While research in deep learning is making breathtaking advances, large enterprises still struggle to apply deep learning and other machine learning technologies successfully because they lack the mindset, processes, or culture for an AI-first world. Indeed, data has historically lived within the office of the CFO: business intelligence and analytics hardware and software are designed for certainty, enabling businesses to accurately account for past transactions to report on business performance.
AI requires a radical shift from a deterministic to a probabilistic mindset, with changes required across all departments. CTOs and CIOs have to tweak Agile development protocol to permit experimentation with data and models. Product designers have to design frontend UIs to communicate technical, statistical output in friendly terms nontechnical users can understand. Management has to make business decisions about minimum confidence rates required for automation (and when to put a human in the loop) and overcome fears of job loss. Privacy and legal functions have to update software contracts to support data-driven products. And compliance has to grapple with interpretability and bias in models that impact consumers. Kathryn Hume explores common failure models that hinder enterprise success and shares a framework for building an AI-first enterprise culture.
Kathryn Hume is vice president of product and strategy for integrate.ai, a SaaS startup applying AI to drive growth and customer satisfaction for large enterprises, and a venture partner at ffVC, a seed- and early-stage technology venture capital firm, where she advises early-stage artificial intelligence companies and sources deal flow. Previously, Kathryn was the director of sales and marketing at Fast Forward Labs (Cloudera), where she helped Fortune 500 companies accelerate their machine learning and data science capabilities, and a principal consultant in Intapp’s Risk practice, focused on data privacy, security, and compliance. A widely respected speaker and writer on AI, Kathryn excels at communicating how AI and machine learning technologies work in plain language. She has given lectures and taught courses on the intersections of technology, ethics, law, and society at Harvard Business School, Stanford, the MIT Media Lab, and the University of Calgary Faculty of Law. She speaks seven languages and holds a PhD in comparative literature from Stanford University and a BA in mathematics from the University of Chicago.
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