Most AI integration failures are predictable at the assessment stage. The team discovers them six weeks into a project, but they were visible on day one to anyone asking the right questions. The code is not the hard part. The data, the infrastructure, and the organizational readiness are.
A real assessment covers four areas: data readiness, infrastructure readiness, use case prioritization, and team capability. Most companies focus on the last one and skip the first three.
AI systems are only as good as the data they operate on. Before integrating any model, you need to know where your data lives, how clean it is, how consistently it is structured, and whether you have enough of it for the use case you are targeting.
Running AI in production requires infrastructure that most companies do not have in place: model call logging, latency monitoring, cost tracking per feature, graceful degradation when the API is slow or unavailable, and rate limit handling.
The best AI use case for your company is the one where the input data is clean, the success criteria are measurable, the failure modes are manageable, and the business value is unambiguous.
A prioritized list of use cases ranked by ROI and feasibility, a gap analysis covering data, infrastructure, and team capability, and a 90-day roadmap with clear milestones.
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