Why Due Diligence Is Critical When Selecting AI Vendors
Choosing the right AI vendor will determine the success or failure of your digital transformation. While established software giants such as Microsoft, SAP, or Salesforce have delivered proven products for decades, the AI space is seeing new vendors emerge daily with bold promises.
However, not every start-up advertising a revolutionary large language model today will still be around in two years. A wrong decision can have consequences ranging from wasted investments to data privacy breaches.
Thomas, Managing Director of a mechanical engineering firm, learned this the hard way: what seemed like a low-cost AI chatbot vendor shut down after eight months. Customer data was suddenly inaccessible.
Systematic due diligence protects you against such scenarios. It not only uncovers technical shortcomings but also organizational risks that could jeopardize your project.
The Three Pillars of Successful AI Vendor Evaluation
Thorough vendor assessment is built on three pillars: technical competence, organizational stability, and contractual safeguards.
Technical competence covers the performance of the AI models, their scalability, and seamless integration with your existing IT environment.
Organizational stability means: can the vendor offer long-term support? Do they have sufficient funding and qualified staff?
Finally, contractual safeguards should cover service level agreements, data protection, and clear exit scenarios.
Detailed Technical Evaluation Criteria
Model Performance and Accuracy
The quality of AI models stands or falls with measurable performance metrics. Always request benchmark results on standardized datasets.
Major providers like OpenAI, Anthropic, or Google frequently publish performance comparisons of their models. Smaller vendors should at least communicate internal benchmarks transparently.
But caution: laboratory metrics often diverge drastically from real-world outcomes. Insist on a proof of concept using your own data.
Anna, Head of HR at a SaaS company, tested three different AI tools for resume pre-screening. Only one produced acceptable results for German CVs.
Scalability and Integration
How does the system perform under load? Modern AI applications must handle hundreds of simultaneous queries without response times ballooning.
Ask about the underlying infrastructure. Does the solution run on established cloud platforms like AWS, Azure, or Google Cloud? Or does the provider use proprietary servers with unclear capacities?
Integration with existing systems is key to practical value. APIs should follow the REST standard and come with comprehensive documentation.
Markus, IT Director at a service group, always checks compatibility with Microsoft 365, SAP, and his CRM system. Even the most brilliant AI tools are worthless without seamless integration.
Data Security and Compliance
Where are your data processed and stored? European companies must comply with GDPR—which often excludes cloud services based in the US, unless appropriate adequacy decisions or standard contractual clauses are in place.
Check the provider’s certifications. ISO 27001, SOC 2 Type II, or BSI IT-Grundschutz indicate a serious approach to information security.
Just as essential: Are your data used to train AI models? Many vendors reserve the right to leverage customer data for model improvement, which could compromise trade secrets.
At Brixon, we exclusively work with vendors who explicitly guarantee that customer data are not used for training. Your information remains private.
Organizational Assessment Factors
Vendor Stability and Track Record
How long has the vendor been in business? Start-ups may provide innovative solutions but come with higher risk of failure.
Look into funding history. Did the company recently complete a financing round? Or is it struggling with cash flow?
Examine the leadership team. Do the founders have extensive experience in AI development or business management? LinkedIn profiles often tell more than press releases.
Reference customers from your industry are invaluable. If the vendor has already delivered successful projects similar to yours, your risk drops significantly.
Support and Maintenance
AI systems require ongoing maintenance. Models need to be updated, data retrained, and errors fixed.
What support levels does the vendor offer? Business-critical applications demand 24/7 support with guaranteed response times under four hours.
Ask about the escalation path. Can you reach the developers directly in critical cases, or do you end up in a generic call center?
German companies value support in German. Make sure your contacts understand your industry’s professional terminology.
Contract Design and SLAs
Service level agreements define measurable quality criteria. 99.9 percent uptime means no more than 43 minutes downtime per month.
But what happens if an SLA is breached? Cosmetic penalties are useless if your business grinds to a halt. Demand appropriate compensation.
Exit clauses are essential. Can you fully export your data at contract end? In what format? How long do you have for migration?
Watch out for hidden cost traps. Some vendors charge extra for every API call. As usage grows, costs can soar quickly.
