Production is precise, but post-sale is still playing catch-up. It’s time to close the service-to-cash gap.
Manufacturers have spent decades perfecting the shop floor. Every process is measured. Every delay has a fix. But once the product ships, that discipline falls apart. Warranty leakage goes unnoticed. Service workflows depend on disconnected systems. And customer uptime—the metric that actually builds loyalty—is treated like an afterthought.
That disconnect is no longer sustainable. As margins shrink and hardware commoditizes, it’s the outcomes that retain customers.
Servitization marks a shift from earning once at the point of sale to earning continuously on performance, uptime, and trust. But most manufacturers aren’t structurally ready to make that shift work.
The Blocker: Closing the Vision-to-Execution Gap in Outcome-Based Service
The value of outcome-based service is clear. The blocker is execution.
If your systems can’t answer which asset failed, why it failed, and who pays, then automation is impossible, scale is out of reach, and proving performance becomes guesswork.
Fragmented Systems: The Real Cost of Siloed Data and Missed Warranty Leakage
ERP tracks assets. CRM manages service cases. Warranty entitlements live in spreadsheets or siloed tools held together by habit. That fragmentation slows everything down—just not in ways that show up on a dashboard.
The consequences play out daily:
- The Blind Technician: Spends an hour piecing together context for a repair because the history lives in three different PDFs.
- The Missed Revenue: A service manager misses a supplier reimbursement window because the claim wasn’t automated.
- The Frustrated Customer: Waits for an update that should have been instant while your SLA clock ticks down.
You’ve promised a 48-hour resolution, but your supplier operates on a 5-day SLA. When the deadline slips, your brand absorbs the frustration and the cost.
This friction persists because the “proof” behind warranty and service decisions—photos, technician notes, PDFs, call transcripts, dealer emails—isn’t structured anywhere. No system can reliably interpret it, so teams chase context rather than delivering service.
The Financial Impact
Meanwhile, the financial drag is real. Suppliers are responsible for nearly 40% of warranty claims, but cover only about 15% of costs. The rest seeps quietly out of margin, one disconnected workflow at a time.
Manufacturers have the intelligence but are missing the connection.
Servitization Demands a Connected Core: Unifying ERP, CRM, and Asset Data
Outcome-based service only works when you can see your business clearly—what assets are in the field, how they’re performing, and what contractual commitments exist across every component.
For most manufacturers, that visibility doesn’t exist. Asset data sits in ERP. Service history lives in CRM. Warranty entitlements are buried in systems never meant to coexist. Leaders end up managing blind spots, not performance.
Modernization resolves that. When systems are unified, teams can manage uptime, automate warranty recovery, and deliver outcome-based contracts with confidence instead of guesswork.
Clean, connected data is what allows AI to operate at the speed and precision servitization demands.
How Agentic AI Enables Scalable Servitization for Manufacturers
Agentforce for Manufacturing pushes automation beyond efficiency. It doesn’t just help teams work faster—it works on their behalf. Identifying failures before customers feel them. Scheduling service. Filing supplier claims. Closing loops across systems that previously had no way to talk to each other.
Unlike Copilot-style tools that help humans move faster inside broken workflows, agentic AI executes the workflow itself. It doesn’t suggest the next step—it completes it.
For manufacturers, that’s the bridge between operational excellence and business model innovation. Agentic AI makes outcome-based service achievable at scale.
The Path Forward: Data Readiness Before AI Automation
At Dreamforce 2025, leaders from Eaton and Goodyear highlighted what it really takes to get there. Before introducing AI, both rebuilt their data foundations—standardizing account structures, reconciling asset data, and aligning systems around the customer instead of internal silos. Only then could their AI agents operate with accuracy and context.
The technology’s ready, but only disciplined teams will see value from it.
Coastal’s Three-Pillar Approach: Your Service Readiness Framework
At Coastal, we work with manufacturers to build that foundation before automation begins. Through our Service Readiness Framework, we help teams close the structural gaps that make AI and servitization possible:
Our Three-Pillar Approach
Installed Base Clarity – Establish a single, accurate view of every asset, its service history, and entitlements.
Process Harmonization – Align the service-to-cash lifecycle so insights actually trigger action.
Architecture Integration – Connect CRM, ERP, and IoT data so every system works from shared intelligence.
The Transformation
Once that groundwork is complete, the transformation is tangible:
- Claims that used to fall through the cracks get filed automatically.
- Contracts and coverage that teams previously missed are now captured.
- Technicians who spent hours hunting for asset history access it in seconds—and use that time to prevent problems instead of just fixing them.
This is how manufacturers turn AI from a concept into real margin impact.
From Data Readiness to ROI: Closing the Service-to-Cash Gap
Servitization is a structural shift in how manufacturers grow. Agentic AI makes it scalable. Unified data makes it possible.
Before investing in AI service automation, assess your readiness.
Coastal’s Data Strategy Lab helps you identify the data gaps limiting profitability and map a clear plan to close them.
Your systems need to work together before AI can work for you. Coastal builds that foundation.
