Intelligence without trust: a risky business

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Companies and entire industries are looking to harness data analytics to make more accurate and effective decisions, within and across organizations. Such real-time and accurate insights have enabled boards and their management to be more effective in conducting their duties. Artificial intelligence (AI) mimics the learning function of the human brain, which means it could be deliberately or accidently corrupted and even adopt human biases, potentially resulting in mistakes and unethical decisions. Control of AI systems by the wrong hands is also a concern. Any AI system failure could have profound ramifications on security, decision-making and credibility, and may lead to costly litigation, reputational damage, regulatory scrutiny, and reduced stakeholder trust and profitability.


Driving AI's potential in organizations

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For some organizations, harnessing artificial intelligence's full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. While testing the waters this way may deliver valuable insights, it likely won't be enough to make your company a market maker (rather than a fast follower). To become a true AI-fueled organization, a company may need to fundamentally rethink the way humans and machines interact within working environments. Executives should also consider deploying machine learning and other cognitive tools systematically across every core business process and enterprise operation to support data-driven decision-making. Likewise, AI could drive new offerings and business models. These are not minor steps, but as AI technologies standardize rapidly across industries, becoming an AI-fueled organization will likely be more than a strategy for success--it could be table stakes for survival. In his new book The AI Advantage, Deloitte Analytics senior adviser Thomas H. Davenport describes three stages in the journey that companies can take toward achieving full utilization of artificial intelligence.1 In the first stage, which Davenport calls assisted intelligence, companies harness large-scale data programs, the power of the cloud, and science-based approaches to make data-driven business decisions. Today, companies at the vanguard of the AI revolution are already working toward the next stage--augmented intelligence--in which machine learning (ML) capabilities layered on top of existing information management systems work to augment human analytical competencies. According to Davenport, in the coming years, more companies will progress toward autonomous intelligence, the third AI utilization stage, in which processes are digitized and automated to a degree whereby machines, bots, and systems can directly act upon intelligence derived from them. The journey from the assisted to augmented intelligence stages, and then on to fully autonomous intelligence, is part of a growing trend in which companies transform themselves into "AI-fueled organizations."


Comment: 'We can't leave Silicon Valley to solve AI's ethical issues'

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So, hands up who was woken up by Alexa this morning? Or now has Google Home finding their favourite radio station for them? Or had fun over the holidays trying to get Siri to tell them a joke? Artificial intelligence is now more accessible and becoming mainstream. The rapid development and evolution of AI technologies, while unleashing opportunities for business and communities across the world, have prompted a number of important overarching questions that go beyond the walls of academia and hi-tech research centres in Silicon Valley.


The Fundamentals of Data Literacy and Data Management Preparing for AI in Enterprise Emerj - Artificial Intelligence Research and Insight

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Implementing artificial intelligence into an existing business is about more than algorithms. In fact, many AI researchers believe that algorithms are the easiest part of an artificial intelligence implementation. Algorithms need data, and for a business to assess, organize, clean, and use it's data requires ways of thinking that are entirely foreign to most existing enterprises. Partnering with Corinium Global Intelligence, we asked six experienced AI and analytics professionals (all speakers at Corinium's Chief Analytics Officer Spring event in on May 14th-16th in San Francisco) the following three important questions: In the sub-sections of the article that follows, we'll explore each of these questions in depth, highlighting the best insights from the professionals we corresponded with. IT procurement, software development, and software aren't new concepts to many experienced executives.


AI-Driven Leadership Thomas H. Davenport and Janet Foutty

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Many companies are experimenting with AI on a small scale, and a few have made a commitment that their organizations will be "AI first" or "AI-driven." But what does this mean? What is AI doing or leading, and, in particular, what is the role of leadership in making organizations AI-driven? We see a lot of confusion around opportunity and action. In the 2018 Deloitte Global Human Capital Trends survey and report of business and HR leaders, 72% indicated that AI, robots, and automation are important -- but only 31% felt their organizations were prepared to address strategy to implement these technologies.