How tech professionals can survive and thrive at work in the time of AI
Despite a spate of fresh headlines about tech layoffs, opportunities for tech professionals are robust -- it's a matter of adapting to a fusion of new technologies and relentless business requirements. Overall employment of software developers, quality assurance analysts, and testers is projected to grow 25% from 2022 to 2032, "much faster than the average for all occupations," states an analysis out of the U.S. Bureau of Labor Statistics, issued in September 2023.
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"Increased demand for software developers, software quality assurance analysts, and testers will stem from the continued expansion of software development for artificial intelligence, Internet of Things, robotics, and other automation applications," according to the BLS statement.
But the potential quantity of tech-related opportunities is only one side of the story. What's really compelling is the quality of tech-related work we will be seeing in the very near future. Technologies such as AI and low and no-code platforms may be pushing away manual work in favor of higher-level tasks. Even those currently working with AI will be in a position to expand their skill bases.
Changes in skill requirements are borne out by research from LinkedIn's Economic Graph Research Institute. "We're in a period of rapid and continuous change in the skills required to perform our jobs," says Dan Brodnitz, global head of content strategy at LinkedIn Learning. Insights from LinkedIn's data suggest "more than half of LinkedIn members hold jobs that stand to be either disrupted or augmented by AI, and the skills required for our jobs will change by up to 65% by 2030," he relates.
With the onset of artificial intelligence and machine learning in day-to-day work, "certain technology-related skills are facing a paradigm shift," says Harshul Asnani, president of enterprise technology at Tech Mahindra. These include anything involving "repetitive and rules-based tasks, traditionally handled by humans. Basic data entry, routine coding for standard applications, and even some aspects of rudimentary data analysis are becoming automated through AI algorithms."
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In addition, over time, skills such as "code abstraction, code conversion, creating visual artifacts through coding, and basic code testing and QA are likely to become less relevant," predicts Asnani.
This does not necessarily mean such skills "will be eliminated, but rather transformed," he continues. Tech professionals "will need to adapt by focusing on more complex, creative, and strategic tasks that AI cannot easily replicate."
Technology-related skills coming to the forefront include "machine learning, data structures, and natural language processing," says Brodnitz. "These technical proficiencies form the backbone of AI applications and are crucial for efficient data handling, learning algorithms, and language-based AI interactions. It's important for IT professionals aiming to stay competitive in today's job market to build and strengthen these skills."
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Tech professionals "who understand how to think bigger and apply AI to make progress against strategic goals will be well-positioned to succeed," says Joe Bradley, chief scientist at LivePerson. "For example, customer service, sales, and marketing functions are increasingly supported by AIs that are customers' first lines of contact, staffing the digital front door. Just like websites are constantly optimized, customer-facing AI can be continually improved to support the goals of service, sales, and marketing leaders."
Important tools or platforms for designing, building, and managing 2020s solutions include "GitHub, Slack, Hugging Face, Reddit, and other existing open-source collaboration platforms," says Asnani. "These resources are useful in accelerating their learning by leveraging the technical knowledge and code contributions of others."
Along with designing and building AI, there is rising demand for human oversight to ensure that technologies deliver results that are trustworthy and meaningful to the business. "Fundamentally, AI and ML technologies deal with data; and to that end, people with data management and science skills will be in high demand," says Srini Kadiyala, chief technology officer at OvalEdge. "People must curate the specific data sets and sources required to fuel AI algorithms and learning models."
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Data preparation "is currently one of the most essential requirements for the efficient running and output from AI modules," Kadiyala observes. "While there is a degree of autonomy, in that AI can complete data preparation tasks, to ensure accuracy and compliance, it is still essential to have a trained professional take the reins in this area."
It's all about the increasing "democratization of both learning and technological development," Asnani states. "Professionals should cultivate the ability to learn, unlearn, and relearn quickly. Their learning scope should also encompass data structures and algorithms, data analysis, mathematics, and software engineering."
Tech professionals' jobs have already gone through major changes with websites, mobile apps, and social media, says Bradley. "They're going to be changed again, reshaped by the people who know where AI will be most effective, where humans need to hold the reins."
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Along with skills demands, new types of job titles are emerging. The LinkedIn platform has seen "an increase in AI-related titles," Brodnitz says. "For example, over the last five years, the number of companies featuring a dedicated 'head of AI' has more than tripled, indicating the growing recognition of AI's significance within organizations."
Additional emerging roles include "AI ethics specialist, smart contracts architect, blockchain network deployer, quantum computing engineer, and VR experience designer," says Asnani. "There is also a growing need for data privacy managers in response to heightened focus on data security and privacy regulations."
As organizations lean ever more heavily on their tech talent to deliver business results and growth, expect to see more movement away from heads-down programming and technical roles. As part of this trend, low-code and no-code platforms will define technology work in the year ahead. The growth of these technologies "is set to accelerate dramatically, propelled by advancements in AI and machine learning," says Asnan.
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This shift means "IT professionals will be relieved from the tasks of writing basic code, conducting code testing, and performing quality assurance." says Asnani. "Instead, their focus will shift towards validating the outcomes of code and ensuring they meet desired objectives and results. This transition necessitates a deeper understanding of specific domains and enhanced functional knowledge."
Going forward, success in technology work hinges on "intellectual fearlessness and curiosity," Bradley says. "Don't get locked into one way of thinking. Seek alternate approaches and perspectives. "Whatever your expertise, don't worry if it perfectly applies to your chosen field, but instead go deep, do it, and enjoy it."
Even if one is adept at AI development, there is still a lot to learn, he continues. For example, "Iyou're a machine learning expert, take a challenging course in product marketing. Don't cede absolute authority to other experts. Know enough and be curious enough to question them."