Glocomp Systems focused on simplifying the AI journey for Malaysian organizations

Continuous education on the capabilities of AI is imperative for Malaysian organizations as key decision makers are still looking for the business outcome from AI investment, explained Joseph Giam, Managing Director for Glocomp Systems.

Despite the increased investments in AI by organizations in Southeast Asia, some businesses are still challenged in getting the board to support their digital journey because of the concerns on the business outcome.

For example, in Malaysia, AI investments have grown but still not at the levels that it should ideally be at. The National AI Office (NAIO), which was formed in 2024, is focused on helping businesses in their AI journey. While the NAIO remains confident that Malaysian organizations are focused on their AI development and deployment, the reality is some organizations are not making decisions as quickly as they should.

This is where tech distributors play an important role in helping businesses understand the true value of AI and also planning their AI strategy.

According to Joseph Giam, Managing Director for Glocomp Systems, a lot of conversations with customers when it comes to AI always involves issues around the business outcome, cost savings, enhanced productivity and profitability.

“A lot of AI projects are geared towards the LLM or the GPT kind of approach, which in most cases is very difficult to address the business outcome. Everybody got more intelligent, they get information faster, it helps indirectly, but it's difficult to quantify in terms of the business outcome,” Giam said.

Getting the right data strategy

Giam explained that data is always the most important ingredient in AI. However, most organizations think they already have data ready, but what they don’t realize is that the data is very disintegrated.

“When I say disintegrated, it doesn't mean that they are in various geographical locations. It means that the data is from various systems, various standards, and when we want to do training, we have to make sure that the data is within a particular standard. Data in different systems and different standards will make it very difficult for data scientists to actually understand and make good of the data,” Giam explained.

As data is core to AI, Giam pointed out that Glocomp has developed a simple data lakehouse initiative to get data ready for AI.

“It's a light ETL and it's not something like from the other big data management vendors, where you need a huge investment. It's not an intelligent or feature-rich or function-rich kind of a data lakehouse, but it is mainly a cabinet where you can store all your data in a very standardized way and prepare AI. So essentially, what businesses will have is a very simplified data lakehouse that is already good enough for you to treat your data and build AI applications,” Giam explained.

Essentially, this means customers do not have to invest heavily in AI. Instead, they can start with a data lakehouse project and that will support their future AI development. Giam added that Glocomp recently acquired a customer in the healthcare industry that has decided to use their services to build a data lakehouse to support their future AI.

The need for AI Innovation Centers

To educate customers on the value of AI and the use cases that would bring them the most efficiency and productivity, some tech vendors and partners have built AI Innovation Centers to address these needs.

Giam stated that Glocomp is also planning to build an AI center of excellence. He said Glocomp plans to invite ecosystem partners to basically come in and exhibit as well as share their success stories, use cases and such.

“So, ours will be a bit different because we will have not just the best-in-solution, but also the solutions from tech vendors in both the East and West. We will have a good mix. And I think essentially, it's about telling people, organizations, businesses that AI is not rocket science. It's there for you to adopt. You don't try to make use of something that you already have and get them to be AI. You can build your data from scratch. We want to make it light and simple. And that will basically make your journey easier because you don't have a package,” he concluded.