Over the past few months, we have been part of conversations where one question has repeatedly emerged: does the standard Business Process Outsourcing (BPO) contract stop working in an AI-first delivery model? The broad consensus is that a shift is underway, but few have gone clause by clause through a standard BPO contract to identify exactly where it breaks.
By a traditional BPO contract, we mean a construct built on three assumptions that have held true for close to three decades: people deliver work and are measured against fixed, time-based metrics, and pricing has a linear relationship with headcount or volume. An AI-first delivery model breaks all three assumptions at once, and it is breaking them faster than most Master MSAs and SoWs can be renegotiated.
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We see five places where the breakdown is already showing up in live contracts.
1. Performance metrics stop measuring anything real
Average handle time, occupancy, and adherence were designed around a workforce with fixed shifts and a fatigue curve. An AI agent has neither. It does not get tired, it does not take breaks, and it can run the same task in parallel across thousands of instances at once. When resolution happens in seconds rather than minutes, and a single deployed agent can handle multiple conversations concurrently, the entire SLA framework built around handle time and occupancy stops telling anyone anything useful. The uncomfortable part is that most SLAs have not been rewritten to reflect this change. They are still being reported on, and still being paid against, using a yardstick the delivery model has already outgrown.
With these measures losing relevance, the contract needs to answer a few basic questions about how performance should be defined in an AI-first model:
- What does FCR mean when an AI agent can resolve a query immediately without the need for a human agent to intervene?
- Should quality be measured at the interaction level or the outcome level?
- Do occupancy, utilization, or agent capacity, as measured in production hours delivered per year, even apply to a system that has no concept of being idle?
2. Liability caps assume a kind of failure that no longer exists
Traditional outsourcing contracts distinguish between measurable service underperformance, harder-to-quantify contractual failures, and losses significant enough to sit outside ordinary liability constructs. Service credits typically address measurable operational misses. Liquidated damages address defined failures such as missed transition milestones and are generally subject to the contractual limitation of liability, while specified indemnities may sit outside those caps. AI complicates this framework because a single model, rule, prompt, or data failure can create many correlated errors before detection, turning what might ordinarily have been a contained operational failure into broader financial or regulatory exposure.
For example, in an AP process, a human reviewer may incorrectly approve a small number of invoices. An AI agent using a flawed validation rule could approve thousands of invoices that should have been rejected before the issue is identified. The underlying failure may be singular, but the resulting loss can compound rapidly across transactions. The contract therefore needs to define more explicitly which AI failures remain service-credit events, which trigger liquidated damages or liability-cap exposure, and which circumstances, such as data breach, regulatory breach, fraud, or specified third-party claims, sit within indemnity or other uncapped or super-cap constructs.
These scenarios raise practical questions about how risk and responsibility should be allocated when a single AI error can scale across many transactions:
- At what point does a repeated AI error become more than a routine service-level miss?
- Who bears the cost if an AI error is discovered only after it has affected thousands of transactions?
- How should responsibility be shared when neither the client nor the provider fully controls the model end to end?
3. IP ownership was never designed for a system that learns
Traditional BPO contracts generally draw a clear line between client-owned data and IP, and the provider’s tools, platforms, and process assets. AI introduces a new layer in between. During the engagement, the provider may create client-specific prompts, workflows, agent configurations, fine-tuned models, knowledge bases, and AI-generated outputs using the enterprise’s data and business processes. Even where the underlying AI platform remains provider-owned and client data is ring-fenced, the contract must clearly define who owns these client-specific assets, who can reuse them, and what happens to them when the relationship ends. Without this clarity, an enterprise could become dependent on AI capabilities it has helped shape but cannot take with it, or inadvertently allow proprietary process knowledge to become embedded in reusable provider assets.
These ownership questions then arise:
- Who owns client-specific prompts, workflows, agent configurations, fine-tuned components, and knowledge assets created using the enterprise’s data and processes?
- What rights does the provider have to reuse learnings, configurations, or derivative assets created during the engagement for other clients?
- If the contract terminates, which AI assets must be transferred to the enterprise versus retained by the provider?
- If the enterprise has funded the development or customization of an AI capability, does it receive ownership, perpetual usage rights, or only access for the duration of the contract?
4. SLA compliance and unit-based pricing get decoupled from actual value
Unit-based pricing rewards completion of a defined unit of work, while SLAs typically measure service outcomes such as timeliness, accuracy, and satisfaction. As AI takes on a larger share of delivery, the number of units processed may remain relevant, but the relationship between volume, delivery effort, and commercial value can change materially. A client ultimately cares about outcomes such as straight-through processing, cost takeout, cycle time, working capital, accuracy, and satisfaction, rather than simply the level of human effort behind them. For a related view, read our blog on AI agent-based pricing. As AI lowers the marginal effort required for some transactions, contracts may need to revisit whether the existing pricing unit still reflects value and whether SLA definitions and evidence remain appropriate when AI performs more of the work.
As pricing and delivery economics shift, contracts need to reconsider how value is priced and how service performance is measured:
- As AI lowers the effort required to process a transaction, does per-transaction pricing continue to reflect value, or should the efficiency gains be shared differently?
- How should SLA measurement and compliance evidence evolve when a model or AI agent performs part or all of the work?
- Should outcome-based pricing carry a floor and a ceiling so that neither party is fully exposed to volatility in a new pricing unit?
5. Governance mechanisms need a new owner, not just a new committee
The steering committees and review cadences built for human delivery teams assume a stable, traceable chain of responsibility. Autonomous, self-improving systems do not stay still long enough for that chain to hold on its own. A model that is retrained, fine-tuned, or upgraded mid-contract can behave differently from the version that was originally scoped and priced, and a quarterly governance call was never designed to catch that kind of drift. What replaces the traditional three-tiered governance structure, and who holds accountability when a system rather than a person approves or rejects an invoice, or marks a case as processed successfully? This shift is one that most contracts have not attempted to answer yet.
This governance gap leaves contracts with a new set of questions around decision rights, traceability, and accountability:
- Who signs off when the underlying model changes mid-contract, and does that trigger a repricing or rescoping event?
- What does an audit trail need to contain for an autonomous decision, and who owns the obligation to produce it on demand?
- Should there be a standing forum focused only on model risk and drift, sitting alongside, not inside, the existing governance layer?
None of these five shifts gets resolved by adding a short AI clause to an existing template. Each one touches the commercial architecture of the contract itself: how work is measured, how risk is allocated, who owns what, what a unit of work even is, and who is accountable when something goes wrong. Most BPO agreements signed in the last few years have not been stress-tested against these shifts. That is a conversation worth having before the next renewal cycle, not after.
If you found this blog interesting, check out, Outcome-based metrics: the new value currency in BPO – Everest Group Research Portal, which delves deeper into another topic relating to BPO.
To take the conversation forward, contact Sahil Shah or Swapnil Varshney.
