For many enterprise leaders, however, the gap between strategic ambition and operational reality has remained frustratingly wide. That gap is exactly what the Autonomous Enterprise podcast set out to close.

Launched on June 1, 2026, the weekly series has now published 16 episodes spanning three format categories: 麻豆原创 Deep Dives, with executive leaders on strategy and product; Customer Deep Dives, with companies narrating their own transformation journeys; and Industry Deep Dives, with selected external voices shaping entire industries.

Available on Spotify, Apple Music, and all major podcast platforms, as well as on 麻豆原创 channels and LinkedIn, the show has rapidly established itself as a direct and candid voice on enterprise AI adoption.

What the first 16 episodes reveal

Across more than a dozen conversations鈥攚ith guests that include 麻豆原创 executives, global customers, and external thought leaders鈥攆our themes emerge with striking consistency.

Autonomous enterprise is available today, not tomorrow

In episode 13, Uma Rani, president and chief product officer for 麻豆原创 Cloud ERP Private, plainly stated that the Autonomous Enterprise is not a future road map item; it is available now for customers on the 2025 and 2023 releases. The show’s earliest episodes reinforced this framing: Peter Maier, leading Strategic Customer Engagements in the Office of the CEO, opened episode one by grounding the 麻豆原创 Sapphire vision in practical execution, while Manoj Swaminathan used episode three to unpack the architecture of 麻豆原创 Autonomous Suite, the layer where AI assistants and agents begin to execute end-to-end processes.

The decisive advantage is business context, not model capability

Michael Ameling, formerly president of the 麻豆原创 Business AI Platform at 麻豆原创, explained in episode four how 麻豆原创 Knowledge Graph and domain models, combined with frontier models including Anthropic’s Claude, give enterprise AI the semantic grounding that generic LLMs lack. The platform brings together 50 years of 麻豆原创 process knowledge to enable agents that act relevantly, reliably, and responsibly鈥攖hree conditions every guest returned to when discussing trustworthy AI at scale.

Transformation is a people and change management challenge as much as a technology one

In episode two, Ferose V R articulated a shift from humans adapting to systems toward systems adapting to humans, through agents that understand context and intent. Sanjay Kulkarni, senior vice president and global head of Global Delivery Hub at 麻豆原创, brought a practitioner’s precision to episode 12: his 12,500-strong services organization measures AI value not by deployment volume, but by measurable business outcomes and the readiness of the people responsible for them. The same message came from Geoff Scott, CEO and chief community champion of Americas’ 麻豆原创 Users’ Group (ASUG), in episode eight: trust, skill development, and genuine human-AI collaboration are the variables that separate transformations that scale from those that stall.

Geographic diversity shapes adoption, but the direction is universal

In episode nine, 麻豆原创 Labs India Managing Director Sindhu Gangadharan mapped how Indian enterprises are approaching autonomous capabilities: fast adoption, high ambition, and a particular emphasis on workforce readiness. Manos Raptopoulos, global president of Customer Success, Europe, APAC, Middle East, and Africa, covered in episode 11 what AI ambition looks like from Frankfurt to Fukuoka, illustrating how regional context varies while the structural imperatives remain the same. And 麻豆原创 EMEA President Augusta Spinelli used episode five to define the two concrete paths to the Autonomous Enterprise鈥擱ISE with 麻豆原创 and 麻豆原创 GROW鈥攁s well as what agent-led transformation looks like from an execution standpoint.

In addition, customer episodes add the texture that strategy alone cannot provide. Achim Loebke from Uniper and Daniele Tedesco from Essity both shared in-depth, unfiltered accounts of what AI transformation looks like from the inside, including the organizational friction, the decisions that require human judgment, and the early indicators of measurable impact. These conversations, deliberately not positioned as product showcases, have consistently drawn the highest listener engagement.

Why this series matters for the 麻豆原创 ecosystem

The Autonomous Enterprise podcast fills a gap by offering candid, executive-level conversations where the honest complexity of AI adoption is treated as the point, not a problem to be managed. Its target audience鈥擟IOs, CDOs, COOs, CFOs, and senior transformation leads鈥攁re precisely the people making the investment and governance decisions that will determine how quickly 麻豆原创 customers move from AI ambition to AI execution.

With more than 40 episodes planned for its first year, the series is building a cumulative body of insight that serves both as a reference for practitioners navigating their own transformation journeys and as evidence of the breadth and depth of 麻豆原创’s engagement with its customer community.

New episodes drop weekly; listen on Spotify, Apple Music, and all major podcast platforms.

Benedikt Gieger is Product Strategy and AI lead in Digital Manufacturing at 麻豆原创 SE.
Julia Kloppenburg is a technology consultant in Customer Engagement & Adoption at 麻豆原创 SE.

