| Description |
1 online resource (1 volume) : illustrations (black and white) |
| Summary |
Artificial Intelligence in its various forms - machine learning, chat-bots, robots, agents, etc. - is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets. Increasing business process and workflow maturity coupled with recent trends in cloud computing, datafication, IoT, cyber security, and advanced analytics, there is appreciation that the challenges of tomorrow cannot be solely addressed with today's people, processes, and products. A recent Gartner article supports our contention that AI is essential because it "promises to solve problems organizations could not before because it delivers benefits that no humans could legitimately perform." There is still considerable mystery, hype, and fear about AI. A considerable amount t of current discourse focuses on a dystopian future - with adversity impacted individuals/employees/society. Such opinions, with understandable fear of the unknown, don't consider the history of human innovation, current state of business/technology, or the primarily augmentative nature of tomorrow's AI. Our book demystifies AI for the enterprise. Our journey takes the reader from the basics (definitions, state of the art, etc.) to a multi-industry journey, and concludes with validated expert advice on everything an organization and its people must do to succeed. Along the way, we also debunk myths, provide practical pointers, and include best practices with appropriate vignettes. In summary, AI brings to enterprises capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow's enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues and achieve better customer/user satisfaction |
| Biography |
Prashant Natarajan Prashant Natarajan is an executive who focuses on the intersection of business outcomes, technology strategy, and digital transformation programs. He is currently the Vice President of Strategy and Customer Advisory at H2O.ai. He is passionate about customer happiness, digital transformation successes and innovation at scale - with AI, advanced analytics, cloud, and data - for global clients in insurance, health sciences, and manufacturing. Previously, Prashant was a Principal at Deloitte Consulting, global leader for data science and analytics at Unum Group, and portfolio director of cloud data platforms and analytics products at Oracle's Health Sciences GBU. He is a keynote speaker, popular panelist/moderator, and has been interviewed on multiple podcasts and in media. Prashant is an author/co-author of five books, all of which are practical, industry-focused titles that demystify digital transformation, data, machine learning/AI, and healthcare informatics. He is an invited Co-faculty Instructor at Stanford University, a Distinguished Fellow at the Health Innovation Alliance, and Member of the Advisory Board at Pistoia Alliance AI Center of Excellence. Prashant has also been invited to contribute as an industry thought leader and expert advisor by members of the US Congress, the White House, and leading private sector organizations & governments in the Americas, Asia, Australia, and Europe. He has an undergraduate degree in chemical engineering and a graduate degree in technical communication and linguistics. Bob Rogers Bob Rogers, PhD, is Expert in Residence for AI at the University of California San Francisco's Center for Digital Health Innovation, where he applies his experience solving problems with advanced analytics and Artificial Intelligence to help build world-class medical AI technologies. He is also co-founder of Orchestrated Intelligence which uses novel technology to automate global supply chains. He is a member of the Board of Advisors to the Harvard Institute for Applied Computational Science. Prior to UCSF, Bob was Chief Data Scientist in the Data Center Group at Intel, and was also co-founder and Chief Scientist at Apixio, a healthcare AI company. Bob began his career as an astrophysicist, developing computer models of physical processes near supermassive black holes. His research expanded to include artificial neural networks. He co-authored the book, Artificial Neural Networks: Forecasting Time Series, which led to a 12-year career as co-founder of a quantitative futures trading fund. In 2006, Bob transitioned into healthcare as a medical device product manager. He received his BA in physics at University of California, Berkeley and his PhD in physics at Harvard. Edward Dixon Edward Dixon's interest in AI stems from a hope that, someday, a robot will iron his shirts. Edward is Principle at Rigr AI, a small consultancy focused on AI for sensitive data, with a special interest in the application of AI to Digital Forensics, stemming from his work with Intel's Safer Children program and the Interpol DevOps technical working group. Jonas Christensen Jonas Christensen has spent his career leading data science functions across multiple industries. He is an international keynote speaker on data science and analytics leadership, a postgraduate educator and advisor in the field of data science and machine learning and host of theLeaders of Analyticspodcast. He holds a Masters of International Finance and a Masters of Accounting from Deakin University as well as a Bachelor of Economics and Business Administration from Copenhagen Business School. Jonas is passionate about what data science and AI can do for the world of business and beyond. He believes data science and AI will be as revolutionary to the way we do business and interact with each other as IT and personal computing has been over the past 40 years. Kirk Borne Dr. Kirk Borne is a data scientist and astrophysicist, providing thought leadership, global speaking, content creation, mentoring, training, and consulting activities in data science, machine learning, and AI across multiple disciplines. He is the Chief Science Officer at DataPrime.ai where he applies his extensive experience and knowledge of the trends in these critical fields to developing data-intensive professions and mentoring data scientists of all experience levels. Previously, he was the Principal Data Scientist, Data