The hype around AI is loud, fast-moving, and hard to ignore. But amid the noise, one thing remains clear: You don’t need to build your own model to get value, but if you do, it needs to be for the right reasons.
So before making the leap, ask yourself and your team, these critical questions:
Strategic Alignment
- What business problem are we solving?
- Is this model driving revenue, efficiency, or differentiation?
- How will we measure success (KPIs, ROI, user adoption)?
Data Readiness
- Do we have access to large volumes of clean, labelled, proprietary data?
- Is the data compliant with regulations like GDPR or HIPAA?
- Do we have data pipelines in place for continuous model improvement?
Capability and Resource Assessment
- Do we have the in-house talent (ML engineers, MLOps, data scientists)?
- Can we support this model for the long term, beyond initial launch?
- Do we have budget allocated for training, infrastructure, and monitoring?
Risk and Compliance
- What are the legal and ethical risks associated with this model?
- Can we explain how the model works if audited?
- What’s our fallback if performance drops or compliance requirements change?
Build vs Buy Viability
- Have we explored what existing models can do before building?
- Could we fine-tune, embed, or securely integrate a foundation model instead?
- Are there existing vendor partnerships we can leverage?
Cultural and Operational Fit
- Does our team have experience with agile, experimental projects?
- Is there executive alignment and patience for AI timelines?
- Are we prepared to pivot or scrap the project if it doesn’t deliver?
What This Means for You
Having your own model is powerful but so is knowing when not to build. These questions aren’t meant to stop progress. They’re here to ensure you build the right thing, at the right time, for the right reasons.
If you're not ready to answer most of these with confidence, a hybrid or pre-built solution is likely your best next step.
