The pre-deployment checklist before AI touches a client

The five checks that matter most

1. Bias testing

Bias testing means systematically checking whether a model's outputs differ unfairly across protected or sensitive groups (gender, ethnicity inferred from name, body size, geography).

For a styling assistant, this means running the same client profile through the model with only one variable changed, say, a name typically associated with a different ethnicity, and comparing recommendations. Do luxury tiers of product get suggested less often? Does language shift in tone or formality?

A simple test harness:

Run this across hundreds of profile pairs before launch, not after a client complaint. Document the results. This documentation is exactly what the EU AI Act expects for high-risk systems, and it is good practice even when the system is not formally high-risk.

2. Escalation paths

An escalation path is the predefined route a flagged case takes to a human. Before launch, the review must answer: what triggers escalation, and who receives it?

Concrete example: if a client asks the styling assistant about a bereavement outfit, or mentions a medical condition affecting fit, the system should stop generating autonomous advice and route to a human client advisor within a defined time window (many maisons target under 15 minutes for VIP tiers). Test this path with simulated edge cases before go-live, not during it.

3. Human override points

A human override point is a place in the workflow where a person can stop, correct, or veto the AI's output before the client sees it. For client-facing luxury AI, the industry norm converging in 2026 is "human-in-the-loop" for anything involving final purchase recommendations above a price threshold, and "human-on-the-loop" (monitoring, not gating) for lower-stakes suggestions like accessory pairing.

The go/no-go review should force a walkthrough: show me, live, where a stylist can intercept a bad recommendation before it reaches the client app. If nobody can demonstrate this in the room, that is a no-go.

4. Brand-voice audit

A brand-voice audit checks whether AI-generated language matches the maison's tone, register, and values, not just factual accuracy. This is distinct from bias testing: a technically unbiased response can still sound wrong for a house known for restraint, or too casual for a house built on formality.

Practically, this means a panel (often merchandising plus brand plus a language model evaluator) scores a sample of 100 to 200 generated responses against a rubric: formality, cultural sensitivity, house-specific vocabulary, absence of competitor mentions. Anything below threshold gets retrained or hard-coded with guardrail prompts.

5. Data lineageData lineageData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.View full definition →

Data lineageData lineageData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.View full definition → is the documented trail of where training and input data came from, how it was processed, and who has rights to it. For luxury, this matters twice over: client purchase history is highly sensitive personal data under GDPR (General Data Protection Regulation, the EU's data protection law), and any imagery or style data scraped from runway shows or editorial content may carry copyright exposure.

The review must confirm: is client data used to train this model, and did clients consent to that specific use under GDPR Article 6 (lawful basis for processing)? If the answer is unclear, that alone is grounds for delay. See the ICO's guidance on AI and data protection for a practical framework, applicable in spirit even outside the UK.

What a go/no-go review actually looks like

In practice, this is a one-hour meeting with a written scorecard, not a vibe check. A realistic structure:

| Check | Owner | Status 3 weeks out | Blocking? |

|---|---|---|---|

| Bias testing across 5 sensitive attributes | Data science | Amber, 2 attributes untested | Yes |

| Escalation path tested end-to-end | Client experience | Green | No |

| Human override demoed live | Product | Red, no demo available | Yes |

| Brand-voice audit, 150 samples scored | Brand/marketing | Green | No |

| Data lineageData lineageData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.View full definition → documented, consent confirmed | Legal/privacy | Amber, awaiting DPO sign-off | Yes |

Three "blocking" reds or ambers means no-go, full stop, regardless of marketing timelines. The scorecard forces the room to separate "we're excited to launch" from "we're ready to launch."

🎬 [VIDEO: "AI Governance Explained: How Companies Manage AI Risk" - youtube.com - a clear walkthrough of enterprise AI risk frameworks applicable to client-facing deployments]

Model risk beyond the five checks

Two broader model risk categories deserve a mention because they recur across luxury AI deployments:

Drift: a model's outputs degrade or shift over time as client behavior, inventory, or seasonal collections change. A styling model trained on last season's line-up may confidently recommend discontinued pieces. Luxury houses should schedule quarterly re-validation, not just at launch.

Hallucination in generative outputs: text or image generation models can invent product details, prices, or availability that do not exist. For a maison, a hallucinated claim about material composition ("this is hand-embroidered silk") when it is not, is both a brand and a consumer-protection problem. Output validation against a live product database, checked before display, is the standard mitigation.

Key Takeaways

• A go/no-go review needs a written scorecard with named owners, not a general discussion; ambiguous answers on bias, override, or data consent should block launch.
• Bias testing must use paired profile comparisons (same request, one sensitive variable changed) and be documented, this documentation also satisfies emerging EU AI Act expectations.
• Human override points must be demonstrated live before launch, not assumed to exist; "human-in-the-loop" for high-value recommendations is becoming the sector norm in 2026.
• Data lineageData lineageData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.View full definition → and GDPR consent for any client data used in training are legal prerequisites, not optional extras, especially for EU-facing brands.
• Model risk does not end at launch: schedule recurring bias re-checks, drift monitoring, and hallucinationhallucinationA hallucination is when an AI model generates output that is fluent and confident but factually wrong, fabricated, or unsupported by its source data.View full definition → validation against live product data.