Science Fellow, and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton from 2015 to 2021. Before that, he was Professor of Astrophysics and Computational Science at George Mason for 12 years in the graduate and undergraduate data science programs. Prior to that, he spent nearly 20 years supporting data systems activities for NASA space science programs, including a role as NASA's Data Archive Project Scientist for the Hubble Space Telescope and 10 years as contract manager in NASA's Space Science Data Operations Office. Dr. Borne has degrees in physics (B.S., LSU) and astronomy (Ph. D., Caltech). He is an elected Fellow of the International Astrostatistics Association for his contributions to big data research in astronomy. In 2020, he was elected a Fellow of the American Astronomical Society for lifelong contributions to the field of astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including keynote presentations at dozens of data science, AI and analytics conferences. He is an active contributor on social media, where he promotes data literacy for all and has been named consistently among the top worldwide social influencers in data analytics, data science, machine learning, and AI since 2013. Leland Wilkinson Leland Wilkinson is Chief Scientist at H2O and Adjunct Professor of Computer Science at the University of Illinois Chicago. He received an A.B. degree from Harvard in 1966, an S.T.B. degree from Harvard Divinity School in 1969, and a Ph. D. from Yale in 1975. Wilkinson wrote the SYSTAT statistical package and founded SYSTAT Inc. in 1984. After the company grew to 50 employees, he sold SYSTAT to SPSS in 1994 and worked there for ten years on research and development of visualization systems. Wilkinson subsequently worked at Skytree and Tableau before joining H2O. Wilkinson is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and a Fellow of the American Association for the Advancement of Science. He has won best speaker award at the National Computer Graphics Association and the Youden prize for best expository paper in the statistics journal Technometrics. He has served on the Committee on Applied and Theoretical Statistics of the National Research Council and is a member of the Boards of the National Institute of Statistical Sciences (NISS) and the Institute for Pure and Applied Mathematics (IPAM). In addition to authoring journal articles, the original SYSTAT computer program and manuals, and patents in visualization and distributed analytic computing, Wilkinson is the author (with Grant Blank and Chris Gruber) of Desktop Data Analysis with SYSTAT. He is also the author of The Grammar of Graphics, the foundation for several commercial and opensource visualization systems (IBMRAVE, Tableau, Rggplot2, and PythonBokeh). Shantha Mohan Dr. Shantha Mohan is a mentor and project guide at Carnegie Mellon University Integrated Innovation Institute's iLab. She co-founded Retail Solutions Inc. (RSi), a leader in retail data analytics and ran its global product development organization. Her prior experiences include technical and educational consulting, and running worldwide product development for Consilium, a Manufacturing Execution System (MES) company (acquired by Applied Materials). She graduated with a Ph. D. in Operations Management from the Tepper School of Management, Carnegie Mellon University. Her undergraduate degree is in Electronics & Communication is from the College of Engineering, Guindy (CEG), India, and is honored to be a Distinguished Alumnus. Shantha is passionate about equality, diversity, and sustainability, and is a member of the Society of Women Engineers (SWE) where she is a volunteer and mentor. She is the author of Roots and Wings: Inspiring Stories of Indian Women in Engineering. She is a Distinguished Toastmaster (DTM) and is active in two clubs. |
|
She serves on the board of CEG alumni, North America (CEGAANA), and is instrumental in the creation of the Ask a CEGian student mentorship program and the CEG Betterment program. |
| Contents |
<P>Chapter 1: AI Strategy for the Executive </P><P></P><P>Chapter 2: Learning Algorithms, Machine/Deep Learning, and Applied AI -- A Conceptual Framework</P><P></P><P>Chapter 3: AI for Supply Chain Management</P><P></P><P>Chapter 4: HR and Talent Management</P><P></P><P>Chapter 5: Customer Experience Management</P><P></P><P>Chapter 6: Financial Services</P><P></P><P>Chapter 7: Artificial Intelligence in Retail</P><P></P><P>Chapter 8: Visualization</P><P></P><P>Chapter 9: Solution Architectures</P><P></P><P>Chapter 10: AI and Corporate Social Responsibility</P><P></P><P>Chapter 11: Future of Enterprise AI</P><P></P><P>Appendix:</P><UL><P><LI>Banking Case Study #1: Get More Value from Your Banking Data -- How to Turn Your Analytics Team into a Profit Centre </LI><P></P><P><LI>Banking Case Study #2: AI in Financial Services -- WeBank Practices </LI><P></P><P><LI>Retail Case Study: 7-Eleven and Cashierless Stores </LI><P></P><P><LI>Supply Chain Case Study: How Orchestrated Intelligence is Utilising Artificial Intelligence to model a Transformation in Supply Chain Performance </LI><P></P><P><LI>FMCG Case Study: Paper Quality at Georgia-Pacific</LI><P></P><P><LI>Healthcare Case Study: GE Healthcare: 1st FDA Clearance for an AI-enabled X-ray Devices -- </LI><P></P></UL><P></P> |
| Subject |
Artificial intelligence -- Industrial applications.
|
|
Business enterprises -- Data processing.
|
|
Intelligence artificielle -- Applications industrielles. |
|
Entreprises -- Informatique. |
|
Artificial intelligence -- Industrial applications |
|
Business enterprises -- Data processing |
| Other Form: |
Print version: Natarajan, Prashant. Demystifying AI for the enterprise. New York : Productivity Press, 2021 9781138491397 (OCoLC)1272896319 |
| ISBN |
9781351032940 (electronic bk.) |
|
1351032941 (electronic bk.) |
|
9781351032919 (electronic bk. ; Mobipocket) |
|
1351032917 (electronic bk. ; Mobipocket) |
|
1351032933 (electronic bk. ; PDF) |
|
9781351032926 (electronic bk. ; EPUB) |
|
1351032925 (electronic bk. ; EPUB) |
|
9781351032933 (electronic bk.) |
| Standard No. |
10.4324/9781351032940 doi |
|
More Resources