Building an evaluation scorecard for AI vendors

Why demos lie (structurally, not maliciously)

Vendors optimize demos for their strengths. A visual search tool will show you a flawless "find similar dress" query on high-resolution studio imagery. Your actual catalog has inconsistent lighting, missing back-view shots, and 12 percent of products with no color attribute tagged.

The scorecard forces every vendor onto *your* data, *your* systems, and *your* success thresholds. It converts vague enthusiasm into numbers you can defend to a steering committee.

The four scoring pillars

Score each vendor 1 to 5 on four pillars, then weight them. Weights should reflect your situation, but a defensible default for a merchandising or CXCXThe overall perception a customer forms of your brand across every interaction, from first touch to post-purchase support.View full definition → pilot looks like this:

| Pillar | Weight | What it measures |

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

| Accuracy on your assortment | 35% | Does it work on your real SKUs? |

| Integration (PLM/PIM) | 25% | Can it plug into your stack without a rebuild? |

| Data ownership and governance | 20% | Who owns the outputs and training data? |

| Pilot success criteria met | 20% | Did the paid trial hit pre-agreed thresholds? |

Multiply each score by its weight, sum, and you get a single comparable number out of 5. We will work an example at the end.

Pillar 1: Accuracy on your SKU assortment (35%)

This is non-negotiable and must run on your data. Ship every vendor an identical test set: a representative slice of your catalog. Include your hard cases on purpose (deep-discount clearance, bundles, seasonal one-offs, products with sparse metadata).

Define the metric that matches the use case:

• Attribute tagging (auto-generating fields like sleeve length, neckline, fabric): measure precision (of the tags it applied, what percent were correct) and recall (of the tags it should have applied, what percent it found). A tool that tags "floral" on 95 percent of true florals but also mislabels stripes has high recall, weak precision.
• Demand forecasting for assortment: measure error against actuals, typically MAPE (mean absolute percentage error, the average size of the forecast miss). Compare it to your current planner's baseline, not to zero.
• Product recommendations (CX): measure uplift in click-through or conversion on a held-out set, not a proxy the vendor invented.

Insist on results broken down by category. A tool that hits 90 percent tagging accuracy on tops but 60 percent on footwear is a footwear problem waiting to happen.

Pillar 2: integration with PLM and PIM (25%)

Two acronyms to define:

• PLM (Product Lifecycle Management): the system where a product is designed, costed, and developed, from tech pack to production. Vendors include Centric Software, PTC FlexPLM, and Bamboo Rose.
• PIM (Product Information Management): the system holding the "single source of truth" for product data used across your channels (descriptions, images, attributes). Players include Akeneo, Salsify, and inRiver.

An AI tagging tool that cannot write back into your PIM just creates a spreadsheet someone re-keys by hand. Score integration on concrete questions:

• Is there a documented, maintained APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → (application programming interfaceapplication programming interfaceApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition →, the connector that lets two systems talk)?
• Does a prebuilt connector exist for *your specific* PIM, or is it "on the roadmap"?
• Can it handle your data volume and refresh cadence (a new-season drop of 3,000 SKUs overnight)?

Ask for the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → docs during evaluation, not after signing. A vendor who hesitates here is telling you something.

Here is the shape of a realistic write-back call, so non-technical readers can see what "integration" concretely means:

Note the ai_confidence and reviewed_by_human fields. Good tooling exposes confidence so you can route low-confidence tags to a human. That is a scoreable feature.

