Autonomous Enterprise

SAP SE

Autonomous Enterprise is SAP’s podcast on how Business AI is reshaping enterprise operations. Through direct conversations with leaders, customers, and experts, the series explores how companies turn data into decisions, automation, and measurable business outcomes.

  1. vor 5 Tagen

    The Enterprise Application Reckoning: What Stays, What Goes, and Why It Matters | Paul Saunders, SAP

    Enterprise application strategy is at a turning point. Paul Saunders, GVP Product Marketing at SAP, joins Julia Kloppenburg and Benedikt Gieger to explore what is actually changing in enterprise software and what will endure. From AI-first design principles to clean core strategy, from the three-gear model of transformation to a frank assessment of why best-of-breed architectures created more problems than they solved, Paul traces how the industry arrived at this moment and where it is heading. The conversation covers the gap between the vision of autonomous enterprise and the practical reality facing most customers, why trust in your underlying data is the prerequisite for any AI investment to pay off, and how the next generation of interfaces is already making today's UX look dated. A grounded, experience-rich conversation for anyone navigating enterprise technology decisions right now. Enterprise applications are being redesigned from first principles, but the transformation is not moving at the same speed everywhere. In this episode, Paul Saunders, GVP Product Marketing at SAP, joins hosts Julia Kloppenburg and Benedikt Gieger for a wide-ranging conversation about what is genuinely changing in enterprise software and what will remain constant longer than most people expect. Paul draws on 35 years in the industry, including time as a CIO and as an industry analyst before joining SAP, to explain why the shift from record-and-report systems to AI systems designed around sense, decide, and act is more profound than a technology upgrade. It requires rethinking process and organizational culture as much as the software itself. The episode covers Paul's three-gear model for enterprise transformation: technology, process, and people. He argues that organizations trying to move all three at once typically stall, and that the most successful transformations sequence them deliberately. The banking analogy he uses throughout: a traditional bank installed ATMs and took tellers off the floor, reducing one form of friction while creating another. A mobile-first bank redesigned the experience entirely. Both happened, but at very different speeds and with very different results for customers. On clean core strategy, Paul explains the underlying logic: separating what a business must standardize, what it can differentiate, and what it should treat as experimental innovation. He uses the BMW and Mercedes comparison to illustrate why two companies that look like direct competitors may need fundamentally different technology approaches based on where their competitive advantage actually lives. The conversation also addresses the practical limits of AI adoption. Paul is direct about the trust problem: a CFO who walks into a board meeting and says "AI thinks revenue could be two and a half billion" will not stay employed long. Reliable AI outcomes require reliable underlying data, and most enterprises have not yet done that foundational work. In the final section, Paul answers the question of what will look most outdated when viewed from 2030: best-of-breed architectures that gave each department the best tool for itself but left nobody accountable for how the pieces fit together or what the data actually meant across systems. What he expects we will be grateful for is simpler: an enduring focus on the customer's customer, and on technology as the stage crew that enables performance rather than the star of the show. Topics covered in this episode: AI-first enterprise design (sense, decide, act) · Clean core and pace layering · Three-gear transformation model · Trust and data quality as prerequisites for AI · Best-of-breed versus integrated platforms · Semantic data and the limits of API integration · Generational differences in UX expectations · The concert crew analogy for technology's role · What 2030 will reveal about today's enterprise architecture decisions

    The Enterprise Application Reckoning: What Stays, What Goes, and Why It Matters | Paul Saunders, SAP

  2. 26. Okt.

    How Agentic AI is Transforming Complex Manufacturing

    Most agentic AI pilots in manufacturing produce impressive proof-of-concept results and then quietly stall. The data is not clean, the processes are not standardised, or the organisation simply does not change how it works. So what does it actually take to get from a compelling demo to an agent running in production, handling real decisions inside real SAP processes? Shashikant Walimbe, VP and Global Head SAP Practice at LTM and an SAP partner deeply embedded in manufacturing operations, gives a practitioner's answer: start with one decision that costs you money every week, measure it, put an agent on it with clear limits, and see whether the numbers move inside a quarter. In this episode, Shashi walks through what agentic AI genuinely means for a manufacturing operation (and what it does not mean), why the skepticism of plant managers and COOs is earned rather than ignorant, the five specific reasons agentic projects fail to reach production, how LTM designed trust into a live deployment at a semiconductor manufacturer, and his honest view on when SAP's AI capabilities win on merit versus when specialist vendors or hyperscaler platforms are the better answer. The conversation closes with a direct discussion of workforce impact: which roles shrink, which change without disappearing, and what responsibility a deploying partner carries in that transition. Episode Chapters00:00 – Introduction: SAP Autonomous Enterprise Podcast00:30 – Guest introduction: Shashikant Walimbe, LTM01:10 – What agentic AI actually means in manufacturing02:24 – Why plant managers and COOs are right to be skeptical03:03 – The honest answer to the COO: start small, measure fast04:33 – Commercial tension: how LTM draws the line on advice06:23 – Why most AI pilots do not reach production07:02 – Five reasons agentic projects stall before scale08:40 – Semiconductor manufacturer case: agents in the SAP backbone10:14 – Designing trust into agentic systems12:53 – SAP versus competitors: when does the SAP route genuinely win?15:29 – Workforce impact: which roles shrink, which change18:52 – Closing thought: start with one decision that matters Listen and SubscribeSubscribe on SpotifyListen on your preferred platform

