Each day, businesses invest in new software tools: marketing platforms, commerce engines, service systems, and sales technology. They are told that assembling the right combination will unlock their digital transformation journey and finally deliver that personalized and seamless experience they wish to provide to their customers.

But what if more or better software is not necessarily the answer? Each solution solves a real problem for a specific team. But collectively, they create a consequence no one planned for: every new tool builds its own data world, without a common language or context across them.

In a 2026 study by Oxford Economics, only 25% of respondents described their customer experience (CX) technology environment as fully harmonized and integrated. Twenty-nine percent remain highly fragmented. Organizations with siloed CX tech are more likely to face an inability to connect customer needs to actionable data insights. Fifty-eight percent reported this challenge, compared to 47% among those with harmonized environments.

More than what tools businesses choose to add to their CX landscape, how they connect and interact with each other becomes even more important.

A unified data strategy sounds straightforward in principle. In practice, most businesses find that the obstacle is not ambition but rather execution. Every integration decision made without a clear data architecture becomes a future campaign mired in manual reconciliation, a customer journey that breaks at the handoff, or a personalization promise the disparate data sources cannot support. Implementation without a validated strategy creates new fragmentation inside the solution meant to eliminate the old. And without a structured way to pressure-test decisions before any commitment is made, even well-resourced organizations find themselves repeating the same cycle: invest, integrate, fragment, repeat.

The real barrier is not budget or technology

A Forrester study of more than 1,000 senior executives found that data quality and integration issues are cited more than any other factor as the top cause of delayed or derailed transformation projects (39%), ahead of budget, technology maturity, and talent. The same study found that 56% of respondents struggle with poor data quality; 55% face persistent data silos. This is not for lack of investment in technology, but because the connections between systems were not designed or maintained effectively.

What bridges that gap is not another platform, it is the expertise to think through data connectivity decisions before they are made and the ongoing guidance to ensure those decisions compound into measurable gains over time. The Advanced Success Plan for SAP Customer Experience solutions provides that guidance along every step of the journey.

Define what success actually looks like

The most common reason data unification projects fall short of expectations is not technical failure, it is a failure to define and measure what success looks like for the entire business before the work begins.

The value management session from the Advanced Success Plan for SAP Customer Experience establishes that definition at the outset: What does a fully unified data strategy actually enable? It means running the next marketing campaign without manual data reconciliation, and presenting an AI readiness road map without caveats. Stakeholders will know, at every checkpoint, whether the investment is moving the business forward, not just moving the project forward.

Design the strategy before building the integrations

A robust data strategy starts with good design. Product guidance from the Advanced Success Plan for SAP Customer Experience covers available out-of-the-box integrations, common usage scenarios, and pitfalls and how to avoid them—all delivered in a live remote session by an SAP expert who can answer questions in real time. The data strategy can be conceptualized and pressure-tested before any budget or technical commitments are made.

Validate every critical decision with expert guidance

With the technical assistance and functional assistance from the Advanced Success Plan for SAP Customer Experience, businesses have continuous access to expert guidance at every critical decision point. Beyond resolving immediate questions, the ongoing access also shares insight on how SAP thinks through problems, strengthening in-house expertise with every interaction. The result is an organization that makes better decisions not just now but for the future.

Measure whether the strategy is delivering

Adoption and innovation checkpoints conducted on a quarterly or semi-annual basis bring the measurement back to where it started: the business outcomes defined at the outset. Do campaigns run without manual reconciliation? Is the AI use case performing against its stated goals? A clear throughline from the value management success KPIs to the adoption and innovation checkpoints proves the benefits of the investment.

Where cycles and silos break

The cycle of invest, integrate, fragment, repeat is not inevitable. It is the predictable result of making technical decisions without an anchoring business imperative, and integration decisions without strategic expert guidance. Organizations that break the cycle do not necessarily have better technology than their competitors—they have better judgment about how to use it.

The Advanced Success Plan for SAP Customer Experience exists for exactly that reason: to put proactive and prescriptive guidance at every decision point where that judgment matters most. When data strategy is designed before it is built, validated before it is committed, and measured against real business outcomes, the technology investment already made starts working harder.

The stack was never the problem. When the thinking behind the technology finally matches its ambition, the personalized, seamless experience becomes a reality.

Tara Tracey, global product owner for the Advanced Success Plan for SAP Customer Experience.
Ella De Torres, product manager for the Advanced Success Plan for SAP Customer Experience.

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.