Pillar 3: Data ownership and governance (20%)

Read the contract, not the sales deck. Key questions:

• Who owns the outputs? If the AI enriches your catalog, that enriched data must be yours to keep and export if you leave.
• Is your data used to train the vendor's shared models? For an apparel brand, your assortment and sell-through patterns are competitive intelligence. You may not want them improving a model your competitor also uses. Look for an opt-out.
• Where is data processed and stored? Relevant under the EU GDPR (General Data Protection Regulation, the EU data-protection law) if any personal data (customer CXCXThe overall perception a customer forms of your brand across every interaction, from first touch to post-purchase support.View full definition → interactions) is involved, and increasingly under US state laws like the California Consumer Privacy Act.
• Does the tool use a third-party foundation model (for example, a large language modellarge language modelA Large Language Model is an AI system trained on vast text data to predict and generate language, enabling tasks like writing, summarizing, and answering questions.View full definition → accessed via APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition →)? If so, your data may transit another vendor's infrastructure. Ask for the data flowdata flowAn automated sequence of steps that moves data from source to destination: ingestion, transformation, validation, and loading, so it arrives clean and ready to use.View full definition → diagram.

For a grounding on why governance belongs in procurement, the NIST AI Risk Management Framework is free and vendor-neutral.

🎬 [VIDEO: "How to Evaluate AI Vendors" - youtube.com - a practical walkthrough of vendor due diligence and pilot design for enterprise buyers]

Pillar 4: Pilot success criteria (20%)

Never score this on promises. Run a paid pilot (paid keeps both sides serious) of 6 to 10 weeks with thresholds agreed *in writing before it starts*. Vague pilots always "succeed."

Good criteria are specific and measurable:

• "Reduce manual tagging time per SKU by at least 40 percent versus current baseline."
• "Achieve attribute-tagging precision of 85 percent or higher across our top five categories."
• "Return recommendations in under 300 milliseconds at peak traffic."

Bad criteria: "improve merchandising efficiency." Unmeasurable, so unscoreable.

Worked example: scoring two vendors

You are choosing between Vendor A (a specialist fashion-attribute tagging tool) and Vendor B (a broad retail AI suite) for auto-tagging your PIM.

You run both on the same 2,000-SKU test set and the same paid pilot. Raw scores (1 to 5):

| Pillar | Weight | Vendor A | Vendor B |

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

| Accuracy on assortment | 35% | 5 | 3 |

| PLM/PIM integration | 25% | 3 | 5 |

| Data ownership | 20% | 4 | 3 |

| Pilot criteria met | 20% | 5 | 4 |

Weighted score, Vendor A:

(5 × 0.35) + (3 × 0.25) + (4 × 0.20) + (5 × 0.20)

= 1.75 + 0.75 + 0.80 + 1.00 = 4.30

Weighted score, Vendor B:

(3 × 0.35) + (5 × 0.25) + (3 × 0.20) + (4 × 0.20)

= 1.05 + 1.25 + 0.60 + 0.80 = 3.70

Vendor A wins on the metric that matters most (fashion-specific accuracy), even though Vendor B integrates more smoothly. The scorecard makes that tradeoff explicit and lets you ask the real question: can we close Vendor A's integration gap with a connector project cheaper than living with weaker tagging?

A note on realistic expectations

No vendor scores 5 across the board. The scorecard is a decision aid, not an oracle. Two disciplines keep it honest:

1. Re-score after the pilot. Pre-pilot scores are hypotheses. Post-pilot scores use evidence. The delta itself is informative: a vendor who overpromised on accuracy is a vendor who will overpromise on support.

2. Watch total cost, not license price. A tool needing heavy integration work or constant human review of low-confidence outputs can cost more than a pricier tool that runs clean. Reflect that in the integration and pilot pillars.

Key Takeaways

• Test on your own SKUs, always. Vendor demos run on curated data; your catalog has the messy edge cases that break tools. Ship an identical, deliberately hard test set to every vendor.
• Weight accuracy highest for merchandising and CX (35 percent in our default), but never ignore PLM and PIM integration: an AI output that cannot write back into your systems is a spreadsheet, not a workflow.
• Nail down data ownership in the contract, including whether your assortment data trains a shared model your competitors could benefit from. Use the NIST AI RMF as a free checklist.
• Run a paid pilot with written, measurable thresholds agreed before it starts, then re-score with real evidence. Track the gap between promised and delivered.
• Convert everything to one weighted number so tradeoffs are explicit and defensible to your steering committee, then interrogate the losing pillar rather than accepting it.