Evolve beyond cost take-out to create capacity for innovation

For years, SAP Application Management Services (AMS) has been defined by a simple objective: lower the cost per ticket.

But that goal alone no longer meets the moment.

Today, organizations often face mounting pressure to not only reduce operating expenses but accelerate digital transformation. Companies are navigating SAP S/4HANA migrations, modernizing data platforms, embedding AI into business processes—all while most of their IT budgets remain locked in “run” operations. (As a recent Gartner research note states¹, “74% of IT budgets relate to ‘run’ or maintenance activities.”) Organizations should not only transform, but realize greater business value post-transformation.

It’s time to rethink the model. Cost reduction remains a key, but it is no longer the end goal—it's the enabler of application evolution, business outcomes and continues innovation. “Legacy managed services are about cost takeout, labor arbitrage, and shifting work offshore,” notes Tim Canonico, PwC Global and US Managed Services Leader. “CIOs and CFOs are tired of not getting innovation and transformation outcomes from their providers.” In today’s “Managed Services 2.0” era, SAP managed services should be judged by how effectively they can extend the value of SAP investments, accelerate business change, and free up capacity for innovation.

PwC’s AI-led approach to SAP Application Evolution Managed Services is not solely about running SAP cheaper. It is also about running SAP smarter so clients can safeguard business operations, accelerate change, and realize more value from their SAP investment over time.

AI-led managed services for SAP Application Evolution Services (AES) fundamentally changes the economics of IT — shifting the focus from labor arbitrage to productivity, automation, and measurable business outcomes.  

IT organizations typically spend the bulk of their budget on support and operations, and less than a third of their spend going to innovation.

AI-led managed services is designed to flip that ratio, moving toward a 30% support and 70% innovation orientation by radically improving run operations.

This isn’t theoretical. It’s operational redesign.

By embedding agentic AI, automation, and intelligent monitoring into SAP environments, organizations can reduce the volume and complexity of manual support work. Fewer repetitive tickets. Faster resolution. Higher first-time-right rates. Reduced escalation.

The result? Operating cost declines — and the freed capacity can be reinvested in transformation initiatives.

IT shifts from cost center to performance engine.  

Traditional AMS is reactive and ticket-centric; AI-led AES is proactive, automation-led and measured by the business value it helps unlock.
Traditional outsourcing models focus on headcount reduction and offshore scale. That approach delivers short-term savings but rarely transforms IT performance.

AI-led managed services introduces a reimagined delivery model — humans and advanced technologies working together to help deliver immediate and long-term value.

This model includes:

• AI-native engineering and intelligent process automation embedded across service delivery
• Agentic AI integrated into service management workflows, enabling proactive issue prevention rather than reactive resolution
• Shift-left automation, expanding self-service and autonomous resolution tiers to help reduce L2/L3 support demand
• Flexible operating models with quarterly scope improvement, enabling realized savings to be reinvested into transformation priorities

The goal isn’t simply to lower rates. It’s to lower the need for manual intervention in the first place.

When automation handles repetitive tasks and AI predicts issues before they disrupt operations, the cost base shrinks structurally—not temporarily.

In many SAP environments, a significant percentage of tickets fall into predictable categories of recurring issues: access issues, recurring configuration errors, batch failures, integration glitches, and routine service requests. That makes it possible to target automation where it can reduce cost-to-serve while improving user experience.

AI-led AES addresses this through three coordinated levers:  

1. Intelligent service desk

Conversational and agentic AI manage common service requests and known errors, helping improve response times and reducing escalation.

2. Proactive monitoring and self-healing

Full-stack observability, combined with AI-driven analytics, can detect anomalies before users are impacted — accelerating movement toward near-autonomous operations.

3. AI-enhanced engineering

GenAI tech-enabled solutions support requirements documentation, test case generation, and code augmentation—helping improve quality while reducing delivery cycle time.

Together, these capabilities can reduce ticket volume, improve user satisfaction, and decrease total cost to serve.  

Cost reduction is often viewed defensively. In an AI-led managed services model, it becomes strategic.

Through smart shoring, supplier consolidation, AI-driven automation, and continuous efficiency levers, meaningful run-cost savings can be achieved over time.

In some environments, operating cost reductions of 50% to 65% are targeted through multi-phase transformation.

But the savings are not the end goal.

They are the funding mechanism. The point is not simply to spend less on run. It is to redirect effort and investment toward the capabilities that can move the business forward.

Freed operating expense can be redirected to:

• SAP S/4HANA modernization

• Process automation

• Advanced analytics

• AI-enabled business capabilities

• Customer experience enhancements

This is how managed services becomes self-funding transformation. Cost reduction matters because it creates room for automation and evolution that helps improve business outcomes.

Traditional AMS contracts measure uptime, ticket resolution, and response times. Those metrics matter — but they don’t tell the overall story.

AI-led AES helps shift performance measurement toward business value.

Service delivery aligns to metrics that matter — time-to-market, operational resilience, business process stability, and user experience.

When service metrics connect directly to enterprise objectives, IT becomes accountable not just for stability — but for growth.

PwC’s differentiation is not simply the use of AI in delivery. It is the combination of SAP and business domain experience, AI-enabled operating design, reusable assets, and a managed services model built to connect execution with measurable outcomes. That is what enables the conversation to move beyond lower-cost support toward continuous value realization.  

Transformation should be pragmatic.

Successful AI-led AES programs follow a phased approach, often described as “Crawl. Walk. Run.”—layering AI and automation capabilities over time.

• Crawl: Stabilize service management, clean up ticket structures, introduce targeted automation.
• Walk: Expand predictive monitoring, embed DevOps and test automation, enhance delivery pods.
• Run: Achieve near-autonomous operations, predictive analytics, and innovation-first funding models.

Each phase reduces risk while increasing maturity.  

Managed services is no longer just outsourcing.

It is the combination of industry insight and AI-enabled delivery to help unlock value faster.

We run your operations with tech and talent — so you can run faster, scale smarter, and lead stronger.

AI-led managed services for SAP AES is a disciplined redesign of operating spend. It helps capture productivity gains, reduce manual effort, and redirect cash flow toward innovation—without requiring incremental capital investment. SAP managed services should be evaluated based on how effectively they can extend the value of SAP investments, accelerate business, and enable innovation.

Smarter operations. Stronger returns. And a new model for SAP Application Evolution Services.