    How Agentic AI is Transforming Complex Manufacturing

  3. 19. Okt.

    Autonomous Enterprise in Latin America | Adriana Aroulho, SAP

    Why 80% accuracy is not enough in enterprise AI. SAP Latin America President Adriana Aroulho on complexity, talent, and partners. Latin America is one of the fastest-growing technology markets in the world, and the autonomous enterprise is not a distant vision for the region. It is the next stage of business transformation. But making AI work at enterprise scale requires more than deploying a capable model. It requires business context: the actual processes, policies, rules, and operational reality of each company. In this episode, Adriana Aroulho, President of SAP for Latin America and the Caribbean, joins hosts Julia Kloppenburg and Benedikt Gieger to explain why the region's regulatory complexity, from Brazil's tax reform to cross-border trade agreements, makes it one of the world's most compelling proving grounds for business AI and intelligent agents. Adriana maps the three-step adoption path she sees across the region — modernising the digital core, establishing trusted data and governance, then scaling to cross-functional AI — and makes the case that autonomous enterprise is not a large-enterprise-only play. She also addresses the talent gap, the role of regional partners in co-creating industry-specific agents, and the question every leader is asking: do you need to complete every maturity step before you can move? Her answer is clear: the foundations cannot be skipped, especially the data piece. But companies do not have to wait until everything is perfect to start generating value. Episode Chapters00:00 – Introduction: SAP Autonomous Enterprise Podcast00:51 – Guest welcome: Adriana Aroulho, SAP Latin America02:02 – Why now: converging trends driving LatAm transformation05:02 – Where AI agents deliver the most immediate value06:33 – Cloud adoption paths: from installed base to business AI09:20 – Is autonomous enterprise universal across company sizes?12:14 – The talent and skills gap: a global, not just regional, challenge15:43 – Partner ecosystem: local complexity and co-creating agents19:07 – Leapfrog versus sequential maturity: can steps be skipped?20:46 – One thing every leader must get right: business context24:20 – Closing and thanks Listen and SubscribeSubscribe on SpotifyListen on your preferred platform

    Autonomous Enterprise in Latin America | Adriana Aroulho, SAP

  4. 12. Okt.

    What does it take to bridge OT and IT in a live refinery? HMEL's Kushagra Verma explains.

    What does it actually take to bridge operational technology and IT inside a running refinery - where the machines that keep crude flowing cannot be touched by a network intrusion? Kushagra Verma, who leads corporate AI transformation at HMEL and has spent 16 years at the company moving from coder to finance to the digital team, brings a practitioner's answer. HMEL runs a 9 MMTPA refinery in Bhatinda, has been on SAP since day one of operations, won the SAP ACE Award for its paperless plant logistics system, and recently launched what the transcript describes as an AI-enabled energy station. This episode covers how the company enforces one-way OT-to-IT data flow, why a network-layer diode technology is being evaluated to air-gap the OT layer entirely, and what it means to build a next-generation refinery command center while SAP remains the single source of truth. Kushagra walks through HMEL's fully automated truck-loading and product evacuation system, where a vehicle enters, is weighed, loads, triggers a real-time SAP sales invoice, receives statutory transport documents, and exits without a single human touch — and explains why the airplane turnaround analogy fits. He addresses the Clean Core challenge honestly: roughly 10,000 custom objects accumulated over 15 years make a full migration very difficult, so the team is starting with housekeeping, inventorying unused objects, and applying Clean Core only to new standalone implementations. His advice for any mid-sized refiner starting this journey: build a culture that lets people fail fast, expose teams to what peer companies are doing, choose a long-term technology partner whose vision aligns with yours, and treat AI on the IT side as non-negotiable right now. Episode Chapters00:00 — Introduction: SAP Autonomous Enterprise Podcast00:30 — Guest introduction: Kushagra Verma, 16 years at HMEL01:56 — OT-IT integration: one-way data flow and the diode technology under evaluation05:14 — Who are the primary internal customers of HMEL's digital transformation07:53 — AI for sulfur threshold management and HMEL's patent filing08:23 — Fully automated plant logistics: how the truck-loading system works10:34 — Partnership architecture: SAP as hub, Amazon and Aveva for specialist functions11:08 — Jewel AI launch: 30 use cases live in less than 30 days14:29 — Clean Core: the honest reality of 10,000 custom objects17:02 — Advice for a mid-sized refiner starting the autonomous enterprise journey21:54 — One thing a leader must get right: people with the right vision and autonomy to execute23:13 — Outro Listen and SubscribeSubscribe on SpotifyListen on your preferred platform

