January 20, 2023After five years of steady, sometimes heady, growth, AI adoption has plateaued, according to The state of AI in 2022, our annual survey of 1,500 companies. Use cases are stable, and the market for tech talent is tight, with new "hot jobs" surfacing every year. But much remains to be done about managing risk and building inclusive teams. "After a period of initial exuberance, we have reached a plateau, a course we've observed with other technologies in their early years of adoption," says partner Michael Chui. He authored the research with senior partners and QuantumBlack, AI by McKinsey leaders Alex Singla and Alex Sukharevsky; partner Helen Mayhew; and associate partner Bryce Hall.

To survive and remain competitive in today's economic and business environment, organizations must lead large-scale changes. Constant organizational change is the new normal in an ever-changing political, social, and economical atmosphere. Artificial intelligence is one of the few innovations that have the potential to assist businesses to transcend significant commercial chasms. AI has immense potential to rocket businesses above the established order and adapts to the new dynamics in business operations. The year 2016 marked the beginning of the business world's recognition of AI potential.

Artificial intelligence (AI) is revolutionizing the way organizations will operate in the future. It is a cornerstone of the digital transformation and will significantly change many business areas we know today. Based on a recent study conducted by Accenture, three quarters of nearly 1500 interviewed C-level executives are afraid of going out of business unless they scale AI [1]. Companies around the globe face the challenge to successfully anchor and spread AI technology in their organization in a value-adding manner. Creating value from AI not only requires operational measures such as technology and infrastructure ramp-up.

I have a confession to make--I'm a back-seat driver! When sitting in a taxi, I can't help but grumble when the ride isn't smooth, or the driver chooses the slowest lane of traffic. I have to fight the urge to take control. When it comes to shopping, I passively accept what is offered for sale. But my wife, who grew up in Asia where haggling is part of the culture, is different.

How many times have you heard a manager respond to employees about organizational change with the words, "It just made sense"? To workers who are adversely impacted by the change, it might not make sense at all. That's exactly why managers who introduce artificial intelligence and enable change should never lose sight of the human impact. Early in my career, I was impacted by organizational changes that affected my job and responsibilities. Later in my career, I was the one making these changes and communicating change to employees.

How many times have you heard a manager respond to employees about organizational change with the words, "It just made sense"? To workers who are adversely impacted by the change, it might not make sense at all. That's exactly why managers who introduce artificial intelligence and enable change should never lose sight of the human impact. Early in my career, I was impacted by organizational changes that affected my job and responsibilities. Later in my career, I was the one making these changes and communicating change to employees.

In recent years, supply chains have become substantially more challenging to manage. Longer and increasingly interlinked physical flows reflect the rising complexity of product portfolios. Market volatility, which has been exacerbated by the COVID-19 pandemic, has elevated the need for agility and flexibility. And increased attention on the environmental impact of supply chains is triggering regionalization and the optimization of flows. As a result, companies and stakeholders have become more focused on supply-chain resilience.

According to McKinsey, "Research shows that 70 percent of complex, large-scale change programs don't reach their stated goals. Common pitfalls include a lack of employee engagement, inadequate management support, poor or nonexistent cross-functional collaboration, and a lack of accountability." Last year I was doing some spring cleaning and looking for space in my home office for a digital piano. As I pulled books from my bookcase, packing them into boxes to go into storage, I found my Blockbuster Video membership card. I'd tucked it inside a book as a bookmark.

Open Government Data (OGD) is being published by various public administration organizations around the globe. Within the metadata of OGD data catalogs, the publishing organizations (1) are not uniquely and unambiguously identifiable and, even worse, (2) change over time, by public administration units being merged or restructured. In order to enable fine-grained analyses or searches on Open Government Data on the level of publishing organizations, linking those from OGD portals to publicly available knowledge graphs (KGs) such as Wikidata and DBpedia seems like an obvious solution. Still, as we show in this position paper, organization linking faces significant challenges, both in terms of available (portal) metadata and KGs in terms of data quality and completeness. We herein specifically highlight five main challenges, namely regarding (1) temporal changes in organizations and in the portal metadata, (2) lack of a base ontology for describing organizational structures and changes in public knowledge graphs, (3) metadata and KG data quality, (4) multilinguality, and (5) disambiguating public sector organizations. Based on available OGD portal metadata from the Open Data Portal Watch, we provide an in-depth analysis of these issues, make suggestions for concrete starting points on how to tackle them along with a call to the community to jointly work on these open challenges.

The era of artificial intelligence (AI) is here and is transforming the insurance industry. Will insurers pivot or perish? The answer lies in how they approach this big disruption. Insurance industry executives need to understand that AI's strength is also its flaw. Not being constrained by rules allows for speedy learning, but it also means that AI is learning without the context that more specific programming or human intelligence and judgment would provide.