Buying the wrong AI tool can cost Dutch SMEs €50k+ in wasted implementation, data exposure, and vendor lock-in. With 45% of mid-market Dutch firms now deploying AI systems, vendor due diligence is no longer optional—it's a critical governance and risk management discipline.
AI Vendor Due Diligence Checklist for Dutch Companies: 12 Questions to Ask Before You Buy Any AI Tool in 2026
For founders, COOs, and tech leaders at Dutch SMEs comparing AI tools and wanting to avoid costly mistakes.
The Dutch market is moving fast. In 2024, 22.7% of Dutch companies with 10+ employees used at least one AI technology, and by 2025 the biggest jump came from firms with 50 to 250 employees, rising from 20% to 45%. At the same time, the Dutch government's business guidance makes clear that companies that provide or deploy AI systems in the EU must deal with AI Act obligations around risk, transparency, and AI literacy. In other words, more companies are buying AI, and more of those purchases now carry operational and compliance consequences.
This is why a comprehensive AI vendor due diligence checklist is no longer optional—it's a critical business protection exercise.
The biggest mistake buyers make
Most companies buy the demo, not the operating reality.
They ask:
- How good is the model?
- How fast can you deploy?
- Can you show us the workflow?
They fail to ask:
- What data does this touch?
- What happens when the output is wrong?
- How do we monitor it?
- How hard is it to exit?
- What exactly are we contractually protected against?
That gap is dangerous because third-party AI risk now sits across privacy, vendor due diligence, DPIAs, transfer assessments, ongoing monitoring, incident management, and contractual controls. PwC's 2025 third-party risk paper ties AI and emerging tech directly to third-party compliance verification, contractual measures, system classification, and ongoing monitoring.
The 12-Question AI Vendor Due Diligence Checklist
1) What business problem are we actually buying a solution for?
Do not buy "AI."
Buy a business outcome.
Ask:
- Which workflow does this improve?
- What metric should move?
- Who owns the current process?
- What would success look like in 90 days?
If the vendor sells general productivity but cannot anchor the product to a specific workflow, you are probably looking at a nice interface, not a high-value solution.
2) Who inside our company will own the workflow after launch?
This is where many projects quietly fail.
A vendor can implement the system, but they cannot own your internal adoption. If no business owner exists, the tool will drift between departments until usage becomes optional and value disappears.
A good buying decision starts with an internal owner, not just an external supplier.
3) Are we the provider, the deployer, or both under the AI Act?
This is one of the first questions serious buyers should ask.
Dutch government guidance is explicit: if you build, sell, have AI built for your own use, or deploy an AI system and are responsible for its use, you fall within the AI Act's scope. That guidance also highlights transparency obligations, AI literacy, prohibited systems, and phased obligations for high-risk systems.
Ask the vendor:
- How do you classify this system?
- Are you acting as provider, processor, subprocessor, or some combination?
- What documentation do you supply to support our obligations?
- What transparency features are built in?
- What use cases do you explicitly advise against?
If the vendor gets vague here, that is already a signal.
4) What happens to our data, prompts, files, and outputs?
This is a non-negotiable diligence area.
Ask:
- Is our data used for training?
- Where is data stored and processed?
- What are the retention and deletion rules?
- Can we export prompts, logs, and outputs?
- What subprocessors are involved?
- What happens to our data when the contract ends?
PwC's third-party risk guidance specifically calls out data-sharing governance, safeguards, controls, portability rights, termination rights, DPAs, DPIAs, transfer assessments, and incident management for third parties that receive or access data.
If your buyer team cannot answer those questions before signature, you are not doing due diligence. You are outsourcing trust.
5) What security controls are actually in place?
You need more than a security page full of badges.
Ask:
- What identity and access controls are supported?
- How are secrets, connectors, and credentials handled?
- What logging is available?
- How are incidents reported?
- What model, plugin, or agent permissions can be restricted?
- How is tenant isolation handled?
OWASP's LLM Applications Cybersecurity and Governance Checklist is explicitly aimed at leaders across executive, tech, cybersecurity, privacy, compliance, and legal roles who want to avoid "hasty or insecure AI implementations."
That tells you something important: security review for AI is not just for the security team. It is a cross-functional buying discipline.
6) What proof do we have that the system works in our context?
Do not accept generic benchmark talk.
Ask:
- What evidence do you have for our use case, not just your best use case?
- How do you evaluate output quality?
- What failure modes are common?
- How often do you test?
- Can we run a controlled pilot with agreed metrics?
NIST's AI RMF treats test, evaluation, verification, and validation as lifecycle responsibilities and also distinguishes procurement, governance, operators, evaluators, compliance experts, and domain experts as separate AI actors. That is useful because it reminds buyers that a model is not "proven" just because the vendor says it works. It must be evaluated in context.
7) Where does human oversight sit?
If an AI system affects customer communication, internal decisions, regulated workflows, hiring, support, or knowledge work at scale, you need to know exactly where humans step in.
Ask:
- What actions require human approval?
- Can we configure review thresholds?
- How are overrides logged?
- What audit trail exists?
- What happens when the model is uncertain?
This matters even more under the AI Act, where human oversight is central for higher-risk systems and transparency matters for deployers.
