Executive Briefing: Business at the speed of AI
Amid the fears of sentient killing robots on one hand and a freezing AI winter on the other, AI has a true potential to transform the enterprise. Actualizing this potential requires a well-informed organizational strategy and consistent execution of best practices regarding people, processes, and platforms.
Bahman Bahmani examines these strategies and best practices and provides insights into their successful execution. At a strategic level, he explores the interplay between AI and organizational value disciplines and looks into how AI can enable different organizational competitive strategies, such as operational excellence (e.g., via automation and augmentation), performance superiority (e.g., via the virtuous cycle of AI), and customer intimacy (e.g., via capturing the long tail).
In terms of people, Bahman outlines how to structure a data science team and the roles on the team, including how proper use of the oft-neglected role of data wrangler, combined with targeted training, can create efficiencies and economies of scale for data science teams that are very difficult and costly to achieve otherwise. He also explores the role of globalization in building a data science team and characterizes the value as well as the intricacies and best practices of building and managing a geographically distributed team. You’ll learn how to design the organizational structure for enterprise-wide AI efforts to maximize ROI.
In terms of processes, Bahman highlights the importance of following a methodical iterative process for AI projects and workflows, starting from the business needs and culminating in successful operational deployments. You’ll look into the role of Agile principles in efficient execution of these processes and explore the differences between data science and software engineering that need to be taken into account for the successful adoption of Agile methodologies in data science projects.
In terms of platforms, Bahman details the key considerations guiding the choice between on-premises versus cloud-based infrastructures, discusses how modern DevOps is even more important for data science than for software engineering, and presents the best practices for risk-managed operationalization of AI in production in the enterprise.
If you’re a business executive, strategic decision maker, or technology or AI leader who’s interested in achieving maximal ROI from your AI initiatives, this is for you. Bahman clarifies the concepts via examples and case studies from real-world, operational, large-scale AI systems for applications such as product categorization, information extraction from unstructured documents, and online ad auctioning. Gain actionable ideas and insights into enabling your organization with the superpowers of AI and data science.
Bahman Bahmani is the vice president of data science and engineering at Rakuten (the seventh-largest internet company in the world), managing an AI organization with engineering and data science managers, data scientists, machine learning engineers, and data engineers globally distributed across three continents, and he’s in charge of the end-to-end AI systems behind the Rakuten Intelligence suite of products. Previously, Bahman built and managed engineering and data science teams across industry, academia, and the public sector in areas including digital advertising, consumer web, cybersecurity, and nonprofit fundraising, where he consistently delivered substantial business value. He also designed and taught courses, led an interdisciplinary research lab, and advised theses in the Computer Science Department at Stanford University, where he also did his own PhD focused on large-scale algorithms and machine learning, topics on which he’s a published author.
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