UK SMEs and charities tell us the same thing: “We’ve tried a few AI demos, but we’re not confident enough to go live.” The missing piece is usually a simple, shared way to measure quality, speed and cost — then decide go/no‑go without drama. This article gives you a practical 7‑day plan to build an AI Quality Scoreboard your executive team, operations leads and DPO can all live with.
We’ll translate well‑known reliability ideas (SLIs/SLOs and error budgets) into plain English for AI features like a helpdesk bot or a document summariser, and we’ll show where to borrow sensible benchmarks without getting lost in research leaderboards. For context, Google’s Site Reliability Engineering playbook remains the clearest explanation of SLIs/SLOs and error budgets — a useful mindset when turning AI performance into business promises. sre.google
Why a “scoreboard” now?
- Models change often and vendor marketing can be inconsistent. Independent, reproducible leaderboards such as Stanford’s HELM focus on transparent methods and use‑case relevance — a good starting point when you need an external yardstick. crfm.stanford.edu
- Assurance is becoming normal procurement hygiene in the UK. Government guidance emphasises measuring and communicating AI performance with evidence, not claims — which is exactly what a scoreboard gives you, even for small pilots. gov.uk
- Costs can swing wildly with long prompts. The major providers now discount repeated context (“prompt” or “context” caching). Your scoreboard should make these savings visible before you scale. openai.com
The 7 metrics that matter (and how to set a first‑cut SLO)
| Metric (what it is) | Why it matters | Starter SLO (first month) | Evidence source |
|---|---|---|---|
| Task success rate (does the answer actually solve the user’s request?) | Predicts satisfaction and re‑contact. Use a small human‑checked sample each week. | ≥80% successful for your “top 3 tasks”. | Lightweight human review + small golden set (see Day 2). Aligns with UK assurance focus on evidence. gov.uk |
| Containment rate (conversations resolved without hand‑off) | Directly links to cost to serve. Official definitions exist in CX tools such as Zendesk. | Target 60–80% for well‑scoped helpdesk bots; lower for complex cases. | Zendesk defines containment as “not transferred to an agent”. secondary-brand.zendesk.com |
| Latency (p95) | Users feel the slowest experiences. Track the 95th percentile, not just the average. | ≤2.5s p95 for answers that don’t call external systems; ≤4s with lookups. | SRE guidance on SLI/SLOs and user happiness traces; adapt to your context. sre.google |
| Cost per resolved task | Combines model price, prompt length and success/containment rate into one figure. | Set a cap that still beats your current cost to serve (e.g., £0.20 per resolved FAQ). | Use provider prompt/context caching to reduce input costs. openai.com |
| Factuality/faithfulness score (is it grounded in your sources?) | Especially important for RAG features; measure with a simple rubric. | ≥90% “fully grounded” in sample reviews. | NIST’s GenAI evaluations highlight believability vs truth — measure both. nist.gov |
| Safety compliance (blocked harmful content, respectful tone) | Prevents failure modes and reputational risk; spot‑check with a weekly audit sample. | 0 critical incidents; ≤1% false positives that block legitimate queries. | Assurance approaches emphasise evidence of testing and red‑teaming. gov.uk |
| Escalation quality (handover clarity, transcript quality) | Ensures poor bot interactions don’t become poor human ones. | 100% of escalations include a concise case summary. | Common CX practice; pairs with containment to detect over‑automation. secondary-brand.zendesk.com |
Tip: Treat these as service level indicators (SLIs). Agree a small set of service level objectives (SLOs) with owners, then use your error budget (how much you can miss the target by) to guide releases — exactly as SRE teams do. sre.google
The 7‑day AI Quality Scoreboard sprint
Day 1 — Pick the “top 3” user journeys and define success
- Choose three high‑volume, low‑risk tasks, e.g., order status, appointment changes, grant eligibility triage.
- Write one‑sentence success criteria per task: “The user can self‑serve order tracking to delivery detail.”
- Nominate an SLO owner per metric (Ops for latency/containment, Finance for cost, Service for success rate).
Day 2 — Build a small “golden set” from real queries
- Collect 50–100 recent queries per journey; strip personal data; add correct answers and links to your sources.
- Tag each item with a difficulty level. This becomes your weekly quality sample.
- UK guidance calls this building “assurance evidence” — simply, a traceable record of how you know the system works. gov.uk
Day 3 — Draft your Scoreboard and SLOs
- Fill in targets for the seven metrics above. Keep them realistic; you can tighten later.
- Write down “exit criteria” for a pilot: e.g., 2 consecutive weeks meeting all SLOs and cost per resolved task under the cap.
Day 4 — Run a fair offline evaluation
- Test 2–3 models side‑by‑side on the golden set. Use consistent prompts and the same knowledge base.
