AI Rising: Asia Pacific Embraces the Age of the Intelligent Machine
Asia Pacific businesses are bullish on AI, and they’re optimistic about the returns, according to our recent study. There’s more to do, however, to prepare for optimal collaboration between humans and intelligent machines.
For many reasons, businesses in the Asia Pacific are particularly well positioned to benefit from the influx of AI into their operations, processes, workflows and business models. Aggressive government artificial intelligence policies, a freedom from legacy assets and an abundance of data position the region as a bright spot for intelligent machine adoption.
According to our recent research, Asia-Pacific businesses are investing heavily in AI, and expect revenue, cost and productivity benefits. However, they also have their work cut out: Just over one-third of the organizations we studied said they feel fully prepared for a future that pivots around intelligent machines.
To learn more about how organizations in the Asia Pacific are preparing for intelligent machines, Cognizant’s Center for The Future of Work surveyed 622 top business and IT executives at leading companies across the Asia Pacific region. We also developed a framework to help them transition to the human-intelligent machine workforce.
Key insights from our research include:
A large majority of respondents said intelligent machines (82%) and human-machine collaboration (76%) were the top two influencers on the future of work in the next five years. Respondents are looking to unlock huge value from intelligent machines, such as boosting revenue (82%), raising employee productivity (73%), acquiring and retaining customers (70%) and generating cost savings and efficiencies (68%).
Most (82%) respondents cited a boost to revenue growth as their number-one reason for adopting intelligent machines, predicting 12% revenue growth in the next five years due to such systems. The retail, banking and financial services, hospitality and insurance industries are expecting the highest gains. Respondents also estimated a 7.4% cost decrease through the use of intelligent machines over the next five years.
Asia Pacific leaders are not afraid to heavily invest in intelligent machine initiatives. On average, companies plan to spend 13.5% of their revenue on building and managing intelligent machines in the next five years. Such investments are necessary for the many initiatives needed to restructure the business and operating models, win new markets, innovate and improve efficiencies, manage maintenance costs during the transition, initiate internal change, set out a man-machine roadmap, provide interdisciplinary company training, hire third-party consultants and possibly acquire companies while attracting digital-savvy talent.
Only 35% of respondents believe they are fully prepared to handle future work with machines, and only 42% are confident about their ability to integrate AI with existing business processes. The top three challenges are misalignment of workforce strategy with business goals (72%), lack of IT infrastructure readiness (71%) and a shortage of required talent and knowledge (70%).
The Five T’s
We’ve outlined five elements for success with intelligent machines: teams, tasks, talents, technology and trust. The key to successful implementation is ensuring all five elements are integrated and aligned to create ultimate value.
Teams: Small, Flexible and Hybrid
Human-machine teaming will change the way organizations manage their workforce, workflows, workspace and culture. One change will be a move from larger hierarchical team structures to smaller teams (72% of respondents). Sixty-three percent of respondents also agreed that a change in workspace dynamics is necessary for human-machine teams to succeed. And with more machines fulfilling worker tasks, businesses will require new roles, such as man-machine teaming managers to identify tasks, processes, systems and experiences to be upgraded by newly available technologies, as well as imagine new approaches, skills, interactions and constructs.
Tasks: Learning to Assign and Share
Businesses will need to deconstruct jobs and identify which tasks are best performed by humans vs. intelligent machines. The majority of respondents plan to create a task allocation framework in the next 12 to 24 months to define roles and responsibilities and set the rules for AI systems and workers to coordinate to accomplish a task.
One of the most prominent findings from our research is that no one task will be 100% driven by a machine or a human by themselves; instead, every task will have some degree of shared involvement. As a result, AI systems can learn to better proceed with new and unknown scenarios, while humans can continue to adapt and focus on higher-value tasks.
Talent: The Fusion of Human and Technical Skills
In a world of pervasive technology, specifically human skills will gain importance, namely leadership (87%), innovation (85%) and interpersonal skills (84%). Technical skills will continue to be in demand, as noted by 81% of our respondents.
Talent scarcity will continue to be one of the biggest leadership challenges in implementing and evolving intelligent machines. Almost 70% of respondents said they struggle to find candidates with relevant skills. Some companies will rely more heavily on flexible workers, and some countries, such as China, will formalize talent cultivation. China’s Ministry of Education has launched a five-year AI talent training program, under which at least 500 teachers and 5,000 students will be trained at top universities.
Technology: IT Matters More than Ever
Whether your organization is recreating a business process from scratch or injecting AI into front-, middle- or back-office processes, success will depend on how well the IT infrastructure is integrated with AI systems. IT infrastructure needs to become agile, responsive, flexible, secure, scalable and simple to manage the transition. Over the next 24 to 36 months, we will see a new phase develop for IT organizations, with 76% of respondents expecting IT to manage the roadmap for a technology infrastructure that integrates AI-driven technologies.
While 50% of respondents said they’d need to replace parts of their legacy systems to enable intelligent machine adoption, 48% said they’re looking for a hybrid approach to ensure the coexistence of legacy infrastructure and intelligent machines.
Trust: The New Battleground for Success
Job changes — and job loss — are key issues when it comes to AI. Only about one-third of respondents said they have a clear understanding about the changes in roles, responsibilities and ownership that will result from changes in work. To grow trust, leaders should proceed sensitively and gradually when introducing intelligent systems, and focus on the human-machine collaboration issue.
Businesses also need to instill trust that the actions and decisions of intelligent machines won’t result in unknown or negative consequences. One approach is to hold AI engineers, designers, developers, investors and innovators accountable for not only defining specific tasks for intelligent machines but also for recognizing their side effects. Businesses should also focus on user acceptance as much as the technology itself by providing adequate training on the use of AI systems, as well as the legal and business consequences of their failures.
Business leaders in the Asia Pacific recognize that the rise of machine intelligence is the ultimate game changer today, and they’re preparing to navigate their companies through the coming years. Elsewhere in the world, organizations will follow suit, knowing inaction will result in irrelevance.