This session explores the impact of AI and IoT on KM, as machines get better and better at sensing the environment, comprehending content, interpreting signals, and anticipating needs. It slices through the hype on AI as a universal job disruptor and looks at how the bigger threat and benefit comes not for high-level cognitive computing, but from very specific skills that eventually outpace the ability of humans to understand what the technology is actually doing. Rasmus explores how sensors extend human reach in terms of distance, simultaneity, types, and volume of data and discusses how the new challenge for KM is not capturing insights from the past, but figuring out how to deal with insights that change based on conditions that may occur in microseconds. He looks at traditional KM and how many of its aspects remain valid even under the new conditions, but why we may lose funding and focus in favor of the need to manage AI and IoT programs. Get a better sense of how the overall world of work is changing and what it means for people who value knowledge, discovery, and curation.

The AI hype is rapidly exploding into C-suites of organizations around the world and with good reason; the promise is compelling. The convergence of AI, robotic process automation (RPA), machine learning and cognitive platforms allows employees to focus their attention on exception processing and higher-value work while digital labor manages low-value, repetitive tasks. While the debate as to whether digital labor will add or eliminate jobs is ongoing, what’s important in today’s enterprise is how digital and human labor can be integrated to improve efficiency and drive innovation. Using real-world examples, this session covers how machine processing, when guided by human knowledge, curation and control, provides assisted intelligence (AI) to organizations which want to streamline processes, reduce operating costs, and improve ROI.

AI technology is destined to change this world beyond our current imagination, even in a world that is creating tons of new knowledge every second. The rate of new knowledge creation has never been seen before and will increase exponentially with time. An organization has to take advantage of outside as well as inside knowledge to keep ahead of its competition. It needs to be agile and change at a rapid pace to cope with new knowledge and connect people with organizational as well as outside knowledge. Human brains have astonishing processing powers but can analyze known information only. When datasets are too large and decision nodes are many, human brains require AI to help make the best decisions. AI can uncover the unknown and empower people to make right decisions. Get ideal scenarios for using AI in a KM program, advantages and drawbacks of AI, ideal AI KM candidates, and new AI-based KM ideas.

Pittman starts off by discussing the many subfields that make up AI and then looks at how various industries are using it to achieve incredible results. The Raytion speaker shows how machine-learning can be applied for sentiment analysis of unstructured data in the context of social media using an example of a large telecommunications organization. Guarino believes AI is already having a significant impact for the U.S. government (including defense and intelligence community uses cases) and is also providing game-changing capabilities for global enterprises in a range of industries, including financial services, healthcare and all areas of technology. She provides real-world examples of how AI is driving measurable benefits today in a range of industry sectors, discusses the importance of Explainable AI to regulated industries, where being able to justify the reasoning behind algorithmic decisions is essential.

Noble explains how NLP (natural language processing) and advanced recommendation engines are delivering intelligent solutions for knowledge-sharing and demonstrates a real example of a cognitive assistant illustrating how it can seamlessly be a part of our everyday lives. Baumgartel discusses whether knowledge workers can outsmart AI and machine learning bots and the practices that may need to be embraced in order to outsmart machines! The M-Files team provides the steps for an AI-powered digital assistant road map: Establishing content in context by breaking down data silos and creating organized data across a company begins with content, and AI holds the key to creating content in context; implementing logical helpers that take spell/grammar check prompts to the next level by understanding and applying keen insight to the contextualized information and providing “logical” assistance; providing document categorization with information extraction, allowing AI to proactively assist with business-critical information, automatically managing information on the back end to send users relevant content and to accompany it with intelligent suggestions at appropriate times.