- Borrow external benchmarks for context rather than as decision‑makers. Stanford’s HELM leaderboards are transparent and reproducible, which helps you sanity‑check your findings. crfm.stanford.edu
- If your vendor offers an “evals” tool, use it to store results. OpenAI provides a framework and hosted evals; any similar tool is fine if it records prompts, answers and scores in one place. github.com
Day 5 — Model costs and speed, then turn on caching
- Measure token usage and latency under load for your best two options. Record cost per resolved task.
- Enable prompt/context caching in production to reduce input costs on repeated content (policies or product data) and to cut latency; providers now discount cache hits substantially. openai.com
- If you’re on Azure OpenAI, check the differences between standard and provisioned caching discounts. learn.microsoft.com
Day 6 — Limited live pilot with guardrails
- Expose the feature to 5–10% of real traffic or a small staff group.
- Track containment, success rate, p95 latency and cost per resolved task daily; pause on any critical incident.
- For helpdesk, review escalations: were summaries clear, tone appropriate, data captured? (Containment must never trump care.) secondary-brand.zendesk.com
Day 7 — Go/no‑go and a 30‑day improvement plan
- Make the decision with your Scoreboard: green across all SLOs and within error budget? Go. Otherwise, fix or wait.
- Agree weekly checkpoints and who owns each metric. Publish a one‑page Scoreboard to your exec team.
Decision checklist: will this pilot scale?
- Do we have 50–100 real queries per journey with checked answers?
- Is our containment rate target appropriate for the issue types we’re automating? gartner.com
- Have we measured p95 latency and cost per resolved task with and without caching? openai.com
- Have we reviewed safety and escalation samples this week and logged findings as assurance evidence? gov.uk
Procurement questions to ask vendors this week
- Evaluation transparency: “Can you show prompt‑level results on our data, and do they match independent methodologies such as HELM‑style reporting?” crfm.stanford.edu
- Cost controls: “What context/prompt caching discounts apply and how are cache hits reported on our bill or usage logs?” openai.com
- Operational limits: “What are your rate limits and how do you signal throttling or back‑pressure?” (Example: CX platforms publish explicit rate‑limit headers.) developer.zendesk.com
- Model changes: “How will you notify us of version changes that might affect quality and what rollback options exist?”
- Assurance evidence: “Can you provide a system card or test summary we can attach to our assurance pack?” gov.uk
Risks and how to de‑risk them early
“Great in the lab, poor in the wild”
Risk Offline scores don’t match live behaviour.
Mitigation Use a small canary pilot with SLOs and an error‑budget policy; treat missed SLOs as a blocker for wider rollout, as in SRE. sre.google
Over‑optimising one metric
Risk Chasing containment harms satisfaction or accuracy.
Mitigation Balance with success rate, factuality and escalation quality; review a weekly sample. secondary-brand.zendesk.com
Unpredictable costs
Risk Long context or repeated prompts inflate bills.
Mitigation Enable caching, monitor cache‑hit rate, and cap cost per resolved task in your SLOs. openai.com
Assurance gaps
Risk Hard to explain why you trust the system.
Mitigation Maintain a lightweight assurance pack: your Scoreboard, test samples, safety audit notes and vendor artefacts. UK guidance encourages this evidence‑based approach. gov.uk
Putting it together: what your one‑page Scoreboard looks like
Keep the “AI Quality Scoreboard” to one page. Include:
- Scope (feature, user journeys), model version, date range, sample size.
- Seven metrics with target vs actual and arrows (up/down) for trend.
- Notes on caching status and cache‑hit rate.
- Incidents and decisions (e.g., “Paused release on 21 Nov due to p95 latency breach”).
If you want to deepen your testing later, you can add task‑specific evals using hosted tools. The point isn’t more tests; it’s the smallest set that predicts quality and cost for your users. github.com
Where this fits with your broader AI plan
If you’re building retrieval‑augmented features, your scoreboard complements a solid retrieval plan and content pipeline. See our 6‑week RAG blueprint and our practical guide to fix retrieval at the source. To stress‑test model choices quickly, pair this scoreboard with the 5‑day evaluation sprint and the 10 tests that predict AI quality.
FAQs
Do we need to be “state‑of‑the‑art” on research leaderboards?
No. External benchmarks are useful for context, but many are not identical to your task. Use transparent sources like HELM for sanity‑checks, and always validate on your own golden set. crfm.stanford.edu
Isn’t this a lot of work for a small team?
It shouldn’t be. The scoreboard asks for a handful of weekly samples and a short pilot. That’s far less effort than firefighting a poorly‑measured rollout.
What about safety?
Build a small weekly audit into the scoreboard, log incidents, and attach vendor artefacts (system cards or their test summaries) to your assurance pack — a pragmatic way to show diligence without slowing delivery. gov.uk