Please read the above case about Artificial Intelligence (AI)1.What are the key differences between AI 1.0 and AI 2.0? List and analyze at least four differences.2.How can the shifting from AI 1.0 to 2.0 impact a firm's strategies at various levels? Please 1) Define corporate, business, and functional level strategies; and 2) Discuss how the shifting impact all three levels differently.3.As a strategic leader, how would you like to revisit and revise your company’s strategic planning approaches? You may want to start by 1) selecting among three planning approaches to strategize for competitive advantages, and 2) offering your reasoning/argument with real-world examples.4.Companies with large logistics demands (e.g., Walmart, and IKEA) can possibly take advantage of AI techniques to improve their operational efficiency. What would be your recommendations to these companies? Discuss at least three recommendations. 1/3110% +Decades ago, artificial intelligence arrived with huge expectations for significant increases in efficiency and productivity. However, despite billions spent ontechnology, project after project stalled-mainly because challenges with company strategies, technical hurdles, and cultures kept the potential power of Alunrealized.Over the last decade, enterprises have migrated en masse to online platforms and cloud providers. This evolution has paved the way for computing capabilitiesto handle much more data while simultaneously generating troves of new data that these systems can now analyze.This migration has laid the foundation for a new generation of automation and analytics-the shift from enterprise Al 1.0 to 2.0. This created the capacity formore sophisticated insights. This includes end-to-end process intelligence powered by focused solutions and machine reasoning that drives exponential gainsin operational efficiency and productivity. Enterprise Al 2.0 is overtaking the shallow learning approaches and simple task automation of enterprise Al 1.0.The organizational shifts underway to embrace these changes from the top down-starting with leaders who understand that future growth is rooted in digitaltransformation-have driven this transition more than anything.Let's take a look at how companies move toward enterprise Al 2.0.From Experiment to Mandate: Getting C-Level SupportEnterprise Al 1.0 was a crucial stepping stone to driving success in the new 2.0 phase. Small wins and incremental advances over the past two decades pavedthe way for the broader buy-in we see across organizations today.However, enterprise Al 1.0 was hamstrung from the start by organizational structures. Al was being applied almost entirely by data scientists with speculative-use cases that often weren't aligned to business objectives, processes, or budgets. That led to a certain amount of irrelevancy and a lack of buy-in, especially athttps://web-s-ebscohost-com.libproxy.csustan.edu/ehost/delivery?sid=9cbb463b-378f-472a-bdad-11e00b8f5414%40redis&vid=1&ReturnUrl=https%3a...3/4/22, 11:58 PMsenior management levels.In one study conducted just before the pandemic hit, 93% of respondents-C-level technology and business executives representing Fortune 100 corporations-identified people and process issues as the key obstacle to implementing Al.EBSCOhostBolstering that assessment, Gartner estimated in 2017 that up to 85% of big data projects fail with other studies putting the failure rate in that range due to alack of buy-in among all levels of management. These failures often stem from data scientists driving Al investments that either don't align with businessobjectives or aren't accessible to frontline teams who could best leverage them.A key difference in enterprise Al 2.0 is the greater ownership of the transformation at all organizational levels, including C-level sponsorship of Al applicationsthat focus on strategic business impact.McKinsey may have been one of the first to study this phenomenon. In 2019, the consultancy found that commitment from management was a significant factorin the success of Al projects. Experts and industry leaders have echoed this idea, including Chris Chapo, senior VP of data and analytics at The Gap, whospoke on the topic at Transform 2019 in San Francisco."Sometimes people think 'all I need to do is throw money at a problem or put a technology in, and success comes out the other end,' and that just doesn'thappen," Chapo said, explaining that companies often "don't have the right leadership support, to make sure we create the conditions for success."In sum, deep support from the C-suite is the foundation of Al success.11/3From Nascent Skills to Citizen Data ScientistsEnterprise Al 2.0 requires a team with an advanced mix of skills at the intersection of machine learning, software engineering, data pipeline engineering,governance and compliance, AlOps and CloudOps. These skill are needed to translate the initial work done by the data scientists within their sandboxenvironments to production-ready