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 Chapters
00:00 — Introduction: SAP Autonomous Enterprise Podcast
00:30 — Guest introduction: Kushagra Verma, 16 years at HMEL
01:56 — OT-IT integration: one-way data flow and the diode technology under evaluation
05:14 — Who are the primary internal customers of HMEL's digital transformation
07:53 — AI for sulfur threshold management and HMEL's patent filing
08:23 — Fully automated plant logistics: how the truck-loading system works
10:34 — Partnership architecture: SAP as hub, Amazon and Aveva for specialist functions
11:08 — Jewel AI launch: 30 use cases live in less than 30 days
14:29 — Clean Core: the honest reality of 10,000 custom objects
17:02 — Advice for a mid-sized refiner starting the autonomous enterprise journey
21:54 — One thing a leader must get right: people with the right vision and autonomy to execute
23:13 — Outro

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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 Chapters
00:00 – Introduction: SAP Autonomous Enterprise Podcast
01:19 – APAC as a transformation landscape: diversity across markets
05:06 – Customer engagement framework: success plans and integrated toolchain
10:01 – Overcoming transformation barriers and the role of AI
14:01 – Customer success stories: Fujitsu and a manufacturing example
26:36 – The first 90 days of AI adoption: people, decisions, blockers
30:23 – Closing advice: start with the business problem
34:36 – Outro

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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 Chapters
00:00 Welcome to the SAP Autonomous Enterprise Podcast
00:30 Guest introduction: autonomous spend and the five domains
02:43 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 point
12:49 Trust, auditability and human intercepts
14:56 The state of agent development
17:00 The honest first step: governance and data

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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 Chapters
00:00 – Introduction: SAP Autonomous Enterprise Podcast
00:54 – Guest introduction: Mark Wheeler, NTT DATA Business Solutions
01:33 – 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 results
09:56 – Why AI transformations fail: economics, feasibility, and user adoption
14:24 – How to identify where to start and which use cases are ready to scale
16:29 – Partner business model transformation: from billable days to outcome delivery
19:51 – Human-centered design in an AI-first world: the SAP App House alliance
22:49 – Final question: the one thing a leader must get right for autonomous enterprise
25:02 – Outro

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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 Chapters
00:00 – Introduction: SAP Autonomous Enterprise Podcast
00:29 – Meet Alexander Kläger: President, SAP Middle and Eastern Europe
00:56 – AI patchwork: what executives are actually doing versus what they say
02:08 – Beyond the hype: decision-makers want results, not participation
03:34 – Behind closed doors: the fear executives will not say out loud
04:49 – The autonomous enterprise in the boardroom: closing the knowing-doing gap
06:18 – The 12-to-18-month window: what you cannot purchase back
07:34 – What unlocks a Mittelstand CEO
09:19 – Why SAP's business context argument lands with executives who have tried generic AI
10:39 – Five years from now: winners will rethink, not just retrofit
11:52 – Outro

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Does AI make us more sustainable, or does it simply accelerate the status quo? In Episode 15, Sophia Mendelsohn, Chief Sustainability and Commercial Officer at SAP SE, brings two decades of sustainability leadership to bear on the question every supply chain executive is quietly asking. Her answer is direct: AI is a mirror. It will only reflect the prompts and training data we give it. If we change the input, we change the outcome. This conversation covers what it actually takes to move from aspiration to autonomous action.

Sophia walks through three concrete stages of what AI-powered sustainability software can do: help leaders see what was previously invisible, fill in data gaps transparently, and automate decisions that today require four or five people on a call. She argues that supply chain leaders should stop thinking about sustainability as a compliance burden and start thinking about it as a financial equation, with measurable ROI on every environmental input. She also draws a clear boundary around what AI cannot do: negotiate with suppliers, manage team dynamics, or replace the human work of driving systemic change.

