This paper introduces the Multi-Step Action Model (MSAM), a closed-source AI model designed by Empsing to address challenges hindering AI adoption in enterprises. Through a holistic examination, this paper explores MSAM's foundational principles, design architecture, and future trajectory. It evaluates MSAM's performance via rigorous testing methodologies and envisions its potential impact on advancing AI adoption within organizations.

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.

In 2019, Venturebeat reported that almost 87% of data science projects do not get into production. Redapt, an end-to-end technology solution provider, also reported a similar number of 90% ML models not making it to production. However, there has been an improvement. In 2020, enterprises realized the need for AI in their business. Due to COVID-19, most companies have scaled up their AI adoption and increased their AI investment.

When Alan Turing published his paper titled "Computing Machinery and Intelligence" in 1950, he was trying to answer a simple question -- Can machines think? He introduced the Turing Test in the paper, where a human had to converse with a bunch of people. Among the people, there was also a machine disguised as a human. The goal was to check whether the human would identify the machine or not. Though the test was not definitive, it opened doors to Artificial Intelligence that we see around us now.

In the current pandemic, AI models are experiencing unique levels of traffic and interest. The coronavirus pandemic has brought many industries to a halt, with businesses reducing hours, sacking employees, and halting new projects. Artificial intelligence projects could definitely fall into the dispensable category, but that's not the case according to a recent report from FICO and the market intelligence firm Corinium. The Building AI-Driven Enterprises in a Disrupted Environment report surveyed more than 100 c-level data and analytic executives and conducted in-depth interviews to understand how organizations are developing and deploying AI capabilities. It found AI demand has risen during the pandemic for a majority of businesses surveyed.

AI has the potential to add $15.7 trillion to the global GDP by 2030 due to labor productivity improvement and automation. Enterprise AI adoption is accelerating in the market, with the projected value of AI (hardware, software, services) to reach $190.61 billion by 2025. The primordial ingredient that is required to make AI deployments successful is the access and availability of quality and clean data to train and feed machine learning and analytical models. Enterprise data architectures have been deployed and developed over decades, leading to a hodge-podge of different technologies, physical and logical architectures, formats and frameworks in organizations. Since machine learning (ML) algorithms rely on data to learn from to evolve prediction models, the need for sufficient, quality, prepared data is paramount.

When it comes to the long-promised mass adoption of artificial intelligence (AI), many have been left wondering what exactly is holding up progress. On one hand, AI is expected to have a $13 trillion impact on the global economy by the end of the next decade. On the other, 77.1% of companies report that business adoption of AI initiatives remains a major challenge. Part of the problem is thought leaders, the media and even the public are talking about AI fervently, not pragmatically. As futurist Martin Ford writes in the introduction to his new book on the subject, Architects of Intelligence, "The result has been a sometimes incomprehensible mixture of careful, evidence-based analysis, together with hype, speculation and what might be characterized as outright fear-mongering."

Enterprises are ploughing on with AI adoption despite facing a skills crisis, says a new survey by O'Reilly, the e-learning provider. According to the survey, 23% of respondents feel that the lack of skilled people is hampering AI adoption. In O'Reilly's 2018 AI survey, 20% of respondents said the lack of skilled people slowed adoption. But despite the skills gap, 81% of respondents work for organisations that already use AI. Furthermore, more than 60% of organisations are planning to spend at least 5% of their IT budget on AI over the next 12 month; 19% are planning to spend at least 20% on their IT budget on AI.

A tech talent shortage is impeding the growth of artificial intelligence (AI) in the enterprise, according to new research from EY. Of 200 senior AI professionals, 56% said a lack of talent in the field is the greatest barrier to AI implementation, EY found. This suggests that tech professionals may need to find ways to grow their skill set to drive the field forward in their companies. Despite the talent shortage, business leaders continue to utilize AI in the enterprise, EY said, with 62% finding the technology beneficial. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) "This year, as businesses strategized how to integrate AI into their operations, they were hampered by a shortage of experts with requisite knowledge of the technology," Chris Mazzei, EY global chief analytics officer, said in the press release.