Artificial intelligence (\u2018AI\u2019) is more and more sneaking up into our daily activities. Anyone using Google, Facebook or a Microsoft product knows this. It\u2019s far from perfect, but it\u2019s improving at a quick pace. Not every enterprise is using AI at the same pace. Has your organization started looking into using AI yet? Do you have any clue on how to tackle and implement AI in your organization? How should your enterprise and business architects examine AI? Where should they start? This article will try to answer these questions using a wealth management example.\nWhat is artificial intelligence?\nThe first mention of artificial intelligence was about 60 years ago. AI has been defined in several ways. The10-minute video below, "What Is Artificial Intelligence Exactly?,\u201d explains AI very well and elaborates on a few definitions:\n\n\n \n\n\nI also find Wikipedia\u2019s definition very appropriate:\n\n\u201cArtificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.\u201d\n\nMuch of the recent enthusiasm about AI has been the consequence of developments in deep learning, which is based on learning data representations, called neural networks, as opposed to task-specific algorithms. Deep learning can be supervised, semi-supervised or unsupervised. Deep learning networks can now easily have over ten layers, with simulated neurons running into the millions, as mentioned in \u201cThe promise and challenge of the age of artificial intelligence."\nThe deployment challenge\nNot everyone has the deep pockets and the technical know-how of Google, Facebook or a Microsoft. Artificial Intelligence will most likely provide value, but its development, its implementation and its practical use is and will remain a real challenge for most enterprises, not to mention for most public organizations. Technical know-how and resources are scarce. Getting the right to, accessing and then analysing existing collected data will continue to be an issue in some circumstances. Finally, positive results from concrete artificial intelligence initiatives may prove longer to materialize then anticipated.\nAs mentioned by Andrew Ng, founder of Google Brain, in a recent article in Forbes:\n\n\u201cAI technology is exciting, but it is also immature. At the risk of sounding sacrilegious, AI technology in isolation is not useful. It needs a lot of customization to figure out exactly how it fits into your business concept. Doing that requires a broad understanding of your company and a reasonably deep understanding of AI. Exploiting the value of AI today requires a team that understands the business context and has cross-functional knowledge of things like how to fit AI into your hospital or how to use AI in your logistics network. Without cross-functional knowledge of how your business runs, it is difficult to customize AI appropriately to drive specific business results."\n\nDeploying artificial intelligence using architecture\nAs indicated by Raj Ramesh in this podcast about how business architecture can help leverage AI:\n\n\u201cBusiness architecture has a huge role to play in the future of organizations. There is no doubt that AI will be an integral part of the future business. Some of the key questions organizations ask related to the application of AI are things like \u201cWhere do we start?\u201d \u201cHow do we mature the capabilities that will enhance our competitive advantage?\u201d These are questions that business architects will help to answer when they map business strategy all the way to execution.\u201d\n\nEnterprise and business architects are also becoming instrumental in designing future scenarios of these organizations using AI among others. Building and deploying AI applications cannot be executed with a chaotic approach. It is impossible to know where to start and make sense of AI without a rigorous business-oriented architecture beforehand. Business and enterprise architects must comprehend the appropriate information, value streams, capabilities, applications and processes that will be impacted by AI.\nAn example: artificial intelligence in wealth management\nThere are at least five ways AI is currently disrupting the financial services industry, as shown here:\n\nInvestments through robot advisors will soon almost eliminate financial advisors;\nChat bots backed by conversational AI abilities will soon enable customer engagement;\nArtificial intelligence has recently started to reduce false positives in fraud detection and risk management;\nAI will eventually be able to \u201clearn\u201d, remember, and comply with all applicable laws for regulatory compliance; and finally\nAI may soon be able to predict the price of stocks and market movements potentially turning upside down wealth management.\n\nLet\u2019s examine more closely AI and Wealth Management. If the circumstances are right, it may be appropriate to tackle an ambitious project and try to replace an investment manager of a non-performing fund with instead artificial intelligence.\n Daniel Lambert\nManaging a fund is essentially about selecting financial instruments to trade as shown in figure 1 above. It includes the following value stages: 1- examine financial instruments available for a selected category, 2- select the evaluation criteria of the financial instruments, 3- evaluate all available financial instruments, 4- select the Quantity and Price for each Financial Instrument of the fund and 5- place orders of the selected financial instruments. There are 16 capabilities in total enabling this value stream. 5 business capabilities enable the \u201cExamine Financial Instruments Available for Selected Category\u201d value stage. 5 capabilities enable the \u2018Select Evaluation Criteria of Financial Instruments\u2019 value stage. 3 capabilities enable the \u2018Evaluate all Financial Instruments\u2019 value stage. 2 capabilities enable the \u2018Select Quantity and Price for Each Portfolio Financial Instrument\u2019 value stage. Finally, 5 more capabilities enable the \u2018Place Orders of Selected Financial Instruments\u2019 value stage. Also note that some enabling capabilities enable more then 1 value stages.\n Daniel Lambert\nAs shown at the bottom of figure 2 above, three critical enabling capabilities of the \u201cSelect Financial Instruments to Trade\u201d value stream need to perform very well for AI to perform as well as the top quartile of the other competing funds in the selected category. The first problematic capability is \u2018Financial Instrument Pattern Analysis\u2019. The second one is the \u2018Financial Instrument Valuation\u2019 capability. Finally, the third problematic capability is the \u2018Portfolio Allocation Determination\u2019 capability.\nApplications, processes and requirements related to these three capabilities will need to be examined in detail to complete the design of this wealth management artificial intelligence strategic initiative. Information concepts and databases related to the same three capabilities will also need to be studied.\n Daniel Lambert\nEnterprise and business architects can also elaborate and evaluate various scenarios to deliver portfolio management using artificial intelligence. Table 1 above describes briefly 4 possible scenarios. There could obviously be more. Financial analysis, impact analyses, and risk analysis will need to be completed for each scenario. Due to the high amount of capital involved and the uncertain timeframe of delivering a positive outcome, postponing the artificial intelligence initiative and stay \u2018as is\u2019 should not be excluded among the possible scenarios.\nAll artificial intelligence initiatives should always be examined in a similar systemic business and enterprise architecture approach, where the cross-functional knowledge of how your business runs is used to customize AI appropriately to drive specific business results. This will increase your odds of delivering a successful AI initiative in synchronization with the business strategies of your organization.