When leaders respond to immediate panic, new business risks and mitigations often emerge. Two recent examples highlight the consequences of rushing to implement and publish positive results from AI adoption. The Wall Street Journal reported in April 2025 on companies struggling to realize returns on AI. Just weeks later, it covered MIT's retraction of a technical paper about AI where the results that led to its publication could not be substantiated. While these reports demonstrate the pitfalls of over-reliance on AI without common-sense guardrails, not all is off track in the land of enterprise AI adoption.

In this work, we reflect on the data-driven modeling paradigm that is gaining ground in AI-driven automation of patient care. We argue that the repurposing of existing real-world patient datasets for machine learning may not always represent an optimal approach to model development as it could lead to undesirable outcomes in patient care. We reflect on the history of data analysis to explain how the data-driven paradigm rose to popularity, and we envision ways in which systems thinking and clinical domain theory could complement the existing model development approaches in reaching human-centric outcomes. We call for a purpose-driven machine learning paradigm that is grounded in clinical theory and the sociotechnical realities of real-world operational contexts. We argue that understanding the utility of existing patient datasets requires looking in two directions: upstream towards the data generation, and downstream towards the automation objectives. This purpose-driven perspective to AI system development opens up new methodological opportunities and holds promise for AI automation of patient care.

The H-1B visa program was intended to bring in specialized talent from abroad, but instead it has become a tool for employers to hire lower-cost labor for ordinary jobs. The result is a distorted job market, where highly skilled workers are being squeezed out of the H-1B visa program by spam applications for ordinary workers who then take entry-level positions that are already in short supply. This misuse of H-1B visas has a negative synergy with growing impact of AI on the job market and is part of a larger problem that urgently needs attention. The impact of this visa-farming problem is particularly acute among young people and recent college graduates, who face a bleak job market despite moderate overall unemployment rates. According to government data, the ratio of unemployment for college grads under 25 to those over 25 has hit an all-time high of more than four to one.

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Last fall, Eric Klopfer decided to conduct an experiment in his undergraduate computer science class at the Massachusetts Institute of Technology (MIT). He divided the class into three groups and gave them a programming task to solve in the Fortran language, which none of them knew. One group was allowed to use ChatGPT to solve the problem, the second group was told to use Meta's Code Llama large language model (LLM), and the third group could only use Google. The group that used ChatGPT, predictably, solved the problem quickest, while it took the second group longer to solve it. It took the group using Google even longer, because they had to break the task down into components.

As many as 300 million full-time jobs around the world could be automated in some way by the newest wave of artificial intelligence that has spawned platforms like ChatGPT, according to Goldman Sachs economists. They predicted in a report Sunday that 18% of work globally could be computerized, with the effects felt more deeply in advanced economies than emerging markets. That's partly because white-collar workers are seen to be more at risk than manual laborers. Administrative workers and lawyers are expected to be most affected, the economists said, compared to the "little effect" seen on physically demanding or outdoor occupations, such as construction and repair work. In the United States and Europe, approximately two-thirds of current jobs "are exposed to some degree of AI automation," and up to a quarter of all work could be done by AI completely, the bank estimates.

As artificial intelligence and automation technologies continue to improve, they will become more important in driving the growth of new industries that are based on data. Artificial intelligence is the development of computer systems that are able to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and problem-solving. AI systems are typically designed to be able to learn from experience, adapt to new inputs, and improve their performance over time. Automation, on the other hand, refers to the use of technology to automate tasks that were previously performed by humans. This can include everything from simple tasks like data entry to more complex tasks like driving a car or managing a supply chain. Automation can be powered by a variety of technologies, including AI, robotics, and machine learning.

The future belongs to intelligent automation. With that said, organizations adopting artificial intelligence (AI) or giving it a top priority in their digital transformation initiatives will be the leaders for tomorrow. AI adoption comes with its own set of challenges and opportunities. One of the most common misnomers of AI adoption is that it poses a threat to jobs, which cannot be farther from the truth. It's time for organizations to embrace AI as "machines for humans" instead of "humans vs. machines." AI can be used to perform specific tasks that generally require human intelligence.

Manual and automated software testing are the two types of testing available. Some manual testing techniques, such as discovery and usability testing, are quite beneficial. Other types of testing, such as regression and functional testing, can be done manually, but it's wasteful to practice for people to repeat the same task repeatedly. Test automation is ideal for this kind of repeated testing. So, before we learn more about AI automation, let's define test automation.

As AI improves and becomes more powerful, its impact on the world economy will become vastly more significant. It will affect virtually every aspect of the world economy -- from unemployment rates to economic growth, productivity, income inequality and more. Some argue that so far, AI has not had a large enough impact, but as its development accelerates, its effects will grow exponentially. Whether we like it or not, automation and job displacement are already here, slowly pushing the human workforce into different domains. Similar patterns can be found throughout history; new technology made certain products and jobs obsolete, and eventually humans were forced to switch to more innovative products and new jobs.