The AI & Machine Learning Imperative
“The AI & Machine Learning Imperative” offers new insights from leading academics and practitioners in data science and artificial intelligence. The Executive Guide, published as a series over three weeks, explores how managers and companies can overcome challenges and identify opportunities by assembling the right talent, stepping up their own leadership, and reshaping organizational strategy.
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Leading organizations recognize the potential for artificial intelligence and machine learning to transform work and society. The technologies offer companies strategic new opportunities and integrate into a range of business processes — customer service, operations, prediction, and decision-making — in scalable, adaptable ways.
As with other major waves of technology, AI requires organizations and managers to shed old ways of thinking and grow with new skills and capabilities. “The AI & Machine Learning Imperative,” an Executive Guide from MIT SMR, offers new insights from leading academics and practitioners in data science and AI. The guide explores how managers and companies can overcome challenges and identify opportunities across three key pillars: talent, leadership, and organizational strategy.
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The series launches Aug. 3, and summaries of the upcoming articles are included below. Sign up to be reminded when new articles launch in the series, and in the meantime, explore our recent library of AI and machine learning articles.
In order to achieve the ultimate strategic goals of AI investment, organizations must broaden their sights beyond creating augmented intelligence tools for limited tasks. To prepare for the next phase of artificial intelligence, leaders must prioritize assembling the right talent pipeline and technology infrastructure.
How Managers Can Enable AI Talent in Organizations
Recent technical advances in AI and machine learning offer genuine productivity returns to organizations. Nevertheless, finding and enabling talented individuals to succeed in engineering these kinds of systems can be a daunting challenge. Leading a successful AI-enabled workforce requires key hiring, training, and risk management considerations. Available Aug. 5.
The Building Blocks of an AI Strategy
Amit Joshi and Michael Wade
AI is no regular technology, so AI strategy needs to be approached differently than regular technology strategy. A purposeful approach is built on three foundations: a robust and reliable technology infrastructure, a specific focus on new business models, and a thoughtful approach to ethics. Available Aug. 10.
Redefining AI Leadership in the C-Suite
Thomas H. Davenport and Beena Ammanath
CFOs who take ownership of AI technology position themselves to lead an organization of the future. While AI is likely to impact business practices dramatically in the future across the C-suite, it’s already having an impact today — and the time for CFOs to step up to AI leadership is now. Available Aug. 12.
Modern Business Models Will Drive the Post-Pandemic World
Lanham Napier, Jim Curry, Barry Libert, and K.D. de Vries
To remain relevant and resilient, companies and leaders must strive to build business models in a way that ensures three key components are working together: AI that enables and powers a centralized data lake of enterprise data, a marketplace of sellers and partners that make individualized offers based on the intelligence of the data collected and powered by AI, and a SaaS platform that is essential for users. Available Aug. 17.
Reskilling Talent to Shrink Technology Gaps
Acquiring the right AI technology and producing results, while critical, aren’t enough. To gain value from AI, organizations need to focus on managing the gaps in skills and processes that impact people and teams within the organization. Available Aug. 19.