Trust is important. It’s not simply trusting that data will be secure, it’s also about the hallucination problem. LLMs work on predictions, but they don’t typically have an option not to predict if they don’t know. So they will make something up. This whole trust aspect has become critical. It’s hard to make them foolproof, but there are tools and technologies, such as RAG. You can guide the models so they don’t give you an answer if they don’t know the answer. You can look at binary models, which use another model on top of the existing model. This ensures that hotel guests, for example, won’t be told about a beach that’s 2,000 miles away in response to a query for a local beach. Ironically, it’s almost as much work to have a model not give you an answer, as it is to give you an answer.
Road maps
KM teams should have a well-defined road map for GenAI adoption. You wouldn’t deploy any other computer system or technology without a project plan, a road map, or set milestones or objectives. Therefore, it’s critical at this point to step back and develop a project plan and a road map. The most common first set of initiatives concerns governance. How will this model be used? Who will have access to it? What models will we use? You don’t want every department contracting with or deploying a different technology. Over time, you’ll lose the benefits and drive costs up. Start with governance processes, standardized models, and deciding access standards, then create a leadership council or a steering committee to evaluate the use cases requiring a business case.