Work in Asia’s data age
How Asian businesses are integrating their HR and technology adoption strategies, and leveraging AI capabilities to reshape their organizations and operations.
In association withCornerstone OnDemand
The steady advance of artificial intelligence (AI) and automation technologies has been reshaping work and jobs for the past decade. Well before covid-19, robust debates were underway about the future of work and what potential scenarios for employment might emerge.
While many Asian markets have met the challenge of containing the spread of covid-19 with relative levels of success, through well-managed lockdowns, social distancing, and tracing programs, the pandemic has placed great pressure on workers and the human resource management systems that support them.
Work in Asia's data age
Technology forecaster Forrester has found nearly half of Asian managers surveyed expect permanent increases in their full-time remote workforce; many will seek to use AI-enhanced workforce engagement tools to try to increase workplace communication to reduce the new distance this creates.
As part of the Global AI Agenda 2021 program, in association with Cornerstone OnDemand, MIT Technology Review Insights surveyed more than 1,500 senior decision-makers and technology leaders to understand how AI is being used in organizations in Asia and globally to accelerate revenue growth and digital collaboration, and to augment human resource capabilities.
AI, top to bottom
Globally, corporates are deploying AI tools and analytics in increasing numbers, to squeeze more productivity out of manufacturing, help employees understand customer requirements more precisely, and support business outcomes. Like many technology adoption strategies, digitally-enabled insight is traditionally seen as a bottom-line tool—for example, more visibility across a supply chain allows a manufacturer to quickly identify places to trim costs. Like many strategic pivots over the last 18 months, the impact of covid-19 has sped this up.
Allan Tate, the executive chair of the MIT Sloan School of Management’s CIO Symposium, refers to this as “the Big Reset: where enterprises undergo two years of digital transformation in two months.” While he concedes that “right now using AI to increase efficiency and reduce costs is probably the most common use case, AI-enabled data usage is quickly becoming a key way of driving revenue for many corporations.”
This view is borne out by our global survey on AI adoption strategies in enterprises: nearly half of our respondents indicate that they have either deployed AI to achieve revenue growth, or are accelerating their efforts to do so. A quarter have plans to step up the use of AI in top-line initiatives, and only 12% indicate that it is a tool only for cost containment.
The perspective from respondents based in Asia largely echoes the global trend, but also reveals a region that is simultaneously behind the curve, and ready to leapfrog it. Asian respondents indicate lower current use of AI for revenue growth than the global average, but are much more likely to undertake “top line” AI initiatives, and over a third have plans to increase its use.
This increasing current emphasis on “top line” AI, which often supports customer-facing teams through increased customer insight, drives business expansion. This, in turn, drives efforts to build capabilities for marketing and business development professionals, such as augmenting their workflows and serving as a catalyst for skills development. Asian respondents, on average, are slightly more aligned toward revenue growth performance in their AI project deployment than the global average (see Figure 2).
Organizations focusing on “bottom line” AI initiatives—which fall into cost efficiency and resource optimization categories—are more likely looking to increase automating functions and drive change in operations, which could lead to task redefinition for operations and internal teams.
Download the full report.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
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