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."

Collaborate with Deloitte’s Ecosystems & Alliances in 2020, as we bring together the leaders, innovators, and insights that are shaping the future of work and helping organizations thrive in a digitally powered world.

This is the last blogpost of this series. We've already talked about conceptual model targets and model performance targets, now it is time to discuss the importance of data in building and evaluating models. More specifically, we will talk about three things: data quality, splitting data for evaluation, and sampling. Before we jump in, let me remind you that in the context of today's post, a model refers to a decision-generating process that applies logical or statistical techniques to transform the data it is provided into a meaningful output. I'll start with the obvious: good data quality is the foundation for producing accurate (and useful) findings from modelling.

From startups to enterprises racing to get new products launched, AI and machine learning (ML) are making solid contributions to accelerating new product development. There are 15,400 job positions for DevOps and product development engineers with AI and machine learning today on Indeed, LinkedIn and Monster combined. Capgemini predicts the size of the connected products market will range between $519B to $685B this year with AI and ML-enabled services revenue models becoming commonplace. Rapid advances in AI-based apps, products and services will also force the consolidation of the IoT platform market. The IoT platform providers concentrating on business challenges in vertical markets stand the best chance of surviving the coming IoT platform shakeout.

The first wave of digital transformation, which is still underway in some businesses, focuses on the digitalization of products, services and business processes. The second wave utilizes AI to improve the quality of decision-making, optimize organizational efficiencies and build closer relationships with customers. While different companies are at different stages of maturity of digital transformation, many organizations already have been experimenting with AI separately to determine how it could benefit the business in the second wave of digital transformation. One reason for the uneven results of the second wave is the understanding or lack of understanding of what AI can do. There has been a general misperception that artificial general intelligence (AGI) can solve any problem when in fact artificial narrow intelligence (ANI) is the state of the art.