Artificial Intelligence

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Why the ‘Just Do Something’ Strategy for AI Won’t Work

For all the giant leaps promised by artificial intelligence, when it comes to business, what we’ve seen so far amounts to just tiny steps. That’s not necessarily a bad thing; many smart people advise companies to start small with AI. But as Boston College professor Sam Ransbotham notes in this week’s Three Big Points podcast, when you think small, you get small results.

The Best MIT SMR Articles of the 2010s

  • Read Time: 2 min 

In the 2010s, MIT Sloan Management Review readers gravitated toward articles that will help them prepare for the future of work — and succeed in an ever-evolving present. Topics of particular interest include digital transformation and competition, global talent management, emerging jobs in the AI era, and strategy execution.

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Designing AI Systems That Customers Won’t Hate

  • Research Highlight
  • Read Time: 13 min 

Though autonomous technology has a large and growing range of potential applications, it also may threaten users’ sense of autonomy and free will, or their belief that they can decide how to pursue their lives freely. But companies can create systems users don’t hate by protecting users’ autonomy, unpredictability, and privacy.

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.

The Five Bestselling MIT SMR Articles of 2019

  • Read Time: 3 min 

This year’s bestselling articles examine perennial challenges for leaders and organizations. From predicting how technology will impact markets and outcomes to creating successful frameworks for strategic decision-making, this collection of articles gives managers practical insights for leading in an age of uncertainty and disruption.

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.

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Closing the Innovation Achievement Gap

  • Read Time: 4 min 

What makes a company an innovation leader? To a great extent, it’s a matter of mindset. Executives whose technology-led transformation projects aren’t delivering the expected value must make strategic shifts in terms of their companies’ technology adoption, technology penetration, and organizational change.

Learning From Automation Anxiety of the Past

  • Read Time: 6 min 

AI and automation might benefit society at large, but there will be losers in the process, and at times even outright resistance, if people feel that their jobs and incomes are threatened. To avoid a backlash against the technology, governments must address its social costs and pursue policies that kick-start productivity growth while helping workers adapt.

Demystifying the Intelligence of AI

  • Read Time: 7 min 

Three common trouble points impede companies moving toward using AI: Leaders are unclear what it means to adopt AI; systems are drawing from too much junky data; and there isn’t a careful balance between customer loss of privacy and the value returned. These problems can be resolved only when leaders pay close attention to the strategic challenges of bringing AI on and approach AI as an integrated element of their processes.

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The Best of This Week

  • Read Time: 2 min 

This week’s must-reads for managing in the digital age: the promise of a symbiotic human-AI strategy, how leaders are responding to regulatory rollbacks, spurring innovation through internal-external partnerships, and the power and potential of metrics done right.

Creating the Symbiotic AI Workforce of the Future

  • Frontiers

  • Research Highlight
  • Read Time: 7 min 

To effectively implement AI, organizations will need to use human-centered AI processes that motivate and retrain workers, which shifts the focus from automation to collaboration between humans and machines. To test that idea, an experiment was designed to see how human workers might augment an existing AI system and embrace their new roles as AI trainers — resulting in a symbiotic system that enabled humans and AI to each work to their strengths.

Winning With AI

AI promises rewards but also comes with risks ― namely, that competitors figure out how to successfully use it before you do. This year’s 2019 MIT SMR-BCG Artificial Intelligence Global Executive Study and Research Report shows early AI winners are focused on organization-wide alignment, investment, and integration.

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