Machine Learning

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The Public Sector Can Teach Us a Lot About Digitizing Customer Service

Digital customer service agents (known as virtual assistants, chatbots, or softbots) are typically used to sift through and process only the most straightforward customer inquiries, such as requests for basic information. At most companies, complex issues get passed along to human agents. In that regard, public sector agencies in Australia are ahead of the curve: They are using digital agents to handle complex inquiries from citizens, and businesses stand to learn much from these applications.

Every Leader’s Guide to the Ethics of AI

  • Blog
  • Read Time: 9 min 

As artificial intelligence-enabled products and services enter our everyday lives, there’s a big gap between how AI can be used and how it should be used. A 2018 Deloitte survey of AI-aware executives found that 32% ranked ethical issues as one of the top three risks of AI, but most companies don’t yet have specific approaches to grapple with the challenges. Here, we list the seven actions that leaders of AI-oriented companies — regardless of their industry — should consider taking.

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Improving Strategic Execution With Machine Learning

Our 2018 Strategic Measurement research shows that companies using machine learning to optimize business processes and decision-making have distinct advantages over those that aren’t investing in ML. By using ML technology to make KPIs more predictive and prescriptive, these data-driven companies are redefining how to create and measure value.

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The Risk of Machine-Learning Bias (and How to Prevent It)

Machine-learning algorithms enable companies to realize new efficiencies for tasks from evaluating credit for loan applications to scanning legal contracts for errors. But they are as susceptible as any system to the “garbage in, garbage out” syndrome when it comes to biased data. Left unchecked, feeding biased data to self-learning systems can lead to unintended and sometimes dangerous outcomes.

Justifying Human Involvement in the AI Decision-Making Loop

Though AI is far from perfect, vast training data has given smart systems formidable accuracy in making independent decisions. Yet even as these decision-making capabilities improve, a Cold War history lesson reminds us that human involvement may still be needed to avoid intolerable consequences of incorrect AI decisions.

Five Management Strategies for Getting the Most From AI

A global survey by the McKinsey Global Institute finds that AI is delivering real value to companies that use it across operations. C-level executives report that when they adopt AI at scale — meaning they deploy AI across technology groups, use AI in the most core parts of their value chains, and have the full support of their executive leadership — they are finding not just cost-cutting opportunities, but new potential for business growth, too.

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Sponsor's Content | Journey to AI: Building a Foundation in Big Data Analytics

  • Content Created By Google Cloud

AI is making headlines — and not just in futuristic technologies like self-driving cars. It’s transforming business processes in established industries, from retail to financial services to manufacturing. But what’s the best way to adopt AI for your organization?

Reshaping Business With Artificial Intelligence

Disruption from artificial intelligence (AI) is here, but many company leaders aren’t sure what to expect from AI or how it fits into their business model. Yet with change coming at breakneck speed, the time to identify your company’s AI strategy is now. MIT Sloan Management Review has partnered with The Boston Consulting Group to provide baseline information on the strategies used by companies leading in AI, the prospects for its growth, and the steps executives need to take to develop a strategy for their business.

Accelerate Access to Data and Analytics With AI

Detailed and data-rich insights won’t help your company if your employees don’t know where to find them — but that’s a problem AI can solve. Machine learning can enable faster organizational learning by helping each employee quickly understand what others in the organization understand — forming a knowledge distribution network.

Ethics Should Precede Action in Machine Intelligence

As analytics and big data continue to be integrated into organizational ways and means from the C-suite to the front lines, authors Josh Sullivan and Angela Zutavern believe that a new kind of company will emerge. They call it the “mathematical corporation” — a mashup of technology and human ingenuity in which machines delve into every aspect of a business in previously impossible ways.

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