INSTRUCTIONS:

After reading the attached article, How To Reskill Your Workforce For AI (Artificial Intelligence).pdf your job is to provide a very brief summary, analysis, and evaluation. We can define these important concepts as follows:

• Summary- A formal, logical, consistent way of highlighting the main points.
  • Purpose:
    • In school - to quickly and accurately describe something you have read
    • In professional life - to provide a faster-to-read version of the material to other readers
    • In personal life - to reflect as accurately as possible on people, events, and one's memories of them.
• Analysis- A taking apart of something to show its parts or pieces, often using a special system, theory, or set of theories.
  • Purpose:
    • In school - to think more about a subject and/or to apply the methods of an academic discipline to a specific text
    • In professional life - to apply a system or idea to a specific situation so that others understand how to use something
    • In personal life - to examine one's own thoughts, actions, and motives logically and consistently from a variety of perspectives.
• Evaluation- A judgment of the value of a text to society or the quality of the way it is argued or organized.
  • Purpose:
    • In school - to show how well or poorly something has been done, or its effects on others beyond its main ideas
    • In professional life - to help decide who to hire, how well people are doing, and the quality and style of your own work
    • In personal life - to look not so much at the contents of one's own thinking and acting, but rather at the quality and value of that thinking and acting.

Each response should be no less than 500 words and no more than 750 words. You should provide in-text citations (APA style) and a reference page (APA style).

How To Reskill Your Workforce For AI (Artificial Intelligence).pdf

ARTICLE:

AI is considered the most disruptive technology, according to Gartners 2019 CIO Survey (it includes over 3,000 CIOs from 89 countries). So yes, this is big reason why there has been a major increase in adoption and implementation.

Yet there is a bottleneck that could easily slow the progress that is, finding the right talent. The fact is that there are few data scientists and AI experts available.

In our recent State of Software Engineer report, we found that demand for data engineers has increased by 38% and demand growth for machine learning engineers has increased by 27% in the last year, said Mehul Patel, who is the CEO of Hired. Based on data from our career marketplace, we believe the difficulty of recruiting for tech talent with specialized skills in machine learning and AI will continue to become increasingly competitive. Machine learning engineers are commanding an average salary of 153K in the SF Bay Area, which is nearly 20K above the global tech workers average salary.

Actually, this is why one approach is to acquire companies that have strong teams! This appears to be the case with McDonalds, which recently paid $300 million for Dynamic Yield. Its an AI company that helps personalize customer experiences.

But of course, this option has its issues as well. Lets face it, acquisitions can be difficult to integrate, especially when the target has a workforce with highly specialized skillsets.

So what are other approaches to consider? Well, heres a look at some ideas:

Automation: With the growth in AI, there has also been the emergence of innovative automation tools, whether from startups or even the mega tech operators. For example, this week Microsoft introduced a new set of systems to streamline the process.

The biggest and most impactful way that organizations can leverage their current team for data science is to implement a data science automation platform, said Dr. Ryohei Fujimaki, who is the founder and CEO of dotData. Data science automation significantly simplifies tasks that formerly could only be completed by data scientists, and enables existing resources -- such as business analysts, BI engineers and data engineers -- to execute data science projects through a simple GUI operation. Automation of the full data science process, from raw business data through data and feature engineering through machine learning, is enabling enterprises to build effective data science teams with minimal costs, using their current talent.

Now this does not mean that a platform is a panacea, as there still needs to be qualified data scientists. But then again, there will be far more efficiency and scale with AI projects.

If organizations have data scientists already, an automation platform frees up highly-skilled resources from many of the manual and time-consuming efforts involved, and allows them to focus on more complex and strategic analysis, said Ryohei. This empowers data scientists to achieve higher productivity and drive greater business impact than ever before.

Reskilling: If you currently have employees who are business analysts or have experience with data engineering, then they could be good candidates to train for AI tasks. This would include focusing on skills like Python and TensorFlow, which is a deep learning framework.

From a training and learning perspective, there are an abundance of online resources via Coursera, Udacity, open.ai, and deeplearning.ai that can help companies develop their employees AI/ML skills, said Mehul. Additionally, it will be valuable for a company to acquire someone with existing experience in AI to be a leader and mentor for developing employees. The interesting thing about AI/data science is that you don't need to be an experienced software engineer to do it. The field is so exciting because of the diversity of talent and backgrounds spanning science, engineering, and economics.

But the training should not just be for a small group of people. It should be company-wide. Without a data-driven culture and mindset, data science and AI cannot be truly implemented, said Ryohei. It is important for enterprise leaders and business teams to understand how to best work with the data science team to meet the organizations key business objectives. While the business stakeholders do not need to be data experts, they need to know How to use AI and How it changes their businesses.