Service Level Agreements measured speed. Experience Level Agreements measure outcomes.

For thirty years, the standard contract between a business and its customer experience function — whether internal or outsourced — has been governed by Service Level Agreements (SLAs). SLAs measure activity: average speed to answer, abandon rate, first-call resolution percentage, average handle time, uptime.

These metrics made sense in an era where customer experience was, fundamentally, an operational problem. Get the volume answered. Keep the queues short. Move people through the system.

This is changing in 2026. We are seeing a move toward Experience Level Agreements (XLAs). These focus on how customers feel and the results they get, rather than just how many calls are handled.

The reason is clear: when AI handles the majority of calls, speed and uptime are no longer enough to distinguish a good experience from a bad one. We need better ways to measure success.

The limits of the SLA era

SLAs have three structural problems that have become impossible to ignore:

1) They measure inputs, not outcomes. "Answered within 30 seconds" tells you nothing about whether the customer's problem got solved. An organization can hit every SLA target and still have declining NPS.

2) They're easy to game. SLA-driven operations develop sophisticated workarounds — closing tickets prematurely, transferring calls to hit handle-time targets, classifying complex calls as resolved when they're not. The numbers look good. The customer experience doesn't.

3) They don't account for AI. When AI handles a call in 90 seconds and "resolves" it without solving the underlying issue, traditional SLAs register a win. The customer registers a loss. SLA-driven AI operations are particularly prone to this disconnect.

What is an XLA, specifically?

An Experience Level Agreement is a contract or operational target tied to customer outcomes and customer-perceived value, rather than operational throughput. In practice, an XLA framework typically includes:

→ Outcome metrics: % of issues fully resolved on first contact (verified by the customer, not by the agent closing the ticket)

→ Effort metrics: total customer time-to-resolution across all touchpoints

→ Emotion metrics: CSAT or sentiment scores on the specific interaction, not just on the brand overall

→ Loyalty impact: whether the interaction increased or decreased the customer's likelihood to renew, refer, or expand their relationship with the business

The shift is from "did we answer the call quickly?" to "did the customer get what they needed, with low effort, in a way that left them feeling positively about the brand?"

Why the shift is happening in 2026

Three forces are converging to make XLAs the new standard:

First, AI is making SLAs trivially easy to hit. Speed-to-answer goes to zero. Containment rates climb. Operational metrics no longer differentiate good operations from bad ones — because everyone's hitting them. The bar has to move up.

Second, what customers expect has changed. People now judge an experience based on how much effort it took and how they felt afterward. "How did it feel?" is now more important for loyalty than "how fast was it?".

Third, it is now much cheaper to measure these experiences. AI can now analyze every single call to check for sentiment and resolution, giving you a continuous view of your performance that was never possible before.

The four pillars of an XLA framework

A practical XLA framework rests on four pillars:

1) Resolution: Did the customer's actual problem get solved? Measured by repeat-contact rate, customer-verified resolution surveys, and downstream behavior (did they cancel, churn, or come back?).

2) Effort: How much did the customer have to spend to get there? Measured by total time across touchpoints, number of transfers, number of repeated explanations, and standardized customer effort scores.

3) Emotion: How did the customer feel? Measured by sentiment analysis on the interaction, post-call CSAT specific to that interaction, and qualitative pattern detection across thousands of calls.

4) Loyalty Impact: Did this interaction make the customer more or less likely to stick with the brand? Measured by NPS shift, renewal correlation, expansion rate, and referral behavior tied to the interaction.

Together, these four pillars give a much more honest picture of what customer experience is actually delivering — and they're far harder to game than traditional SLAs.

How to build XLAs into AI-powered operations

Operationalizing XLAs in an AI-augmented CX environment requires four shifts:

→ Move from sampled QA to 100% AI-driven QA. You can't manage experience metrics on 3% of calls. Every interaction needs to be scored for resolution, effort, and emotion.

→ Connect customer outcomes to interaction data. If you can't tie a renewal decision back to specific interactions, you can't manage loyalty impact. This requires CRM connectivity that most companies don't currently have.

→ Make XLA metrics visible to frontline teams. Agents and AI systems both need real-time visibility into the metrics that matter, not just the operational ones.

→ Restructure incentives. As long as agents are compensated on AHT and containment, that's what they'll optimize for. Pay for resolution and CSAT, and behavior shifts.

The procurement implications

If you outsource any portion of your customer experience function — to a BPO, to a voice AI vendor, to a managed services provider — your contracts need to evolve.

Traditional outsourcing contracts are SLA-based: penalty clauses tied to abandon rates, handle times, and answer speeds. These contracts incentivize the wrong behavior in an AI-augmented world. A vendor optimizing to hit SLAs may actively damage your customer experience while hitting every contractual target.

Forward-looking procurement leaders are building XLA terms into new contracts:

→ Bonuses tied to CSAT and resolution rate, not just operational metrics

→ Penalties tied to repeat-contact rate and customer effort scores

→ Shared accountability for downstream customer outcomes (renewals, NPS, churn)

→ Joint ownership of QA standards rather than vendor-defined ones

Partners who can deliver on these experience-based results will be the leaders in the coming years. Those who only focus on old metrics will likely struggle as AI continues to take over routine tasks.

If you're negotiating a CX contract in 2026 — internal or external — XLA terms should be on the table. The companies that lock these in early will have a structural advantage. The ones still negotiating on AHT and ASA will be measuring the wrong things on the wrong contracts for the wrong era.

VINSI is built to support XLA-driven operations end-to-end — with AI-driven QA on 100% of calls, unified outcome metrics across voice, ticketing, and CRM, and dashboards designed around customer experience, not operational throughput. Book a demo at vinsi.ai/contact.