There is a 6-dimension scorecard that separates the AI vendors who deliver enterprise ROI from those who run a great demo and disappear three months after signing. Here it is, ready to use in your next RFP.
According to MIT NANDA's 2025 Generative AI Pilots study, 95% of enterprise AI pilots produce zero measurable return on the USD 30 to 40 billion organisations have spent. BCG's 2026 Build for the Future report shows the failure pattern is concentrated, vendor-led deployments succeed 67% of the time while in-house builds succeed roughly one-third of the time. The vendor decision is not a procurement formality. It is the single highest-leverage decision in your AI programme.
What is an AI vendor evaluation framework?
An AI vendor evaluation framework is a structured scorecard that rates competing vendors across the same set of weighted dimensions, producing a defensible numeric comparison instead of a subjective preference. It replaces gut-feel decisions made after polished demos with evidence the procurement, security, finance, and business teams can all challenge before signing.
For Hong Kong enterprises, the framework needs one extra layer most generic templates skip, jurisdictional fit. HKMA's November 2025 circular on generative AI in banking, the HK PCPD Model Personal Data Protection Framework, and the HKPC AI Governance and Safety Assessment Service all set context for what acceptable vendor behaviour actually means in this market.
Why does AI procurement need a different framework than SaaS?
AI procurement is not ordinary SaaS procurement because AI vendors do not sell a fixed product. They sell a system whose behaviour depends on your data, your prompts, your workflow integration, and the model's evolving training. Switching costs are higher, observable defects appear later, and the regulatory surface is wider than a typical CRM or ERP purchase.
A Forrester 2026 Total Economic Impact study of enterprise AI deployments found that contracts written with the same procurement template used for SaaS produced 2.4 times more change orders within the first 12 months. Cost overruns averaged 37% above the signed price. The procurement framework, not the negotiation, was the variable that moved the outcome.
What are the six dimensions every AI vendor scorecard should cover?
The six dimensions every AI vendor scorecard should cover are technical fit, data and integration, governance and security, operating model, commercial terms, and measurable business value. Each dimension is scored 1 to 5 and weighted by your organisation's priorities, producing a single comparable score per vendor for every shortlisted option.
Below is what each dimension actually contains in practice.
Dimension 1: Technical fit — does the model and architecture match your problem?
Technical fit asks whether the vendor's model class, context window, latency profile, and tool-calling architecture match your specific use case. A vendor strong in document summarisation may be the wrong choice for an agentic workflow that needs reliable function calling across a 50-step process.
Three concrete questions belong here. First, can the vendor demonstrate accuracy on a representative sample of your data, not their marketing demo? Second, what is the p95 latency at your expected concurrency? Third, does the architecture support the integration pattern you need, REST API, dedicated VPC, or on-premise deployment for sensitive workloads?
Dimension 2: Data and integration — where does your data live and how does it move?
Data and integration covers where customer data is stored, how it traverses the vendor's pipeline, what is logged for audit, and how easily the system connects to your existing core systems. For Hong Kong financial services and professional services, this dimension carries the highest regulatory weight.
The HK PCPD March 2025 Generative AI Employee Use Checklist requires organisations to map data flows for any AI system handling personal data. A defensible vendor answer includes a written data residency commitment, a list of every sub-processor in the pipeline, and a documented retention and deletion process. Vendors who cannot produce this in writing should not be on the shortlist.
Dimension 3: Governance and security — what controls protect your business?
Governance and security covers identity and access management, model audit logs, prompt injection defences, content filtering, and the vendor's incident response track record. According to the IBM 2025 Cost of a Data Breach Report, organisations with shadow AI in their environment paid an average USD 670,000 more per breach than those with governed AI deployment.
For Hong Kong banks, the HKMA's principle that boards remain accountable for AI outcomes means your vendor's governance must be auditable by your second line of defence. SOC 2 Type II is the floor. ISO 42001 AI management certification, if available, separates serious vendors from aspirational ones.
Dimension 4: Operating model — who runs this once it is live?
