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The executive’s AI playbook
The executive’s AI playbook. It's time to break out of pilot purgatory and more effectively apply artificial intelligence and advanced analytics throughout your organization. Our interactive playbook can help. Value & Assess: Size the opportunity and determine data needs. Execute: Learn best practices to realize the value. Beware: Know the warning signs of AI program failure. Potential total annual value of AI and analytics across industries: $9.5T - $15.4T. This data visualization shows the potential business applications and economic value for a range of analytics and artificial-intelligence (AI) techniques. It is based on a study of more than 400 use cases, covering 19 industries and nine business functions. For each use case, we estimated a range of the annual value potential of applying AI and other analytics across the entire economy based on the structure of the global economy in 2016. We aggregated these values at the industry, function and domain levels and display the high end of the value range in this visualization. Note that figures may not sum to total due to rounding. We define traditional AI and analytics and advanced AI as follows: Traditional AI and analytics: Traditional machine learning (e.g., clustering) and statistical techniques (e.g., basic regression). Advanced AI: Deep learning neural networks (e.g., convolutional neural networks): Our library of use cases, while extensive, is not exhaustive, and thus it may overstate or understate the potential for certain sectors. See “Notes from the AI frontier: Applications and value of deep learning” for more details on our methodology. Learn best practices to scale AI: Applying AI and analytics across an organization requires much more than the latest technologies and modeling techniques. Through a survey of more than 1,000 companies globally and our work with clients, we’ve identified the practices that better position companies to achieve full scale and value from AI and analytics. Aligning on strategy: To reap the value from AI and analytics, organizations need to plug these technologies into critical strategic areas of the company, which typically cut across business functions (e.g., customer experience). Doing so requires a clear, coordinated approach and focused investment. Organizations achieving better scale and value, what we call “breakaways,” are more likely to engage in the practices below. Know the warning signs of AI program failure: We've detected what we consider to be the ten red flags that signal an AI and analytics initiative is in danger of failure. In our experience, business leaders who quickly respond to these alerts will dramatically improve their companies' chances of success in as little as two or three years. Know the warning signs of AI program failure: We've detected what we consider to be the ten red flags that signal an AI and analytics initiative is in danger of failure. In our experience, business leaders who quickly respond to these alerts will dramatically improve their companies’ chances of success in as little as two or three years. 1) The executive team doesn’t have a clear vision for its AI and analytics initiatives. 2) No one has determined the value that the initial use cases can deliver in the first year. 3) There’s no AI and analytics strategy beyond a few use cases. 4) AI and analytics roles—present and future—are poorly defined. 5) The organization lacks analytics translators. 6) Analytics capabilities are isolated from the business, resulting in an ineffective analytics organization structure. 7) Costly data-cleansing efforts are started en masse. 8) Analytics and AI platforms aren’t built to purpose. 9) Nobody knows the quantitative impact that AI and analytics are providing. 10) No one is hyperfocused on identifying potential ethical, social, and regulatory implications.
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