Autonomous Enterprise

Execution over announcements. Nicole Berg of valantic on why AI agents fail without data, guardrails, and people-first change management.

What separates companies that actually deploy AI agents from those stuck in proof-of-concept mode? Nicole Berg, Partner and Executive Managing Director at valantic, has seen both sides: 18 years inside SAP across CRM, strategy, and IT leadership, then T-Systems, then one of Europe's fastest-growing SAP consulting firms. Her answer is blunt: execution. Not announcements, not POC portfolios, not agent counts. Execution, grounded in data strategy, compliance readiness, and real change management for every role affected by AI.

In this episode Nicole and the hosts cover the honest case for partner-built AI agents versus SAP-standard ones, why the consulting billing model is broken and what outcome-as-a-service could look like, how valantic built its own orchestration layer for multi-environment agent deployments, what the "Magic Five" skill profile means for the next generation of SAP consultants, and why physical AI and robotics are the next frontier for German engineering companies. The closing leadership question gets a one-word answer worth hearing in context.

Episode Chapters
00:00 Introduction: SAP Autonomous Enterprise Podcast
00:08 Nicole Berg joins from the podcast studio in Walldorf
02:10 valantic as SAP AI focus partner and the 42 percent study question
04:25 Tax agent case: 15 days to 2 hours cycle time reduction
06:37 Partner relationship: eye level, challenge, and trust
07:44 Autonomy needs guardrails: Nicole on her CRN article position
10:02 Three prerequisites: data strategy, compliance, change management
12:53 How AI changes every role, from developers to leaders
16:48 Hiring and upskilling: the "Magic Five" skill profile
20:45 Pricing models: from time-and-material to outcome-as-a-service
24:15 The consulting business model transformation
27:51 Physical AI and robotics partnership
31:20 University partnerships and talent development
32:13 Leadership question: one thing to get right for autonomous enterprise

Listen and Subscribe
Subscribe on Spotify
Listen on your preferred platform

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

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 Chapters
00:00 – Introduction: SAP Autonomous Enterprise Podcast
00:30 – Guest introduction: Shashikant Walimbe, LTM
01:10 – What agentic AI actually means in manufacturing
02:24 – Why plant managers and COOs are right to be skeptical
03:03 – The honest answer to the COO: start small, measure fast
04:33 – Commercial tension: how LTM draws the line on advice
06:23 – Why most AI pilots do not reach production
07:02 – Five reasons agentic projects stall before scale
08:40 – Semiconductor manufacturer case: agents in the SAP backbone
10:14 – Designing trust into agentic systems
12:53 – SAP versus competitors: when does the SAP route genuinely win?
15:29 – Workforce impact: which roles shrink, which change
18:52 – Closing thought: start with one decision that matters

Listen and Subscribe
Subscribe on Spotify
Listen on your preferred platform

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 Chapters
00:00 – Introduction: SAP Autonomous Enterprise Podcast
00:51 – Guest welcome: Adriana Aroulho, SAP Latin America
02:02 – Why now: converging trends driving LatAm transformation
05:02 – Where AI agents deliver the most immediate value
06:33 – Cloud adoption paths: from installed base to business AI
09:20 – Is autonomous enterprise universal across company sizes?
12:14 – The talent and skills gap: a global, not just regional, challenge
15:43 – Partner ecosystem: local complexity and co-creating agents
19:07 – Leapfrog versus sequential maturity: can steps be skipped?
20:46 – One thing every leader must get right: business context
24:20 – Closing and thanks

Listen and Subscribe
Subscribe on Spotify
Listen on your preferred platform

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

Listen and Subscribe
Subscribe on Spotify
Listen on your preferred platform

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

Listen and Subscribe
Subscribe on Spotify
Listen on your preferred platform

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

Listen and Subscribe
Subscribe on Spotify
Listen on your preferred platform

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

Links
Subscribe on Spotify
Listen on your preferred platform

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

Links
Listen on Spotify
Subscribe via PodLink

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

Links
Listen on Spotify
Subscribe via PodLink

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

Links and Resources:

• Listen on Spotify
• Subscribe via PodLink

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.

