Jason L. Anderson - Artificial Intelligence for Business

The production step is the process of converting the prototype into a full-fledged system. This includes conducting a technological evaluation, building user security models, and establishing testing frameworks.

Technological EvaluationDuring the prototype phase, developers select technologies appropriate for a prototype, including using technologies and languages that are easy to work with. This mitigates risk by determining the project's feasibility quickly before investing a lot of time and money. That said, during the production step the technology must be evaluated for other factors as well. For instance, will the technology scale to a large number of users or massive amounts of data? Will the technology be supported in the long term and be flexible enough to change as requirements do? If not, pieces of the prototype might have to be rebuilt to accommodate.User/Security Model

During the prototype phase, the project is typically only running on locked-down development machines or internal servers. While they require some security, high levels of security are not typically needed during prototyping and will only slow down the prototyping process. Work, such as integrating an organization's user directory (single sign-on [SSO]) and permission structures, will be part of the production process.Testing Frameworks

To ensure code quality, testing frameworks should be built alongside the production code. Testing ensures that the code base does not regress as new code is added. Development teams may even adopt a “test first” approach called test-driven development (TDD) to ensure that all pieces of code have tests written before starting their implementation. If TDD is used, developers repeat very short development cycles, writing only enough code for the tests to pass. In this way, tests reflect the desired functionality and code is written to implement that functionality.

Once you have adopted AI and your organization is realizing its benefits, it is time to switch into the lifecycle mode. At this point, you will be maintaining your AI systems while consistently looking for ways to improve. This might mean leveraging system usage data to improve your machine learning models or keeping an eye on the latest technology announcements. Perhaps the AI models you have implemented can also be used in another part of your organization. Furthermore, it is important that the knowledge gained during the implementation of your first AI system be saved for future projects. As we will discuss in this book, this can take the shape of either an entry in your organization's model library or a lessons learned document.

Adopting artificial intelligence in your organization can feel like a daunting task, especially since the technology is changing so frequently. The main idea is to be aware of all the benefits, as well as the pitfalls, so that you can adequately discern between them and navigate your way to success. Mistakes are inevitable. Keeping them small and easy to recover from will ensure that your AI transformation has the resilience it needs to prevail. To minimize the likelihood of mistakes, we list the common pitfalls associated with each step at the end of each chapter so you can take notice and avoid them. With sufficient planning and foresight provided by this book, you will be able to acquire the tools necessary to make your organizational adoption of AI a great success.

1 1 https://preferred.jp/en/news/tag/fanuc/

2 2 www.bloomberg.com/news/features/2017-10-18/this-company-s-robots-are-making-everything-and-reshaping-the-world

3 3 https://en.wikipedia.org/wiki/File:FANUC_R2000iB_AtWork.jpg

4 4 www.hrblock.com/tax-center/newsroom/around-block/partnership-with-ibm-watson-reinventing-tax-prep/

5 5 www.blackrock.com/aladdin/offerings/aladdin-overview

6 6 https://ir.blackrock.com/Cache/1001247206.PDF?O=PDF&T=&Y=&D=&FID=1001247206&iid=4048287

7 7 www.institutionalinvestor.com/article/b1dn7pgfhbxpsg/BlackRock-s-Aladdin-Adds-Alts-Power

8 8 www.ft.com/content/9ab2d910-1816-11e9-9e64-d150b3105d21

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