Traditional service provider metrics tell a simple story: how many transactions processed, how quickly completed, at what cost? These measurements worked when service delivery was human labour at scale. But as AI transforms service delivery, these metrics become inadequate and potentially misleading. Our research reveals an industry confronting how to measure success when value creation has changed.
Kumar Lalit from Genpact provides concrete evidence: "AI enables service providers to interpret information at scale, triangulating documents and data sources to quickly identify issues like invalid claims. This accelerates processes , reducing claim processing from 90 days to just days , improving cash flow and delivering tangible value to clients." He quickly moves beyond cost: "The growing adoption of AI is driving a shift from FTE-based commercial models to outcome-based and value-based models. Rather than charging for the number of employees deployed, service providers can offer commercial arrangements based on specific outcomes."
This multidimensional value requires new measurement frameworks. Traditional metrics capture only cost, missing the transformation being created.
Ankur Saxena articulates measurement importance: "Obviously, it's also equally important to measure it, to really understand whether we are getting the ROIs that we wanted. As opposed to just putting faith in AI and moving forward without proper assessment." This isn't measurement for its own sake but proving value in ways that matter to modern clients.
The evolution reflects how value is created and perceived:
From efficiency to effectiveness. Traditional metrics focused on speed or cost. New frameworks measure business outcomes achieved. The question shifts from "How many transactions?" to "What business value?"
From point-in-time to continuous improvement. Static SLAs give way to metrics capturing continuous enhancement. AI's learning ability means performance should consistently improve.
From activity to outcome. Rather than measuring activities, providers focus on business outcomes. This requires closer client alignment and sophisticated measurement.
From single to integrated. Where traditional metrics focused on cost or speed alone, AI-first services deliver value across dimensions simultaneously.
The evolution reflects how value is created and perceived:
From efficiency to effectiveness. Traditional metrics focused on speed or cost. New frameworks measure business outcomes achieved. The question shifts from "How many transactions?" to "What business value?"
From point-in-time to continuous improvement. Static SLAs give way to metrics capturing continuous enhancement. AI's learning ability means performance should consistently improve.
From activity to outcome. Rather than measuring activities, providers focus on business outcomes. This requires closer client alignment and sophisticated measurement.
From single to integrated. Where traditional metrics focused on cost or speed alone, AI-first services deliver value across dimensions simultaneously.
Modhura Roy emphasises measuring throughout the journey: "The services that we offer are starting from the business case to implementation, to post implementation. Looking at adoption of the technology which is landed and then using that adoption, we can also measure what is the effectiveness of the services which are being delivered."
This end-to-end approach represents a shift in how providers think about value demonstration.
Suri Babu Komma from EXL provides practical insight: "Today, it's not only about SLAs but business outcomes. For example, in accounts payable, clients previously focused on processing metrics like volume and accuracy. Now they expect insights about optimising cash outflow, vendor relationships, and fraud analysis."
He continues: "Success metrics vary by objectives, whether cost reduction, penalty avoidance, or revenue enhancement. In accounts payable, efficient processing saves money while early payments can generate additional income through discounts."
The new metrics setting includes:
The new metrics setting includes.
Revenue generated, costs avoided, risks mitigated. Metrics tying directly to client objectives rather than operational statistics.
Innovation indicators.
Process improvements suggested, new capabilities developed, problems solved previously impossible.
Quality beyond accuracy.
Quality expands to include consistency, compliance, insight generation, and proactive problem identification.
Adoption and utilisation.
Measuring not just AI capability but effective usage. High adoption indicates successful change management.
Continuous improvement velocity.
Rate of AI system improvement, measured through performance gains and expanded capabilities.
The challenge extends to commercial models. Traditional pricing based on headcount makes little sense when AI changes productivity. New models must price value delivered rather than effort expended.
Leading organisations develop sophisticated approaches:
Baseline establishment.
Before AI implementation, carefully document current performance across dimensions, creating clear comparison points.
Outcome attribution.
Analytics attributing business outcomes to specific AI interventions, proving causation.
Stakeholder-specific views.
Different metrics for different audiences. Operational metrics for teams, business metrics for clients, strategic metrics for executives.
Value dashboards.
Real-time visibility into value creation, accessible to providers and clients. Dashboards tell stories, not just display numbers.
Predictive value modelling.
Using AI to predict future value based on trends and planned improvements.
Timeline pressure intensifies measurement challenges. Ankur Saxena's 12-month warning means organisations must show value quickly and continuously. This creates pressure for metrics demonstrating immediate impact and progress toward transformation.
Transforming metrics isn't technical exercise but reimagining how providers demonstrate value. Those mastering this gain competitive advantage: proving worth not in activities but business value created. In an era demanding AI ROI, measuring and demonstrating return becomes critical differentiation. The message: what you measure matters as much as what you deliver.
The transformation of metrics reflects broader changes in how services are delivered globally.
About Enate
Enate is the leading SaaS solution for business services. Enate orchestrates work from start to finish, giving clients the visibility and control needed to deliver better services. From email management and data analysis to intelligent document processing, Enate also offers a host of touch-button AI features designed to slash the time spent on manual work. Trusted by global service teams, Enate ensures smooth, consistent operations that help clients perform at their best.