Future warfare will require Command and Control (C2) decision-making to occur in more complex, fast-paced, ill-structured, and demanding conditions. C2 will be further complicated by operational challenges such as Denied, Degraded, Intermittent, and Limited (DDIL) communications and the need to account for many data streams, potentially across multiple domains of operation. Yet, current C2 practices -- which stem from the industrial era rather than the emerging intelligence era -- are linear and time-consuming. Critically, these approaches may fail to maintain overmatch against adversaries on the future battlefield. To address these challenges, we propose a vision for future C2 based on robust partnerships between humans and artificial intelligence (AI) systems. This future vision is encapsulated in three operational impacts: streamlining the C2 operations process, maintaining unity of effort, and developing adaptive collective knowledge systems. This paper illustrates the envisaged future C2 capabilities, discusses the assumptions that shaped them, and describes how the proposed developments could transform C2 in future warfare.

As a chief technology officer and professor in emergent technologies, I've made it my life's work to understand how humans work with technology to create meaningful progress. But the last few years have brought about rapid change that the general workforce is struggling to keep up with. As an advisor on Dell Technologies' recent Breakthrough study, I was among other voices in the tech community calling for a reformation in organizational relations that are inhibiting innovation. Dell's Breakthrough study polled 10,500 respondents worldwide across industries and business functions, investigating workers' openness to digital transformation. Their findings concluded that employees are experiencing burnout at unprecedented levels, while still being tasked with the expectation to excel and scale alongside rapid technological advancement.

Businesses that want to be digital leaders in their markets need to embrace automation, not only to augment existing capabilities or to reduce costs but to position themselves to successfully maneuver the rapid expansion of IT demand ushered in through digital innovation. "It's a scale issue," says John Roese, global chief technology officer at Dell Technologies. "Without autonomous operations, it becomes impossible to keep up with the growing opportunity to become a more digital business using human effort alone." The main hurdle to autonomous operations, says Roese, is more psychological than technological. "You have got to be open-minded to this concept of rebalancing the work between human beings and the machine environments that exist both logically and physically," he says. "If you're not embracing and wanting it to happen and you're resisting it, all the products and solutions we can deliver to you will not help." Technology and infrastructure-driven AI and machine-learning discussions are expanding beyond IT into finance and sales--meaning, technology has direct business implications. "Selling is a relationship between you and your customer, but there's a third party--data and artificial intelligence-- that can give you better insights and the ability to be more contextually aware and more responsive to your customer, says Roese. "Data, AI, and ML technologies can ultimately change the economics and the performance of all parts of the business, whether it be sales or services or engineering or IT." And as companies gather, analyze, and use data at the edge, autonomous operations become even more of a business necessity. "Seventy percent of the world's data is probably going to be created and acted upon outside of data centers in the future, meaning in edges," says Roese. "Edge and distributed topologies have huge impacts on digital transformation, but we also see that having a strong investment in autonomous systems, autonomous operations at the edge is actually almost as big of a prerequisite … to make it work."

You don't have to be a prophet to foresee that artificial intelligence will also play an essential role in the field of human resource management. It will have a decisive impact on the way we connect people in the future. Using human-machine partnerships to improve the process of connecting people to the right job is relatively new to how most organizations hire. While there are many favorable advancements and novel solutions that promote more inclusive hiring, there are several risks to consider. First and foremost, we must challenge the assumption that hiring managers know what constitutes an ideal employee.

THuMP is a multi-disciplinary project, with the ambitious goal of advancing the state-of-the-art in trustworthy human-AI decision-support systems. ThUMP will address the technical challenges involved in creating explainable AI (XAI) systems, with a focus on Visualization for Explainable Planning and Argumentation, so that people using the system can better understand the rationale behind and trust suggestions made by an AI system. This project is conducted in collaboration with three project partners: Schlumberger and Save the Children, which provide use cases for the project, and a law firm whowill cooperate in considering legal implications of enhancing machines with transparency and the ability to explain. The candidate will be responsible for conducting research around the interfaces required to support explainability in the context of decision making in human-machine partnerships. Tasks will involve designing new visual layouts, building the interaction infrastructure for the project, developing a prototype interface for communicating with users, designing and conducting experiments with human subjects based on the use cases that will be co-created with the project partners.

It stands to reason that human workers who can demonstrate exceptional judgement, creativity, empathy, intuition, awareness and vision will find themselves in very high demand, no matter how much machine learning, smart automation and artificial intelligence have infiltrated an organization's business processes. The best strategy for human workers may be to focus on what makes them better humans rather than on becoming better technology users.

Macdeo was commenting on recent research which was conducted by Dell Technologies and the Institute for the Future, an independent research group based in California, which found that the work and learning environments of 2030 are already being shaped by the technology trends of today. Human and machine partnerships will create more equitable workplaces by evaluating candidates based on their capabilities, rather than gender, age or class. Employees will collaborate in entirely different, immersive ways using technologies such as XR, empowering workers more than ever before. AI will complement and augment human capabilities rather than replace them, and a deep understanding of AI and human and machine systems will unlock human potential and set workers apart. The research explored how technologies such as collaborative AI, multimodal interfaces, extended reality (XR), and secure distributed ledgers could change the congruence between humans and machines, while simultaneously enhancing collaboration within organisations.

It stands to reason that human workers who can demonstrate exceptional judgement, creativity, empathy, intuition, awareness and vision will find themselves in very high demand, no matter how much machine learning, smart automation and artificial intelligence have infiltrated an organization's business processes. The best strategy for human workers may be to focus on what makes them better humans rather than on becoming better technology users.

Dell Technologies Australia and New Zealand managing director Angela Fox has painted a future where humans and machines learn to live in harmony and machines evolve to be consumers. Delivering the Dell Technologies Forum keynote in Sydney last week, Fox discussed research that was conducted with the Institute of the Future, which looked at the next era of human-machine partnerships. Fox touched on three developments that she expects will shift the economy in the future, with the first being autonomous commerce. "We believe that you'll see machines evolving into consumers. They will use a mix of sensors, software updates, and artificial intelligence (AI) to determine when they -- and the people they serve -- are functioning sub-optimally, but more importantly, they will find ways to remedy it autonomously," Fox said.

Despite complexities of the human brain, scientists today are ostensibly creating one from scratch with one subset of artificial intelligence called deep learning. The basic building blocks of deep learning are artificial neural networks--algorithms that replicate the biological structure of the brain through "neurons" that contain discrete layers and connections to one another. Every layer of a neural network focuses on a certain type of task, such as recognizing patterns in digital images. Collectively, these layers form depth--hence the moniker, "deep learning". To combat these challenges, methods for collecting, standardizing, labeling, and cleansing data sets are becoming more prevalent.Getty