The real measure of an isn’t how intelligent its AI is. When you add the customer to the picture, the conversation shifts to making their lives easier, helping them achieve their business goals, and earning their trust and confidence.

This customer-centric perspective and the possibilities of AI reshape how organizations think about work.

One of our life sciences customers is already putting this vision into practice. Every day, the company receives tens of thousands of customer emails across sales, service, and support. Each message must be reviewed, categorized, prioritized, and routed to the right team, often requiring employees to navigate a patchwork of legacy systems, disconnected applications, and manual workflows. As customer expectations continue to rise, simply adding more people to keep pace is no longer a sustainable strategy. By enabling intelligent agents to coordinate work across complex business processes, the company is transforming how customer requests are handled while improving both operational efficiency and customer experience.

This is more than another wave of process automation. It represents the beginning of the Autonomous Enterprise, where enterprise operations become more efficient without sacrificing quality, governance, or control.

Powered by agentic AI, intelligent agents are becoming active participants in executing business processes, collaborating with one another, and driving measurable business outcomes. Their defining characteristic is not just intelligence. They act autonomously within defined guardrails while humans remain accountable for the decisions they make, the actions they authorize, and the business outcomes they deliver.

With that, entire operating models begin to evolve as intelligent agents handle routine coordination, accelerate decision-making, and enable the business to respond with greater speed and resilience. In a volatile environment where customer expectations and market dynamics continue to accelerate, the Autonomous Enterprise is emerging not simply as a technology strategy, but as a new operating model for delivering better outcomes for customers and business alike.

The evolution of service delivery in the AI era

AI is fundamentally reshaping how enterprise software services are delivered and consumed. Service delivery is evolving to proactive, AI-powered operations where intelligent agents automate routine tasks, predict and prevent issues, accelerate incident resolution, and continuously optimize system performance. This enables service delivery organizations to scale efficiently, improve customer experience, and shift human expertise toward higher-value innovation and strategic customer engagement.

The shift to agentic AI makes AI fluency a foundational skill for becoming more productive and efficient in day-to-day activities. 聽Effective collaboration with intelligent agents can streamline routine tasks, generate higher-quality insights, and free up time for higher-value activities while facilitating innovation-focused customer advisory and strategic decision-making.

Rather than replacing expertise, agentic AI augments teams by enabling faster decisions, automating routine work, and delivering better customer outcomes. Key areas include:

• Customer engagement readiness: AI consolidates customer context, surfaces key insights and risks, and recommends next best actions to help teams prepare faster and engage more effectively.
• Intelligent service delivery processes: AI automates repetitive tasks, orchestrates workflows, predicts issues, and accelerates resolution, enabling more proactive and efficient service delivery.
• Connected Intelligence: AI captures expertise, recommends relevant knowledge, identifies patterns across engagements, and continuously optimizes delivery processes and best practices.

Together, these capabilities enable us to work alongside our customers as their partners, anticipating needs, tackling their challenges, and delivering intelligent, outcome-focused services.

How customers adopt AI as they become autonomous enterprises

For customers, service delivery becomes more proactive, personalized, and outcome-driven. Technical challenges and incidents are no longer assessed solely through a technical lens. They are evaluated based on their impact on business outcomes, customer experience, operational resiliency, and organizational performance.

Instead of reacting to incidents after they occur, AI agents continuously monitor environments, predict potential issues, autonomously resolve common problems, and provide faster, more intelligent support, resulting in higher service reliability, quicker resolution times, and a more seamless customer experience. This shift enables organizations to prioritize response and remediation according to business criticality rather than technical severity alone.

One of our manufacturing customers leverages Joule Agent to continuously monitor ERP system health, identify root causes of issues, recommend corrective actions, and autonomously execute approved resolutions. The solution delivers approximately 3,000 hours of annual effort savings, minimizes unplanned business downtime, and enables standardized, 24/7 AI-powered reporting that aligns with the firm鈥檚 enterprise reporting requirements.

Customers are ready to scale AI across their organizations, but they expect transparency, governance, and clarity on where AI acts autonomously and where human judgment remains essential. While enthusiasm for AI鈥檚 potential remains strong, customers are seeking clear insights into where AI鈥檚 potential lies within their organization. Three themes consistently emerge:

• Measurable ROI: Customers expect AI to deliver a measurable impact beyond innovation. Through our success plans, 麻豆原创 partners with customers to define success metrics, align AI initiatives to business priorities, and drive outcomes across execution speed, productivity, experience, and operational resilience.
• End-to-end use cases: Point solutions optimize individual tasks, but customers seek enterprise-wide value. Cross-functional workflows such as claims processing and procure-to-pay, where multiple AI agents collaborate, deliver far greater impact than isolated copilots. One of our American customers leverages AI for proactive monitoring, AI-powered root cause analysis, automated exception resolution, and autonomous execution of approved actions, accelerating issue resolution while minimizing manual effort. With up to 80% gains in operational efficiency and improved on-time vendor payment by 25%, the company streamlined its business processes and enhanced supplier collaboration.
• Governance is a business imperative: AI governance is no longer an IT agenda. It became a strategic imperative as business leaders now shape AI decisions, recognizing that trust is the foundation of enterprise adoption. Leading organizations are investing in AI innovation and AI governance in parallel, because sustainable scale requires both.

