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The Quarterly Review Said Green. The Business Unit Said Otherwise.
Picture a center in Hyderabad that built a claims-intake assistant for its parent company in the US. The project plan was met: all 140 stories closed, the release went out on the agreed date, and the quarterly review showed green across the board. Three months later the claims team stops trusting the assistant because answers got worse after a model upgrade, replies slow down every Monday morning, and nobody on the parent side knows whom to call. The project is finished. The product is failing. This is an illustrative case, but most GCC leaders will recognize the shape of it.
A delivery SLA answers the question "did we ship what we promised?" It counts tickets, milestones, and sprint velocity, and it ends when the release does. A product service level answers a different question: "is the thing working for the people who depend on it today?" Those are different measurements, owned by different roles, reviewed on different calendars. The Nasscom-Zinnov report describes centers moving from delivery engines to enterprise nerve centers, and the mandate has already changed in most of the newer ones. The way work is measured has not changed with it.
AI makes this gap costly sooner than traditional software does. A conventional application that passed testing keeps behaving the same way until someone changes it. An AI product shifts when the underlying model is updated, when retrieval content changes, when traffic patterns move, or when users start asking questions nobody anticipated. Quality can slide without a single deployment. A team that only measures delivery will not see it, because from the delivery plan's point of view nothing happened.
"If your center owns the product, the business unit is your customer, and customers do not care how many tickets you closed."
Five Service Levels, and What a Delivery SLA Misses in Each
These are the places where product ownership shows up as a measurement. For each one, the right-hand column describes how a team that only tracks delivery tends to find out something has gone wrong.
| Service level | How a delivery-only team finds the problem | Severity |
|---|---|---|
| Answer quality on live traffic | A business user complains weeks after a model upgrade, and nobody can say when quality started to drop | Critical |
| Escalation time to a person | Hand-offs from agent to human queue up unseen, and the first signal is a customer escalation on the parent side | Critical |
| Cost per completed task | The monthly cloud bill arrives higher than forecast, with no link to the prompt or retrieval change that caused it | High |
| Speed and uptime | Slow Monday mornings are reported as anecdotes, because nobody agreed what slow means or who watches it | High |
| Error budget and release rules | Feature work keeps shipping while quality is already below target, since no rule says when to stop | Moderate |
| Consumer support agreement | Business units raise defects through personal contacts, so fixes depend on who knows whom | Lower |
Not sure where your AI service level gaps are?
10decoders works with GCC leaders on operating models for AI products they own. We review each live product against the five service levels and show which ones you can already measure today.
Book a Free AI Assessment →Why Quality Is the Hard One
Uptime and latency are familiar. Most centers already have monitoring for both, and an on-call rotation if the product is customer-facing. Quality is the service level that has no obvious owner, because "correct" depends on the use case. For a claims-intake assistant it might mean the right form is selected and the right fields are extracted. For an internal policy assistant it might mean the answer cites the current version of the document. Either way, the definition has to be written down by someone who understands the business outcome, then turned into a set of graded examples the team can run on demand.
That example set is the center of the whole arrangement. It gets run before every model change, every prompt change, and every retrieval content update, and it gets run on a schedule between changes so drift is caught without a deployment. A sample of real conversations is scored in parallel, because live traffic always contains questions the example set missed. The result is a quality number with a trend line, and that number is what you bring to the review with the parent company.
Cost per completed task belongs beside it. A model swap that cuts latency but triples spend per task can look like a win in a delivery report. Seeing both numbers in one place stops that kind of local improvement from becoming a budget problem two quarters later.
Delivery Milestones Are the Scoreboard
The center reports stories closed and releases shipped. Quality is judged by complaints, and no one is named as the service owner after launch.
Uptime Is Watched, Quality Is Not
Latency and availability dashboards exist and an on-call rotation covers the product. Answer quality and cost per task are reviewed occasionally by hand.
Five Service Levels With an Error Budget
Each product has an owner, targets for all five levels, and a rule that pauses feature work when a target is missed. The parent company has seen and agreed the numbers.
Checklist: What a Product-Ready GCC Can Show for One AI Product
Choose the AI product with the most users and ask the team to demonstrate each item from a dashboard or a document, not from memory.
"The mandate moved from projects to products. The scoreboard should move with it, starting with one product this quarter."
What to do this week
01 Pick one product and name its service owner
Choose the AI product with the most daily users and write one name beside it: the person who answers when quality drops. If the answer today is "the project team," that is the finding. Agree with your leadership that this person has authority to pause releases, because a service owner who cannot say no is only a contact point.
02 Write the quality definition with the business owner
Sit with one business user and list twenty real requests the product handled recently. Mark each answer as acceptable or not, and write one line on why. Those twenty rows are the start of your graded example set. Add ten more each week from live traffic, and keep the cases where the product failed, since those are the ones that guard against regressions.
03 Put quality and cost per task on one page
Pull the last thirty days of usage and model spend, divide spend by completed tasks, and plot it next to the quality score from your example set. If you cannot compute completed tasks, define one now, even a rough one. The first version will be imperfect, and that is fine, because a rough number on a shared page produces better conversations than a precise number nobody sees.
04 Agree the error budget with the parent company
Propose a single rule: if answer quality or availability misses its target for a month, the next sprint is fixes only. Take it to the parent-side product owner before you need it, and write the targets on one page that both sides sign off on. Agreeing in calm conditions is far easier than negotiating during an incident, and it gives your center a documented basis for saying no to new scope.
Let 10decoders Review Your GCC's AI Service Levels
We take your highest-traffic AI product, define the five service levels with your business owner, build the first graded example set from real requests, compute cost per task, and draft the error budget rule for the parent company. You leave with a one-page service agreement and a ranked list of gaps.