In this post, I highlight some of the best thought-leadership articles and reports that cross my desk. I note why they rise to the top of the pile and are worth reading (or skimming), even if they focus on functions or industries outside your areas of interest. Among the criteria I use to make the selections are freshness and provocativeness of insights and timeliness, analytical rigor, depth of prescriptions, and overall readability.
In the March 2023 Gold Standard, we highlighted the increasing publishing efforts of professional services firms on the applications of artificial intelligence (AI) in business. Since then, they (and almost everyone else who writes about technology) have focused intensively on generative AI, algorithms such as ChatGPT and DALL-E that can be used to create new content. The firms are offering early thoughts on how businesses can use this fast-developing technology, its risks, and its potential impact on the workforce. (They are also teaming up with technology companies to offer generative AI service lines.) Here is some of the more interesting work from the first half of 2023.
This article offers a good overview of generative AI. Its biggest contribution, though, is an analysis of how generative AI could affect work by industry and job category.
“Generative AI will disrupt work as we know it today, introducing a new dimension of human and AI collaboration in which most workers will have a “co-pilot,” radically changing how work is done and what work is done,” the authors say. “Nearly every job will be impacted – some will be eliminated, most will be transformed, and many new jobs will be created. Organizations that take steps now to decompose jobs into tasks, and invest in training people to work differently, alongside machines, will define new performance frontiers and have a big leg up on less imaginative competitors.”
Apart from the intriguing title, this article presents a view about how generative AI could affect retailing. It groups emerging use cases in retailing into four categories: personalized marketing, customer engagement and service transformation, operations and productivity, and customer and industry insights. For each category, the authors offer concrete ideas about how generative AI could be applied, such as new ways of analyzing customer sentiment and loyalty.
These BCG authors were fast to the marketplace with this guide to getting started in generative AI. They focus their recommendations on three areas: potential, people, and policies. Potential refers to the use cases companies could pursue; people, the impact of generative AI on jobs; and policies, assessing the risks of generative AI and erecting guardrails as necessary.
“From our perspective, the priority for CEOs isn’t to fully immerse themselves in the technology; instead, they should focus on how generative AI will impact their organizations and their industries, and what strategic choices will enable them to exploit opportunities and manage challenges,” the authors write.
I would also recommend this BCG article: “Engaging consumers in a generative AI world.” The piece explores the technical choices that companies must make about how to interact with customers.
Authors from the Deloitte AI Institute have produced an excellent overview of generative AI’s potential. It serves as a primer on the subject while also summarizing possible consumer and business use cases, adding a discussion of how companies across the value chain can develop sustainable business models.
“The far-reaching impacts and potential value when deploying generative AI are accelerating experimental, consumer, and soon, enterprise use cases. And even though much media coverage has focused on consumer use cases, the opportunities are widespread–and some are already here,” the authors write. “Still, questions remain about how individuals and enterprises could use generative AI to deliver efficiency gains, product improvements, new experiences, or operational change. Similarly, we are only beginning to see how generative AI could be commercialized and how to build sustainable business models.”
In this report, Goldman analysts came fast to the market with an analysis of the potential impact of generative AI on the labor market.
“If generative AI delivers on its promised capabilities, the labor market could face significant disruption,” the authors wrote. “Using data on occupational tasks in both the US and Europe, we find that roughly two-thirds of current jobs are exposed to some degree of AI automation, and that generative AI could substitute up to one-fourth of current work. Extrapolating our estimates globally suggests that generative AI could expose the equivalent of 300 million full-time jobs to automation.”
These KPMG authors reflect on how generative AI might change software development in companies, concluding that the technology will become the most valuable coding partner developers will have.
“If it lives up to its promise, generative AI will herald a paradigm shift every bit as significant as the cloud or DevOps, the development process that enables faster delivery of more reliable software products and services. In terms of how corporations develop and maintain software, it will prompt changes as big as, and likely even more impactful than, those created by Agile development methods, which enable rapid responses to changing software requirements and customer feedback.”
In this comprehensive report, McKinsey authors assessed the impact of generative AI on productivity, business functions (such as sales and marketing and software development), and jobs. They concluded that generative AI could create trillions of dollars in value across sectors. Among the findings are the following:
This article, aimed at CEOs, focuses on the value creation opportunities in generative AI. A distinctive feature is a section on how companies can pursue generative AI through four example cases dealing with organizational effectiveness, from those requiring minimal resources to resource-intensive projects.
I would also recommend this McKinsey article:, “Exploring opportunities in the generative AI value chain.” This piece discusses the six main pieces of the generative AI value chain and which business functions are most likely to experience the first wave of generative AI applications.
Risk is one of the most important considerations in rolling out generative AI use cases. This piece summarizes PwC’s responsible AI framework and how to use generative AI responsibly. The authors conclude, “If there’s a golden rule for responsible AI (and trusted technology in general), it’s this: It’s better to implement trust by design and ethics by design from the start rather than racing to close gaps after systems are up and running.”