From the course: AI Strategy Foundations for Data Scientists and Team Leaders

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Managing trade-offs

Managing trade-offs

From the course: AI Strategy Foundations for Data Scientists and Team Leaders

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Managing trade-offs

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Feel like there's a lot on your plate for your AI project, or do you have competing tasks? You might need to make trade-offs. Trade-offs aren't easy. As a partner in AI strategy, you need to focus on balance, current resources, and scaling your solution. Rule 1: Strike a balance between innovation and practicality. AI projects fail because we don't strike the balance between these. Push for innovation, but prioritize practicality. So what to look for? Prioritize impact over complexity. Avoid equating complexity with value. Sometimes the most simple solutions are the most cost effective and usable. For example, an NLP solution to classify documents may be more useful than an LLM like GPT. Match solutions to real needs, solve business problems and enhance existing processes. Don't pursue innovation for its own sake. I find teams often get bogged down solving strategic business needs that don't exist. Weigh innovation against usability. Strive to make things easy for the user. Avoid AI…

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