Businesses that focus only on customer experience may be missing a significant opportunity to connect with people on a deeper level. We don't pour that delightful first cup of life-giving coffee and think, "I am the end user of this coffee." So why does the business world insist on grouping--and trying to understand--people as customers when, before anything else, we are human? We are messy, inconsistent, and, perhaps most of all, emotional--and it's time for businesses to acknowledge, respect, and account for this. The fields of philosophy, religion, social science, psychology, biology--and yes, marketing--have spilled much ink defining what it means to be human.

Companies today need what only truly modern operations can provide: broad and deep visibility into the operational value chain, including supply, manufacturing, distribution and demand; accurate forecasts of what might impact those operations; resilience in the face of potential disruptions; tools to help them quickly sense and predict changes in market demand; and the ability to quickly adapt the value chain to deliver on its promises to customers. The technologies to support this scenario are available today or will be soon.

In many labor markets, workers and firms are connected via affiliative relationships. A management consulting firm wishes to both accept the best new workers but also place its current affiliated workers at strong firms. Similarly, a research university wishes to hire strong job market candidates while also placing its own candidates at strong peer universities. We model this affiliate matching problem in a generalization of the classic stable marriage setting by permitting firms to state preferences over not just which workers to whom they are matched, but also to which firms their affiliated workers are matched. Based on results from a human survey, we find that participants (acting as firms) give preference to their own affiliate workers in surprising ways that violate some assumptions of the classical stable marriage problem. This motivates a nuanced discussion of how stability could be defined in affiliate matching problems; we give an example of a marketplace which admits a stable match under one natural definition of stability, and does not for that same marketplace under a different, but still natural, definition. We conclude by setting a research agenda toward the creation of a centralized clearing mechanism in this general setting.

The Deloitte Centre for Regulatory Strategy is a powerful resource of information and insight, designed to assist financial institutions manage the complexity and convergence of rapidly increasing new regulation. With regional hubs in the Americas, Asia Pacific and EMEA, the Centre combines the strength of Deloitte's regional and international network of experienced risk, regulatory, and industry professionals – including a deep roster of former regulators, industry specialists, and business advisers – with a rich understanding of the impact of regulations on business models and strategy.

Today's billion-dollar unicorn start-ups have the advantage of building these capabilities into their process design at the outset. In contrast, businesses running on more-established processes may find themselves encumbered by inefficient legacy systems, rigid silos and fixed schedules. Most were designed and built decades ago to solve for specific needs, with a premium placed on consistency and simplicity rather than on speed, scale and agility. Fortunately, the evolution of technology has lifted a lot of the constraints facing seasoned enterprises, so the past doesn't have to dictate the future. Mature companies can incorporate today's rapidly advancing and increasingly accessible tech enablers to create Living Process.

IBM, Amazon, Deloitte, DXC, and Accenture are partnering with digital human experts at UneeQ to push the advance and ongoing adoption of advanced conversational AI, according to a recent press release. Artificial Intelligence (AI) has found its way into a wide spectrum of human experiences -- from routine transactions to meaningful, life-changing events. Soon it may be hard to tell where organic interactions end and AI features begin. Digital humans are AI-powered life-like virtual beings that exist both in the real world and online, helping customers and businesses worldwide. A leap beyond standard chatbots, they even look like us, and are becoming a trend among major companies.

Alex Fly's article presents some key statistics related to the development of AI and the utilisation of AI-based technologies within enterprises. The included statistics and figures are sourced from reputable surveyors such as Deloitte, Gartner, McKinsey & Company etc. The statistics and figures presented in the article paint a picture of how AI is transforming the entirety of a typical enterprise day to day functions. AI impact is felt from hiring initiatives to profitable revenue sources. One major takeaway from Alex's article is that AI is here to stay and enterprises are adapting at a pace that will see entire companies, industries and nation transform within the next five years.

A year ago, we concluded that the window for AI competitive advantage might be closing.1 We based this assessment on data from the second edition of Deloitte's State of AI in the Enterprise survey: 57 percent of executives at AI-adopting firms believed that AI would substantially transform their businesses within three years, and 38 percent believed the technologies would do the same for their industry during that time frame (see figure).2 The 19-point gap suggested that AI adopters had a fairly small window before industry competitors cut into their lead. We've released the results of the third edition of the Deloitte State of AI study,3 and adopters continue to be bullish: More than eight in 10 report that AI will be "very" or "critically" important to their business success in the next two years, and the portion who regard it as critically important is poised to grow from 23% today to 41% in two years. And they're continuing to grow their investments: 71% of adopters expect to increase their AI spending in the next fiscal year.

This part of the series looks at the future of AI with much of the focus in the period after 2025. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Some of the classical approaches to AI include (non-exhaustive list) Search algorithms such as Breath-First, Depth-First, Iterative Deepening Search, A* algorithm, and the field of Logic including Predicate Calculus and Propositional Calculus. Local Search approaches were also developed for example Simulated Annealing, Hill Climbing (see also Greedy), Beam Search and Genetic Algorithms (see below). Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. A non-exhaustive list of examples of techniques include Linear Regression, Logistic Regression, K-Means, k-Nearest Neighbour (kNN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forests, XG Boost, Light Gradient Boosting Machine (LightGBM), CatBoost. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion.

Consulting and professional services firm Deloitte recently issued a report, "Unbundling the cloud with the intelligent edge," which looks at how updated connectivity in the cloud, AI, and edge computing can be exploited by the enterprise. Elements making up this changing ecosystem include the adoption of 5G and Wi-Fi 6 in the next phase of wireless connections, says Jeff Loucks, executive director of Deloitte's Center for Technology, Media, & Telecommunications. He says the report posits the displacement of incumbent wireless by 5G in the next three years. New tiers of wireless may act as force multipliers, according to the report, by expanding the potential of other new technologies. "The way we're thinking about the intelligent edge," says Loucks, "it's a combination of processing power, artificial intelligence, and advanced connectivity that's located near devices that generate and consume data."