Jude's AI Blog

The recent rise of artificial intelligence has changed the question from, "will they replace us in the workforce" to "when will they replace us in the workforce?" As organizations continue to adopt AI-driven tools and robotic process automation, many tasks once performed by humans are being executed faster, cheaper, and with greater accuracy by machines. From routine administrative work to complex data analysis, AI is reshaping the employment landscape and challenging the security of traditional job roles. While this shift presents opportunities for innovation and efficiency, it also comes with the inevitability of job displacement. With this in mind, does the government have a responsibility to play a role in reskilling initiatives, or is it all on the businesses? This is the question I asked ChatGPT, and this was its response: "Governments should indeed play a stronger role in reskilling initiatives, because the effects of automation and artificial intelligen...

 Automation presents considerable opportunities for both organizations and the workforce. By automating repetitive and time-consuming tasks, employees are able to focus on higher-value responsibilities that require human judgment, creativity, and problem-solving. This should be viewed as a positive in my opinion.  In addition, the advancement of technologies such as artificial intelligence, robotics, and data analytics is generating entirely new career fields, including AI development, data science, and cybersecurity. The demand is there for people who are willing to adjust their skill set. For businesses, these changes often translate into increased efficiency, improved productivity, and cost savings, while employees who are willing to adapt can gain access to more rewarding career paths. Despite these opportunities, automation also introduces significant challenges. Job displacement is a major concern, especially in industries where work is routine and easily automated. Wor...

The first four chapters of Discovering Data  have been an introduction to data. What data actually is, where it comes from, where it goes, and the reasons why it is valuable to businesses. Chapter five takes all this information and shows it in action. Data was used for marketing, predictive analytics, customer segmentation, supply chain management, and risk management for fictional companies. Although these are not actual statistics, my takeaway is that there is always a need for individuals who understand data and can utilize data analysis tools.  My other takeaway from the chapter is that business owners have more access to information about their operations, customers, and markets than ever before. With powerful data analysis tools available, owners are no longer limited to intuition or guesswork when making decisions. Instead, they have insights into customer behavior, sales trends, supply chain efficiency, and financial performance at their disposal. This wealth of data ...

 I prompted ChatGPT to identify potential risks associated with automation projects and the ways these risks can be mitigated. "Automation projects come with several potential risks that organizations must plan for carefully. One major concern is operational disruption, as new systems may interrupt workflows or cause errors during implementation. This can be mitigated by starting with small pilot projects, testing thoroughly, and scaling gradually while keeping manual backup processes in place. Another risk is employee resistance, since workers may fear job loss or struggle to adapt to new tools. Early involvement, clear communication about the benefits, and proper training can help employees embrace automation as a way to reduce tedious work rather than replace them. Cybersecurity threats are also significant, as automated systems can become targets for hacking or misuse. Strong access controls, encryption, monitoring, and regular audits are essential to safeguard data.  Comp...

 Chapters 5 and 6 of Automation with AI  continue to build on the concept of identifying tasks that are repetitive and error prone and implementing automation to do these tasks. Chapter 6 goes further into the implementation process and describes ways that businesses can smooth the transition into automation. Automation comes with controversy because it is associated with replacing people in the workforce. There is no denying it, RPA's and AI have already started replacing humans in certain areas of business, but there are plenty of jobs AI has created and/or increased the demand for. Machine learning engineers, data scientists, robotics engineers, are just a few examples. Any industry that has replaced human workers with AI or automation, has also created a job to work on or improve that technology.  The real-world case studies in chapter six have shown that integration of AI can be successful in various industries. AI is not a one size fits all asset for businesses eith...

 For this week's AI post, I prompted ChatGPT to compare the cost-effectiveness of human labor vs. automation over a 5-year period, for a logistics company. "When comparing the cost-effectiveness of human labor versus automation in a logistics company over a five-year period, the financial picture changes significantly once upfront investments and long-term savings are considered. Human labor carries recurring expenses such as wages, benefits, payroll taxes, training, and turnover costs. For example, a warehouse with 100 staff members earning $40,000 per year each would spend approximately $4 million annually on wages alone. Adding benefits and overhead increases this to roughly $5.2 million per year, or about $28 million over five years when factoring in modest wage inflation. Automation, on the other hand, requires a high upfront capital investment—around $12 million for robotic systems, integration, and setup—but the ongoing annual costs for maintenance, energy, software, an...