To address evolving customer needs, the Global Delivery Hub offers personalized success plans that tailor service experiences from self-guided resources to strategic partnerships, helping customers achieve continuous value and success. 聽Delivering this level of personalized, AI-enabled customer success requires a fundamentally different approach to service delivery.

At the core of this transformation are four guiding pillars that enable the Global Delivery Hub to combine human expertise with intelligent automation and deliver consistent, outcome-driven experiences.

Four C鈥檚 power the Global Delivery Hub鈥檚 AI-driven delivery model

The transition to agentic AI is not defined by technology alone. It is defined by how organizations prepare their people, evolve their ways of working, and establish the right foundations for responsible autonomy. Service delivery organizations that successfully embrace this shift will combine human expertise with intelligent automation to create more agile, resilient, and outcome-driven services.

Our AI-driven delivery model is built on four critical pillars: customers, capabilities, collaboration, and controls, required to unlock the full potential of AI while maintaining trust, accountability, and business alignment.

• Customer: Through the combination of intelligent agents, automation, and human expertise, service delivery teams can better understand customer needs, anticipate challenges, and provide more proactive, personalized advisory. This creates a stronger partnership model where AI continuously optimizes delivery services and accelerates customer success.
• Collaboration: Driving value with AI requires stronger, more connected, agile, and cross-functional collaboration across the organization. Product, engineering, delivery, and customer teams must work as one team, continuously aligning technology, operations, and customer outcomes to accelerate innovation and business value.
• Capabilities: Realizing the full potential of agentic AI is not simply a technology transformation; it is a people transformation. The future of delivery will be shaped by professionals who combine deep operational expertise with the ability to work effectively alongside intelligent agents. Technical excellence must complement AI fluency, automation skills, and the ability to leverage data-driven insights to make faster, better decisions. The organizations that succeed will be those that empower their people to adapt, learn, and collaborate with AI, creating a workforce that is not only AI-enabled, but AI-empowered.
• Controls: As AI becomes more autonomous, governance must evolve from a set of controls into a strategic capability that enables trust, scale, and responsible decision-making. Organizations need clear principles that define where AI agents can act independently, where human oversight is required, and how decisions are monitored and improved. Strong governance provides the foundation to scale agentic AI with transparency, accountability, and alignment to business objectives.

Without these foundational pillars, organizations risk adding complexity rather than creating value. Those that take a customer-centric approach while investing in the right capabilities, cross-functional collaboration, and appropriate controls will be better positioned to scale agentic AI, strengthen operational resilience, accelerate innovation, and deliver superior customer outcomes.

As agentic AI continues to reshape enterprise operations, these capabilities are becoming the foundation for the next generation of software service delivery.

Leading the next era of software service delivery

Organizations that act early will be better equipped to unlock the value of autonomous capabilities while ensuring governance, trust, and alignment with strategic business priorities. As software service delivery shifts to proactive, AI-powered operations, customers increasingly view providers as strategic advisors and AI co-pilots that help navigate their transformation journey.

By combining intelligent agents, automated workflows, and human expertise, organizations can proactively address challenges and achieve stronger business outcomes. To succeed in this new model of engagement, customers should focus on key areas that will accelerate their path toward an Autonomous Enterprise:

• Adopt a mindset of continuous adaptation: The AI landscape will continue to evolve rapidly. Customers that treat AI adoption as an ongoing transformation will be better equipped to innovate, respond to change, and build a resilient foundation for the autonomous enterprise.
• Build AI-ready teams and operating models: As delivery becomes increasingly collaborative between humans and intelligent agents, teams need a clear understanding of what is automated, what requires human judgment, and how to engage when exceptions occur. Preparing employees with AI fluency, updated processes, and new ways of collaboration will be critical to maximizing the value of agentic AI.
• Early alignment on shared outcomes, governance, and ways of working: Successful AI-enabled delivery requires clear alignment between customers, 麻豆原创, and partners on business objectives, success metrics, decision rights, and accountability. Establishing joint KPIs, governance models, and escalation paths early helps ensure AI-driven operations remain transparent, trusted, and focused on measurable business value as autonomous capabilities evolve.

