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Apr 1 2021

Machine Learning Is Everywhere (But Where Are the Workers?)

Signature Consultants General

Machine learning has come a long way since the term was first coined in 1959 by computer gaming and AI pioneer Arthur Samuel—and its growth has accelerated in the last decade, particularly as deep learning has opened up machine learning to many software services and applications in our day-to-day lives. Machine learning has now entered the mainstream, and it’s enabling all types of businesses to do things that were only a figment of someone’s imagination—or a sci-fi novel—in the not-so-distant past.

Machine learning is a segment of artificial intelligence (AI) that’s focused on building applications that “train” algorithms, or statistical engines, to find patterns and features in huge amounts of data. As the algorithms learn from data, they are able to make increasingly accurate outcome predictions and decisions over time. The better the algorithm, the more accurate the predictions will become.

The Expanding Value of Machine Learning

Machine learning is everywhere—and it’s not taking a backseat in the AI realm anytime soon. Digital voice assistants such as Apple’s Siri, Google Assistant, and GPS technology are increasingly becoming a part of our homes, our cars, and even our fitness routines, and they’re using machine learning and natural language processing, a machine learning application, to improve their results. News and media platforms like Spotify, Netflix, and Amazon use deep learning models to curate personalized lists and recommendations based on what a user, or someone in their network, has liked or played. And machine learning models are being employed for everything from hyper-personalization to fraud detection, cybersecurity protection, medical image analysis, chatbots, facial and voice recognition, and self-driving cars.

Global spending on artificial intelligence (AI) is forecast to double over the next four years, growing from $50.1 billion in 2020 to more than $110 billion in 2024, according to the International Data Corporation. As big data continues to expand and computing becomes both more powerful and more affordable, and data scientists continue to develop better algorithms, machine learning will continue to drive more and more efficiency in our work and home lives. As Sri Elaprolu, senior leader at Amazon Machine Learning Solutions Lab, says, “The Cloud enables extremely low-cost compute and storage, which opens up opportunities for more modeling. There’s lots of innovation yet to happen. We are barely scratching the surface.”

Machine learning’s fast-growing adoption across all kinds of industries is a testament to its ability to quickly solve complex human problems. It’s bringing improvements to all types of industries, including manufacturing, government, marketing, healthcare, finance, banking, science, and education. The predictive behavior created by machine learning is enabling real-world problem solving that’s extending beyond day-to-day business to medical and environmental breakthroughs, such as cancer detection. Leading-use cases for AI include automated customer service agents, sales process recommendation and automation, automated threat intelligence and prevention, and IT automation, according to the IDC—and use cases are growing all the time.

Machine learning can give businesses increased visibility and insights into data;  improve efficiency of internal and external processes (such as optimizing a company’s value chain and operations); help companies better understand prospects and customers, more closely target their services to what they need, and improve customer experience; reduce costs; and fuel innovation. By effectively employing machine learning in their organizations, businesses can gain a competitive edge in an economy that’s not waiting around for AI to make its mark. “Companies will adopt AI—not just because they can, but because they must,” says Ritu Jyoti, program vice president, Artificial Intelligence at IDC. “AI is the technology that will help businesses to be agile, innovate, and scale.”

COVID-19 is driving investment in AI, with 86 percent of organizations in S&P Global’s “Vote AI ML Use Cases 2021” survey saying the pandemic has or will cause their organizations to invest in new AI initiatives. It’s no surprise, then, that “AI and Machine Learning Specialists” is the number 2 role seeing increasing demand across industries, surpassed only by “Data Analysts and Scientists,” according to The Future of Jobs Report 2020. Many other AI and tech-related roles are among those seeing the most increasing demand, which, World Economic Forum writes, reflects the acceleration of automation as well as the resurgence of cybersecurity risks.

An Urgent Need for Skilled Workers

There is, however, a caveat. This explosive growth requires an abundance of support—which includes talented workers to fill necessary roles as machine learning models expand. Many companies are struggling to find the right skills for the research, production, and adoption phases of machine learning. Simply having large stores of data does not always equate to successful AI algorithms; in fact, data can be a major hurdle in planning and adopting an AI strategy. According to a Rackspace survey of 1,870 IT leaders in various industries, data quality remains the top barrier in terms of using machine learning to extract valuable insights—and getting access to quality data is not always easy. This is especially true in the industrial, health, and government sectors, where data is often scarce or subject to strict regulations.

