Artificial Intelligence by Harvard Business Review;Thomas H. Davenport;Erik Brynjolfsson;Andrew McAfee;H. James Wilson;

Author:Harvard Business Review;Thomas H. Davenport;Erik Brynjolfsson;Andrew McAfee;H. James Wilson;
Language: eng
Format: epub
Publisher: Lightning Source Inc. (Tier 2)
Published: 2019-09-17T00:00:00+00:00

4. Are you accelerating your pilot project with credible partners?

If you are still building up your AI team, consider working with external partners to bring in AI expertise quickly. Eventually, you will want to have your own in-house AI team; however, waiting to build a team before executing might be too slow relative to the pace of AI’s rise.

5. Is your project creating value?

Most AI projects create AI value in one of three ways: reducing costs (automation creates opportunities for cost reduction in almost every industry), increasing revenue (recommendation and prediction systems increase sales and efficiency), or launching new lines of business (AI enables new projects that were not possible before).

You can create value even without having big data, which is often overhyped. Some businesses, such as web search, have a long tail of queries, and so search engines with more data do perform better. However, not all businesses have this amount of data, and it may be possible to build a valuable AI system with perhaps as few as 100–1,000 data records (though more does not hurt). Do not choose projects just because you have a lot of data in industry X and believe the AI team will figure out how to turn this data into value. Projects like this tend to fail. It is important to develop a thesis upfront about how specifically an AI system will create value.

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