systems,0GW 2/3From Nascent Skills to Citizen Data ScientistsEnterprise Al 2:0 requires a team with an advanced mix of skills at the intersection of machine learning, software engineering, data pipeline engineering,governance and compliance, AlOps and CloudOps. These skill are needed to translate the initial work done by the data scientists within their sandboxenvironments to production-ready systems.100% +Enterprise Al 2.0 leverages sophisticated technology platforms and packaged solutions that streamline, simplify, and accelerate Al-driven innovation. Ratherthan cobbling together disparate tools and siloed environments, teams work with integrated approaches to manage data and machine learning pipelines fromearly development through production deployment and ongoing management. Purpose-built solutions abstract the underlying data and model developmentcomplexities while significantly hastening time to value.Enterprise Al 2.0 will also see the growth of new platforms that unleash the power of Al for employees at all levels of training, throughout entire organizations -the democratization of technology. These business users will use next-gen tools that harmonize data and automatically build predictive models and intelligentapplications.These employees become citizen data scientists who can use Al, low-code/no-code platforms, and their deep domain expertise to overcome businesschallenges and exploit latent opportunities. They accomplish this in self-service mode, thus becoming critical enablers across the entire enterprise.From Machine Learning to Machine ReasoningThe predominant predictive modeling approach used in enterprise Al 1.0 is based on supervised learning, leveraging shallow algorithms.In contrast, enterprise 2.0 will usher in a wide variety of modeling approaches, including lightly-supervised, semi-supervised, self-supervised, low-shot, andunsupervised learning. In addition, we will build more intelligent systems that go beyond merely identifying patterns within data. We'll create a more nuancedunderstanding by deriving meaning from enterprise data and user interactions, understanding reasons for a particular behavior or phenomenon.These next-generation systems, based on domain-specific semantic intelligence, will leverage machine reasoning powered by propositional or probabilisticknowledge. This will work in tandem with machine learning to bring Al closer to human-level intelligence.Also see:For example, consider an intelligent system that uses multimodal sensors to detect the operating state of a centrifugal pump in an industrial environment. Thesystem can ingest sensor measurements, including pressure, temperature, flows, and vibration, to predict any upcoming performance degradation or equipmentfailure. By drawing upón a library of failure modes and effects analysis, the system can automatically act or propose mitigation advisories.From Narrow Tasks to Intelligent SystemsEnterprise Al 1.0 machine learning has a narrow scope and simply added automation and intelligence to tactical capabilities. Enterprise Al 2.0 capabilities willbroaden Al automation, so entire business processes and decisions can be more policy-driven and autonomous.Imagine systems of intelligence that can help retailers understand each of their target markets to anticipate shopper demand. This allows sellers to executepersonalized promotions, streamline supply chain logistics, ensure ideal inventory levels, and automatically set pricing to maximize quarterly businessobjectives.The evolution of governance is also crucial to enabling enterprise Al 2.0. Companies deploying Al will need to make sure they self-impose system regulation tosupervise Al-based decisions. This allows them to root out imprecisions, biases, non-compliance, or other problems as Al technology digests models.Remember how exciting it once was when Al evolved to answer FAQS or score and sort a sea of leads for the sales team? Yes, enterprise Al 1.0 solutionshandled simple tasks well. This functionality isn't going anywhere. But we can do so much more.https://web-s-ebscohost-com.libproxy.csustan.edu/ehost/delivery?sid=9cbb463b-378f-472a-bdad-11e00b8f54f4%40redis&vid=1&ReturnUrl=https%3a...About the Author:3/4/22, 11:58 PMEBSCOhostCompanies are already changing their cultures, upgrading their data infrastructure, enhancing their systems and technology, and refining their processes toembrace enterprise Al 2.0 tools and solutions. These changes coupled with Al analytical advances can help companies exploit the full potential of enterprise Al2.0.Eshwar Belani is an operating partner at0 6W2✪2

Artificial intelligence which refers to the ability of a computer or robot that are controlled by a…

Answered: Please read the above case about…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

See similar textbooks

Please read the above case about Artificial Intelligence (AI) 1.What are the key differences between AI 1.0 and AI 2.0? List and analyze at least four differences.