    What does it take to bridge OT and IT in a live refinery? HMEL's Kushagra Verma explains.

  5. 5. Okt.

    APAC AI adoption isn't one journey. Yee-Ching Wang on trust, governance, and what it takes to reach the autonomous enterprise.

    Across 26 markets in Asia-Pacific, SAP's Customer Engagement and Adoption team is navigating one of the most complex AI transformation landscapes in the world. In this episode, Yee-Ching Wang, SAP Senior Vice President and Head of Customer Engagement and Adoption for Asia-Pacific, joins Julia Kloppenburg and Benedikt Gieger to address the question that every executive in the region is sitting with: if the technology is ready, why isn't adoption keeping up? The answer, Yee-Ching argues, is almost never the technology. It is trust, governance, and the organisation's readiness to change at the pace the business actually requires. Drawing on customer journeys across regulated industries, large multinationals, and fast-moving mid-market businesses, Yee-Ching walks through how SAP's integrated toolchain and tiered success plans translate vision into measurable operational outcomes. Two case studies anchor the conversation: Fujitsu's global consolidation from more than 4,000 internal systems toward a single-process, AI-enabled operation, and a second example of AI-powered productivity on the manufacturing shop floor. The episode closes with one piece of advice for any leader starting the journey: begin with the business problem, not the technology, identify the handful of processes where AI delivers clear value, prove it, and then scale with confidence. Episode Chapters00:00 – Introduction: SAP Autonomous Enterprise Podcast01:19 – APAC as a transformation landscape: diversity across markets05:06 – Customer engagement framework: success plans and integrated toolchain10:01 – Overcoming transformation barriers and the role of AI14:01 – Customer success stories: Fujitsu and a manufacturing example26:36 – The first 90 days of AI adoption: people, decisions, blockers30:23 – Closing advice: start with the business problem34:36 – Outro Listen and SubscribeSubscribe on SpotifyListen on your preferred platform

    APAC AI adoption isn't one journey. Yee-Ching Wang on trust, governance, and what it takes to reach the autonomous enterprise.

  6. 28. Sept.

    AI agents already negotiate bids, suggest awards and buy in production. The hard line: humans still hold every strategic decision.

    What does it take for procurement to become genuinely autonomous? In this episode, Fang Chang, General Manager and Chief Product and Technology Officer, describes how AI agents already negotiate bids, suggest awards and navigate catalogs in production, not in demos. He explains where the hard line sits: customers are not ready to let agents make major strategic decisions on an organization's behalf, so humans review and adjust before execution. The conversation covers the data foundation behind reliable agents, why auditability is built into every step, and what a CFO or CPO should get right first when moving from pilot to production this quarter. The readiness gap varies: some organizations have harmonized their data, others still run a heterogeneous landscape, and closing that gap is a shared responsibility across SAP, the customer and the partner ecosystem. Network effects multiply the value: the larger the supplier network, the more valuable it becomes for every buyer on it. And when trust is the question, the answer is governance plus data. Agents record every step so decisions stay auditable, and they are grounded on the customer's own buying policies, rules of engagement and sustainability goals. Episode Chapters00:00 Welcome to the SAP Autonomous Enterprise Podcast00:30 Guest introduction: autonomous spend and the five domains02:43 How real is the vision?06:59 Where is the hard line for agents?08:34 Data readiness: how big is the gap and who closes it?10:39 The business network and its tipping point12:49 Trust, auditability and human intercepts14:56 The state of agent development17:00 The honest first step: governance and dataListen and SubscribeSubscribe on SpotifyListen on your preferred platform

    AI agents already negotiate bids, suggest awards and buy in production. The hard line: humans still hold every strategic decision.

