Unlocking the next era of IT talent: Impact of AI on the future technology workforce

Although artificial intelligence (AI) has existed for the past few decades and has been at the forefront of technology in digital transformation, the introduction of OpenAI’s ChatGPT has brought to light AI’s true potential, particularly in generating human-like content and emulating human creativity, revolutionising various aspects of human life and work. This shift has led to a spike in interest and investment from organisations across the globe. The rapid evolution of AI technology has ushered in a new era of technological advancement and is poised to have a profound role in transforming industries and reshaping the future of the workforce.

AI’s ability to process vast amounts of data, learn from it and perform tasks with increasing autonomy is metamorphosing industries across the board. From banking, financial services and insurance (BFSI) to healthcare, human resources (HR) to marketing, AI is not only streamlining operations but also augmenting human capabilities. This blog delves into the intricate interplay between AI and the future technology workforce, examining the challenges, possible remedies and opportunities that lie ahead.

Exploring the evolution of the IT skill landscape with the increasing adoption of AI

• The rapid adoption of AI has given rise to an unprecedented demand for traditional data and AI-related roles.
  Key roles, including data scientists, data engineers, AI/machine learning (ML) engineers and AI-ops engineers, have become the linchpins for businesses seeking to leverage AI to enhance their operations and decision-making processes. The data scientist’s expertise in analysing complex datasets and extracting valuable insights, the data engineer’s role in building and maintaining robust data pipelines, the AI/ML engineer’s ability to design and develop AI algorithms, and the AI-ops engineer’s proficiency in deploying and monitoring AI systems are all pivotal in the integration of AI into an organisation.
• Niche and specialised roles are emerging in AI to address the unique demands and intricacies of AI applications in different industries and functions.
  The increasing complexity and diverse use cases of AI necessitate professionals who can provide specialised expertise. These positions, such as Natural Language Processing (NLP) engineers, prompt engineers, AI research scientists, computer vision engineers, generative AI engineers, AI ethicists and AI auditors have become the vanguards of innovation within the AI domain. NLP engineers, for instance, focus on developing AI systems capable of understanding and generating human language, a skill crucial in applications like chatbots, sentiment analysis and language translation, whereas prompt engineers specialise in crafting prompts for AI models, enhancing the model’s ability to provide more precise and tailored responses.

Roadblocks ahead in the form of skill shortage

• The talent shortage has emerged as a key impediment to AI’s pervasive and sustainable adoption.
  The rapid growth in demand for AI professionals, from data scientists to machine learning engineers, has far outstripped the supply of qualified individuals, and with this demand-supply gap for key advanced analytics and AI roles as high as 30-35%, it is difficult to bridge the gap in the near term. For instance, among all global companies that specialise in building AI-based solutions, less than 1% claim to have a holistic talent base for building generative AI solutions.
• The evolving landscape of AI is ushering in new skills and roles at a rapid pace highlighting the need for adaptability and flexibility.
  As AI is still in the nascent stage and continues to grow at a faster pace there is uncertainty about which roles or skills will become more important in the future. Organisations need to monitor the market trends and ongoing skill shifts closely to adapt very fast and stay ahead of peers. For example, a year ago roles like prompt engineer and generative AI developer/engineer were non-existent in the market, but now these roles have become forefront of driving innovation through generative AI.
• AI not only leads to the emergence of new roles and skills but also poses the prospect of rendering existing skills obsolete and underscoring the demand for an AI-literate workforce.
  As AI technology continues to advance, it brings forth a dual challenge for the workforce. On one hand, it generates opportunities by creating new roles and demanding fresh skill sets that cater to the development, implementation, and maintenance of AI systems. On the other hand, AI poses a formidable challenge by potentially making existing skills and jobs obsolete. As AI becomes more adept at automating routine, repetitive or rule-based tasks, it could lead to job displacement in certain industries. This highlights the need for continuous upskilling and reskilling efforts at a scale and speed. Organisations need to prepare professionals to embrace the culture of lifelong learning, ensuring they remain agile and relevant in the AI-driven future.

Key considerations to overcome these challenges

• Organisations should strengthen their sourcing capability to expand the horizon of recruitment and enhance access to the untapped talent pool.
  Diversified sourcing channels have a pivotal role to play in swiftly alleviating the talent shortage issue in the near term. This may enable organisations to focus beyond their domestic talent pool and look for cross-border talent. It will help them to tap into a larger global talent pool to source quality talent for high-demand roles to combat talent shortages.
• Organisations need to adopt a data- and AI-driven approach to drive precision in skilling and build personalised career and learning pathways to lay the foundation for the culture of lifelong learning.
  While hiring from the external market may provide some respite in the near term, in the long-term the key strategic priority for organisations should be strengthening their learning and development ecosystem to build a diverse talent pool. Along with in-house skilling efforts, organisations need to partner with third-party platforms and technology providers to offer employees access to a wide range of courses and technology.
• As AI continues to advance and become an integral part of our daily lives, it’s imperative to cultivate a diverse AI workforce to reduce bias in AI-driven technology.
  A diverse team brings together individuals with unique backgrounds, perspectives and experiences, which, in turn, helps identify and rectify biases in AI algorithms, thus fostering fairer and more inclusive technology. Organisations should democratise access to learning and development opportunities to build a diverse talent pool that includes women, early career talents and older individuals, ensuring that everyone, regardless of age, gender or background, has an equal chance to learn, grow and contribute to the collective and sustainable future with AI.

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