead to regulatory fines or reputational damage. ◆ 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: The belief that AgenticAI will seamlessly replace backend business logic oversimplifies enterprise ecosystems (also an interesting Satya prediction). Consider ERP platforms like SAP or Oracle—systems deeply entrenched in operations. Moving to an AI-centric architecture would require an overhaul, fraught with risks and costs. ◆ 𝗚𝘂𝗮𝗿𝗮𝗻𝘁𝗲𝗲𝗱 𝗥𝗢𝗜: Enterprises are still unlocking value from past automation investments like RPA. Without clear, measurable outcomes, agentic AI risks following a similar path of unfulfilled ROI promises, with hidden costs in maintenance and scaling. ◆ 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗡𝗲𝘂𝘁𝗿𝗮𝗹𝗶𝘁𝘆: Automating roles like data entry could displace workers and create regulatory risks from opaque AI decisions, especially in sensitive industries like finance. Blending deterministic workflows with probabilistic agents sounds innovative, but in high-stakes environments, even minor errors can cascade into major consequences. The shift to an "AI-PI" interoperability paradigm may sound groundbreaking, but replacing decades of API standardization is no small feat. And safeguarding proprietary enterprise data in AI systems adds another layer of complexity. #AgenticAI undoubtedly holds incredible promise, particularly for unstructured workflows like document analysis. But the 𝐉𝐚𝐧𝐮𝐬-𝐟𝐚𝐜𝐞𝐝 nature of this technology reminds us to proceed with both optimism and caution. Transformative potential requires rigorous planning, measurable value delivery, and careful consideration of societal impact. What’s your take? Are these challenges surmountable, or are we rushing to embrace agentic AI without addressing the big questions? Let’s discuss! #AgenticAI #Automation #AIinBusiness #EnterpriseAutomation #FutureOfWork #AIEthics #Innovation #Leadership #AutomationStrategy

The word 𝒅𝒆𝒄𝒊𝒔𝒊𝒐𝒏 comes from Latin, which translates to "𝘤𝘶𝘵𝘵𝘪𝘯𝘨 𝘰𝘧𝘧." This skill especially becomes more ruthless when tradeoffs are concerned. How do you decide when two ends hang in a subtle balance with each other? You are required to "cut off" sharply, precisely, and sternly without breaking the balance of the system. ⚔️ 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐃𝐢𝐥𝐞𝐦𝐦𝐚 𝐨𝐟 𝐀𝐈 𝐩𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭.  Do you embed it deeply into your platform? Do you share it as a common layer for all? Or do you treat it as an external utility, consumed when needed? Each choice cuts off possibilities while amplifying others. ⚖️ Embed AI within, and you gain tight integration but risk rigidity. ⚖️ Keep it as a horizontal layer, and you expand reach but accept shared complexity. ⚖️ Push it outside, and you maximise flexibility for consumers but cede some control over context and depth. These tradeoffs define not just technical architecture, but organisational power. Where you place AI determines who it serves, who it empowers, and how much leverage it generates for the enterprise. 🔱 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐭𝐡𝐫𝐞𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧𝐬: 𝐓𝐲𝐩𝐞 1️⃣: AI Embedded Within the Data Platform (AI-for-You) For enterprises prioritizing tight feedback loops and context-rich insights directly inside their data ecosystem. 𝐓𝐲𝐩𝐞 2️⃣: AI as a Shared Horizontal Layer (AI-for-You-and-Them) For organizations seeking shared AI services that balance control with extensibility across teams and use cases. 𝐓𝐲𝐩𝐞 3️⃣: AI as an External Utility (AI-for-Them) For those who want maximum flexibility by exporting structured data for external AI consumption, where user-facing capabilities sit outside the core platform. The decision is strategic. Where you place your AI is where you cut, and where you cut determines what grows. ℹ️ This piece by Jeevan Reddy and Ritwika C. on Modern Data 101 jumps into the details of these choices, types, and tradeoffs organisations and teams are considering to place AI right in their enterprise infrastructures: https://lnkd.in/dbzgcZeY Which one has been your approach, or is it one that falls beyond these categories? #AIArchitecture #DataforAI #DataStrategy

