Incumbents like CBA are increasingly looking to A.I. technology to solve their business problems and are eyeing external tech partners to source those A.I. solutions. But these traditional companies have faced challenges nurturing meaningful collaborations that maximize the support they get from A.I. players. Only 1 in 5 incumbents found the right kind of A.I. player, like H20.ai is for CBA, that offers access to custom technology, as well as support for talent, training, and change management, prompting the incumbent to overhaul its processes. We call these A.I. players that provide such support transformers. For industry incumbents that are able to identify and effectively collaborate with a transformer, the value is clear.

Without the ability to estimate and benchmark AI capability advancements, organizations are left to respond to each change reactively, impeding their ability to build viable mid and long-term strategies. This paper explores the recent growth of forecasting, a political science tool that uses explicit assumptions and quantitative estimation that leads to improved prediction accuracy. Done at the collective level, forecasting can identify and verify talent, enable leaders to build better models of AI advancements and improve inputs into design policy. Successful approaches to forecasting and case studies are examined, revealing a subclass of "superforecasters" who outperform 98% of the population and whose insights will be most reliable. Finally, techniques behind successful forecasting are outlined, including Phillip Tetlock's "Ten Commandments." To adapt to a quickly changing technology landscape, designers and policymakers should consider forecasting as a first line of defense.

AI systems may be better thought of as peers than as tools. This paper explores applications of augmented collective intelligence (ACI) beneficial to collaborative ideation. Design considerations are offered for an experiment that evaluates the performance of hybrid human- AI collectives. The investigation described combines humans and large language models (LLMs) to ideate on increasingly complex topics. A promising real-time collection tool called Polis is examined to facilitate ACI, including case studies from citizen engagement projects in Taiwan and Bowling Green, Kentucky. The authors discuss three challenges to consider when designing an ACI experiment: topic selection, participant selection, and evaluation of results. The paper concludes that researchers should address these challenges to conduct empirical studies of ACI in collaborative ideation.

The need to integrate Responsible AI (RAI) practices has become an organizational imperative. As Generative AI systems such as ChatGPT gain traction, it will quickly become easier for companies to adopt AI, thanks to lowered barriers to access. Already, as many experiment with these systems, they are unearthing serious ethical issues: scientific misinformation that looks convincing to the untrained eye, biased images and avatars, hate speech, and more. Our research has shown that investing in RAI early is essential; it minimizes failures as companies scale the development and deployment of AI systems within their organization. But we've also found that it takes three years on average for an RAI program to achieve maturity.

Four years ago, the $70 billion Alibaba Group, one of the world's biggest artificial intelligence users, teamed up with Mars, the $35 billion global leader in confectioneries, to figure out the types of candy and chocolates that consumers in China prefer. The fresh consumer data that Alibaba continually gathers from the millions of people shopping on its various platforms turned up the counterintuitive finding that many Chinese who buy chocolates also purchase spicy snacks at the same time. Using that data-driven insight, Mars developed a sweet-and-spicy product: a candy bar that contains Szechuan peppercorns, the source of China's spicy "mala" flavor. Even though Mars didn't conduct any other consumer research to reinforce the A.I.-driven insight, Spicy Snickers proved to be a winner on the mainland. Depending on A.I. also saved the company time; instead of the two to three years that it normally takes to launch a product, Mars was able to bring Spicy Snickers to market for the first time in August 2017, less than 12 months after the collaboration with Alibaba started.

In early 2020, when scientists rushed to develop a vaccine to take on the SARS-CoV-2 coronavirus that causes COVID-19, it seemed like a really long shot. The fastest a vaccine had ever previously been developed was for mumps, back in the 1960s--an effort that took 48 months. Still, just nine months later, in December 2020, the American pharmaceutical giant Pfizer and a German deep-tech startup, BioNTech, had developed the first COVID-19 vaccine, validating the use of the new technology of mRNA-based vaccines. The first studies on DNA vaccines began 25 years ago, and the science of RNA vaccines too has been evolving for over 15 years. One outcome was mRNA technology, which required the convergence of advances in synthetic biology, nanotechnology, and artificial intelligence, and has transformed the science--and the business--of vaccines.

In January 2020, an unknown American facial recognition software company, Clearview AI, was thrust into the limelight. It had quietly flown under the radar until The New York Times reported that businesses, law enforcement agencies, universities, and individuals had been purchasing its sophisticated facial recognition software, whose algorithm could match human faces to a database of over 3 billion images the company had collected from the internet. The article renewed the global debate about the use of AI-based facial recognition technology by governments and law enforcement agencies. Many people called for a ban on the use of the Clearview AI technology because the startup had created its database by mining social media websites and the internet for photographs but hadn't obtained permission to index individuals' faces. Twitter almost immediately sent the company a cease-and-delete letter, and YouTube and Facebook followed suit.

Africa has a unique opportunity to develop its competitiveness through artificial intelligence (AI). From agriculture and remote health to translating the 2,000-odd languages spoken across the continent, AI can help tackle the economic problems that Africa faces. Africa faces several known challenges in developing AI such as a dearth of investment, a paucity of specialised talent, and a lack of access to the latest global research. These hurdles are being whittled down, albeit slowly, thanks to African ingenuity and to investments by multinational companies such as IBM Research, Google, Microsoft, and Amazon, which have all opened AI labs in Africa. Innovative forms of trans-continental collaboration such as Deep Learning Indaba (a Zulu word for gathering), which is fostering a community of AI researchers in Africa, and Zindi, a platform that challenges African data scientists to solve the continent's toughest challenges, are gaining ground, buoyed by the recent "homecoming" of several globally-trained African experts in AI.

At the 2020 virtual Web Summit I had occasion to meet virtually with Francois Candelon, Global Director at the BCG Henderson Institute about a recent study they did with the MIT Sloan Management Review on the value that companies are getting from their artificial intelligence (AI) initiatives. I also spoke with Alan Baratz, President and CEO of D-Wave, about the practical uses of quantum computers. They had an interesting lesson to teach on how data should be handled and the best way to make AI and advanced processing technologies work best. The lesson in short, is that these technologies work best when they are used to augment humans, changing the way they work. The BCG Henderson Institute and MIT Sloan Management Review study was conducted with more than 3,000 executives worldwide and revealed that more than half of respondents are deploying AI and six out of ten have an AI strategy in 2020, up from four out of ten in 2018.

Among the many companies investing in artificial intelligence, there is one surprisingly exclusive group: companies that generate value from AI. And right now, at least, the odds against gaining admission are sobering. According to a survey of more than 2,500 executives - conducted for a new report by MIT Sloan Management Review, BCG Gamma, and BCG Henderson Institute - seven out of ten companies report minimal or no gains so far from their AI initiatives. Why do some efforts succeed, but many more fail? The only one that struck me was #3.