We have AI initiatives, and they start from business needs, not from "let's implement GPT and see what happens." Someone is responsible for AI at the company-wide level, not "a little bit in every department." We have criteria for evaluating the effectiveness of ML/AI solutions: metrics, business results, and ROI.
AI Process Maturity Checklist
Check how closely your company's AI initiatives connect to business goals, performance metrics, and sustainable adoption into processes.
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AI Process Maturity Checklist
We know which data can be used for training and who owns that data. We have infrastructure for retraining and deploying models, not just a single Jupyter notebook.
We know how to identify and eliminate bias in models instead of pretending the problem does not exist. We have a pipeline: from idea → to model → to integration into a product or process. Our ML models in production are monitored, logged, and alerted on, so they are not a black box. Our AI projects go through checks for regulatory compliance and ethics. We have fallback scenarios for what the system does if the model returns an error or fails.
We assess AI implementation risks the same way we assess financial or legal risks. We have a clear role structure: data scientist ≠ ML engineer ≠ product owner. AI is built into processes, for example recommendations, planning, and quality control.
We test hypotheses in a sandbox/PoC, rather than launching them straight into production.
Our goal is not just to implement AI, but to scale it across business units.
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