Organizations in Asia Pacific have been quick to embrace technology in response to COVID-19 restrictions, but are we crossing the edge of an ‘AI winter’ into spring?
Over the past year, COVID-19 has caused tremendous disruption to various industries globally, forcing organizations to rethink their strategies and adapt to an increasingly contactless, digital world.
One of the key findings from Cognizant’s recent report titled ‘Getting Ahead with AI: How APAC Companies Replicate Success by Remaining Focused’ was that business leaders view AI as being essential to future business development, increased productivity, improved customer satisfaction and employee engagement.
When it comes to wide-scale deployment of AI projects, Asia Pacific is keeping pace with the rest of the world, indicating that the region has a strong foundation to build upon to further improve its AI efforts.
These efforts have also yielded positive outcomes, with businesses being able to progress in key areas such as preparing platforms for data management, scaling machine intelligence and other people-related areas such as coordinating teams, creating ethical standards, and addressing privacy and security concerns.
To understand how companies are getting ahead with AI, and how it can help to drive growth and performance, DigiconAsia discussed some of the key findings from the Cognizant report with Newton Smith, Vice President, Digital Business and Technology, Cognizant.
Have COVID-19 restrictions accelerated the adoption of AI in the Asia Pacific region?
Smith: Amidst the pandemic, AI has evolved from an exciting technology into a critical business tool. Cognizant recently surveyed 1,200 senior executives worldwide — including more than 370 from APAC, to understand how AI is being deployed in an increasingly uncertain world.
The survey found that as work and commerce moved online in the wake of the pandemic, APAC organizations quickly realized the need to boost their dependence on AI across their operations, with 93% of respondents naming AI as having the highest impact on the future of work, compared to 89% in North America and Europe.
Across all industries in the region, 71% of respondents see AI as an essential ingredient for business success, while 45% consider themselves to be either in the ‘leaders’ or ‘advancers’ category, suggesting the potential for sustained growth of AI adoption in the region.
At a glance, AI is already being deployed to reduce costs, speed up decision cycles, and help identify emerging opportunities for innovation and disruption. More recently, AI has proven to be a significant boon in the race to discover the COVID-19 vaccine. By utilizing AI-enabled systems, researchers were able to churn through years-worth of data to derive actionable insights on potential cures within months.
In what ways are organizations in the region, especially ASEAN and China, investing in and using AI to help drive performance and growth?
Smith: From automating driverless cars and detecting digital threats to predicting profitable market segments for specific products, AI’s potential has come far enough that it is only limited by human imagination, with the pandemic set to accelerate its adoption.
Businesses in China are receptive to new AI-based products and are relatively fast in bringing these technologies into the market. For example, agriculture businesses in China are automating seeding, pesticide spraying, and even weather monitoring to ensure crop growth. Manufacturing companies are also relying on autonomous models that can maneuver themselves in factories and on warehouse floors.
In ASEAN, businesses are also pushing ahead with AI adoption. The healthcare industry in the region is relying on AI, not only for disease prevention and recovery, but also on democratizing healthcare in less developed countries, addressing doctor shortages, and tackling threat and management of infectious diseases such as COVID-19. In Singapore, some healthcare institutions use AI to predict whether patients are likely to befall risks and allow management to take the necessary preventative measures.
Are organizations yielding results from their AI investments yet, or do they have to look at a longer term? What are some key challenges enterprises encounter when speeding up AI adoption?
Smith: Businesses in APAC have seen the early promise of AI and see it more as a source of revenue than a means to reduce costs. While productivity gains from targeted projects are often quick, businesses in the region need to broaden the scope of their use cases across the enterprise.
Indeed, the transformational effects of AI will be truly experienced when it has a pervasive, ubiquitous presence across enterprise work. This means identifying multiple uses cases and delivering on those that generate value. In the post-pandemic world, the improved capability to leverage AI across different value models will further separate leaders from the rest of the pack.
The biggest challenge that businesses face is that many business leaders fail to incorporate AI investments as part of their long-term strategy. AI strategy should always be part of a broader business strategy for digital transformation. This means incorporating key business metrics to ensure that AI helps the organization maintain an unwavering focus on business outcomes. Even though many business leaders are expressing interest, formulating ideas or experimenting with AI, not many of them are building strategies.
For businesses to accelerate AI adoption successfully, it is critical to get a holistic view of data and use data analytics to drive decision-making across the organization. However, businesses cannot start with unorganized data and build out an AI solution that churns out reliable insights. Factors such as volume, collection, labelling, and even accuracy of data can affect how well an AI model can perform.
Lastly, AI adoption is about collaboration and partnerships with employees, vendors, and relevant stakeholders. Non-technical workforce may find AI integration intimidating if they do not have the skills and knowledge for these technologies. When a business adopts AI without communicating its importance and potential with employees, staff may feel a sudden pressure to prove their relevance. Business leaders need to educate employees on what this change means for them and help address any false assumptions or unrest amongst employees.
When adopting AI, what areas are of highest concern – such as ethics, privacy, skills gap, job displacement, high cost etc. – among organizations in this region?
Smith: Regarding talent, AI will continue to test businesses’ ability to retrain existing employees while attracting and retaining fresh AI talent in the near future. AI requires businesses to master a broad set of technologies — and reinventing traditional businesses through AI, requires new skillsets in areas such as machine learning security and data.
There also remain concerns associated with the rise of AI, including job losses and privacy concerns. While the concerns are not unfounded, its repercussions are overblown. AI is expected to create new jobs over the next 10 years even as repetitive tasks are automated. These jobs will be built around data and how it can be deployed to create new experiences. Businesses must therefore strive to create a new organizational mindset because without one they risk losing their ability to compete.
Another emerging concern seen in APAC regarding AI is the management of AI risks and ethics. Businesses in the region appear to place more emphasis on ethical considerations at the initial stages, when they should maintain a consistent focus on ethics throughout the solution’s deployment.
If businesses were to solely rely on customer or employee feedback reactively, it could adversely impact user experience, defeating a key AI goal of achieving a leap forward in customer and employee satisfaction. A sustained focus on ethics can ensure that AI solutions are minimally affected by human biases, intended or not, and build or reinforce trust between the business and its customers and employees.
How should organizations develop a robust AI maturity framework, to better analyze performance and to benchmark best practices?
Smith: Business leaders need to realize that these tools must be implemented in the right processes to help solve the right challenges. To do so, they can start by implementing a roadmap for automating processes across the enterprise, or in some cases, to integrate isolated processes.
Companies ought to kick off with pilot testing programmes, working closely with business teams to identify use cases and demonstrate their value through such trials. It is important to identify multiple use cases, since some AI initiatives will fail. This allows businesses to learn from past mistakes and make them aware of potential challenges that may arise when scaling projects across the organization.
Additionally, businesses must deconstruct jobs and identify which tasks are best performed by humans vs. intelligent machines. Looking ahead, there will be an increase in complex and routine decisions executed by machines, thus business leaders need to achieve an optimal balance of human-machine collaboration. As data analysis continues to move beyond human scale, AI-driven approaches will be key to deriving insights that can supplement human decision-making.