The AI Technology Workforce is Exploding

ICYMI, Artificial Intelligence (AI), in all its forms, is poised to become the foundational technology tool for many (if not most) organizations. It’s already taking over many, many mundane tasks and duties, freeing human workers to focus on more enterprise-centric concerns. Looking ahead, advances in AI computing and programming (most of which are accomplished using the AI software itself) promise even more opportunities for companies to reduce costs, improve performances, and ramp up profitability.

However, those organizations will need a well-trained AI workforce if they intend to accomplish those goals, and that workforce requires advanced AI training. According to Gartner, evolving AI training capacities will not only sharpen the emerging AI programming workforce but up to 80% of all current software designers and developers will also need additional training to become truly competent using the novel AI programming options.

Further, the growth of the AI programming population will be critical to the success of the economy – any economy – because so many of the industries that drive the economy will be embedding the new AI capacities into their regular business activity cycles. Consequently, companies that don’t invest in AI strategies risk losing market share to organizations that embrace this completely unique opportunity.

AI tech use cases emerging in the healthcare and manufacturing sectors provide examples of why AI is already – and will become more so – the basis of today’s and tomorrow’s corporate and economic success.

AI in Healthcare

Technology is already at the heart of the functionality of America’s healthcare system. The 2010 passage of the Affordable Care Act (ACA) mandated that all Americans have access to safe, private, and comprehensive healthcare options, regardless of pre-existing conditions or other factors that had previously eliminated some of the population from accessing medical services. A significant aspect of the law requires that medical providers document their activities per patient and share that data as necessary among the patient’s medical team. Advanced technologies provided the tools needed to accomplish these mandates.

The emergence of AI expands the use of technology to accomplish a variety of healthcare goals, including enhanced patient care, more comprehensive diagnostics, and accelerating drug development, to name just three:

• Enhanced Patient Care – In some cases, AI programs in the form of ‘virtual health assistants’ can help patients find answers to medical questions simply by logging into the accompanying application. One app, Ada, was developed by doctors to review symptoms and provide guidance to patients who don’t have or can’t access traditional medical facilities. The program collects patient information, scans billions of records, and directs the user to information relevant to their concern. Since 2016, the Ada app has offered diagnostic assessments in multiple languages to over 30 million users.
• Doctors are using AI-assisted programming, too, often to create and track patient-specific treatment plans and strategies. The software analyzes both patient data and the vast database of potential medical information to detail a healthcare plan specific to the individual. Using that information, the program (and its human doctor affiliate) can optimize dosages, suggest multiple or alternate therapies, and even ward off complications by predicting and preventing possible side effects.
• The use of data-driven analysis is already fully vested in the medical imaging arena. Traditional and AI software programs now routinely scan X-rays, MRI scans, CT scans, and more to find anomalies that are too small or too well hidden to be detected by the human eye. This style of technology is most helpful in areas where emergency medical services are scarce or non-existent.

By all accounts, AI technology will continue to enhance and advance medical care for the foreseeable future across all healthcare industry sectors.

AI in Manufacturing

Technology in the manufacturing sector isn’t new – robots have been building cars for decades, and more recent innovations in predictive analytics and machine learning have improved their performance and profitability significantly. However, even as those robots have been advancing in their capacities, AI just expands their capabilities even further:

• Cobots are becoming all the rage on the manufacturing floor. These “collaborative robots” – ‘cobots’ – are smaller and lighter than their traditional robot siblings, so they are more adaptable for smaller, more precise functionality. Their size and shape facilitate use cases even on small workbenches or in tight quarters; their programming software allows for swift and easy reprogramming to accomplish other tasks. Their biggest asset is their capacity to provide accurate, detailed replication of minute or tiny tasks that are impossible for a larger robot to perform and too delicate to trust to a human hand.
• Additive Manufacturing (AM) has replaced ‘3D printing’ as the name of this unique manufacturing service. Also not new (the 3D printer was invented in 1981), AM is now making inroads across multiple industries, offering a more controlled design and development option to the legacy system of metal machined parts. Using these sophisticated printing machines to devise the precisely right tool or part for the purpose, then reproduce it as needed. While this manufacturing adjunct is not as widely embraced as other technologies (so far), the inclusion of AI into its methodology and accuracy will enhance its capacities to identify key variables, optimize processes, and improve efficiencies.
• Predictive Maintenance – Along the same line, AI will also allow even more efficiency and accuracy in predictive programming, such as those programs that scan for predictive maintenance metrics. A typical cost factor in all companies is the need to maintain and sometimes replace machines that are failing due to age, usage volume, or other reasons. Too often, a complete mechanical failure within any system will stop production altogether while waiting to complete expensive emergency repair or replacement activities. Adding AI to predictive maintenance software programming facilitates a more comprehensive oversight of the system as a whole and each of its parts separately. Using it allows leadership to schedule repairs/replacements during downtimes or in off-cycles, reducing the risk of loss due to machine failure.

As revealed by the examples above, AI is fast becoming the software of choice across these and other industrial sectors, making its adoption and usage almost required for companies seeking to master their markets. To ensure the achievement of that goal, these organizations will need to hire and train an AI-focused workforce and enhance the AI and technology skills of their existing staff. There’s been no better time than now for learners looking for a career to find the training and occupations they desire through an AI and technology-focused education program.