The recent selloff in SaaS stocks misreads the real disruption. It reveals why enterprise software is more essential than ever. 

Artificial intelligence is the most consequential technology shift since the internet—and the most transformative thing to happen to enterprise software. Not because AI threatens it, but because AI needs it. The breakthroughs in reasoning, code generation, and autonomous agents are real, and they will reshape every industry. 

I see it firsthand. AI is driving double-digit efficiency gains across our own operations. In more than two-thirds of our 2025 fourth quarter cloud deals, customers chose to include AI capabilities. Manufacturers are using AI agents to automate quotation processes, cutting response times dramatically. Consulting teams are reclaiming a quarter of their working week for higher-value work. This is not hype; it is happening, at enterprise scale. 

Every major platform shift follows a pattern. Early on, value accrues to the lowest layers of the stack: the compute, the models, the infrastructure—the shovels in the gold rush. Over time, enduring value migrates upward to the application layer, where technology translates into business outcomes. The internet made that clear. Cloud computing confirmed it.

AI will be no different. Software is not reaching the end of the line; it’s just getting started. In other words: software is becoming AI’s superpower. 

Where the real value lies 

Across industries, companies are pouring billions into AI, driven by real breakthroughs in capability and productivity. Yet many are struggling to translate experiments into measurable, enterprise-wide outcomes. The root causes are well-known: fragmented data landscapes, siloed processes, inconsistent governance, and AI bolted on to aging legacy systems.  

Regardless of their industry or size, every customer I speak with wants one thing: AI that deeply understands their business and does so securely and reliably. That requires integrated applications, harmonized business data, and clear controls. Without these, AI operates in a vacuum, disconnected from business reality. 

If it doesn’t understand how finance connects to procurement, how a supply chain interacts with manufacturing, what compliance rules govern a transaction, or how to handle exceptions, AI cannot reliably run a business. The smallest mistake—using outdated, incomplete, or incorrect data—can quietly cascade into wrong decisions, faulty transactions, and significant losses before anyone notices.

Far from eliminating software, AI exposes the indispensability of the systems that coordinate work at scale.  

Enterprise AI succeeds where agents and governance meet 

Building an agent is becoming increasingly easier—the tip of the iceberg. Deploying it across end-to-end supply chains or financial close processes, with full compliance and audit trails, is where most of the effort lies. Orchestration, policy enforcement, and workflow determinism are the gatekeepers of trust. The more autonomous agents you deploy, the more valuable the governed systems that constrain and supervise them become, and that’s where the platforms that already run the world’s core operations come into their own.

What agents need to operate at scale 

To deliver real outcomes reliably, agents need three things. First, deep domain and industry knowledge encoded in systems, so agents understand context, relationships, and end-to-end processes. Second, accurate, semantically rich business data that provides a reliable source of truth. And third, enterprise-grade governance: validation rules, compliance checks, approval flows, identity management and audit trails to keep autonomy safe.  

These are the elements that separate the AI that can truly and reliably run a business from the AI that merely impresses in a demo. 

What changes, what stays true 

AI makes software faster and cheaper to build. Large language models will be commoditized. Business models will evolve as usage patterns shift from users to agents. Entirely new interfaces will emerge. Users will increasingly converse with AI rather than navigate applications, and front-ends will be generated dynamically in real time. 

But the need for continuously updated, governed systems only grows. AI raises the bar for secure updates, telemetry-driven improvement, and shared controls: all strengths of mature SaaS. AI agents don’t replace enterprise software. They rely on it.  

The winners will not be those who own marginally better foundation models. They’ll be those who deliver value at the application layer: business outcomes grounded in deep domain expertise, integrated across functions, and governed for deployment at scale. 

Software is becoming the operating system for trusted autonomy. The companies that recognize this will embed AI into the systems that run the world’s economy. The rest will run more experiments, generate more prototypes, and wonder why the outcomes lag the hype. 

Long live software. 

Christian Klein is CEO of SAP SE.

For decades, BSH Hausgeräte GmbH has been a quiet force in kitchens worldwide, with brands like Bosch, Siemens, and Gaggenau in its portfolio. Now, the company is rewriting its own recipe—not for food, but for finance. In partnership with SAP, BSH is transforming its IT landscape to a cloud-first foundation, aiming to free employees from manual tasks and put insights at the center of decision-making.

In an interview, BSH Head of Governance, Methods, and Systems Heiko Schletz explained how the company is reshaping finance and why its move to the cloud is a critical ingredient to a successful AI-enhanced future.

Technology follows vision

Represented in more than 50 countries, BSH manufactures home appliances in 39 factories worldwide. Schletz’s team oversees group controlling and is responsible for ensuring that financial data flows smoothly from its core systems, such as ERP, all the way up to the consolidated group level. His team manages how financial data is structured and integrated across all global entities, ensuring it can be used effectively for company-wide reporting and decision-making.

BSH is working to bring accounting and controlling together into one integrated process, supported by real-time data and analytics. One principle is guiding this transformation: technology follows vision, not the other way round. As part of its transformation journey, BSH is embracing change by testing new technologies to support its vision.

To simplify reporting efforts, for example, BSH is currently piloting SAP Datasphere, SAP’s next-generation data management platform that can unify and govern all SAP data and seamlessly connect with third-party data.

A recent use case automatically connected accounting balances, controlling P&L data and market metrics in SAP Datasphere and delivering consolidated reports without spreadsheets and manual effort. “This shows where the journey is going—joining sources, bringing them together,” Schletz says.

Breaking down silos to empower AI

BSH’s long-term goal in the financial area is to get rid of silos between accounting, controlling, and treasury. Schletz envisions a parallel ledger architecture that supports both—legal entity and consolidated group views—enabling advanced analytics such as value-driver trees. By moving to SAP S/4HANA Cloud Private Edition, integrated with SAP Datasphere and SAP Analytics Cloud, BSH aims to create a single source of truth for finance spanning from subsidiary ledgers to group-level consolidation.

Schletz is convinced that with a cloud-based, synchronized toolset, his finance team can deliver the latest figures for decision-making faster and with less manual consolidation. “SAP’s AI evolution is running in the direction we also want to go,” Schletz explains. “The technology meets our vision and that’s why it’s a perfect fit.”

The company has relied on SAP solutions for decades, starting with SAP R/3 and now running SAP S/4HANA, SAP Business Warehouse, and SAP Analytics Cloud. The next milestone is cloud migration: RISE with SAP. “In the next two years, we go into the cloud,” Schletz says. “We want a synchronized toolset that gives us a holistic view.”

To get the most out of analytics and AI functionalities, BSH is currently consolidating and simplifying its comprehensive business application landscape. The company’s target is to move from six separate ERP solutions to one global SAP S/4HANA environment that covers all subsidiaries and geographies.

How to prepare an SAP S/4HANA transformation

Schletz’s advice for other organizations exploring the RISE with SAP journey is to start with a clear vision. “If you don’t begin with a concept that combines accounting and controlling, don’t start with SAP S/4HANA,” he says, noting that a finance transformation is not an isolated IT project—it requires alignment across logistics, sales, and customer service. “The SAP S/4HANA conversion is a cross-functional adventure,” he adds.

BSH’s journey is ongoing, but the direction is set. Cloud migration via RISE with SAP, integrated data, and a finance function designed for insight rather than manual effort. “We want the machine to do what it does best, so people can focus on creating value,” Schletz concludes.