AI systems are often introduced with high expectations, yet many fail to deliver, resulting in unintended harm and missed opportunities for benefit. We frequently observe significant "AI Mismatches", where the system's actual performance falls short of what is needed to ensure safety and co-create value. These mismatches are particularly difficult to address once development is underway, highlighting the need for early-stage intervention. Navigating complex, multi-dimensional risk factors that contribute to AI Mismatches is a persistent challenge. To address it, we propose an AI Mismatch approach to anticipate and mitigate risks early on, focusing on the gap between realistic model performance and required task performance. Through an analysis of 774 AI cases, we extracted a set of critical factors, which informed the development of seven matrices that map the relationships between these factors and highlight high-risk areas. Through case studies, we demonstrate how our approach can help reduce risks in AI development.

Late last year, the European Union introduced the Artificial Intelligence Liability Directive (AILD) to "improve the functioning of the internal market by laying down uniform rules for certain aspects of non-contractual civil liability for damage caused with the involvement of AI systems." Bad AI is AI that isn't trustworthy--AI that is based on biased or incomplete data that then, in turn, could perpetuate harmful outcomes. And with AI expecting a compound annual growth rate of 20% by 2030--to reach nearly US $1.4 trillion--the technology, media and telecommunications (TMT) industry has a critical responsibility to not only develop the most trustworthy AI but also model the most trustworthy AI behavior to their business customers and society at large. While AI may have seemed like the stuff of science fiction, it has now entered the realm of reality and offers incredible potential to make businesses more competitive. According to Deloitte's AI Dossier, there are six key ways AI can help businesses create value: But while AI presents amazing potential for business value, AI has an equal amount of potential to go wrong.

What can marketers bring to the mix when AI is so powerful? Don't miss a MarketingProfs podcast, subscribe to our free newsletter! Passion, for one thing, says Peter Prodromou of Boathouse. "If you're in the upper right-hand corner with passion, chances are people are going to want to work with you or buy your product," he says on the latest episode of Marketing Smarts. "Think about Apple and Tesla; those are two brands that are very much about passion. Your ability to convey that is critically important." AI is just an algorithm, after all. "Everybody is going to shop at Amazon because they have the best algorithm, and there may or may not be passion for it," Peter says.

As Vice President of Strategy and Products, my role is to help our customers develop products based 100% on their business and market strategy. Our customers are evolving what they think their future needs might be, and we're helping them by democratising AI for the enterprise. What H2O means by that is we're giving them tools to deal with the immense volumes of data, helping them convert that into not just smart insights, but enable action and create value. So, my role is to understand the business market and societal trends around healthcare, sciences, insurance, banking, and other verticals. I work with customer executives and leaders--not just in business and operations, but also in data science and technology.

The health care revolution is not just an opportunity but an urgent and essential need. Our existing health care models are not sustainable in the long run. The cost of health spending continues to rise with the rapid worldwide growth of costly chronic diseases. Meanwhile, the global health care workforce faces a predicted shortfall of 18 million health workers by 2030, a gap which will accelerate the necessary adoption of digital technologies. Yet while these trends are widely acknowledged, health care organizations and stakeholders need to recognize that we now also have the tools for transformation, which will not only drive efficacy of care and personalization, but also, and equally importantly, better access and efficiency.

The Data Engineer has been gaining popularity in the past 10 years, but what exactly do Data Engineers do? Data Engineers in my experience wear many hats, and often sit in the middle of a triangle of Business Intelligence, Software Engineering, and Data Science. One primary role of the Data Engineer is to partner with downstream teams, such a Business Intelligence and Data Science to understand the data needs of the business, and build data integrations to supply these data. The other role can be to partner with Software Engineers to consume application data; typical of new software development efforts, or "0 to 1" projects. Data Engineers are often hidden in the shadows; monitoring data quality dashboards, listening to engineering sprints, and eavesdropping in analytics meetings.

A large ecommerce company has allocated a hefty budget for marketing activities before its upcoming sale event--its biggest in the year. The marketing team needs to come up with the messaging to be delivered during the marketing campaign to attract maximum customers. The big question is what should that messaging be? Should it be, "Get the best discounts," or should it be, "Get the best international brands," or will "Get best quality at best rates" work better? Should all three messages be employed?

History does not repeat itself but often rhymes. When it rhymes, it can be predicted by AI algorithms. In this context, the prediction refers to the future price of a house after renovation within a range of a few months. The volatility that we experienced in the labor market or supply chain during the past two years, makes the prediction near impossible compared to the times before the pandemic. AI is a powerful tool for prediction; however, it only works if the future is built upon the past.

The Deloitte AI Institute unveiled a new report that examines the most compelling business use cases for artificial intelligence (AI) across six major industries. The report, "The AI Dossier," helps business leaders understand the value AI can deliver today and in the future so that they can make smarter decisions about when, where and how to deploy AI within their organizations. "The AI Dossier" illustrates use cases across six industries, including consumer; energy, resources and industrial; financial services; government and public services; life sciences and health care; and technology, media and telecommunications. For each industry, the report highlights the most valuable, business-ready use cases for AI-related technologies – examining the key business issues and opportunities, how AI can help, and the benefits that are likely to be achieved. The report also highlights the top emerging AI use cases that are expected to have a major impact on the industry's future.

Jupyter notebooks are where machine learning models go to die. Unlike what you probably learned in University, building models in a Jupyter notebook or R Studio script is just the very beginning of the process. If your process ends with a model sitting in a notebook, those models almost certainly didn't create value for your company (some exceptions might be it was only for analytics or you work at Netflix). But it probably does mean the people paying you are not super excited by the outcome. In general, companies don't care about state-of-the-art models, they care about machine learning models that actually create value for their customers.