Covering Disruptive Technology Powering Business in The Digital Age

Written By: James Ang, Senior Vice President – Dataiku

Southeast Asia has entered its digital decade as it continues to stand resilient against the macroeconomic challenges we face today. With the region’s digital economy expected to reach USD $1 trillion by 2030, Artificial Intelligence (AI) will be a critical force to reckon with. In fact, according to PwC, AI could contribute more to the global economy in 2030 than the output of China and India combined!

Despite AI investments growing steadily, with most concentrated in Singapore, adoption in Southeast Asia is still in its early stages. With huge potential to drive economic growth and transform the region’s industries, governments and businesses will be ramping up momentum to prioritise AI strategies. But when it comes to scaling AI, the top two blockers for companies are hiring people with specific skills and developing strong and valuable business cases. Candidates with specialist skills and niche knowledge that can address both issues are certainly a rare breed. Unsurprisingly data scientists with the desirable mix of analytical and business skills have come to be referred to as “unicorns.”

Singapore is home to over 3,800 tech-enabled startups, but the talent shortage is one of the biggest challenges facing the city-state. The Singapore Salary Report & Market Outlook 2022/2023 has revealed that 52% of employers are looking for AI skills—considered among the top 10 business development skills.

As AI adoption continues to grow and business value is realised, organisations will aim to increase their talent pool by recruiting data scientists and specialists that can do it all. However, for a business to scale AI, hiring more data scientists is not necessarily the obvious or sensible option. The reason is they are in high demand and challenging to recruit—and even harder to retain as they become more specialised and experienced. According to McKinsey, outsourcing is not a way out of this issue either, as core capabilities need to remain in-house.

What is increasingly evident is that hiring unicorn specialists with every specific skill set is no longer the answer to building an AI-driven organisation, particularly at a time when budgets and resources are stretched. Instead, businesses need to think about moving AI mainstream by tapping into collective intelligence and building communities of interdisciplinary business and data professionals across the entire organisation.

Building Cross-Functional AI Teams

There are proven alternatives to the AI talent crunch that have succeeded in many companies and industries: Upskill your current talent, monitor their performance and nurture your rising AI stars.

With the drastic volume of data generated daily, business analysts need to move on from spreadsheets and support business decisions with data-driven insights; likewise, data analysts need to understand and communicate the wider business implications of data. To bridge the talent gap, establishing hybrid teams that encompass business intelligence and data intelligence is the way forward.

“AI could contribute more to the global economy in 2030 than the output of China and India combined!”

The approach has worked in the private sector across many industries. PepsiCo went so far as to state that hiring individuals who can do it all is a myth. Instead, companies should look to develop cross-functional AI teams.

AI strategies are not one-size-fits-all. Rather, organisations must ensure that they are adopting a strategy that is aligned with their ultimate goals. At Dataiku, we use the 5E framework:

• Explore. Explore what AI means to the business, define priorities and solidify the reasons to leverage AI.
• Experiment. Estimate the value of AI with early projects and raise overall awareness.
• Establish. Create tangible value from initial use cases and lay foundations for scalability of the business’s framework.
• Expand. Expand the business’s use of AI across all departments within the organisation and accelerate overall business value.
• Embed. Embed the use of AI across all business activities and ensure that it is part of the organisation’s DNA.

Change is difficult, indeed. But how can you enable AI to be more pervasive without the challenge being compounded by other transformation requirements?

Here are three proven strategies for success:

1.  Adopt a Common AI/ML Platform

A prevalent issue with scaling AI is that innovation is only in the hands of a few experts. Many AI functions developed today are fairly similar to how things were made in the pre-industrial revolution era, produced by artisanal experts in small batches, keeping knowledge closely guarded. This means that productivity is low, but maintenance and costs are high.

Similarly, businesses struggle with the complexities of bringing AI to fruition with expertise being in the hands of only a select group of business specialists and analysts. When other members of the organisation are unable to understand AI models applied to projects, business continuity and shared institutional knowledge are at risk. What organisations need to strive for is shifting AI from being something that only a small group of people can access and understand to an Everyday AI model that permeates across the business and all functions.

Weaving data into an organisation’s DNA is necessary for their survival. That is where a common AI/ML platform helps unite workers of all skill levels to use it, without eliminating the ability for data scientists to continue developing models. The optimal platform would facilitate a distinctive collaborative space that brings together AI consumers and creators—regardless of their coding abilities.

2.  Invest in an Adoption Program

Any technology investment is only as successful as its adoption. It does not matter how great the platform may be: If nobody uses it, then no value is generated. Likewise, data scientists using the platform may create excellent models, which are not going to contribute to output if not used in business workflows.

The goal of an adoption program is to evangelise the use and application of the AI platform to move it from a stage of intent to action, both from a data worker and business workflow point of view. This can be done through several activities such as training programs, communication campaigns and regular monitoring of satisfaction metrics. You could even inject a bit of fun and strengthen awareness through branded badges and laptop stickers or run hackathons and internal competitions to celebrate wins.

3.  Create an Upskilling Program

A successful upskilling program creates a positive cycle where business analysts learn AI capabilities and add value. This increased awareness of AI and knowledge capacity invariably leads to identifying new users and a new cohort of data workers looking to be upskilled.

Whereas an adoption program is designed to encourage the rapid uptake of a new platform, an upskilling program ensures the benefits are widely spread across the organisation through tools such as self-service training. Supported by a common AI/ML platform, there are two models of upskilling programs that have been successful in our experience: interdisciplinary (grouping workers with the same skills regardless of the business function they are from) and functional (workers with similar skills in the same business functions learn together).

“What is increasingly evident is that hiring unicorn specialists with every specific skill set is no longer the answer to building an AI-driven organisation, particularly at a time when budgets and resources are stretched. Instead, businesses need to think about moving AI mainstream by tapping into collective intelligence and building communities of interdisciplinary business and data professionals across the entire organisation.“

Regardless of which model works best for your organisation, the long-term goal is to create a sustainable community of professionals and drive ROI across many business units and functions.

Closing the AI Skills Gap

As a Smart Nation, Singapore’s national AI strategy aims to address the need to develop homegrown talent across the entire range of AI-related job roles, to prepare the workforce for a digital-led future. To successfully and sustainably scale AI, enterprises should look beyond hiring data unicorns and build unicorn teams to close the AI skills gap.
Data scientists are consistently in high demand and with tech shortages not likely to abate soon, Singaporean businesses should look beyond technology and focus on the community. AI capabilities should be augmented with human capital, making data science less mythical and more achievable, less restrictive and more accessible.

(0)(0)