Building an AI-enabled Asean for the intelligent revolution

TALK of Industry 4.0 started over a decade ago, but public and private enterprises are only just starting to understand the long-term benefits of artificial intelligence (AI).

Despite its reputation as a startup hub, South-east Asia is still in its infancy when it comes to AI, with just 15 per cent of businesses in the advanced stages of AI implementation, according to a Kearney report. In terms of investment, 83 per cent dedicate less than 0.5 per cent of revenue to embedding AI solutions into their operations.

Beyond financial returns, AI can address everyday issues faced by South-east Asians. Millions of un-banked individuals can gain access to affordable loans via machine learning-enabled credit models. Complex supply chains can optimise demand forecasting, production planning, and traceability. Policymakers can rethink urban planning by harnessing geospatial intelligence. In short, the use cases of AI are myriad, multifaceted, and brimming with untapped potential.

However, several big challenges remain in driving widespread AI adoption. The first is inadequate, and often outdated, data infrastructure. Open and public data is imperative for innovation, but governments and businesses in emerging markets often lack a robust and interconnected data ecosystem that allows for collaboration. Compared to more mature, homogenous markets such as Europe and North America, South-east Asia's diversity also adds another layer of complexity in data hygiene. As such, data collation efforts are less sophisticated and often done commercially instead of at a national level.

AI integration is the opposite of a "get-rich-quick" scheme, with a significant upfront investment required to generate long-term returns that take decades to reap the full benefits of. There are high costs involved in enterprise AI, from implementation to infrastructure management. Unlike mature markets where AI is often used to automate manual and repetitive labour, manpower in emerging nations is more cost-efficient, resulting in a greater hesitancy to invest in technology.

On the other side of the coin, the breakneck speed of digital adoption in the region can be attributed to an emerging middle class leapfrogging into mobile-first technologies. This presents nimble businesses with an opportunity to start their digitalisation from a clean slate, forgoing the struggle of integrating legacy systems.

One simple step organisations can take is to break down organisational silos to extend AI beyond its usual sandbox of the IT department. Instead of spending years building a solution without being able to prove results, conducting trials on stand-alone projects allows for greater agility, as employees can monitor outcomes and consistently fine-tune processes per organisational needs. Potential use cases identified can then be multiplied across different departments to build a digital pathway that connects the organisation.

It is these small-scale deployments that allow for user familiarisation - dispelling any fears of technology replacing workers, while helping the entire workforce to upskill prior to mass roll-out.

NURTURING DIGITAL NATIVES

On the same note, there is still a lack of data-centric management culture in the boardroom. Transitioning to data-driven decision-making is essential to a successful digital transformation, and roadblocks include a lack of understanding around the business value of AI and capacity building around mid-level managers throughout the organisation.

A common misstep for organisations trying to digitalise is to designate innovation as the remit of solely the IT department. The need to invest in workforce retraining is equally as important as implementing technology. Digital literacy cannot be limited to IT employees as this hinders the ability for mass adoption across all departments, which can stall growth especially when individuals are reluctant to rely on data-driven insights as a basis for decision-making. Consequently, these businesses cannot monetise the trove of data amassed as it sits in silos with fragmented ownership; where it is collected but not analysed.

Workforce transformation is an often overlooked aspect of any organisational revamp. Leaders must invest in nurturing a tribe of digital natives to break silos and get every employee engaged in experimenting with AI.

Encouraging knowledge sharing at a grassroots level ensures learnings are passed down and codified for current and future employees. This not only maintains continuity of understanding, but also reduces a singular reliance on IT teams.

For a region synonymous with being at the forefront of innovation, AI is underutilised and brimming with untapped potential, especially when it is projected to uplift 10 to 18 per cent of the region's gross domestic product by 2030, an equivalent US$1 trillion in economic value. Yet, AI investments are primarily led by startups and technology firms when ironically, it is traditional industries such as manufacturing and transport that need to transform now or risk entering unprepared into an age of disruption.

Unlike cloud technology, AI is still in the nascent stages. It is time South-east Asia takes the lead on the intelligent revolution, instead of the current wait-and-see approach adopted by so many. With 5G hyperconnectivity at our fingertips within the decade, now is the time for both the public and private sector to invest in AI. More than just a competitive advantage, digitalisation is crucial to the very survival of organisations in this brave new world.

• The writer is chief operating officer of DataSpark, NCS.