The European Union took a landmark step with the EU Pay Transparency Directive, requiring employers to make pay practices more transparent and equitable. This represents a significant move toward greater accountability at a time when the gender pay gap across the EU still averages 11%, despite decades of equal pay legislation throughout Europe. 

Now, the countdown is on. By June 7, all 27 EU member states are expected to adopt the directive into national law, marking what many HR leaders are calling “Day One” of a new era in workplace transparency.  

But while the deadline is fast-approaching, many organizations are still far from operationally ready. Even though employers will be required to share pay information with both employees and candidates during the recruiting process, current practices suggest a significant gap. For example, across Europe, salary disclosure in job postings remains inconsistent and often limited, according to recent data.

For HR leaders, the challenge is no longer understanding the directive—it’s executing on it with confidence.

The barrier to execution 

For many organizations, the challenge often starts with the state of their HR and compensation data. In multinational organizations, compensation data often spans multiple systems, payroll providers, spreadsheets, and local processes. Job classifications vary across countries, salary bands are not consistently defined, and workforce data remains siloed across regions. 

As a result, many organizations struggle to produce consistent pay comparisons, define standardized salary ranges, explain compensation decisions clearly, and generate accurate reporting across multiple countries. 

Without a connected and reliable workforce data foundation, pay transparency becomes difficult to operationalize at scale. 

Building a foundation for continuous transparency  

The organizations making the most progress are focusing first on data consistency, workforce visibility, and connected HR processes. 

This is where technology is becoming critical. AI can help organizations move beyond manual reporting by identifying pay anomalies, surfacing unexplained pay variance, and accelerating workforce equity analysis across large, complex data sets. 

With pay transparency insights (generally available on June 5),  a capability within the People Intelligence package in SAP Business Data Cloud, organizations can unify compensation and workforce data across systems while applying AI-assisted analysis to help identify inconsistencies, support explainable pay decisions, and improve reporting readiness. 

Instead of relying on fragmented systems and disconnected reporting processes, organizations can move toward a more consistent and scalable approach to transparency. 

Three areas HR teams need to execute now 

With the right data foundation in place, organizations are better positioned to address the directive’s three major operational requirements. 

1. Enabling employee pay transparency 

Under the directive, employees have the right to request information about average pay levels by gender for comparable work. For many organizations, this immediately exposes data consistency issues. Comparable roles may be classified differently across countries or business units, while compensation data often lives in disconnected systems that were never designed to work together. 

SAP SuccessFactors HCM helps organizations provide employees with pay transparency statements through SAP SuccessFactors Employee Central while supporting more consistent comparisons across worker groups. These statements can give clear insight into the employee’s annual pay and the average pay of the same worker category broken down by gender.  

2. Preparing for candidate pay transparency 

The directive also requires employers to disclose salary ranges in job postings or before interviews and prohibits asking candidates about salary history. While this may sound straightforward, many organizations are discovering they lack standardized pay ranges, consistent job architecture, or alignment between recruiting and compensation systems. 

SmartRecruiters for SAP SuccessFactors allows organizations to display salary ranges directly within job postings and support more transparent hiring experiences. AI-driven recommendations can also help organizations establish more consistent pay ranges aligned to internal equity, external benchmarks, and evolving workforce needs. 

3. Meeting gender pay gap reporting obligations 

Mandatory gender pay gap reporting represents one of the directive’s most operationally demanding requirements. Annual reporting obligations begin in 2027 based on 2026 workforce data, meaning organizations need to prepare now.  

For many HR teams, the challenge is turning complex, multi-country workforce data into accurate and defensible reporting. With pay transparency insights, organizations can use AI-assisted analysis to identify potential drivers behind pay gaps, surface workforce equity insights more quickly, and support more proactive decision-making before reporting deadlines arrive.  

What HR should do now 

The EU Pay Transparency Directive is not just introducing a new compliance requirement. It’s accelerating a broader shift toward continuous transparency in how organizations manage compensation, hiring, and workforce equity. 

The organizations best prepared for this shift are taking action now to: 

• Unify workforce and compensation data 
• Standardize job and pay structures 
• Improve reporting readiness 
• Build more consistent, explainable compensation processes across the business

As transparency expectations continue to grow among employees, candidates, regulators, and business leaders, pay equity can no longer operate as a periodic reporting exercise. It is becoming an ongoing operational capability. 

Watch the webinar replay to learn how to move from policy to execution and prepare your organization for EU Pay Transparency requirements at scale.  

Maryann Abbajay is chief revenue officer for SAP SuccessFactors.

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This year at SAP Sapphire, SAP introduced Autonomous CX as a core pillar of the Autonomous Enterprise, including the principle that every customer promise must be backed by operational reality.

The Advanced Success Plan version for SAP Customer Experience solutions, part of the SAP Services and Support portfolio, is the expert-led engagement model that translates that vision into execution, helping organizations adopt, activate, and scale the SAP Customer Experience and AI innovations announced at SAP Sapphire.