  7. 21. Sept.

    AI Partner Strategy: From Implementation to Outcomes | Mark Wheeler, NTT DATA

    Is your SAP partner truly ready to deliver the autonomous enterprise, or are they repackaging traditional implementation work with an AI label? Mark Wheeler, Global Head of Product Engineering and AI Customer Success Delivery Operations at NTT DATA Business Solutions, gives a candid answer. He describes a three-strand delivery framework, called Cloud ERP Elevated, that separates partners who are genuinely industrialising AI delivery from those who are not. And he names the three reasons AI transformation fails: misaligned business economics, underestimated technical feasibility, and users who were never brought on the journey. Wheeler shares concrete outcomes from NTT DATA customer engagements: 90% touchless supply confirmations for a manufacturing customer and 98% straight-through processing for unstructured sales orders at a UK chemicals company. He explains why human-centered design still matters even as AI capability accelerates, and closes with the single question he asks every CIO before any tactical decision: what is your platform strategy? The answer, he argues, shapes everything that follows for an autonomous enterprise. Episode Chapters00:00 – Introduction: SAP Autonomous Enterprise Podcast00:54 – Guest introduction: Mark Wheeler, NTT DATA Business Solutions01:33 – Is the partner ecosystem ready to deliver the autonomous enterprise?03:10 – Cloud ERP Elevated: three strands of AI delivery (ready, template, custom AI)06:17 – Customer outcomes: Leinmuller and Synthema use cases with concrete results09:56 – Why AI transformations fail: economics, feasibility, and user adoption14:24 – How to identify where to start and which use cases are ready to scale16:29 – Partner business model transformation: from billable days to outcome delivery19:51 – Human-centered design in an AI-first world: the SAP App House alliance22:49 – Final question: the one thing a leader must get right for autonomous enterprise25:02 – Outro LinksSubscribe on SpotifyListen on your preferred platform

    AI Partner Strategy: From Implementation to Outcomes | Mark Wheeler, NTT DATA

  8. 14. Sept.

    AI Without Business Context Is Just Noise | Alexander Kläger, SAP

    Why do most enterprise AI initiatives stay stuck? SAP's MEE president on closing the knowing-doing gap. Most companies say they are already doing AI. But when you look closer, what you find is a patchwork: several tools running in parallel, rising technical complexity, and integration dead ends that stall real progress. Alexander Kläger, President of Middle and Eastern Europe at SAP SE, has sat across from hundreds of senior executives this year. His verdict is unambiguous: decision-makers are past the hype. They want outcomes, and they want them delivered on the right foundation, not bolted on top of one they are already questioning. In this episode, Alexander explains why the fear he hears most behind closed doors is not "will AI replace my people?" but rather "are we making the right bet?" He describes how the autonomous enterprise closes the gap between knowing and doing, why business context is the actual differentiator between generic AI and AI that acts inside real processes, and why the next 12 to 18 months represent a learning window that cannot be bought back later. He also shares what finally unlocks Mittelstand CEOs and why MEE has every reason to lead this transformation, not simply follow it. Episode Chapters00:00 – Introduction: SAP Autonomous Enterprise Podcast00:29 – Meet Alexander Kläger: President, SAP Middle and Eastern Europe00:56 – AI patchwork: what executives are actually doing versus what they say02:08 – Beyond the hype: decision-makers want results, not participation03:34 – Behind closed doors: the fear executives will not say out loud04:49 – The autonomous enterprise in the boardroom: closing the knowing-doing gap06:18 – The 12-to-18-month window: what you cannot purchase back07:34 – What unlocks a Mittelstand CEO09:19 – Why SAP's business context argument lands with executives who have tried generic AI10:39 – Five years from now: winners will rethink, not just retrofit11:52 – Outro LinksListen on SpotifySubscribe via PodLink

    AI Without Business Context Is Just Noise | Alexander Kläger, SAP
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Autonomous Enterprise is SAP’s podcast on how Business AI is reshaping enterprise operations. Through direct conversations with leaders, customers, and experts, the series explores how companies turn data into decisions, automation, and measurable business outcomes.

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  • Erstellt von
    SAP SE
  • Jahre aktiv
    2026
  • Folgen
    23
  • Bewertung
    Unbedenklich
  • Copyright
    © SAP SE
  • Sendungswebsite
    Autonomous Enterprise

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