The last few years have seen tremendous advancement in the field of AI and ML. This is largely driven by the ability of AI and machine learning techniques to exhibit promising results in multiple areas across industries from retail, pharmaceuticals, to e-commerce.
However, every business looking to introduce artificial intelligence into their operations ponders the larger question of whether to 'build' or 'buy' their AI capabilities. Although most of these organizations are excited and aware of the value that AI and machine learning (ML) could offer for their business, they have little or no idea about the best ways of making this success a reality.
With growing awareness and expectations of AI/ML technology, the issue of the AI skills gap has been increasingly discussed and documented in recent times.
This blog aims to explore the impact of investing in AI technology/professionals, how an AI skills gap can impact businesses, what are the important skills needed by AI/ML professionals, and what organizations can do to build a future-ready AI workforce.
Why Does Each Organization Need to Invest in AI and AI Professionals?
Artificial Intelligence (AI) needs no introduction today. It is a cutting-edge technology that aims at imbibing human intelligence into machines.
Likewise, Machine Learning (ML), an important sub-branch of AI, trains machines to learn, adapt and conclude based on experience instead of a pre-defined set of rules. This enables them to come up with various analytical/statistical models and make them capable of mimicking human actions.
There are various ways organizations can reap benefits by investing in AI/ML technologies as well as hiring AI professionals for their business processes.
Some of these are listed below:
Efficiency and productivity gains are among the key benefits of implementing AI within a business. This is simply because technology handles various day-to-day tasks at a pace and scale that humans can't match.
Besides, by eliminating mundane tasks from your staff's responsibilities, AI allows these employees to move to higher-value tasks that technology can't assist with, thus helping businesses to minimize the costs associated with performing mundane, repeatable jobs.
New capabilities and business model expansion
Professionals trained in AI capabilities can deploy data and analytics into the business, thus opening up various new opportunities to participate in different areas.
For example, autonomous vehicle companies could identify new revenue streams related to insurance with all the data they collect.
AI can take in and process massive amounts of data in real-time, thus enabling organizations to implement near-instantaneous monitoring capabilities such as recommending actions, initiating responses, and more.
AI's monitoring capabilities can also be effective in several other areas, such as enterprise cybersecurity operations, where massive amounts of data need to be analyzed and understood.
In addition to the advantages mentioned above, AI can also facilitate numerous industry-specific improvements, such as:
- The pharmaceutical sector can use AI technology to perform drug-discovery data analysis and accurate predictions that otherwise can't be done with conventional technologies
- Retailers can use AI/ ML technology to target their marketing efforts better, develop a powerful and efficient supply chain, and better calculate pricing for optimal returns
- The financial industry can use AI/ML technology to understand and strengthen its fraud detection efforts better.
How the Widening AI Skills Gap can Affect Organizations
While an increasing number of organizations are fully invested in AI today, more than half of them don't have the required in-house skilled talent to execute their AI strategy.
A recent study also suggests that even though over 93% of US and UK organizations consider AI to be a business priority, most of them don't have the right mix of skilled AI talent in-house to bring their plans and strategies to life.
One of the key reasons for this AI skills crisis is that today's academic and training programs can't keep up with the pace of innovation and discoveries with AI/ML technology.
Apart from requiring official training, AI professionals also need robust on-the-job experience. This is why there aren't enough experienced AI professionals to step into the leadership roles required by most organizations that are just beginning to adopt AI strategies into their operations.
This acute lack of skilled talent is considered the main barrier to progressing their AI initiatives, followed by aspects such as lack of access to the right technology, shortage of budget, and lack of access to useful/relevant data.
While there is no doubt that artificial intelligence, as a technology, has the potential to change organizations for the better, for that to actually happen, business leaders around the world will have to shift their focus on how people are reskilled to get the maximum out of these emerging technologies.
The shortage of AI skills is seen as a major barrier to the pace of technology adoption in any organization. A recent poll also confirmed that 56% of senior AI professionals are of the opinion that a lack of additional, qualified AI professionals was one of the main hurdles to be overcome in terms of achieving the required level of AI implementation across business operations.
Between upskilling internally and recruiting externally, organizations are often left with inefficient and impractical methods for sourcing AI talent. The need here is to democratize AI as a technology, lacking which the future of AI will largely shift simply to low-code or no-code technology.
Overall, although a huge demand already exists for AI skills, the shortage of AI talent is slowing down hiring significantly. Without new/qualified AI hires, organizations simply cannot move forward and implement their AI strategies.
Skills Needed by AI and ML Professionals
AI engineering is one of the most popular and rapidly developing career fields that can provide an abundance of opportunities in the future.
The basic qualification required to become an AI engineer is a bachelor's degree in a related field such as computer science, information technology, statistics, or data science.
