Data ownership, labelling infrastructure, integration architecture, and team capability.
Data ownership, labelling infrastructure, integration architecture, and team capability, these four dimensions determine whether your AI project will ship or stall. Before you sign anything, you should be able to answer all of these.
Who owns the data your AI system will train on? If it's a third-party platform, do your contracts give you access to the raw data, or just to the platform's API? Can you export it? In what format? This is non-negotiable: if you don't control the training data, you don't control the model.
Where does the data live, and who can access it? GDPR, HIPAA, and sector-specific regulations impose constraints on what data can be used for AI training. Know your constraints before your vendor starts designing the system.
Most AI systems require labelled training data, examples where the correct answer is already known. Who will produce those labels, and at what cost? This is a question most clients underestimate. Getting 10,000 clinical notes labelled by doctors for a clinical AI system is not a trivial logistics problem. Building a product recommendation training set requires customer data that may not exist in the right form.
Do you have a process for maintaining label quality? Labels degrade in usefulness over time as the world changes. A labelling process that worked in 2023 may need updating for 2025 data.
Where will the AI system sit in your stack? An AI model is not a standalone product. It has to connect to your existing data sources, serve predictions through your existing interfaces, and operate within your existing latency and reliability constraints.
Who owns the integration work? In our experience, integration is where most AI projects stall. The model works. The data pipeline works. But nobody owns the work of connecting them to the existing system. This ownership question has to be answered before the project starts.
Who will maintain the system after handoff? AI systems require ongoing operational oversight. Who in your team understands what the system does well enough to notice when it starts going wrong?
Is there a retraining plan? Models drift. Retraining is not a one-time event, it's an operational process. Does your team have the tooling and the process to run it?
If you can't answer these questions confidently, the first investment isn't in AI, it's in getting to a position where AI will work.
We run 2-hour feasibility calls at no cost. We'll tell you what applies to your project.