Thirty percent of respondents in the Rackspace survey said a lack of the capabilities or talent to effectively manage their data was their biggest barrier to extracting actionable insights from machine learning models. Data engineering problems such as data and software being stored in separate silos in an inconsistent way, and in incompatible frameworks and models are also major barriers to successful machine learning implementation, and technology itself won’t dig a company out of these holes. The right people must be in place to identify and thwart roadblocks before they have a real chance of hindering a company’s success.

In addition, 27 percent of respondents in The Future of Jobs Report 2020 said that skills gaps in the labor market, and an inability to attract the right talent (such as data scientists and machine learning engineers who can build AI models) were their biggest barriers to AI and machine learning adoption. When business leaders were asked to rate the ease of finding skilled employees across a range of new, strategic roles, AI and machine learning specialists, data analysts and scientists, and software and application developers were consistently among the roles they said they had the most difficulty hiring.

In-Demand Skills and Roles in Machine Learning

There is, however, good news for both employers and candidates. According to The Future of Jobs Report 2020, many transitions into emerging positions in data and AI don’t require a full skills match between the source and destination occupation—widening the talent pool to more qualified candidates who aren’t necessarily coming from a traditional AI or data science background. Skills traditionally needed for machine learning are shifting as use cases become more mainstream. Anand Rao, global artificial intelligence lead at PwC, cites a growing need for operational machine learning skills to support projects.

It’s clear that the right talent must be in place to make machine learning work in real-life applications—and that a lack of talent can have detrimental effects. Every machine learning solution is designed, built, implemented, and optimized by a team of highly trained professionals. They include software engineers, machine learning engineers, machine learning scientists, applied scientists, data scientists, data engineers, development managers, cloud architects, and technical program managers—and those job titles are expanding. The roles popping up, as well as the skills needed for them, are becoming more diverse. While data scientists are still in short supply, other roles are rising in demand. The increasingly multidisciplinary nature of AI and machine learning is reflected in the variety of roles that companies are recruiting for, writes Nick Patience, lead analyst for AI and machine learning research at 451 Research. These roles, he says, require a mix of machine learning skills, general IT skills, and an understanding of particular domains of the business. With the influx of AI and machine learning into the mainstream workplace, IT professionals must be able to determine which data has value, and how that value can be applied to real business problems. Soft skills such as critical thinking and analysis, problem-solving, and self-management skills like resilience, stress tolerance, and flexibility are increasingly being sought out by employers, as the in-demand skills across jobs continue to change over the next five years, according to World Economic Forum’s Future of Jobs Report 2020.

A trend toward smarter, more responsible, scalable AI at organizations is, as Gartner put it, a “mission-critical investment” as we move toward a post-pandemic world, and it requires AI researchers, developers, and engineers with the right mix of hard and soft skills and the ability to support ethically responsible, resilient AI solutions.

Preparing for a Post-Pandemic Future

Some companies are already putting plans in place—and employees in a place to succeed—to prepare for an increasingly AI-powered workplace.  JPMorgan Chase, for example, has announced a $350 million investment in re-skilling related to AI-related job changes to help workers gain needed AI skills in their existing jobs or transition to new ones.

Many companies are also leaning on outside experts to help fill their skill shortages with AI talent. The push to invest in digital transformation as a result of COVID-19 will require a mix of IT experts, including project consultants, in order to be successful and get ahead in a post-pandemic economy. Consider both re-skilling your employees and bringing in an expert partner to fill your existing technology talent gaps and plan for future projects.

Good talent is hard to find, but it’s also essential for successful machine learning implementation and adoption. At Signature, we’ll match you with expert IT consultants who have the talent and breadth of experience to see your AI and machine learning goals through. Lean on us to help you put the right team in place.

About Signature Consultants, LLC

Headquartered in Fort Lauderdale, Florida, Signature Consultants was established in 1997 with a singular focus: to provide clients and consultants with superior staffing solutions. For the ninth consecutive year, Signature was voted as one of the “Best Staffing Firms to Work For” and is named the 15th Largest IT Staffing Firm in the United States (source: Staffing Industry Analysts). With 29 locations throughout North America, Signature annually deploys thousands of consultants to support, run, and manage their clients’ technology needs. Signature offers IT staffing, consulting, managed solutions, and direct placement services. For more information on the company, please visit https://www.sigconsult.com. Signature Consultants is the parent company to Hunter Hollis and Madison Gunn.

Signature Consultants General