8) What will implementation really require from us?
Many AI tools look easy until integration starts.
Ask:
- What systems need to be connected?
- How much internal engineering is needed?
- What data cleanup is assumed?
- What change management is needed?
- What is the realistic timeline to production?
- What does the vendor need from our team each week?
This is where demos hide labor.
A cheap subscription with heavy internal rework is not a cheap solution.
9) What is the real cost, not just the list price?
AI pricing often looks clean at the top and messy underneath.
Ask:
- What drives usage costs?
- What happens when prompt volume grows?
- Are there overage or model-tier surprises?
- What services are extra?
- What admin burden sits on our team?
- What is the 12-month total cost, including implementation and governance?
The right question is not "What does it cost?"
It is "What will it cost once people actually rely on it?"
10) How portable is this if we need to switch?
This is one of the most ignored AI buying questions.
OECD warns that restrictive data licensing can create data lock-in, while vendor lock-in can leave the buyer heavily dependent on proprietary technology and data formats. ENISA's 2025 advisory opinion goes even further, recommending AI procurement contracts and standardized clauses to verify vendor compliance and improve trust in AI systems and services.
Ask:
- Can we export prompts, configurations, logs, and evaluations?
- Are we tied to your proprietary orchestration layer?
- Can we switch models without rebuilding everything?
- What survives if we leave?
If the exit path is fuzzy, the buying decision is incomplete.
11) Is this vendor likely to be viable for the length of our dependency?
You are not just buying software. You are buying dependency.
Ask:
- What is the vendor's support model?
- Who owns onboarding and escalation?
- How often do they change models or pricing?
- What happens if a core provider changes terms?
- What roadmap stability do they offer?
NIST notes that technologies acquired from third parties may be complex or opaque, and that the vendor's risk tolerances may not align with those of the deploying organization.
That is why financial health, roadmap discipline, and support quality matter as much as feature velocity.
12) What must be in the contract before we sign?
This is where procurement needs to get serious.
At minimum, your contract review should cover:
- data use restrictions
- retention and deletion
- security obligations
- audit rights
- incident notification
- performance commitments
- change notification
- model substitution rules
- termination rights
- portability support
- liability and indemnity boundaries
- compliance cooperation
PwC's third-party risk paper and ENISA's advisory both point in the same direction: AI vendor management now needs contractual controls, compliance verification, monitoring, and clearer clauses around security and ongoing responsibilities.
The red flags that should stop the deal
Pause the purchase if you hear any version of these:
- "We can discuss security after the pilot."
- "Our customers usually do not ask that."
- "The model handles that automatically."
- "You do not need to worry about AI Act scope for this."
- "Export is possible, but only through professional services."
- "We cannot show how quality is measured."
- "We do not support detailed logging yet."
Those are not small issues.
They are future costs.
What a strong AI buying process looks like
A good AI buying process is not vendor-hostile.
It is disciplined.
For most SMEs and mid-market companies, the strongest pattern is:
- define the workflow and owner
- classify the use case and risk
- run technical, privacy, security, and legal diligence together
- pilot with real metrics
- negotiate the contract around actual operating risk
- build an exit path before you need one
If that sounds heavier than your current process, that is the point.
AI procurement has become a leadership issue, not just a software purchase.
Where First AI Movers fits
This is exactly where First AI Movers can help.
We help companies cut through demo theater and evaluate AI vendors against the questions that actually matter. With services like our AI Readiness Assessment and AI Governance & Risk Advisory, we focus on:
- business fit
- governance and AI Act exposure
- data and privacy controls
- security posture
- evaluation discipline
- implementation burden
- contract risk
- portability and long-term dependency
If you are about to buy an AI tool, agent platform, or implementation service, a structured due-diligence review can save you far more than it costs.
FAQ
What questions should I ask an AI vendor before signing?
Ask about workflow fit, data use, retention, security, evaluation, human oversight, integrations, pricing, portability, vendor viability, and contract protections.
Do Dutch companies need AI vendor due diligence now?
Yes. If the tool will touch real workflows, data, customers, employees, or regulated processes, vendor diligence is now part of responsible procurement and risk management.
What is the biggest risk when buying an AI tool?
Usually not the demo quality. The biggest risks are hidden implementation burden, weak governance, unclear data handling, poor portability, and contracts that do not protect the buyer.
How do I avoid AI vendor lock-in?
Ask upfront about exportability, model portability, contract exit support, data rights, orchestration dependence, and how much of your workflow becomes proprietary to the vendor.
Should procurement handle AI vendor selection alone?
No. Strong AI buying decisions usually require input from business owners, technical leads, privacy, security, legal, and the people who will operate the workflow after launch.
Further Reading
- EU AI Act Audit Governance Model Guide
- AI Readiness Assessment Dutch SMEs 2026
- Build vs Buy AI Systems 120k Decision Framework 2026
- Internal AI Lead vs External Partner Dutch SMEs 2026
Written by Dr Hernani Costa | Powered by Core Ventures
Originally published at First AI Movers.
Technology is easy. Mapping it to P&L is hard. At First AI Movers, we don't just write code; we build the 'Executive Nervous System' for EU SMEs.
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