Practical Checklist for Vendor Selection
Technical Evaluation
| Criterion | Check Point | Weighting |
|---|---|---|
| Model Performance | Benchmark results on relevant datasets | High |
| Latency | Response times under normal and high load | High |
| API Quality | REST standard, documentation, versioning | Medium |
| Scalability | Support for horizontal and vertical scaling | High |
| Offline Capability | Does the system function if the internet goes down? | Low |
Security and Compliance
- Data location: Processing and storage within EU/Germany
- Certifications: ISO 27001, SOC 2, BSI IT-Grundschutz
- Encryption: End-to-end for transmission and storage
- Access controls: Multi-factor authentication, role-based permissions
- Audit logs: Full traceability of all actions
- Data usage: No use for model training without explicit consent
Organizational Criteria
- Company age: At least 18 months of operations
- Funding: Verified liquidity for the next 24 months
- Team qualifications: Experienced AI developers and business experts
- References: At least three successful projects of similar scope
- Support hours: Response within 4 hours on critical issues
- Roadmap: Public product development plans for 12 months
Contractual Safeguards
Explicitly request the following points in your contract:
- Availability SLA of at least 99.5 percent
- Appropriate penalties for SLA breaches
- Termination periods under 90 days
- Full data export at contract end
- Price protection for 24 months
- Escalation path up to C-level
Typical Pitfalls and How to Avoid Them
The Demo Effect
Impressive demonstrations often mask practical limitations. Vendors show perfectly curated examples but gloss over problems encountered with real enterprise data.
Insist on tests with your own datasets. That’s the only way to check if the system processes German umlauts properly or fails on your specific terminology.
Vendor Lock-In through Proprietary Formats
Some vendors store your data in proprietary formats. Later migration becomes virtually impossible or extremely expensive.
Demand data export in standard formats such as JSON, CSV, or SQL. Your data belongs to you—not the vendor.
Hidden Scaling Costs
How much does the system cost if user numbers double? Many providers lure you in with low entry prices, only to impose disproportionate surcharges as usage grows.
Model out different growth scenarios. Costs should rise linearly or even decline per user—not exponentially.
Unclear Data Privacy Agreements
Terms and conditions often contain vague wording on data usage. “Anonymized data for product improvements” could really mean your business information leaves the company.
Insist on a separate Data Processing Agreement in line with GDPR standards. All aspects relevant to data protection must be clearly defined here.
Actionable Recommendations for Your AI Strategy
Start with a Structured Selection Process
Begin by precisely defining your requirements. Which specific business processes should AI improve? What integration is needed? What budget is available?
Create a long list of 8-10 potential vendors. Rely on trade journals, analyst firms, and recommendations from your network.
Narrow this list down to 3-4 candidates through desk research. Only then invest time in detailed discussions and proofs of concept.
Develop a Risk Management Plan
What if your preferred provider fails? Plan for backup scenarios from the outset.
Avoid single points of failure. Opt for vendors with multi-cloud strategies, or keep a secondary provider on standby.
Document all configurations and customizations. In an emergency, this allows for much quicker migration to alternative solutions.
Invest in Internal Expertise
The best AI solution is worthless if your employees can’t use it effectively. Allocate sufficient budget for training and change management initiatives.
Appoint an internal AI lead. This person becomes the primary interface between your company and the provider.
At Brixon, we don’t just guide you in vendor selection, but also in building up internal AI expertise. Our workshops equip your teams with the know-how needed for successful AI adoption.
Start Small, Think Big
Begin with a manageable pilot project. This helps you gain hands-on experience and minimizes risk during your first AI implementation.
Choose a use case with measurable ROI. Automated quote generation or intelligent document classification deliver fast, tangible results.
Think about scalability right from the pilot phase. What further applications might follow? How does the chosen system fit into your long-term IT strategy?
Frequently Asked Questions
How many AI vendors should I evaluate in parallel?
Three to four vendors are ideal for thorough evaluation. More candidates dilute your comparison, fewer overly limit your options. Start with desk research on 8–10 vendors, then create your final shortlist.
Which certifications are especially important for AI vendors?
ISO 27001 for information security and SOC 2 Type II for operational controls are essential. For European companies, also look for GDPR compliance and ideally BSI IT-Grundschutz certification.
How long should a proof of concept take?
Plan 4–6 weeks for a meaningful PoC. One week is too short for realistic tests, and more than two months only delay your decision unnecessarily. Define clear success criteria and exit conditions in advance.
How much does a professional AI vendor assessment cost?
A structured due diligence process with external support typically costs €15,000–35,000, depending on your requirements’ complexity. This investment pays off quickly if it prevents costly missteps running into six figures.
Should I favor start-ups or established providers?
That depends on your risk appetite. Start-ups offer innovative solutions and flexible customization, but come with higher risk of failure. Established providers like Microsoft or Google are stable but less flexible when it comes to custom requirements.
How can I spot vendor lock-in early?
Warning signs include proprietary data formats, lack of export features, APIs incompatible with standards, and overpriced migration support. Before signing, demand a complete data export in standard formats and test portability.
What’s an appropriate SLA uptime for AI systems?
For business-critical applications, require a minimum uptime of 99.5%—that’s up to 3.6 hours downtime monthly. Premium vendors offer 99.9% or more. Pay attention to how measurement periods are defined—some exclude planned maintenance windows.
