Applied artificial intelligence tops the list of 14 most influential technology trends in McKinsey & Company's "Technology Trends Outlook 2022" report. For now, applied AI (which might also be referred to as "enterprise AI") is mainly the use of machine learning and deep learning models in real-world applications. A closely related trend that also made it to McKinsey's top-14 list is "industrializing machine learning," which refers to MLOps platforms and other tools that make it easier to train, deploy, integrate, and update ML models in different applications and environments. McKinsey's findings, which are in line with similar reports released by consulting and research firms, show that after a decade of investment, research, and development of tools, the barriers to applied AI are slowly fading. Large tech companies, which often house many of the top machine learning/deep learning scientists and engineers, have been researching new algorithms and applying them to their products for years.

C3 AI "…could become the de facto AI platform standard for the world's most complex industries" "Ahead of its time, C3 AI's strategy is to make AI application-centric by building a growing library of industry solutions, forging deep industry partnerships, running in every cloud, and facilitating extreme reuse through common data models," the report states. "We are pleased to be recognized as a leader in AI and ML platforms," said Thomas Siebel, C3 AI CEO. "I'm delighted to see C3 AI's significant investments in enterprise AI software be acknowledged. I believe that Forrester Research has made an important contribution, having published the first professional comprehensive analysis of enterprise AI and Machine Learning platforms," Siebel continued, "changing the dialogue from a focus on disjointed tools to the importance of cohesive enterprise AI platforms. This is certain to accelerate the market adoption of enterprise AI and simplify often protracted decision processes."

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. While there is a lot of excitement about how advances in artificial intelligence will help the enterprise sector, the reality is that most efforts fail. Study after study shows that organizations of different sizes are struggling to bring machine learning into their operations, and many initiatives end up being shelved or used in a very limited capacity. The adoption of applied AI is very difficult and costly, wrought with pitfalls, and requires fundamental changes at different levels. However, as the tools and processes mature, more companies will be able to take advantage of enterprise AI while reducing the risks and costs of adoption.

Reaching out to you with a request for Career related advice / input. I am a professional with about 18 years of experience in advisory services and implementation of SAP solutions across industries and geographies, presently working with a consulting firm in the U.S. I am a Chartered Accountant from India by education. From 2018, I started focusing on ML/AI as my parallel interest / career in addition to SAP. For the last four years, I started putting efforts to learn fundamentals of the ML/AI domain. I read multiple blogs, books, completed courses as well as completed PoC for a few Embedded Machine Learning solutions of SAP.

Expertly serving the industries like e-Commerce, retail, social, and more, consumer AI is focused on driving customer experiences. Consumer AI interacts with customers to help the business scale and automate customer engagement. At the same time, with insights gained from this, organizations can gain insights to personalize their offerings, thereby impacting customer delight positively. On the other hand, enterprise AI is more organizational-focused and strongly emphasizes creating tangible value for the brands. The results are deduced based on pre-decided KPIs and contribute to adding value over time.

Artificial intelligence is reshaping businesses and how they handle innovation. AI may force management to rethink a company's entire innovation process, given rapid technical growth and the displacement of human organizations. The relative balance of AI use is shifting dramatically and permanently in businesses across industries. Each company will have as many possibilities as obstacles due to AI applications. Enterprise AI can alter the enterprise ecosystem in a variety of ways.

The criticality of getting corporate restructuring right is hard to overstate. As you may know, restructuring is normally carried out when an organization is not in the best financial health. A complete overhaul of existing working methods and the overall structure of an organization to avoid financial crises and stabilize business performance necessitates the proper extraction and use of data and resources. Corporate restructuring involves adhering to a robust business strategy while carrying out SWOT analysis, creating new strategies for the future, adding and eliminating operations and resources depending on financial requirements and launching a new brand language, if necessary, to turn the fortunes of a failing business around. Corporate restructuring is a data-driven process.

AI adoption has witnessed a monumental surge in recent years. The COVID-19 pandemic has accelerated enterprise AI adoption as businesses pushed for digital transformation while a majority of the workforce was working remotely. However, generating significant return on investment (ROI) from AI-powered applications can be a complicated task for business leaders. Business leaders need to be aware of the changing landscape of their industry and use an agile approach for AI implementation. Along with these, businesses need to understand how to identify and utilize the strengths as well as assess the risks of AI utilization in a specific situation.

Today, artificial intelligence applications are widespread and perceptible even in everyday life. Consider, for example, the ability of a car to brake automatically or not to exit the lane during a curve or, also, to the health sector, where the AI is able to monitor and report anomalous trends as well as helping to streamline processes to free up doctors' schedules and therefore reduce costs. Let's think for example of a computer able to identify the famous person we have in mind, asking ourselves no more than 10 questions, or how AI is constantly being adopted by many organizations in the finance and business sectors to restructure companies, improve earnings and experiences, reduce risks, and increase opportunities for the financial engines of our modern economy. These are already existing technologies of more or less advanced artificial intelligence, which are changing the way we live, work and study. When the machine learns independently, thanks to these and through experience, managing to improve its performance over time by providing more answers or more functions, you will have machine learning.

Living in a world of big data which keeps on multiplying regardless of the size of enterprises. It's hard to ignore the fact that AI plays an important role in managing it. Now it's hard to think of enterprises without AI. There's so much potential in AI. enterprises are incorporating AI with the aim of saving cost, boosting efficiency, and getting new clients. There is an end number of AI tools to enhance productivity, leverage manual power, save time.