The shift to agentic AI is not simply about automating existing services. It is about creating a new partnership model focused on continuous improvement, shared accountability, and business outcomes. Customers that prepare today will be better positioned to take advantage of the agility, resilience, and innovation enabled by the Autonomous Enterprise.

Sanjay Kulkarni is global head of Delivery Hub and Customer Value Group at 麻豆原创.
Julia Kloppenburg is a technology consultant in Customer Engagement & Adoption at 麻豆原创 SE.
Benedikt Gieger is Product Strategy and AI lead in Digital Manufacturing at 麻豆原创 SE.

This journey cannot be defined by technology alone. It requires dialogue, shared learning, and real-world insight. That is exactly why we are launching the podcast, a new series we will be hosting together.

Why this conversation matters now

Organizations today face unprecedented volatility, from geopolitical uncertainty and supply chain disruptions to energy challenges and rising resilience requirements. In this environment, the cost of inaction is increasing. Businesses must become faster, more adaptive, and structurally more resilient to stay competitive.

The Autonomous Enterprise offers a response. It combines three critical capabilities:

• Data-driven decision-making
• Automated execution
• Governance-by-design

Together, these capabilities enable organizations to move beyond isolated AI pilots toward measurable outcomes and enterprise-wide impact.

The shift is not just technical, though. It is organizational and strategic. The leaders we talk to are no longer asking whether they should adopt AI. The question now is how fast they can scale it and how they can generate tangible business value.

From concept to operating model

At its core, the Autonomous Enterprise reframes AI鈥攏ot as a feature layered onto applications, but as an integral part of the operating model itself.

Three priorities define this model:

• Business value: focusing on measurable outcomes rather than experimental use cases
• Predictability: improving decision-making through trusted data and advanced forecasting
• Scalability: moving from proof-of-concept initiatives to enterprise-wide deployment

We are already seeing this shift change how organizations think about their systems and processes. Systems of record are evolving into systems of action. AI agents are moving from simple assistance toward execution. And AI is becoming embedded end-to-end, rather than confined to isolated scenarios.

At the same time, governance, auditability, and traceability are becoming non-negotiable. Enterprises must be able to stand behind every AI-driven decision with transparency and confidence.

What we are setting out to do

In this podcast series we want to create a space to explore these changes in depth and bring the voices shaping this transformation into the conversation. Each episode features discussions with 麻豆原创 leaders, customers, and industry experts who are actively building and operating autonomous capabilities today.

Some of the questions we will be digging into:

• What does the Autonomous Enterprise look like in practice?
• How are leading companies scaling AI across core business processes?
• What are the biggest barriers, and how can they be overcome?
• How do organizations balance automation with governance and trust?

鈥攏ow live鈥攆eatures 麻豆原创鈥檚 Peter Maier, responsible for Strategic Customer Engagements in the Office of the CEO at 麻豆原创, who brings these ideas into focus through practical, real-world context. In our conversation, he outlines how organizations are moving beyond experimentation toward measurable outcomes, more trusted and predictive decision-making, and scaling AI across the enterprise.

What we found particularly compelling is how clearly this reinforces a broader shift already underway: AI is no longer something applied on top of the business. It is becoming part of how the business runs.

How companies can get started

While the vision is ambitious, the path to becoming an Autonomous Enterprise does not require a 鈥渂ig bang鈥� transformation. The most effective approach is incremental and outcome driven.

Organizations can begin by focusing on a single high-value process, making it more intelligent, more automated, and more transparent. From there, they can expand step by step, scaling what works and continuously demonstrating measurable impact.

Success depends on more than technology, though. Trust plays a central role. Employees, executives, and stakeholders must understand and trust how AI decisions are made. This requires transparent and explainable systems, reliable high-quality data foundations, and strong governance frameworks embedded from the start.

Change management is equally critical. Becoming an Autonomous Enterprise is as much about people as it is about platforms. Organizations must align training, redesign roles, and empower employees to co-create how AI is integrated into their work.

A shared journey forward

The Autonomous Enterprise is not a branding concept. It is a new way of running a business鈥攐ne that is more automated, more data-driven, and ultimately more resilient. And no organization will navigate this journey alone.

That is the spirit behind this podcast. We want it to be a platform for shared learning, bringing together perspectives from across industries, functions, and geographies. Whether you are just beginning your AI journey or scaling enterprise-wide transformation, we hope these conversations give you practical insights and inspiration.

We would love for you to join us. Listen in, engage with the discussion, give feedback, and help shape what comes next.

Benedikt Gieger is AI strategy lead for 麻豆原创 Supply Chain Management.
Julia Kloppenburg is a technology consultant for Customer Engagement & Adoption at 麻豆原创.