The AI vendor market has exploded. Every software company now claims to be "AI- powered," and a new crop of startups launches weekly promising to revolutionise everything from customer service to compliance. Separating the genuinely useful from the overhyped is harder than it's ever been.
We've helped organisations evaluate dozens of AI vendors across different sectors. The patterns of good and bad vendors are remarkably consistent. Here's what we've learned.
Red Flags in AI Vendor Pitches
Before you evaluate capabilities, learn to spot the warning signs that should make you pause:
"Our AI is 99% accurate." Any vendor quoting accuracy without context is either naive or dishonest. Accuracy depends on the task, the data, and the definition of "correct." Ask them: 99% accurate at what? Measured how? On whose data? If they can't answer these questions clearly, walk away.
"We use proprietary AI." In 2026, most AI applications are built on foundation models from OpenAI, Anthropic, Google, or open-source alternatives. There is nothing wrong with this — the value is in the application layer, not the model. But vendors who obscure this fact are often hiding a thin wrapper around an API call.
No discussion of data handling. If a vendor does not proactively address where your data goes, how it's stored, whether it's used for training, and what happens when you terminate the contract, that is a serious red flag. Data governance should be front and centre, not an afterthought buried in the terms and conditions.
Resistance to a proof of concept. Good vendors welcome the chance to prove their solution works on your data, with your workflows. If a vendor pushes for a long-term contract before demonstrating value, they are not confident in their product.
Vague pricing. "It depends on usage" is fair for consumption-based models, but you should be able to get a clear estimate based on your expected volume. Vendors who cannot give you a ballpark figure are either disorganised or hoping to upsell you later.
Evaluation Criteria That Actually Matter
When you move past the pitch and into evaluation, here's what to assess:
Performance on your data. Not demo data. Not synthetic data. Your actual data, with all its messiness. This is the single most important test. We've seen vendors whose demos look flawless fall apart when confronted with real-world data quality issues.
Integration complexity. How does the vendor's solution connect to your existing systems? A tool that requires six months of custom integration work might deliver less net value than a simpler one you can deploy in weeks. Ask for a technical architecture diagram and assess the integration burden realistically.
Explainability. Can you understand why the AI made a particular decision? For customer-facing or compliance-sensitive use cases, explainability is not optional. If the vendor cannot explain how their system reaches its conclusions, that is a problem.
Support and SLAs. What happens when something breaks at 2 AM? What is the vendor's uptime commitment? How quickly do they respond to issues? A brilliant AI solution with poor support will cost you more in the long run than a decent one with excellent support.
Roadmap alignment. Where is the vendor heading? Does their product roadmap align with your needs over the next 12 to 24 months? A vendor that is pivoting away from your use case is a risk, no matter how good their current product is.
Running a Proper Proof of Concept
A proof of concept (PoC) is your best protection against a bad vendor decision. But most PoCs are structured poorly. Here's how to run one that actually tells you what you need to know:
- Define success criteria upfront. Before the PoC starts, agree on exactly what constitutes success. Measurable metrics, not vibes. For example: "Process 500 invoices with 95%+ extraction accuracy and under 3 seconds per document"
- Use real data. Insist on testing with your actual data. If the vendor cannot handle your data format, document quality, or volume, you need to know now, not after signing a contract
- Time-box it. A PoC should take 2 to 4 weeks, not 3 months. If it takes longer, the vendor is either struggling or padding the timeline
- Include edge cases. Feed the system your hardest examples — the poorly scanned documents, the unusual formats, the exceptions. These are what will determine whether the solution works in production
- Evaluate the whole experience. The PoC is not just about the technology. Assess the vendor's responsiveness, technical competence, and communication. This is a preview of what the ongoing relationship will look like
Contract Considerations
AI vendor contracts have unique risks that standard software procurement processes do not cover. Pay particular attention to:
Data ownership and usage. Confirm in writing that your data remains yours and is not used to train the vendor's models or improve their product for other customers. This is non-negotiable.
Exit provisions. What happens when you want to leave? Can you export your data, your configurations, your training data? Vendor lock-in is a real risk in AI, especially with solutions that learn from your data over time.
Liability for AI outputs. Who is responsible when the AI gets it wrong? Most vendors disclaim liability for AI-generated outputs, which means the risk sits with you. Make sure your contract reflects this and that you have appropriate human oversight in place.
Price escalation. AI costs can increase significantly as usage grows. Negotiate volume discounts and price caps upfront. Understand the pricing model thoroughly — per-API-call pricing can become very expensive at scale.
Build vs Buy vs Partner: A Decision Framework
Sometimes the right answer is not to buy from a vendor at all. Here's how we help clients think through the decision:
- Buy when your use case is well-defined, a mature product exists, and speed to deployment matters more than customisation. Examples: AI-powered search, document processing, standard chatbots
- Build when your use case is unique, involves proprietary data or processes, or requires deep integration with your existing systems. Examples: custom underwriting models, bespoke recommendation engines, workflow-specific agents
- Partner when you need external expertise to design and build a solution, but want to own it long-term. A good AI consultancy can help you build something tailored without the overhead of hiring a full internal team
The worst decision is defaulting to "buy" because it feels safer. A poorly chosen vendor product can cost more in integration, workarounds, and eventual replacement than building the right thing from the start.
At Grove AI, we help organisations evaluate AI vendors, run structured proof of concepts, and make the right build vs buy decision. If you're navigating the AI vendor landscape and want an independent perspective, book a free consultation and we'll help you cut through the noise.