Sanjay Kulkarni, SVP and Global Head of SAP's Global Delivery Hub, explains how a 12,500-strong services organization translates AI ambition into measurable business value - and why people, not technology, remain the decisive factor in every successful enterprise AI transformation.

What does it actually take to move from AI ambition to measurable enterprise value? In this episode, Sanjay Kulkarni - SVP and Global Head of the Delivery Hub within SAP's Customer Value Group - brings a direct, frontline perspective from leading an organization of 12,500 to 13,000 delivery experts, consultants, architects, and project managers who help customers across every SAP solution and industry turn software investments into concrete business outcomes.
Sanjay opens with a clear diagnosis of where enterprise AI conversations have shifted: from exploratory use cases and proof of concepts to hard questions about scale, governance, and ROI. Customers are no longer asking whether AI is interesting. They are asking how to operationalize it, how to put a hard dollar value on it, and how to make it run responsibly at scale across connected global ecosystems.
Three strategic priorities frame the conversation. First, accelerating time to value - customers expect concrete business outcomes from software, not just successful implementations. Second, embedding AI into core processes in ways that produce tangible, measurable results. Third, using AI to make services delivery itself dramatically more efficient, freeing consultants from repetitive tasks to spend more time solving complex business problems. At the heart of all three, Sanjay returns repeatedly to the same conviction: his top three priorities are people, people, and people.
The General Motors case study illustrates the pattern in action. GM - a long-standing SAP customer managing finance, procurement, supply chain, manufacturing, and HR on SAP - has been embedding Joule and Business AI directly into workflows: helping planners identify supply constraints, supporting procurement sourcing decisions, and enabling finance teams to extract real-time insights from operational data. The goal is not AI as a standalone tool but AI woven into the fabric of how the business runs.
The conversation then turns to what separates organizations that have successfully scaled AI from those still running isolated pilots. Sanjay identifies three consistent markers: a business-outcome-first mindset (not a technology-first one), clean and well-governed data, and genuine organizational transformation - with executive sponsorship, change management, workforce enablement, and a culture of continuous learning treated as non-negotiables.
On the evolving role of SAP consultants, he introduces what he calls a "beautiful trifecta": industry and process knowledge, core product capability, and the technology foundation. All three must come together to shift the consultant's role from functional implementer toward strategic partner who works across lines of business to solve real problems.
The episode closes with a leadership principle that applies well beyond SAP: stop thinking about AI as automation of existing processes, and start rethinking which decisions should remain human, which should be AI-assisted, and which can be fully autonomous. Accountability, Sanjay argues, is not reduced by automation. If anything, it is strengthened.

Manos Raptopoulos, SAP’s Global President Customer Success Europe, APAC, Middle East & Africa, joins Julia Kloppenburg and Benedikt Gieger on what turns AI adoption into real transformation, how regions differ, and what the autonomous enterprise takes across the world.

Manos Raptopoulos, Global President Customer Success Europe, APAC, Middle East & Africa at SAP, joins Julia Kloppenburg and Benedikt Gieger for a journey across a region that spans from the industrial powerhouses of Germany and Japan to the fast growing digital economies of the Middle East and Asia. They explore what separates the organizations achieving measurable business value from those simply deploying more AI, where customers are most eager to experiment with AI and where they need reassurance and trust, and what companies really expect from SAP as a long term partner beyond contracts, licenses and SLAs. Manos shares a customer story that made him proud, practical advice on where to start on the path to the autonomous enterprise, and the one thing every leader needs to get right. It is a transformation that is anything but uniform, yet universal in its ambition from Walldorf to Tokyo, Riyadh, Lagos and Munich.

A look inside Essity: Daniele Tedesco, Global Process Owner (eCom & CX), joins Julia Kloppenburg and Benedikt Gieger on running global digital commerce, and how customer experience, reliability and trusted AI shape the path to the autonomous enterprise.