The proactive, expert-led engagement model is built to de-risk transformation, accelerate time to value, and sustain measurable outcomes across customer experience initiatives. It combines guided adoption, prescriptive functional and technical assistance, AI-powered best practices, and continuous value realization aligned to the realities of modern customer experience (CX): AI at the core, unified data, omnichannel at scale, retention over acquisition, service-led growth, and persistent skills gaps in a rapidly evolving digital landscape.

At its heart, the Advanced Success Plan for SAP Customer Experience brings together the right expertise at the right time, program governance, solution experts, value advisors, and adoption specialists. This helps teams execute faster and smarter with SAP Customer Experience.

What sets the Advanced Success Plan apart

• Outcome-based: Business outcomes and key value indicators are co-defined with teams, with milestones and workstreams aligned to deliver measurable Autonomous CX results.
• Proactive by design: AI Assistants, adoption checks, and innovation accelerators are embedded throughout, reducing risk and compressing time to value as agentic capabilities evolve.
• Continuous enablement: Role-based best practices and coaching are tied directly to the Autonomous CX road map, closing skills gaps at pace as new AI and platform capabilities become available.
• Cross-solution orchestration: Unified processes and shared business context across marketing, commerce, sales, and service break silos and enable enterprise-scale execution.

This is the first of a planned series to deep dive on the topics below. Here, we start with introducing how the Advanced Success Plan for SAP Customer Experience helps operationalize seven macro trends shaping modern customer experience.

1. AI‑powered customer experiences

AI now underpins everything from next best engagement to intelligent service resolution. The Advanced Success Plan embeds AI adoption patterns directly into the delivery approach, identifying high value use cases, calibrating data prerequisites, and guiding model governance.

The results are prioritization of high‑impact starting points, a plan to scale with guardrails, accelerating time from pilot to production and grounding every decision in SAP’s CX AI capabilities and product road map.

2. Hyper‑personalization at scale

Personalization demands more than algorithms; it requires clean, consent‑aware data, robust decisioning, and experimentation discipline. The Advanced Success Plan delivers:

• Data readiness assessments and integration patterns to enrich customer profiles and segments
• Governance and testing playbooks to validate personalization hypotheses at scale
• Prescriptive journeys to operationalize next best action across every customer channel

The result: hyper personalization moves from proof of concept to standard operating model.

3. Unified customer data and breaking down silos

Siloed data undermines CX. We help establish a unified data foundation and harmonized identities, aligning business, data, and integration teams. With technical guidance and adoption accelerators, users can move faster toward a single view of the customer to fuel analytics, personalization, and service excellence.

The results are unified profile use cases, data quality baselines, and source‑of‑truth decisions to reduce duplication and latency.

4. Omnichannel commerce and B2B digital transformation

Modern buyers expect seamless journeys across web, mobile, marketplace, and partner portals, especially in B2B. The plan accelerates omnichannel capability build‑out by uniting commerce, order sourcing, pricing, and fulfilment patterns, supported by outcome‑based governance.

The result: Channel consistency, catalogue and contract complexity, and the alignment of service and sales motions are all addressed, driving measurable improvement in conversion rates and repeat purchase.

5. Customer retention over acquisition

Acquisition costs are rising and retention is the new growth engine. The Advanced Success Plan helps operationalize retention strategies, churn prediction, intelligent engagement, loyalty, and proactive service across the CX stack.

The result: We align metrics such as retention rate, customer lifetime value, and service‑to‑revenue contribution, and ensure the data foundation supports them.

6. Service as a revenue driver

Service is no longer a cost center; it’s a growth channel. We guide users to productize services, monetize value‑added offerings, and embed outcome‑based contracts. The plan includes:

• Playbooks for cross‑sell/upsell from service interactions
• Knowledge and field service patterns to improve first‑time fix and attach rates, KPI frameworks for service‑led growth

The result: With prescriptive governance and AI‑driven intelligence, service organizations move from reactive cost management to consistent, measurable contribution to top‑line revenue and customer retention.

7. Navigating digital transformation complexity and skills gaps

Large transformation programs falter on orchestration and capability enablement. The Advanced Success Plan addresses both by:

• Establishing a cadence of value sprints and decision forums
• Providing role‑based enablement covering functional and technical assistance, data, product ownership, end-user adoption, and change management
• AI-guided best practices embedded throughout delivery to eliminate rework and accelerate quality outcomes across Industry AI scenarios

Organizations execute with confidence, even amid shifting requirements, resource constraints, and rapidly evolving agentic AI capabilities.

