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Artificial Intelligence has many applications in marketing and advertising. However, it may prove useful in the near-term future for businesses to start looking into AI solutions for after they generate a lead. AI already has applications for converting leads into paying customers and making progress on various steps in the sales process overall.
According to a 2017 report by the Financial Stability Board, artificial intelligence (AI) and machine learning firms managed assets of over $10 billion in 2017, with further growth projected in the next five years. The reasons for this are clear; AI can now present wealth managers with new capabilities to enhance and further personalize their services, at scale.
Data is essential when it comes to building machine learning models for business applications. A strong AI strategy is predicated on data that is specific to the business problem a company is trying to solve, as outlined in our recent article: Data Collection and Enhancement Strategies for AI Initiatives in Business. When it comes to executing on that strategy, oftentimes the first thing a company needs to do is collect some or all of the data it will need to build the right machine learning model for its use case.
When it comes to planning an AI initiative, a business will need to determine the method by which to acquire the data necessary to meet their objectives. Data is essential when it comes to succeeding with AI. An effective AI strategy is built on top of data that is specific to the business problem a company is trying to solve.
There’s more to successful AI adoption than picking the right technology. Business leaders should be aware of the technical requirements of the initiative they’re undertaking, and few of those requirements are as important as data.