| | April 20178CIOReviewBuilding A Cognitive Enterprise ArchitectureBy Josh Sutton, Global Head, Data & Artificial Intelligence, Publicis.SapientYou can't turn around today without somebody talking to you about the role of AI and cognitive in society, media, and specifically your business. It seems like everybody has an opinion about how you should be deploying AI solutions, from the most senior leadership of your company through to the intern that just joined. They are correct in their belief that these technologies could have a profound impact on your business, but transforming your enterprise architecture to leverage them is not nearly as simple as most people might like to believe.I have had the opportunity to work with a number of Fortune 1000 companies and help them to develop their strategies related to cognitive capabilities. In this article I will share some of the best practices that I have seen work successfully as well as some of the lessons that I have learned along the way.Start with the Full Set of Use CasesOne of the most common mistakes that I see firms make is that they attempt to start by either selecting a single platform (Watson, Amelia, etc) IN MY OPINIONor a single use case (chatbot, etc). The rationale is often that they will learn from this exercise and be able to make better decisions going forward. This misses the key point that the combination of artificial intelligence and big data assets are transformative technologies that build off of themselves. The right first step is to understand all of the use cases that you believe might be impacted, or even created, as a result of leveraging these new capabilities. They don't need to be completely accurate or comprehensive, but they need to cover the full range of solutions that you believe might be impacted by AI. These can then be segmented into categories of use cases that will build off of one another. The most common categories of use cases that I have seen tend to fall into the following categories:· Insight generation: Machine learning, inclusive of deep learning tools, is enabling us to generate insights from our data assets faster and more reliably than ever before. The applications of this range from optimization of marketing spend through to analysis of call center trouble areas. Some of the latest technologies even enable this benefit to be derived from unstructured data such as images, call center conversations, and social media.· Conversational engagement: Engaging with technology in the same way that we engage with one another, whether by voice or text, is becoming more and more of a reality every day. This ability to use technology to handle basic interaction is central to a number of customer and employee empowerment use cases, not to mention the allure of conversational commerce platforms.· Knowledge work acceleration: I personally haven't seen a great deal of use cases that are focused on full-scale automation, however I have seen a tremendous amount of opportunity that firms have identified related to automating individual tasks and components of a person's job. The net result is a more effective and efficient workforce that is better enabled to deliver value on behalf of your company.Map Your Use Cases to Services and Technology PlatformsOnce you have a full set of use cases that you have summarized, it becomes a fairly straight forward exercise to extract out the set of core services that will be needed to enable the implementation of these transformative capabilities. Typically, I would expect to see high level services identified around a number of areas such as machine The opportunities provided by cognitive technologies are compelling, however the execution of solutions isn't as easy or simple as many would like
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