Schedule: AI in the Enterprise sessions

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making. Read more.

AI is a powerful tool, but often companies get more excited about their technology than in the customer value they’re creating. Radhika Dutt, Geordie Kaytes, and Nidhi Aggarwal share a framework for building customer-centered AI products. You'll learn how to craft a far-reaching vision and strategy centered around customer needs and balance that vision with the day-to-day needs of your company. Read more.

AI will fundamentally change (and power) the way the world works together. So what does the future of AI in the enterprise look like? Faizan Buzdar explains how intelligence is being applied to enterprise content in practical ways that will revolutionize the most important business processes for companies of all sizes and across all industries. Read more.

Few organizations have mastered integrating AI technology into their business processes and offerings, and many who want to don’t fully understand the work that lies ahead. David Kiron shares surprising insights about businesses’ appetite for and approach to AI, drawn from global collaborative research conducted by MIT Sloan Management Review and The Boston Consulting Group. Read more.

Ben Vigoda offers an overview of idea learning, a new approach to deep learning that has been funded since 2013 as one of DARPA's largest investments in next-generation machine learning. Ben details the process of teaching machines with ideas instead of labeled data and demonstrates use cases with state-of-the-art performance on applications in unstructured enterprise data. Read more.

Danny Lange offers an overview of deep reinforcement learning—an exciting new chapter in AI’s history that is changing the way we develop and test learning algorithms that can later be used in real life—and explains how the crossroads between machine learning and gaming offers innovations that are applicable in other fields of technology, such as the robotics and automotive industries. Read more.

AI delivers value to many facets of the automotive value chain, including smart manufacturing, supply chain management, and customer engagement. Andre Luckow discusses how to assess AI technologies, validate use cases, and foster fast adoption and shares lessons and best practices learned from developing computer vision and natural language understanding applications. Read more.

Large enterprises struggle to apply deep learning and other machine learning technologies successfully because they lack the mindset, processes, or culture for an AI-first world. AI requires a radical shift. Kathryn Hume explores common failure models that hinder enterprise success and shares a framework for building an AI-first enterprise culture. Read more.

Susan Etlinger shares use cases, emerging best practices, and design and CX principles from organizations building consumer-facing chatbots, covering the risks and opportunities of conversational interfaces, the strategic implications for customer experience, business models, brand strategy, and recent innovations. Read more.

What is the impact of AI and deep learning on clinical workflows? Enhao Gong and Greg Zaharchuk offer an overview of AI and deep learning technologies invented at Stanford and applied in the clinical neuroimaging workflow at Stanford Hospital, where they have provided faster, safer, cheaper, and smarter medical imaging and treatment decision making. Read more.

Entrusted with the financial data of 42 million customers, Intuit is in a unique position to take advantage of AI to solve some of its customers’ biggest financial pains. Ashok Srivastava discusses technology’s role in solving economic problems and details how Intuit is using its unrivaled financial dataset to power prosperity around the world. Read more.

Drawing on his experience leading two successful AI companies that implemented machine learning and NLP solutions in over a hundred organizations, Robbie Allen details patterns and characteristics of successful machine learning implementations (and those that predict failure) and explains how to build and cultivate ML talent within your organization in an increasingly competitive job market. Read more.

AI is about more than automating tasks; it's about augmenting and extending human capabilities. James Guszcza discusses principles of human-computer collaboration, organizes them into a framework, and offers several real-life examples in which human-centered design has been crucial to the economic success of an AI project. Read more.

Drawing on his experience bringing AI to the public sector, Sumeet Vij offers perspectives on public sector AI trends, dispelling myths around barriers to entry and sharing approaches and opportunities as he highlights examples of successful AI adoptions. Read more.

Advancements in computer vision are creating new opportunities across business verticals, from programs that help the visually impaired to extracting business insights from socially shared pictures, but the benefits of applied AI in computer vision are only beginning to emerge. Ophir Tanz explores the tools and image technology utilizing AI that you can apply to your business today. Read more.

Regardless of industry, every executive is concerned with the same thing: their customers. Omar Tawakol details the building blocks of speech technologies, including natural language processing, automatic speech recognition, and neural networks, that are necessary to implement voice-activated artificial intelligence and more importantly, enable a customer-centric enterprise. Read more.

Ron Bodkin explains how Google is using AI internally to enhance understanding and experiences for its digital customers and enabling external businesses, such as Spotify and Netflix, to do the same. Along the way, Ron shares examples of deep learning use cases that enable improved recommendations, help companies better understand their customers, and drive engagement in the customer lifecycle. Read more.

AI and its related subtechnologies are being introduced into operational decision making throughout the enterprise. The most promising and risky experiments involve the way people are selected and utilized, but the use of AI in HR raises the specter of software product liability. John Sumser offers an overview of the available use case solutions and the accompanying ethical issues. Read more.

In video games, players learn by failing, sometimes “dying” hundreds of times before learning how to succeed. By enabling us to simulate scenarios and predict outcomes, AI has essentially made the world like a game. Scott Weller explores the role of failure in machine learning, explaining how to set realistic expectations and sharing examples of good and bad AI deployments in the wild. Read more.

AI scores points for providing better answers to your company's challenges and for requiring you to get your data house in order. Jana Eggers explains why AI's hat trick is how it can transform your company into a learning organization. Jana reviews the benefits of a learning org and details how to build an AI program that can support you in achieving those benefits. Read more.

What are the latest initiatives and use cases around data and AI within different corporations and industries? How are data and AI reshaping different industries? What are some of the challenges of implementing AI within the enterprise setting? Michael Li moderates a panel of experts in different industries—including Lori Bieda, Saar Golde, Sassoon Kosian, and Len Usvyat—to answer these questions. Read more.

With all the buzz around machine learning, it can be difficult to distinguish what is disruptive from what is merely a marginal improvement. Rachel Silver shares a new taxonomy of machine learning approaches that categorizes both models and learning algorithms with respect to technical complexity and explains how to use it to identify approaches that provide compelling competitive advantage. Read more.

There are major challenges when combining cutting-edge AI with real-world, practical applications for traditional industries. Ryan Kottenstette shares lessons learned from building practical and scalable enterprise AI solutions for insurance, finance, and agriculture. Read more.

Great AI products are more than technology; they are built on a clear (computationally tractable) model of customer success. Getting that model right can be more challenging than building the AI models themselves; and getting it wrong is very expensive. Shane Lewin outlines common pitfalls in defining AI products and explains how to organize teams to solve them. Read more.

Chatbots are expected to make machine communication feel human, but high-quality bot experiences are very hard to build. Ofer Ronen explores the challenges in optimizing chatbots and shares ways for developers to address them quickly and efficiently. Read more.

Although AI technology seems to be everywhere, implementing AI in practice is a real challenge. The technology needs to be scalable, trusted by the humans that use it, and easily accessible for those with limited AI expertise. Nicole Eagan shares the unique insights on building practical and successful AI applications Darktrace has gained from its 4,000+ deployments. Read more.