Covering Disruptive Technology Powering Business in The Digital Age

Written by: Bailey Martin, Tech Journalists, AOPG.

IBM, long known as the titan of tech, has been a constant in the ever-evolving world of business since its founding in 1911. The company’s longevity is a testament to its enduring focus on building technology solutions specifically designed to address the unique challenges and opportunities faced by enterprises.

It’s no surprise, then, that IBM Think’s Singapore stop this year focuses on critical issues surrounding Artificial Intelligence (AI) adoption and readiness for businesses in ASEAN. The event highlights IBM’s achievements with its watsonx AI platforms, which are instrumental in overcoming these challenges. At IBM Think 2024 in Singapore, a central theme emerged: IBM’s openness. The company is transparent about the challenges enterprises face with AI and is dedicated to helping them adapt safely and effectively. Even IBM’s watsonx platforms are open-source, allowing clients to customise and innovate with AI – I will share more on this later, along with IBM’s findings on the enterprise AI landscape for ASEAN. There’s much to explore, so let’s dive in.

What’s Holding Up AI Advancement for Enterprise in ASEAN?

This notion may come as a shock given the recent major investments in AI servers and data centres across Asia to accommodate the implementation of AI solutions throughout the region. But looks can be deceiving. A significant percentage of observed organisations in ASEAN had overestimated how ‘transformative’ they were in terms of AI adoption compared to their actual ‘AI readiness’ scores, as pointed out by Ullrich Loeffler, CEO of Ecosystem. IBM and their partner Ecosystems discovered a significant gap between companies’ optimism on their AI readiness being in the ‘transformative stage’ (39% of participant organisations) and the actual percentage of organisations that were scored as ‘transformative,’ which was considerably lower (only 4% of participant organisations). So, while ASEAN countries have made great strides towards the accessibility and use of AI, it turns out that some companies may not be as well prepared to properly implement AI solutions into their operations.

This issue can be down to a few factors, one of which is the need for more talent and upskilling on AI in these regions. Catherine Lian, GM of ASEAN, IBM pointed out that ‘Talent Readiness’ is what appeared to be one of ASEANs challenges. Overall, a more AI-capable workforce across all departments is an additional requirement for seamless utilisation of AI. The first step in solving a problem is recognising there is one… There are indeed some companies who are aware of this problem and have begun investing in the right resources early to overcome this ‘hurdle.’ For instance, Lee Li Foon, CIO of Bank Simpanan Nasional (BSN) in Malaysia, had shared that major investments by BSN and blue-chip organisations have been focused on training and upskilling the upcoming workforces on AI as well as bringing more AI-specific ‘talent’ into Malaysia.

These are major looming issues that require acknowledgement from groups across the ASEAN region before taking steps to solve them. Of course, there are additional complexities when it comes to AI for enterprises and that is that a fixed model, no matter how extensively configured, is not reliable to be implemented across so many different departments. Some people-focused departments such as HR or CRM may have different aims for AI implementation, such as automation of repetitive processes, compared to IT teams which would focus on innovating like code generation. This is exactly why IBM’s watsonx AI is open-sourced.

IBM’s Hard-Fought Successes and Approaches to Enterprise-designed AI

As previously mentioned, watsonx is open-sourced. This allows for fine-tuning and augmented data retrieval so that a business’s AI can be better equipped to handle specific tasks. This helps enterprises with greatly differing methods or focuses in using AI. However, IBM’s real key to success lies in their segmented platform variants of watsonx. IBM offers watsonx platforms to optimise: AI, Data, Governance, and Assistance. Each of those specialised platforms focuses on progressing AI in the corresponding factors, Organisational Strategy, Data Foundation, People and Skills, and Governance.

With these pre-prepared open-sourced platforms, companies may use watsonx across multiple disciplines for different departments, dealing with the challenge of different AI needs within the same organisation mentioned earlier. watsonx allows for room to innovate while still providing a discipline-specific pre-trained model to serve as a solid foundation. Overheads are reduced, tuning is vastly more cost-effective, and a solid framework greatly reduces the odds of incidental AI hallucinations.

Now, onto the issue of AI preparedness across ASEAN enterprises: Talent and upskilling are crucial, as well as having businesses that are self-aware regarding their AI readiness. While not all enterprises may recognise this, thankfully IBM and their vendor partners do. IBM Consulting has been working alongside partners to actively engage with businesses and support them through all stages of AI adoption and adaptation.

Erika Valenciano, Product Manager at VST-ECS in the Philippines, clarified that there is no ‘one-size-fits-all’ approach to dealing with this problem. Although there are guidelines to follow, it is truly a case-by-case basis that requires VST-ECS to work closely with clients, consult them, and educate them on watsonx implementation. Even after AI adoption is complete, IBM Consulting emphasises the importance of aftercare and follow-up. As IBM’s Catherine, stated at IBM Think’s panel, “AI is not an end-point solution – it’s a journey.” Companies will always need to reconfigure their AI models over time, and IBM Consulting will be there to support enterprises through these changes.

For example, SingHealth’s use of IBM AI before Covid-19 wreaked havoc on the world, allowed them to quickly tune their AI to change processes and accommodate new Covid-specific regulations. IBM may not claim to predict the future – but they can certainly claim to have the resources to react and adapt to whatever the future throws at us.

Where Will IBM Go From Here?

To be honest, this question is rhetorical, as one cannot predict what the future holds. IBM once emphasised the application and promise of quantum computing, but this has now been sidelined and overshadowed by watsonx, given its almost spontaneous burst into the commercial landscape—something that quantum computers are too ahead of their time for. Things changed quickly, and IBM reacted to what the issues observed in AI were. What can be appreciated, however, is the almost global acceptance of LLMs and GenAI. IBM certainly has a significant part to play in this and thanks to their openness on key issues around AI for enterprise, as well as their facilitating for innovation with watsonx platforms, we can be confident that enterprises won’t be forced to navigate upcoming AI obstacles in the dark.

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