MIT Sloan Management Review and BCG have assembled an international panel of AI experts that includes academics and practitioners to help us gain insights into how responsible artificial intelligence (RAI) is being implemented in organizations worldwide. This month's question for our panelists: Should RAI be a top management agenda item at organizations across industries and geographies?1 Eighty-six percent of them (18 out of 21) agree or strongly agree that it should be. In aggregate, their replies offer a compelling rationale for top management to oversee RAI efforts. We distill and explain this rationale below. We also conducted a global survey of more than 1,000 executives that generated similar findings: Eighty-two percent of managers in companies with at least $100 million in annual revenues agree or strongly agree that RAI should be part of their company's top management agenda.

AI model deployment is very important in the life cycle of an AI project. Though AI model is at the core of any AI project, only after the proper deployment the AI model will make true sense. The process of taking a trained ML/AI model and making it's predictions available to the users and other systems is known as "deployment". Let's start from the initiation of an AI project till deployment and look at the best practices in AI model deployment. If a data scientist wants to use scikit-learn models, he just needs to subclass the Model class and implement the necessary methods.

From customer support, business intelligence, service management, lead generation to information retrieval, chatbots have gained widespread adoption across functions. The reason why organizations are actively embracing bot technology is that chatbots not only have several high value business use cases but also are easy to deploy with minimum risks. If you believe that your organization can greatly benefit from investing in chatbots but your top management is still on the fence about it, we are here to help. Here are some ways you can strengthen your business case and persuade your leadership/executive sponsors to deploy enterprise chatbots. Data and facts help you sharpen your pitch and make decision-making simpler for your stakeholders.

For many companies, coming up with a marketing plan is mainly about evaluating past results, tweaking them, and perhaps get inspiration from competitors, i.e. doing exactly what others do and thus ending up with mediocre results at best. Likewise, many marketing plans revolve purely around deciding on an agency or ad frequency and then wait for others to come up with ideas – ideas constrained by budgets and bad decision-making by top management. But at the end of the day, marketing is all about creativity, and creativity is in short supply. Bringing creativity into the planning process is difficult, mainly because you probably have exactly the same people as last year sitting around the table, steeped in group-think and stifled by the decades of experience the CMO or CEO, or worse, company owner, use to justify shooting down any idea that is in any way risky or simply different. The reasons why 90% of companies end up with (b) is primarily psychological.

A fintech talent war is pitching corporations against startups in pursuit of the AI skills required for the digital transformation of financial services. Our #RefinitivSocial100 thought leaders discuss hiring, retaining and educating data science talent. Advances in artificial intelligence (AI) have made hiring and retaining the best fintech talent one of the most pressing challenges facing the financial services sector today. This race to attract the right skills is exacerbated because corporates and startups search for the same profile within a limited pool of fintech talent. It's also apparent that the education system is not geared to producing thousands of students with the required AI skills.

A chatbot is the most in-your-face use case of AI, but it's easy to underestimate the opportunities that AI can help us realize. By some estimates, by 2023 around 40% of all internal operations teams in Enterprises will be AI-enabled. The flip side is that even though the growth opportunities are huge, it will take time, effort, and a concerted strategy to realize the true potential. Let us look at the key considerations to factor in while embarking on the AI journey. It is imperative to have a definite use case in mind before one thinks of implementing AI in your Enterprise.

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning--and the need for it. In 2007 Fei-Fei Li, the head of Stanford's Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.1 1.Fei-Fei Li, "How we're teaching computers to understand pictures," TED, March 2015, ted.com. Last November, Li's team unveiled a program that identifies the visual elements of any picture with a high degree of accuracy.

Martin Hiesboeck is an international business consultant with a focus on companies' operations in Asia.

Digitalising value streams effectively and efficiently is, without question, a move that will provide an organisation with future competitive advantage. However, to undertake such a digital transformation, top management must focus on developing the right capabilities and establishing the necessary mindset throughout the organisation – a necessity also common to lean management. The potential of digital technologies to transform performance is now widely recognised. However, most companies struggle to find the right approach to effectively grasp the benefits of this digital promise. Indeed, choosing from among the plethora of new options provided by digital technologies is a real challenge.

Charismatic CEOs enjoy leading and inspiring people, so they don't like delegating critical business decisions to smart algorithms. Who wants clever code bossing them around? But that future's already arrived. At some of the world's most successful enterprises -- Google, Netflix, Amazon, Alibaba, Facebook -- autonomous algorithms, not talented managers, increasingly get the last word. Elite MBAs (Management by Algorithm) are the new normal.