The operating model dimension asks who is responsible for monitoring, model updates, prompt iteration, and incident response after launch. A common failure pattern is vendors who provide great pre-sales support and weak post-launch operations, leaving your IT team to debug production issues with no run-book.
Three practical checks separate strong from weak operating models. First, does the contract specify named human contacts and response time SLAs, not just a ticketing portal? Second, who pays when a model update breaks an integration, the vendor or you? Third, is there a published quarterly cadence for model performance reviews and capability updates?
Dimension 5: Commercial terms — what is the real total cost of ownership?
Commercial terms is where most enterprise AI procurement loses money. The headline price often excludes inference cost overruns, integration work, fine-tuning, and the consulting hours required to operationalise the system. FinOps Foundation 2026 State of FinOps reports 73% of enterprises ran their AI programmes over budget by an average of 41% in the first year.
A defensible commercial scorecard quantifies licence fees, inference and token costs at projected production volume, integration and professional services, training, and the ongoing cost of governance. For Hong Kong enterprises with strong currency hedging requirements, ask whether pricing is in HKD or USD and who absorbs FX volatility on a multi-year contract.
Dimension 6: Measurable business value — what KPI moves and by how much?
Measurable business value is the dimension that decides whether you can defend the investment to the CFO at the 12-month review. Every AI vendor will quote efficiency claims. The framework asks for specifics, which KPI, what is the current baseline, what is the target, and what is the measurement method?
According to McKinsey's State of AI 2025, only 6% of enterprises qualify as AI high performers reporting more than 5% EBIT contribution from AI. Their common pattern is a small set of business-led KPIs measured before deployment, not a long list of vanity metrics produced after launch. Build the measurement plan into the contract before you sign.
How do you weight the six dimensions for your organisation?
You weight the six dimensions based on the regulatory profile of your industry, the maturity of your AI programme, and the strategic role of the use case. A regulated bank weights governance and data residency higher. A logistics firm chasing efficiency weights operating model and commercial terms higher. There is no universal weighting.
A practical starting allocation for Hong Kong mid-market enterprises is 20% technical fit, 20% data and integration, 20% governance and security, 15% operating model, 15% commercial terms, 10% measurable value. Adjust upward on whichever dimension carries the highest organisational risk, and run the scorecard with three internal reviewers, business, IT, and risk, before consolidating scores.
What are the most common pitfalls when applying the framework?
The most common pitfall is letting the demo drive the score. Vendor demos are designed to maximise positive impression on dimensions one and four while obscuring weakness on dimensions two and three. A disciplined evaluation requires evidence, signed answers to written questions, code-level integration tests, and reference calls with deployed customers, not slide decks.
A second pitfall is skipping the proof-of-concept stage. The framework rewards vendors who can deliver a 4-week paid POC against your data, with success criteria written into the contract. Vendors who refuse paid POCs almost always score lower on dimension six once the system reaches production.
A third pitfall is letting procurement run the framework without business and risk in the room. AI vendor decisions cross five organisational boundaries, technology, data, finance, legal, and the line of business that will operate the system. Single-function evaluation reliably produces single-function blind spots.
The bottom line for Hong Kong enterprise leaders
The 95% AI pilot failure rate reported by MIT NANDA is not primarily a technology problem. It is a procurement problem. Organisations that adopt a structured 6-dimension framework, weight it deliberately, and apply it across a multi-disciplinary team, move from gut-feel selection into evidence-led decisions their boards can defend.
The vendor you select will shape the next three to five years of your AI programme. Treat the selection as the strategic decision it is, not a procurement task pushed downstream. 懂AI，更懂你 — UD相伴，AI不冷. With 28 years partnering Hong Kong enterprises through technology cycles, we know that the difference between AI ROI and AI write-off is rarely in the model. It is in the framework you used to choose it.
Now that you have the framework, the next step is identifying which AI investment is the right entry point for your organisation. We'll walk you through every step — from AI readiness assessment, to vendor scorecard scoring, to a board-ready procurement memo, drawing on 28 years of enterprise technology experience in Hong Kong.