After gaining a bachelor's degree, candidates also have the option to pursue a postgraduate degree specializing in the specific field of AI. Besides that, earning certifications in machine learning, data science, and deep learning can be very beneficial in a job search in the field of AI and can give candidates a comprehensive understanding of relevant concepts.
In terms of the key skills, professionals need to take advantage of the growing opportunity to build great ML/AI solutions, including:
To become an expert In AI and machine learning, professionals need to have experience with programming languages.
Exploratory data analysis
To be able to perform exploratory data analysis on a dataset is one of the most important skills for AI/ML professionals because it allows them to discover relevant and interesting patterns in data, identify anomalies, and test hypotheses.
Knowledge and specialization in AI are must-haves if professionals seek a successful career in AI.
AI professionals must have expertise in machine learning, neural networks, deep learning, etc. Having this kind of domain knowledge helps them understand the industry better in terms of the risks/challenges that they need to address.
Statistics and probability distribution
Statistics form the very base of data science, which is an integral part of AI/machine learning.
AI/ML engineers, therefore, must be proficient in probability concepts, including conditional probability, Markov models, etc., and also need to be aware of univariate as well as multivariate statistical analysis, which forms the base of most ML techniques.
Data modelling and model validation
Proficiency in data modelling techniques is another essential skill needed by AI/ML professionals as they are extensively used in machine learning to identify valid patterns and classifications in datasets.
Neural network architectures
AI and machine learning are used for various complex tasks that are beyond human capability to code.
Understanding neural networks is an important skill to manage these tasks and are considered the most precise way of countering several problems such as speech recognition, translation, and image classification.
Applied math and algorithms
AI professionals need to have a solid foundation and expertise in algorithm theory as this is one skill set that enables understanding subjects such as gradient descent, quadratic programming, partial differential equations, convex optimization, and summations.
How Organizations Can Build an AI Future-Ready Workforce
There's no single way to solve the AI skills gap crisis, and it will require a multipronged approach over an extended period of time.
While there is no simple and easy solution to navigate this challenge, organizations that plan to use AI today or in the future must thoroughly consider how they will deal with the AI talent gap.
Some of the ways organizations can build a future-ready AI workforce include:
- Investing in their employees to help them adapt to the latest AI/ML technologies by enrolling them in various upskilling and reskilling programs. Incorporating AI/ML courses in the curriculum can be an excellent way to make the workforce future-ready.
- Organizations should offer their IT workforce personalized training to democratize learning
- AI education should start early on and take place at every education stage. Academia, organizations, and public sector officials need to work together and ensure comprehensive AI curriculums are available
- One of the other ways to address the AI skills gap is to increase resources for digital, math, and technical education at all levels
Continuous education of current employees is an ongoing challenge for most organizations. Different operations/mechanisms are required to address these concerns. One of the critical factors here is to increase management awareness and knowledge regarding the opportunities AI will bring and navigate your L&D program for AI professionals accordingly.
Major Application Areas of AI and ML in Future Everyday Jobs
Some of the sectors where AI is likely to have a strong impact in particular include:
AI is tremendously impacting the world of transportation and automobiles with the advent of autonomous vehicles and autonomous navigation.
Artificial Intelligence has a range of diverse applications in the healthcare sector. With the medical industry exploding with a robust amount of data, the same can be utilized to create predictive models related to healthcare. Other AI applications in healthcare include:
- To build sophisticated machines that can help detect diseases and identify cancer cells
- To analyze chronic conditions with medical data to ensure early diagnosis
- AI can also use a combination of historical data and medical intelligence for the discovery of several new drugs
AI is likely to play a pivotal role in e-commerce in the future, in almost every aspect of the industry, from user experience to fulfillment and distribution. Some of the other AI applications in e-commerce are:
- AI-powered assistants
- Personalized shopping
- Fraud prevention
Robotics is another area where artificial intelligence applications are most commonly used. Powered by AI, robots typically use real-time updates to identify any kind of trouble or obstacles in their path and instantly pre-plan their journey. Some of the AI applications in robotics include:
- Inventory management
- Transporting goods to factories and warehouses
- Cleaning offices/large equipment
Apart from these, AI technology has a range of applications in sectors such as finance, gaming, education, chatbots, travel, and more.
The AI/ML industry is booming with opportunities today. However, the market is still in a nascent stage, with a huge demand for a skilled workforce. This makes it essential for both employers and employees to put in the time and make sustained efforts to bridge the skill gaps and take the AI/ML industry to the next level.
When creating an L&D program for AI professionals, organizations need to keep in mind the difference between good and great AI talent in terms of strong communication/analytical skills, business acumen, and intellectual curiosity. By approaching the AI and ML skills gap with a fresh perspective, any organization can find the right talent to successfully move AI adoption forward.