Daniele Tedesco, Global Process Owner (eCom & CX) at Essity, joins Julia Kloppenburg and Benedikt Gieger to discuss how the global hygiene and health company is approaching the autonomous enterprise. They talk through Essity's perspective on customer experience as the driver: running truly global digital commerce operations across consumer and B2B channels, pairing reliability and flexibility on the S/4HANA and SAP Commerce Cloud backbone, and building trust with both employees and customers as AI and automation move into everyday operations.

Interview with Sindhu Gangadharan, MD of SAP Labs India and Head of Customer Industry Solutions at SAP, on how Indian enterprises are adopting business AI and autonomous enterprise capabilities.

In this episode, Julia Kloppenburg welcomes Sindhu Gangadharan, Managing Director of SAP Labs India and Head of Customer Industry Solutions at SAP, to explore how Indian enterprises are embracing the Autonomous Enterprise vision. Sindhu shares how customers reacted post-Sapphire, which industries in India are moving fastest and how her team turns the Autonomous Enterprise vision into concrete customer projects through co-innovation and design thinking. The conversation also covers governance models, the importance of CEO-led AI strategies, upskilling as a board priority, balancing global strategy with local Indian market requirements, and what to expect in enterprise AI adoption over the next two to three years.

Insights from the CEO and Chief Community Champion of ASUG, the Americas’ SAP User Group, on how SAP customers are adopting the Autonomous Enterprise, trust in AI agents and closing the skill gap.

This is a special episode as for the first time we recorded in video format, in the podcast studio in Walldorf, with Geoff Scott, CEO and Chief Community Champion of ASUG, the largest independent SAP user community in the world. In an exchange with Julia Kloppenburg and Benedikt Gieger, he shares the honest customer reaction to SAP's Autonomous Enterprise vision, discusses whether organizations still on ECC can realistically pursue AI-driven transformation, and explores what it takes for customers to trust AI agents in core business processes. We also cover the skills gap, the evolving role of SAP partners, and ASUG's influence on shaping SAP's strategy through customer feedback.

A look inside Uniper: Achim Löbke, Head of Digital Enablement & Automation, joins Julia Kloppenburg and Benedikt Gieger on enablement as the foundation for the autonomous enterprise and the shift toward AI agents.

Achim Löbke, Head of Digital Enablement & Automation at Uniper, joins Julia Kloppenburg and Benedikt Gieger to discuss together how the energy company is approaching the autonomous enterprise. They talk through Uniper's perspective on enablement as the foundation: bringing people along, driving digital transformation toward autonomous AI agents, and pairing automation with the enablement it takes to give end users seamless support as agents move into everyday operations.

Stephan Klein talks about what it takes to move from AI enablement to real transformation in the enterprise, and what autonomous domain blueprints mean in practice.

In this episode, Julia Kloppenburg and Benedikt Gieger talk with Stephan Klein, Chief Business and Delivery Officer for Customer Engagement and Adoption at SAP.

Stephan supports the Agentic AI Zone initiative. In this conversation, they dig into what good delivery actually looks like today, why enablement and transformation are not the same thing, and where most organisations get stuck when trying to move forward with AI. They also talk about what it means to shift from human-centric workflows to machine-centric process design, and why that mindset shift is harder than most teams expect.

The episode also touches on where enterprise AI is heading: from assistants to agents to fully autonomous business domains, and what that means for how organisations will operate in the next few years.

Augusta Spinelli, SAP's President for Europe, Middle East and Africa, joins Julia Kloppenburg and Benedikt Gieger on the two paths to the autonomous enterprise: RISE with SAP and SAP Grow, agent-led transformation, and moving from ambition to execution.

Augusta Spinelli joins Julia Kloppenburg and Benedikt Gieger to unpack the two paths to the autonomous enterprise: RISE with SAP for established enterprises and SAP Grow for fast-scaling companies, both leading to a modern, AI-enabled cloud ERP. Augusta explains what the autonomous enterprise means in practice, the role of clean core, and the new agent-led toolchain in RISE that embeds AI directly into the transformation. She also covers how eligible customers can bring Joule assistants and agents to their on-prem systems during the hybrid transition, the four pillars of SAP Grow, and the one thing leaders need to get right: building a solid data and process foundation.

