Various types of vehicle automation is increasingly used in a variety of environments including road vehicles such as cars or automated shuttles, confined areas such as mines or harbours, or in agriculture and forestry. In many use cases, the benefits are greater if several automated vehicles (AVs) cooperate to aid each other reach their goals more efficiently, or collaborate to complete a common task. Taxonomies and definitions create a common framework that helps researchers and practitioners advance the field. However, most existing work focus on road vehicles. In this paper, we review and extend taxonomies and definitions to encompass individually acting as well as cooperative and collaborative AVs for both on-road and off-road use cases. In particular, we introduce classes of collaborative vehicles not defined in existing literature, and define levels of automation suitable for vehicles where automation applies to additional functions in addition to the driving task.

Data lies at the core of all business strategies. It has been generated at an unparalleled rate since the outbreak of the epidemic that has led the entire world to go digital. Organizations have experienced an exponential increase in footfall on their web properties and become data-rich. Now, the majority of organizations want to tap into this data asset to improve their business further and gain a competitive edge. Once the business objective is decided, the executives bake it into their strategic goals which is what the business wants to position itself in the next three to five years.

According to Gartner, AI applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decision-making, and take action. In essence, the concept of AI centres on enabling computer systems to think and act in a more'human' way, by learning from and responding to the vast amounts of information they're able to use. AI is already transforming our everyday lives. From the AI features on our smartphones such as built-in smart assistants, to the AI-curated content and recommendations on our social media feeds and streaming services. As the name suggests, machine learning is based on the idea that systems can learn from data to automate and improve how things are done – by using advanced algorithms (a set of rules or instructions) to analyse data, identify patterns and make decisions and recommendations based on what they find.

Applying ERM principles to AI initiatives can help organizations improve governance of AI, manage risks, and drive performance to maximize achievement of strategic goals. The project, commissioned by COSO and co-authored by Deloitte & Touche LLP, focuses on the need for organizations to design and implement governance, risk management, and oversight strategies and structures to realize the potential of humans collaborating with AI. The COSO ERM framework, coupled with Deloitte's Trustworthy AITM Framework, can provide an overarching and comprehensive approach that aligns risk management with strategy, reduce performance variability, and improve the likelihood of success for AI initiatives.

During the inaugural HIMSS State of Healthcare digital event on Tuesday, Thomas Kiesau, director and digital health leader at The Chartis Group offered some useful benchmarking for hospitals and health systems wondering how their IT investments and digital maturity compare to their peers. The Chartis Group surveyed 226 health system executives nationwide, at hospitals and health systems of all shapes and sizes. They were asked their perspectives on digital health, artificial intelligence and machine learning – and about the financial health of their respective organizations. The health system execs had generally positive sentiments about digital health. Most agreed with the sentiments, said Keisau: "It should be integrated into the care model. It will advance care over the long term. It can create a competitive advantage. It can help close care disparities. Across the board what we saw from our respondents was that they believe those relatively strongly."

No organization can afford to sit back and ignore the potential of AI. Yet, rushing to adopt AI just because the technology exists can be just as damaging to a business. To fully realize the potential of AI – indeed, any new technology – you need to approach it with a clear business goal or need in mind. In other words, you need to identify and narrow down to the most valuable AI opportunities for your business (which means your approach could be quite different from that of your competitors). Let's explore each step in a little more detail.

No organization can afford to sit back and ignore the potential of AI. Yet, rushing to adopt AI just because the technology exists can be just as damaging to a business. To fully realize the potential of AI – indeed, any new technology – you need to approach it with a clear business goal or need in mind. In other words, you need to identify and narrow down to the most valuable AI opportunities for your business (which means your approach could be quite different from that of your competitors). Let's explore each step in a little more detail.

Whether your company is in the field of retail, utilities, healthcare, media, or logistics, the bet is that artificial intelligence (AI) is already touching your industry, even if you aren't aware of it. There are endless possibilities for what AI can do for your business. Just to name a few, some of the common use cases for which it's being applied are fraud detection, smarter services, research insight, demand forecasting, patient diagnosis, inventory optimization, recommendation systems for e-commerce, and the list goes on. According to the research "Reshaping Business with Artificial Intelligence" by MIT Sloan Management Review and The Boston Consulting Group, in a global survey of more than 3,000 executives, managers, and analysts across industries, almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage. Nevertheless, only one in 20 companies have extensively incorporated AI in offerings or processes and less than 39% of all companies have an AI strategy in place.

It's critical that leaders realize the world of recruiting is now changing at the fastest pace in its history. I estimate that in the past, a corporate recruiting function could go for nearly five years without a major transformation. Today, your corporate recruiting function could become obsolete within as few as 18 months. In addition, if you don't continually jump on emerging trends, your competitors may be ahead of you before you have time to react.Not only is the rate of change blistering, but much of the change is coming to completely new areas. As a result, if you don't want to get blindsided, you need to constantly monitor new industry movements, emerging trends, and their associated (and rapidly developing) "next practices."