The rise of robotics and artificial intelligence (AI) is a significant development for the workforce. The shortage of skilled professionals across industries has made automation increasingly appealing to employers. It is evident that automation and AI have the potential to replace a considerable portion of tasks currently performed by humans.

Reactions to this trend are varied, ranging from optimism to apprehension. An Oracle study reveals that 93% of individuals would readily follow instructions from robots in the workplace, demonstrating a high level of trust in automation. Ericsson’s 2019 trend forecast includes the concept of “mind gyms,” where 31% of consumers anticipate engaging in cognitive training to enhance their thinking abilities as daily decision-making becomes progressively automated. However, there’s also a degree of negativity surrounding these advancements.

Lieu Yew Fatt, Managing Director of Omron Electronics, notes that some companies face internal resistance to automation from employees who perceive it as a threat to their jobs. Simultaneously, businesses grapple with a skills gap in cutting-edge technological fields such as robotics, data science, and AI.

Ronen Naishtein, General Manager for Asia, Hong Kong, and Taiwan at Oracle NetSuite, emphasizes the importance of workforce preparedness in the successful integration of AI. He stresses the need for organizations to address potential AI skills gaps by investing in training programs.

Manish Bahl, Assistant Vice President of the Center for the Future of Work at Cognizant Singapore, echoes the sentiments of Lieu and Naishtein. He observes that while businesses in the Asia Pacific region have begun exploring the potential of AI to enhance productivity, profitability, and engagement, they face a substantial challenge in striking a balance between AI adoption and the evolving relationship between humans and machines. Equipping employees to work effectively alongside intelligent machines is not merely an optional strategy, but a critical requirement for maintaining a competitive edge in the digital era.

The ideal scenario involves a symbiotic relationship between humans and AI. Dan Sommer, Senior Director and Global Market Intelligence Lead at Qlik, highlights that AI is more likely to create jobs than eliminate them. He points out that the disparity between data generation and human capacity to process and act upon it is frequently overlooked, as is the availability and adoption of advanced analytical tools. Bridging these gaps will ultimately empower the workforce.

Equipping both the current and future workforce with skills aligned with the age of AI is paramount. Bahl emphasizes the importance of fostering a lifelong learning mindset among individuals. He argues that educational institutions and businesses should prioritize skills-based learning and adapt their curriculums and training programs to incorporate relevant content for both academic pursuits and on-the-job training.

One way to thrive in this evolving landscape is to emphasize higher-value skills, particularly in data analysis. Chua Hock Leng, Managing Director for Singapore at Pure Storage, asserts that roles such as data scientists and AI specialists will become indispensable, offering a significant competitive advantage. He anticipates increased collaboration between businesses and educational institutions to bridge the talent gap in these domains.

Leslie Ong, Southeast Asia Country Manager at Tableau Software, observes that LinkedIn’s recent reports on emerging jobs in various Asia Pacific markets consistently rank “data scientist” among the top five in demand. He attributes this to the growing recognition of data’s value across industries. However, Ong also points out that while job requirements are evolving to include data literacy, a PwC study reveals that only one in five workers in the Asia Pacific region possess this skill. He underscores the need for joint efforts from both the private and public sectors to upskill the existing workforce and equip the next generation with essential data skills.

Ben Elliott, CEO of Experian Asia Pacific, agrees that data science will continue its ascent, with companies placing greater emphasis on data analytics capabilities as data volumes expand exponentially. He emphasizes the need for ongoing advancement, upskilling, and retraining initiatives to keep pace with data science’s mainstream adoption.

The future workforce will consist of formally trained data scientists and individuals who acquire skills through on-the-job learning. Sumir Bhatia, President of the Asia Pacific Data Center Group at Lenovo, predicts the rise of “citizen data scientists”—individuals without formal data science credentials who leverage accessible data and AI platforms to generate insights. He also anticipates the emergence of new roles, such as machine learning engineers, and increased collaboration between business and IT departments, particularly in organizations that may not have the resources to assemble large teams of data scientists or invest in sophisticated platforms.

Ong believes that 2019 will mark a turning point where data becomes accessible to all. He attributes this to the democratizing effect of AI-powered technologies that simplify analytics adoption by automating various stages of the process. Ong, like Bhatia, envisions a future where individuals with limited analytics expertise can still derive valuable insights from data.

Ong predicts that organizations will prioritize the creation of data-driven cultures. This involves promoting engagement through internal communities of practice, where data experts initially guide novices in adopting BI tools. As these users become proficient, they, in turn, share best practices, disseminate knowledge, and foster a shared understanding of data definitions.

Thomas LaRock, Head Geek at SolarWinds, shares the view that data will be recognized as a crucial business driver. He asserts that operations teams must embrace a data-centric mindset, identifying and refining data within their departments to generate insights that benefit the entire organization. LaRock advocates for the adoption of DataOps, a methodology that enables IT teams to function as data science teams by prioritizing a data-first approach.

According to LaRock, DataOps will play a pivotal role in extracting, analyzing, and distilling the most relevant data into a compelling and easily digestible narrative that resonates across the organization. He anticipates a shift from mere data reporting and tracking to leveraging data for informed decision-making. LaRock suggests that the ability to communicate these actionable insights may even earn technology professionals a seat at the strategic planning table.

Elliott predicts that the secure and seamless management of data will remain a critical skill across industries. He foresees companies investing in talent acquisition or partnerships with third-party providers to effectively manage, analyze, and mitigate the risks associated with increasing data volumes.

Ong argues that the most sought-after skills of the future will extend beyond data analytics alone. He believes that modern data scientists must possess a combination of hard skills, such as proficiency in advanced statistics and machine learning, as well as soft skills, including strong communication abilities. Additionally, they will require a strategic business mindset and a deep understanding of their respective industries.

Shaun McLagan, Senior Vice President for Asia Pacific and Japan at Veeam Software, suggests that technicians need to become versatile generalists. He attributes this to talent shortages, the convergence of on-premises and cloud-based infrastructures, and the rise of SaaS. McLagan acknowledges the continued importance of specialization but emphasizes the increasing need for IT professionals to possess a broader business understanding and contribute value across multiple IT domains.

Bahl anticipates a shift in future job scopes towards skills that are difficult for robots to replicate. These include providing human interaction and empathy, overseeing and managing machines, extracting high-level insights from data processed by algorithms, and collaborating effectively across departments by leveraging machine intelligence.

Bahl predicts that hiring managers will prioritize human-centric qualities and behaviors over technical skills alone. Emotional intelligence, problem-solving, and crisis response are among the skills expected to be in high demand across industries.

Facilitating skills acquisition and learning will become a top priority for organizations. Bahl emphasizes the need to shift the perception of learning from a departmental responsibility to a boardroom imperative. He advocates for reskilling and learning to be treated as strategic priorities for senior executives.

Chua believes that academia will play a crucial role in addressing the skills gap. He envisions the development of courses tailored to equip students with in-demand skills, supported by flexible university curriculums that adapt to industry trends. Chua contends that these efforts will accelerate the growth of the data science talent pool.

Bahl stresses the importance of a collaborative ecosystem to support workforce development. He highlights the need for collaboration between public institutions, businesses, and educational institutions to leverage each other’s strengths and create a resilient workforce adaptable to constant change.

Sommer concludes that AI has the potential to make data and analytics more human-centric. He believes that organizations that prioritize designing AI systems around human capabilities will achieve far greater success in the next five years compared to those focused on automating humans out of processes.