Machine Learning

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An Executive Guide to the Winter 2020 Issue

MIT Sloan Management Review’s Winter 2020 issue explores the dilemmas managers face in using blockchain, machine learning, and marketing analytics effectively; strategies to recognize potential threats to your business; the underpinnings of successful organizational transformation; and meeting the emotional and educational needs of your employees.

Why Multinationals Should Consider Geographic Complexity First

  • Research Highlight
  • Read Time: 9 min 

Well-executed international expansions can provide access to new markets, customers, and revenue streams. But many companies underestimate operational complexity and end up with a country portfolio that slowly and subtly erodes profitability. New research offers a framework for managing successful country portfolios and making some key decisions before the next recession hits.

People-Centered Design Principles for AI Implementation

  • Video | Runtime: 0:59:03

As the AI field currently stands, deep learning is playing an increasingly critical role. As organizations begin adopting deep learning, leaders must ensure that these artificial neural networks are accurate and precise–lest they negatively affect business decisions and potentially hurt customers, products, and services.

Is Deep Learning a Game Changer for Marketing Analytics?

The technology that underpins deep learning is becoming increasingly capable of analyzing big databases for patterns and insights. Before long, companies will be able to integrate a wide array of databases to discern what consumers want, and then leverage that information for market advantage. Deep learning might also be used to design products to meet consumers’ personal needs. Different types of organizations will try to harness the powers of deep learning in their own ways.

Tell Your Colleagues: MIT SMR Is Unlocked Today Through Thursday

  • Read Time: 2 min 

On Oct. 8-10, MIT SMR is dropping its paywall — all of the content is freely available to visitors. Readers will have immediate access to ideas, research, benchmarks and tools, all grounded in the reality of our technologically driven economy and society. We’re offering some recommendations based on what readers tell us are some of the most pressing problems they’re facing right now.

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Three People-Centered Design Principles for Deep Learning

As organizations begin adopting deep learning, leadership must ensure that artificial neural networks are accurate and precise to avoid negative impacts on business decisions that hurt customers, products, and services. A designed-centered approach helps address both these short-term concerns as well as the long-term concerns that machines might displace humans when it comes to business decision-making.

Self-Driving Companies Are Coming

  • Read Time: 9 min 

Automation can go far beyond cars. Self-driving company capabilities are closer than many leaders realize. And just as automobile manufacturers are rethinking the meaning of driving within the context of self-driving technology, business leaders are being forced to rethink an equivalent question: What does it mean to manage an enterprise once some of the work can be done autonomously?

What Does an AI Ethicist Do?

  • Read Time: 7 min 

Microsoft has been active in advocating for an ethical perspective on artificial intelligence, and in 2018 it appointed its first general manager for AI policy and ethics. Tim O’Brien, who had been with the company for 15 years, says his activities as “AI ethics advocate” include extending the community of people who are focused on the ethics topic, meeting with Microsoft customers, and leading a research effort to develop a global perspective on tech ethics.

Sponsor's Content | The New Era of Personalization: Why CPG Brands Must Own the Direct-to-Consumer Experience

  • MIT SMR Connections | Executive Scholar Exchange

As consumers gain more choices in how they buy products and interact with brands, consumer packaged goods companies can no longer rely on retailers for sales customer feedback. Instead, brands must take a direct-to-consumer approach across the life span of the customer relationship.

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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

  • 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

  • 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

  • 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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