Artificial Intelligence for Business. Jason L. Anderson

Artificial Intelligence for Business - Jason L. Anderson


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we make. There is no one-size-fits-all solution, especially not in a field like AI that is constantly evolving.

      Ahead of the recent boom in AI technologies, many organizations have already successfully implemented intelligent solutions. Most of these organizations followed an adoption roadmap similar to the one we will describe in this book. It is insightful for us to take a look at a few of these organizations, see what they implemented, and take stock of the benefits they are now realizing. As you read through these organizations' stories, keep in mind that we will be diving into aspects of each approach in more detail during the course of this book.

      Science fiction has told of factories that run entirely by themselves, constantly monitoring and adjusting their input and output for maximum efficiency. Factories that can do just-in-time (JIT) ordering based on sales demand, sensors that predict maintenance requirements, the ability to minimize downtime and repair costs—these are no longer concepts of speculative fiction. With modern sensors and AI software, it has become possible to build these efficient, self-bolstering factories. Out-of-the-box IoT equipment can do better monitoring today than industrial sensors from 10 years ago. This leap in accuracy and connectivity has increased production threshold limits, enabling industrial automation on a scale never before imagined.

      Most factories today are capable of utilizing these advancements with minor modifications to their processes. The gains that can be achieved from such changes will be able to exponentially elevate the output of any factory.

Photograph of a FANUC Robot model that will make Apple iPhone cases in the near future.

       FIGURE 1.1 Example of a FANUC Robot3

      H&R Block saw the opportunity here to leverage the use of AI to compile, cross-reference, and analyze all of these notes. Natural language processing (NLP) can be applied to identify the core intent of each note, which then can be fed into the AI system to automatically identify possible deductions. The system then presents the tax professionals with any potentially relevant information to ensure that they do not miss any possible deductions. In the end, both tax professionals and their customers can enjoy an increased sense of confidence that every last applicable deduction was found.

      Financial markets are a hotbed for data. The data can be collected accurately and in real time for most financial instruments (stocks, options, funds, etc.) listed on stock markets. Metadata (data about data) can also be curated from analytical reports, articles, and the like. The necessity for channeling the sheer amount of information that is generated every day has given rise to professional data stream providers like Bloomberg. The immense quantity of data available, along with the potential for trend prediction, growth estimations, and increasingly accurate risk assessment, makes the financial industry ripe for implementing AI projects.


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