Measurable outcomes

• Accelerated time to first value through prioritized, AI-ready use cases aligned to Autonomous CX capabilities
• Higher adoption and sustained performance via continuous enablement
• Reduced program risk through proactive governance, telemetry, and structured decision forums
• Measurable gains in conversion rates, customer retention, and service-led revenue contribution across the full CX stack

Getting started

• Define Autonomous CX priorities: Identify two to three priority outcomes for the next two quarters facilitated by the SAP Value Management service.
• Assess readiness: Evaluate data, integration, governance, and enablement gaps to define a 12 to 18 month engagement plan.
• Engage the Advanced Success Plan: Align workstreams, milestones, and metrics with our expert team. 
• Industrialize and scale: Convert proven delivery patterns into reusable accelerators, deployable across regions and lines of business.

This series will examine each of the seven trends in depth, demonstrating how the Advanced Success Plan for SAP Customer Experience translates CX strategy into repeatable execution and measurable business outcomes.

Tara Tracey is a global product owner at SAP.

Today, most companies are experimenting with AI. Many of them can point to demos that impressed, pilots that worked, and tools that saved time in narrow tasks. Far fewer can say AI has changed their business across functions, processes, and teams. 

The difference is not the model. It is context: the ability for AI to understand how a business actually runs. 

Much of today’s AI discussion centers on agents, along with models and benchmarks. Which model performs best? Which system completes the most tasks? Which interface feels most natural? These factors matter, but they do not solve the central enterprise challenge.

Companies run workflows that cut across teams, policies, approvals, authorizations, and data. They plan, source, produce, hire, pay, and serve through systems that carry real business consequences. AI only creates durable value at scale when it operates inside this reality.

Models generate answers. An agent can complete a task. But running a business requires something more. It requires an understanding of how work gets done, who is authorized to act, which rules apply, and how decisions connect across functions. Without that context, AI simply can’t deliver on its promise.

That is one reason I believe AI raises the premium on software with deep business context. It allows companies to fundamentally reinvent how work gets done. When AI agents understand end‑to‑end processes, they can operate across functions, execute workflows autonomously, and coordinate actions in real time. Instead of automating individual steps, AI can run processes end to end, freeing employees from repetitive coordination and enabling them to focus on higher‑value judgment, oversight, and strategy.

This is what we describe as the Autonomous Enterprise, a fundamental shift from systems of execution to systems that can reason, decide, and act. A vision where SAP is poised to lead. 

For more than five decades, we have powered the core processes that run the world’s leading organizations. Our systems don’t just store data; they encode how businesses actually operate: their processes, rules, and decisions. Our ERP is the institutional memory and the brain of many companies across industries and around the globe. Our new SAP Business AI Platform brings together enterprise data, processes, and governance into a unified context for AI.

Building on this foundation, Joule is the interaction layer that connects people with AI and redefines how they interact with software. Joule Assistants collaborate with users, while Joule Agents execute business workflows end to end. This is how intelligence becomes embedded directly into operations, not added on top. We call this the SAP Autonomous Suite.

“Show me how my financial forecast for the year could change based on the latest pipeline and supply chain data.” On the surface, this looks like a simple prompt directed to a large language model. But disconnected from enterprise systems, the answer is mere speculation.

Grounded in the full context of the business, the system first identifies the correct business process from hundreds of mission‑critical processes and understands the specific configuration that governs how this process runs in your organization. It then selects exactly the right data from millions of data fields stored across the ERP landscape. Finally, every step is checked against identity, authorization, and access controls, ensuring the result is accurate, compliant, and trustworthy. This is how enterprises move beyond generic, probabilistic answers toward decisions they can rely on.

Reaching this state requires more than adding a chatbot or layering AI on top of existing systems. Many enterprises still operate with fragmented landscapes, data spread across systems, and processes shaped by years of incremental change. In this environment, AI cannot simply be “bolted on” or layered onto fragmented, outdated systems. It does not accelerate progress. It amplifies inefficiency and risk. Companies must rethink how their processes, data, and infrastructure work together and how humans and AI share responsibility. This is not only a technical shift. It is a change‑management challenge. 

New technology only creates value when it is accompanied by real change. AI does not replace transformation. It raises the return on transformation done well. And it comes to life only when every element of the system—the agent, the process, and the human—works together by design. People need to understand how to work with AI agents, and processes must be intentionally shaped to embed intelligence where decisions and execution happen.

This is why change management is foundational. It means reskilling employees, re‑engineering processes to connect them directly with data and AI, and modernizing the underlying landscape. 

That is why we are introducing new AI-led RISE with SAP and SAP GROW offerings and fundamentally resetting our services model: to help companies modernize, navigate change, and turn AI from potential into sustained business value at their own pace. 

This marks the beginning of a new era of enterprise software: where intelligence is not separate from operations but embedded within them. The companies that lead will not be those with the most advanced models in isolation, but those that connect AI to the way their business actually runs—with context, governance, and trust. 

This is the dawn of the Autonomous Enterprise, and SAP is uniquely positioned to help the world’s leading organizations realize its full potential. 

Christian Klein is CEO of SAP SE.