Machine Learning

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People and Machines: Partners in Innovation

Thoughtful adoption of intelligent technologies will be essential to survival for many companies. But simply implementing the latest technologies and automation tools won’t be enough. Success will depend on whether organizations use them to innovate in their operations and in their products and services—and whether they acquire and develop the human capital to do so.

Customer Centricity in the Digital Age

  • Blog
  • Read Time: 4 min 

As AI moves from the hype stage to implementation within organizations, retailers and marketers have new competitive opportunities with customer centricity. AI enables companies to apply data about their customers’ wants, needs, and preferences to customize their offerings, create personalized shopping experiences, and make the purchase process simpler and more convenient.

The Perils of Applying AI Prediction to Complex Decisions

While AI is brilliantly placed to solve decisions that are concrete and well-defined, in other contexts it can fail spectacularly, showing connections between facts or events but stumbling when the need to disentangle cause from correlation arises. Human input in the form of subject matter knowledge and common sense are often needed to complement AI. And executives must understand which challenges are right for these new technologies to address.

Does AI-Flavored Feedback Require a Human Touch?

With customized and continuous data-driven feedback becoming a new normal, managers are revisiting the role they should play in delivering, facilitating, and curating face-to-face employee feedback. Does direct managerial involvement complement or compete with data-determined performance reviews?

The Machine Learning Race Is Really a Data Race

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  • Read Time: 6 min 

Companies are racing to apply machine learning to important business decisions, only to realize that the data they need doesn’t even exist yet. In essence, the fancy new AI systems are being asked to apply new techniques to the same old material. The result is a visible arms race as companies bring on machine learning coders and kick off AI initiatives alongside a behind-the-scenes, panicked race for new and different data.

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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.

Machine Learning in the Travel Industry: The Data-Driven Marketer’s Ticket to Success

Leading marketers in the travel sector are using machine learning not only to measurably improve business outcomes but to fundamentally redefine what those outcomes should be. Travel marketers who take advantage of the large volumes of data their organizations collect will continue to pull ahead of their rivals.

Creating Satisfying Customer Experiences With Machine Learning

  • Video | Runtime: 0:02:16

According to Northwestern Mutual’s chief marketing officer, marketing should be more than a support function — it should be a strategic growth driver. In this video, Aditi Javeri Gokhale describes how the company used KPIs to train machine learning algorithms to build an online experience that pairs customers with financial advisors. Its success rate: over 95%.

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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.

Showing 1-20 of 36