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."

Beena is a managing director with Deloitte Consulting LLP and is an award winning senior executive with extensive global experience in artificial intelligence and digital transformation. Beena is the founder and CEO of Humans For AI Inc. She has co-authored the book "AI Transforming Business." A well-recognized thought leader and keynote speaker in the industry, Beena also serves on the industrial advisory board at Cal Poly College of Engineering, and she has been a board member and advisor to several startups including Flerish, Predii, iguazio, CliniVantage, and ProjectileX. Beena has been honored several times for her contribution to tech and her philanthropic efforts, including: UC Berkeley 2018 Woman of the Year in Business Analytics, San Francisco Business Times' 2017 Most Influential Women in Bay Area, WITI's Women in Technology Hall of Fame, National Diversity Council's Top 50 Multicultural Leaders in Tech, CIO.com and Drexel University's Analytics 50 innovator, Forbes Top 8 Female Analytics Experts, and Women Super Achiever Award from World Women's Leadership Congress.

For the many companies that rely on pricing as a competitive advantage, they need to start evaluating AI and machine learning on their IT platform roadmaps now. Staying at competitive parity and turning AI- and machine learning-based expertise into a pricing and revenue management strength needs to be a priority. Data is a proven panacea for fear, and given the new market dynamics many companies are facing, it's the most reliable way to make decisions. Harnessing Pricing Power to Create Lasting Value, Bain & Company, February 24, 2020. Harnessing Pricing Power to Create Lasting Value, Bain & Company, February 24, 2020.

Machine learning creates room for continuous business model innovation. One recent summer, Charles Weinstein, CEO of New York City-based accounting firm EisnerAmper, had an epiphany: machine learning could either destroy his business or remake it. A 35-year veteran of the industry, Weinstein sensed that the practice of accounting--issuing financial statements three months after the quarter closes--while still necessary, was losing relevance in the real-time, data-driven economy. So he organized a three-day partner meeting to consider how machine learning capabilities in particular might remake the traditional accounting firm for the digital era, enabling it to help its clients look into the future rather than simply reporting on the past. Weinstein invited a partner in charge of global innovation at a Big Four accounting firm (not a direct competitor) to talk about the moves his firm was making.

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Similar to a constellation where you can spot different stars, artificial intelligence (AI) can be brought down into different types. To help you decide what AI type will shine brightest and contribute to your business' stellar performance, our data science consultants will define each. However, let's first dispel the clouds to have a clear look at AI as a whole. Artificial intelligence enables a computer system to be trained and apply the gained knowledge to new inputs. This ability rests upon math and algorithms and is applicable only to the tasks that the system has been trained to perform.

Deloitte announced the launch of the Deloitte AI Institute, a center that focuses on artificial intelligence (AI) research, eminence, and applied innovation across industries. The Institute will bring together the brightest minds in the field of AI to apply cutting-edge research to help address a wide spectrum of relevant AI use cases. "The Deloitte AI Institute is being established to advance the conversation and development of AI for enterprises," said Nitin Mittal, AI co-leader and principal, Deloitte Consulting LLP. "Our goal is to blend Deloitte's deep experience in applied AI with a robust network of some of the most intelligent AI minds in the world to challenge the status quo. Through the power of this center, we aim to deliver impactful and game-changing research; and innovation to help our clients lead in the'Age of With,' a world where humans work side-by-side with machines."

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