SAP / Executive Software Inc (ESG) AI-augmented delivery models for ECC → S/4HANA migrations. Here’s the breakdown:  1. Impact on Headcount AI doesn’t eliminate the need for experienced SAP consultants, but it reduces dependency on large junior-heavy teams. Instead of 10–15 consultants handling manual tasks, you may only need 5–8 senior consultants augmented by AI tools. Examples where AI reduces labor: Custom Code Remediation → AI accelerates code scanning, impact analysis, and adaptation suggestions. Testing → AI-driven test automation (e.g., Tricentis with SAP AI) reduces manual test cycles. Data Cleansing & Migration → AI helps detect duplicate/erroneous data and automates mapping between ECC and S/4. Process Mapping → Signavio + AI identifies inefficiencies, reducing the need for large analyst teams. Net effect: Smaller but more senior project teams — fewer “hands” needed, more value-driven expertise.  2. Impact on Timeline AI accelerates many traditionally slow phases of an S/4HANA migration: Assessment Phase → What used to take 3–4 months (system analysis, custom object inventory) can now be done in weeks. Testing Cycles → Automated regression testing reduces cycle times by 30–50%. Cutover Planning & Data Validation → AI can simulate cutover scenarios and identify risks earlier. Net effect: Migration timelines can often be shortened by 20–30%, depending on complexity and readiness. 3. Key Caveats AI is not a silver bullet — skilled SAP architects, functional experts, and project leadership are still critical. For highly customized ECC landscapes, AI accelerates analysis but human judgment is required to decide what to keep, redesign, or retire. Change management and business readiness are still human-led and usually remain on the critical path.  Bottom Line Headcount: Yes, AI reduces the need for large teams, particularly at the junior/analyst level. Projects shift toward smaller, senior-led teams. Timeline: Yes, AI can compress project schedules by 20–30% by automating code analysis, testing, and data prep. Value: Lower cost, faster delivery, and higher quality — but still require senior SAP expertise to guide transformation. Please contact randy@esgit.com to arrange a discussion.

Auditing in the Age of Agentic AI – Part 3: SAP Joule, Delegated Intent, and AI-to-AI Risk (For Risk, Compliance, External and Internal Audit Leaders) This is applicable to the next version of ERPs with AI embedded. For example as SAP rolls out Joule, its embedded AI copilot, a new paradigm of risk emerges—not just in what the AI does, but in whose intent it’s acting on, and who (or what) it’s interacting with behind the scenes. With Joule, users in finance, procurement, and operations can now: • Trigger reports • Request recommendations • Automate transactions • Ask the system to “take action” But here’s the key shift: Joule doesn’t just follow commands—it interprets intent and may trigger actions that affect downstream systems, agents, or APIs. Now layer in SAP’s integration capabilities—and we have Agent-to-Agent (A2A) communication across platforms, suppliers, and business processes. What happens when Joule initiates a supplier negotiation via Ariba, or adjusts forecasts in Integrated Business Planning (IBP) based on user prompts and agent logic? Who authorized it? Was it logged? Can we explain it later? Here’s what Internal Audit, Compliance, risk and audit leaders (External and Internal) need to start thinking about: 1. Delegated Intent: Joule may execute actions on behalf of a user—but what if the user lacks full understanding of the downstream impact? 2. Dynamic Context: Prompts like “optimize working capital” or “escalate vendor issue” sound simple—but Joule may execute multi-step decisions across modules. Are those steps governed? 3. Cross-System Decisions: An action triggered in S/4HANA Finance may cascade into Ariba, Concur, or SuccessFactors. Are those cross-agent handoffs observable and auditable? 4. Explainability and Logs: Can Joule provide an interpretable summary of why it took certain steps? And is that summary preserved for audit or compliance purposes? 5. Ownership and Accountability: If a forecast changes or a vendor is offboarded—who owns the outcome: the user, the AI, or the process designer? Bottom line: SAP Joule represents the future of business interaction—context-aware, agent-led, and outcome-driven. But as its capabilities grow, so must our governance maturity. Now is the time to: • Define intent boundaries • Require human validation where appropriate • Log agent interactions across modules • And build “chain of intent” visibility into enterprise assurance frameworks This is the new frontier of digital governance. Curious—how are you preparing for Joule-enabled workflows in your SAP landscape? Piers C. Maree-Louise Kernick Renata Sanfona Zunaid Mangera Shaylin Moodley, CISA, C-EH, CIMA cBA Hari Vignesh Iyer Kaviren Govender Sree Krishna Rao Krishna Iyer Jainil Gandhi Jason Walters Alistair Grange Abhishek Mohal Kevin Duthie #SAPJoule #AgenticAI #A2A #DigitalAssurance #SAPGovernance #AuditInnovation #AIControls #IntentTracing #EnterpriseAI #SAPSecurity