Episode Chapters
00:00 – Introduction: SAP Autonomous Enterprise Podcast
00:30 – Welcome and guest introduction
01:05 – AI as a mirror: does it make us more sustainable?
03:16 – The elephant in the room: AI's resource footprint
05:14 – SAP sustainability portfolio and the three-stage AI framework
09:43 – SAP as its own first customer: lessons learned
11:31 – Scope 3 emissions: what AI can and cannot do
14:44 – From compliance burden to competitive advantage
17:51 – Two decades on the front line: one old prompt to delete
20:54 – Architecting sustainability into autonomous agents
23:34 – Greenwashing, trust, and the coming wave of consumer power
26:20 – Closing advice for leaders: start with your data
27:21 – Individual call to action: update the prompt
28:07 – Outro

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Enterprise AI competitive advantage isn't about model access. Eric van Rossum (SAP) explains why growth mindset is what wins.

When every company can access the same foundational AI models, where does competitive advantage actually come from? Eric van Rossum, CMO Autonomous Suite, Product Marketing and Learning at SAP, has a clear answer: the organisations that win will be those grounded in deep process knowledge, proprietary business data, and enterprise-grade governance. But his more provocative argument is that the technology itself is not the constraint. The bottleneck is human and organisational. In a market where AI adoption is compressing from years to weeks and months, the one skill that separates those who adapt from those who don't is not a technical skill. It is growth mindset: the willingness to stay permanently on the learning curve, to be comfortable being consciously incompetent, and to keep adapting faster than the tools do.

Eric walks through SAP's three-part competitive differentiation - process domain knowledge, proprietary enterprise data, and governance - and explains why these create a durable layer above commoditised frontier models. He introduces the competency pyramid as a practical framework for navigating the AI adoption curve and argues that every AI project should start with the business outcome and work backwards, not the other way around. He shares how his teams moved from distributed experimentation into governance-led standardisation, and estimates that 30 to 40 percent of a product marketer's execution work is already shifting to AI, freeing capacity for strategic positioning.

When every company can access the same foundational AI models, where does competitive advantage actually come from? Eric van Rossum, CMO Autonomous Suite at SAP, explains why the bottleneck in enterprise AI is human and organizational, not technical, and why growth mindset is the defining leadership competency of this era.

Episode Timestamps and Chapters:

• 00:00 - Introduction and guest welcome
• 00:48 - AI and the future of work: will AI replace jobs?
• 03:28 - Growth mindset: the defining enterprise AI skill
• 07:12 - From executor to orchestrator: the shift already happening
• 08:24 - Organisational bottleneck: why the hard part is not technical
• 11:38 - SAP's three-part competitive edge: process knowledge, data, and governance
• 18:19 - Closing the POC-to-production gap: start with the business outcome
• 21:45 - How Eric's teams use AI daily: from distributed experimentation to governance
• 28:08 - One thing every leader must get right to reach the autonomous enterprise
• 30:51 - Key takeaway and outro

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Uma Rani, President and Chief Product Officer for SAP Cloud ERP Private, sits down with Julia Kloppenburg and Benedikt Gieger to make the autonomous enterprise tangible for the companies that run the world's most complex SAP landscapes. Her starting point is direct: autonomous enterprise is not a future vision - it is available today for customers on the 2025 and 2023 releases, and SAP is actively working to extend it further into the install base.

The conversation covers three interlocking themes. First, clean core: Uma explains why 80% of upgrade effort - 50% testing, 30% custom code - is the direct consequence of accumulated modifications, and why that bill comes due now that the pace of AI-driven innovation has accelerated. The Hitachi Hi-Tech story makes it concrete: 8,000 modifications reduced to 120, upgrade timelines cut from 18 months to weeks. Second, hybrid AI: a newly announced capability that gives customers still running ECC or S4 on-premise - provided they have committed 50% or more of their maintenance revenue to a RISE subscription -access to selected cloud-native agents and the business AI platform without waiting for a full migration. Third, the upgrade cadence argument: with SAP shipping more than 500 innovations in a single feature pack, customers who skip release cycles are not saving effort - they are accumulating it, and eventually pricing themselves out of the AI opportunity.

Uma closes with a leadership challenge that cuts across all three themes: autonomous enterprise is technology plus change management, and the technology half is the easier half. Organizations moving from a deterministic world of automation to a probabilistic world of AI agents need to rewire not just their systems but their readiness to operate differently. SAP is Customer Zero — and that accountability, she argues, is what makes the commitment credible.