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The ecosystem economy, holistic employee well-being, Tech for Execs, and more big reads for the weekend

Harmony Internal - McKinsey

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CURATED PICKS FOR YOUR DOWNTIME, FROM OUR EDITORS

Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities

The weekend awaits so take a moment to relax and enjoy these essential reads from this week on the ecosystem economy, women in European tech, holistic employee well-being, Tech for Execs, and more.

In a new Author Talks interview, McKinsey senior partners Venkat Atluri and Miklós Dietz make the case that traditional sector siloes are breaking down, and discuss how ecosystems—or networks of organizations across different sectors—are driving the global economy.

Create the best value

Image of two young women working on a computer

What’s the key to building competitive advantage and growth in Europe? Recruiting and retaining women in the tech sector, according to senior partner Sven Blumberg and his coauthors.

Close the gap

The latest Five Fifty from McKinsey Quarterly is all about employee well-being and how a holistic view of health can help workers live longer, better, and more productively.

Understand the shifts

Our weekly briefing note on sustainable and inclusive growth explores promoting digital equity and inclusion among Black Americans, four interventions that could improve women’s representation in European tech, and more.

Get updated

TECH FOR EXECS

Machine learning engineers put AI into play

Our experts serve up a periodic look at the technology concepts leaders need to understand to help their organizations grow and thrive in the digital age.

What does a machine learning (ML) engineer do? The person in this role ensures that the algorithmic models built by data scientists operate efficiently and accurately within a live AI application, for example, one that determines a loan applicant’s creditworthiness. They test the models, build the infrastructure to run and flow data through them, and monitor and optimize model performance. ML engineers also build capabilities to automate as much of this work as possible.

Why don’t data scientists do all of that? It takes a team of skilled technical professionals, often using MLOps to guide their operations, to build reliable AI applications as quickly as businesses need them today. Each team member specializes in certain aspects of application development and deployment . Data scientists are experts at building machine learning models that help solve business problems as well as identifying the data needed to train the model. They’re increasingly expected to understand how their models fit into applications, but ML engineers have way more knowledge about this and can do the engineering work to make it happen.

How do I know I need one? If your organization is building a lot of AI applications and wants to get the most business value from them, you’ll likely need an ML engineer. Organizations seeing the highest returns from AI were twice as likely to hire an ML engineer last year as all other companies—in fact, 58% did so.

Where do I find an ML engineer? Many organizations upskill data scientists or software engineers to transition them to ML engineering. Others hire externally. ML engineers often have computer science backgrounds and pursue machine learning in specialized professional courses or advanced degree programs.

QUOTE OF THE DAY

“‘In this decade of action, it’s really imperative that every company deals with its impact on climate change, biodiversity loss, and mounting inequality.”

—John Revess, senior director of Net Zero Transformation at the World Business Council for Sustainable Development, on how companies are tackling decarbonization in a recent episode of McKinsey Talks Operations

chart of the day

Chart of construction permit delays by country

See today’s chart

Ready to unwind?

What is the future of work?

Just like the world at large, the world of work shifts and changes over time. The future of work refers to an informed perspective on what businesses and other organizations need to know about how work could shift (given digitization and other trends), plus how workforces and workplaces can prepare for those changes, big and small.

Understand what's coming

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Data Points: Your weekly quiz

How well do you know the numbers behind the changes in business, society, and the world? Take the McKinsey weekly quiz to find out.

Take the quiz

2-Down: Much briefer way to text “While I don’t presume to think that what I say is the only right answer, as I see it”

Know the answer? Test yourself with the latest McKinsey Crossword, It’s a Formality | No. 112.

Keep with tradition

— Edited by Joyce Yoo, editor, New York

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by "McKinsey Daily Read" <publishing@email.mckinsey.com> - 06:15 - 27 Jan 2023

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