Clean your data: As I noted recently about Agentic RAG strategies, many enterprises suffer from terrible data. It’s out-of-date, error-ridden, obtained from dubious sources, and might unintentionally contain sensitive data (including PII and health data) that is not supposed to be there. No genAI magic can ever work if the data foundation is a mess. Have your team generate pristine data and your AI ROI has a chance.
Select more ideal projects: This is actually a twofer: First, talk with your team about genAI particulars so you can identify where the technology can help. GenAI can indeed handle anything, but it can only handle a very small subset really well. Secondly, far too many projects have been selected because, as an experiment, execs wanted to see what genAI can truly do. You need to be far more selective if you want to give genAI a fair chance.
Assess your hallucination comfort zone: This is arguably the most crucial. GenAI will hallucinate, and it will do so with no predictability. There are mechanisms you can deploy to reduce hallucinations a small degree — such as using AI to double-check AI, as is being attempted by Morgan Stanley, as well as limiting the data sources genAI is permitted to use.
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