Michael Ameling, President of the SAP Business AI Platform, joins Julia Kloppenburg and Benedikt Gieger on why enterprise AI just changed: one unified platform to build, run, and govern agents at scale, grounded in 50 years of SAP process expertise.

Michael Ameling joins Julia Kloppenburg and Benedikt Gieger to unpack the new SAP Business AI Platform: SAP's shift from single AI features to one unified platform for building, running, and governing agents at enterprise scale. Michael covers how the SAP Knowledge Graph and SAP RPT-1 ground agents in real business context, the three pillars of Build, Contextualize and Reason, and Govern, and how Joule Studio, the SAP Integration Suite, and the SAP AI Agent Hub work together. He also explains how SAP combines its own domain models with frontier models from OpenAI, Anthropic, and Mistral through the Generative AI Hub.

Manoj Swaminathan joins Julia Kloppenburg and Benedikt Gieger to break down SAP's Autonomous Suite, what it is, how it works, and why it changes everything for enterprise AI.

What does it actually mean for an enterprise to run autonomously? In Episode 3 of the Autonomes Enterprise Podcast, Julia Kloppenburg and Benedikt Gieger sit down with Manoj Swaminathan, one of SAP's leading voices on AI and the Autonomous Suite, to answer exactly that question.

Manoj walks us through the vision behind the Autonomous Suite, how SAP is moving beyond automation into truly autonomous systems that can reason, decide, and act. He explains how Joule and Agentic AI fit into the bigger picture, and what this shift means for IT leaders and business users alike.

Together, they dig into real-world applications, the readiness of enterprises to embrace autonomy, and what separates organizations that are thriving in this new era from those still catching up.

A must-listen for anyone building, buying, or curious about the next generation of enterprise software.

Ferose V R, breaks down the Autonomous Enterprise concept from its foundations—explaining how AI agents transform work, what it means for people, and why humanity must stay at the center.

In this foundational episode, Julia and Benedikt welcome Ferose V R to break down the Autonomous Enterprise concept from the ground up. With so many years at SAP and teaching experience at Columbia University, Ferose explains how enterprise software is fundamentally shifting—from humans adapting to software, to software adapting to humans through AI agents that understand intent and orchestrate across systems.

He illustrates this transformation with concrete examples, showing how multiple AI agents collaborate behind a single prompt. Ferose addresses the real fears around job transformation, emphasizing how work evolves from being operators of processes to orchestrators of outcomes. He shares why leaders must communicate with honesty, humanity and clarity, and explains the essential skills needed: systems thinking, comfort with ambiguity, and lifelong learning. This episode establishes the core concepts and human-centered philosophy that will guide the entire podcast series.

At SAPPHIRE, Christian Klein presented the Autonomous Enterprise as a central part of SAP’s strategy. In this episode, Peter Maier explains how governed Business AI turns enterprise data into decisions, automation, and verifiable business results.

In this kickoff episode of the “Autonomous Enterprise” podcast, hosts Julia Kloppenburg and Benedikt Gieger speak with Peter Maier, who leads Strategic Customer Engagements for SAP’s CEO. They discuss how the Autonomous Enterprise is becoming a concrete operating model for leading companies, built on enterprise‑grade, governed Business AI embedded in ERP. Peter shares key observations from SAPPHIRE in Orlando and Madrid, where board and C‑level discussions shifted from testing AI in pilots to scaling it into production for measurable outcomes.

The conversation focuses on three core dimensions: business value beyond token consumption, better predictability based on trusted business data, and the ability to scale AI agents from a single use case to end‑to‑end processes. They also address governance, auditability, and change management so organizations can stand behind every AI‑driven decision. The episode closes with a clear recommendation for executives: start with one critical, traceable process, validate the impact, and then expand the autonomous enterprise step by step.