While I have been learning about Structured Query Language (SQL) in one of my other classes this semester, this is my first experience using it. I am glad that Chapter 4 of Discovering Data provides resources to use SQL and see how effective it is at searching through large, structured data. It is one thing to read about SQL, but I could not have given a good definition of what it does until I was able to try it out. Even as I was typing in the prompts provided by the book I was still coming back with syntax errors and other entry problems. To figure out why I was getting these errors, I would put my queries in ChatGPT and ask what I was doing wrong. ChatGPT would then walk me through what I was doing, and I was able to get a better understanding of the SELECT and FROM commands, and why I had to type them out so specifically.  I attempted the W3Schools SQL exercises and some of the exercises and quizzes on SQLZOO. It took around 30 minutes of my day, and I already have a much bette...

 Chapter 3/4 in Automation with AI  covered how different industries used software bots and RPAs in their operations. One industry that has been flooded with AI created content is social media. I was curious on how this process worked so I prompted ChatGPT with this, "How do AI social media posts work and how much money do they generate." "AI-generated social media posts work by using tools like ChatGPT, Jasper, or Canva to create captions, hashtags, images, and even videos. These tools can generate content based on trending topics, user prompts, or specific brand styles. Once the content is created, platforms like Buffer or Hootsuite can be used to schedule and automatically publish the posts. Some advanced tools even offer A/B testing to compare variations of a post and determine which one performs better. In some cases, AI bots are used to simulate engagement by liking, commenting, or following other accounts, though this is often against platform rules. Additionally,...

 If I was a business owner, I would absolutely be looking at ways software bots could help my company grow. Chapter 3 of Automation with AI  breaks down how different industries are applying Robotic Process Automation into their day-to-day operations. When industries that have little in common, banking and manufacturing are two that the chapter references, are able to use the same tool (RPA's) it is not something that can be ignored by any business. I was not aware that there were different kinds of bots for specific tasks. Knowing that now, I feel less intimidated about trying to learn how to use them. In fact, I think I would be doing myself an injustice if I did not try a platform like Automation Anywhere. The worst that could happen is I have a resume booster with experience on RPA platforms. No matter how long you have worked at a job, there will always be questions from bosses, coworkers, and customers that we might not know the answer to. This is a scenario where I feel...

As automation technologies continue to be integrated into our jobs and industries, the importance of learning how to adapt to the changes cannot be ignored. Let's face it, technologies such as software bots are able to do repetitive tasks more efficiently than humans can. I will give an example in my industry, accounting. QuickBooks now has software that links bank accounts and updates bank statements continuously. When it is time to reconcile the account, all I have to do is hit the reconcile button, and all the credits and debits are categorized, and the account is reconciled. Doing this manually, inputting all of the transactions, is time-consuming, and I am more likely to transpose or miss a transaction than the software. While the software is not always perfect, it saves the accountant's time and gets the job done accurately. That is just one example in the accounting field. This frees up our time to do other accounting tasks like tax planning or talking to clients. Moving...

At the end of Chapter 1 of Discovering Data , the readers are given a hands-on exercise to go on LinkedIn and search for job postings related to data analysis. While I expected there to be demand for this skill, I was blown away at the number of companies needing this position filled, the salary they were offering for this position, and the fact that some of the most well-known companies in the US were needing this position filled all around the country. Companies like Meta, PayPal, and Life360 are all offering jobs with six-figure salaries with titles like "People analytics" or simply "Data analytics". Businesses understand how important it is to not only have access to the data but to also have people who can interpret the data and use it efficiently. While having data is great, having analysts who understand the data is where you start to separate yourself from your competitors.   Data is described in Chapter 2 as an asset, and if you look at the different ways d...