What matters most? Eight CEO priorities for 2024 - McKinsey & Co | Gen AI: A guide for CFOs - McKinsey & Co | Who is productive, and who isn’t? Here’s how to tell. - McKinsey & Co | The economic potential of generative AI: The next productivity frontier

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What matters most? Eight CEO priorities for 2024 - McKinsey & Company

What matters most? It’s a question we’ve been investigating for a few years now (here are reports from 2022 and 2021). This year, we’re reminded that what matters most are family, friends, values, principles, and commitments.

One of our commitments is to CEOs. It’s a tough job and getting tougher all the time. Just in the past few years, they’ve had to cope with a global pandemic, busted supply chains, war, stubborn inflation, and many other disruptions. Any one of these is enough to derail a CEO’s agenda. Taken together, it’s the most difficult operating environment we can remember.

Both of us talk to hundreds of CEOs every year, and many of our colleagues do the same. We admire how CEOs are leading their companies for the benefit of all stakeholders. We’ve consolidated the views that have come out of these conversations and are pleased to offer what we’ve heard about how companies can do better for society, communities, and employees—and the prosaic business of how they can pay for it all, and reward investors too.

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Gen AI: A guide for CFOs - McKinsey & Company

Technology changes every business, often radically, and the pace of change is getting faster. Now, generative AI (gen AI) is beginning to show its disruptive potential (see sidebar “Gen AI: A primer”). The technology won’t affect all businesses equally, and certainly not at the same time. Yet across industries and geographies, gen AI could present substantial opportunities for significant value creation.

Generative AI (gen AI) is a predictive language model that produces new unstructured content such as text, images, and audio. Traditional, or analytical, AI, by contrast, is used to solve analytical tasks such as classifying, predicting, clustering, analyzing, and presenting structured data.

Gen AI technology is powered by artificial intelligence models called foundation models, which are trained on a broad set of data, including the outputs from analytical AI. It can be adapted to generate (hence the name) content that seems human, such as written documents, audio conversations, software programming, charts, and visual images. But it doesn’t create the way a human does: it predicts what a human would enjoy or find useful. And unlike traditional, analytical AI, gen AI doesn’t calculate or do math. The technology, therefore, won’t displace traditional AI. Instead, the ideal is that each will complement and enable the other, with new innovations in robotics and automation, to make human lives better, more creative, and more self-fulfilling.

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Who is productive, and who isn’t? Here’s how to tell. - McKinsey & Company

If productivity is down in your organization, it may be a sign that at least some employees are unmotivated and unhappy—and that other employees are feeding on that dissatisfaction, further eroding productivity and creating attrition. On this episode of The McKinsey Podcast, McKinsey senior partner Aaron De Smet and partner Angelika Reich talk about McKinsey’s latest research on employee productivity and share their perspectives on a few types of workers, ranging from those who bring fellow employees down to those who raise them up.

The McKinsey Podcast is cohosted by Roberta Fusaro and Lucia Rahilly.

This transcript has been edited for clarity and length.

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The economic potential of generative AI: The next productivity frontier - McKinsey & Company

AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. As a result, its progress has been almost imperceptible. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness.

Generative AI applications such as ChatGPT, GitHub Copilot, Stable Diffusion, and others have captured the imagination of people around the world in a way AlphaGo did not, thanks to their broad utility—almost anyone can use them to communicate and create—and preternatural ability to have a conversation with a user. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it.

This article is a collaborative effort by Michael Chui, Eric Hazan, Roger Roberts, Alex Singla, Kate Smaje, Alex Sukharevsky, Lareina Yee, and Rodney Zemmel, representing views from QuantumBlack, AI by McKinsey; McKinsey Digital; the McKinsey Technology Council; the McKinsey Global Institute; and McKinsey’s Growth, Marketing & Sales Practice.

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