1 min readArtificial intelligence is regarded as a leading technology capable of solving numerous problems—but business leaders can’t look to AI for every solution.
AI requires big data and domain knowledge.
Big data is necessary to glean patterns for effective AI—but not just any big data will do. The intricacy of the problem depends on specific data location, flow of data, quality of data, and system integration. Often, despite a promising use case and a large amount of data, AI cannot be implemented due to signals and patterns that ultimately don’t align.
Platforms—such as Google Cloud and Amazon Web Service—lower the barrier of entry and make AI readily available. They include frameworks for developing, testing, and training machine learning models as well as leveraging pre-built models as appropriate. But despite the user-friendly nature of these platforms, subject matter and AI domain experts are necessary for complex problem-solving.
Without big data and domain expertise, AI will not produce valuable results.
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