Transforming Disconnected Data into Revenue: TriSeed's AI Solutions

Singapore, Singapore Aug 28, 2026 ALN: Why do so many organisations struggle to turn enterprise AI adoption into financial impact and what are the few that succeed doing differently?

The latest evidence shows that adoption is no longer the main hurdle. McKinsey’s 2026 State of AI survey found that nearly nine in ten organisations now use AI regularly in at least one business function. Yet only 37% reported a positive impact on earnings before interest and taxes, while just 6% qualified as AI high performers. The gap is increasingly between using AI and redesigning the business around it.

The problem, increasingly, isn’t access to AI. Almost any company can experiment with powerful models today. The harder part is stitching AI into the messy reality of an operating business, its existing systems, data, workflows and people in a way that actually produces results. This challenge is compounded by the fact that many businesses have legacy systems and processes that were not designed to accommodate the rapid advancements in AI technology.

Some companies are beginning to crack that problem. They are finding innovative ways to integrate AI into their operations, leading to improved efficiencies and enhanced decision-making capabilities. The Independent spoke with TriSeed, an enterprise AI and data engineering company and certified member of Anthropic’s Claude Partner Network, about what it takes to move AI beyond the pilot stage and into the everyday operations of a business.

With its technology centre in the Philippines, TriSeed has built capabilities around Claude implementation, data engineering and enterprise workflow automation. Its engineers work directly with businesses to connect fragmented systems and turn the data already sitting inside an organisation into infrastructure that AI can actually use. This hands-on approach is crucial because it allows TriSeed to tailor solutions that fit the specific needs and challenges of each client.

One recent engagement with a multi-brand restaurant group offers an interesting look at how that works in practice — and some useful lessons for any enterprise embarking on its own AI transformation. The restaurant group faced significant operational challenges due to the disparate systems it used to manage its various brands.

What It Really Takes to Make Enterprise AI Work

Three weeks into the build, a source file arrived with its columns in a different order than the week before. Nothing upstream had changed. Same system, same export button, same person running it every morning. Only the shape of the file had drifted, the way a file drifts when a process depends on someone repeating the same manual steps by hand, day after day. That file was the moment the design changed.

The client was a multi-brand restaurant group. Like many multi-site operators, it ran its business across several systems that had never been built to talk to each other: an enterprise resource planning platform for finance, a point-of-sale system for each brand, a human resources system, and a handful of operational tools besides. None of them offered a direct connection, an interface, or a warehouse feed. To get anything out, someone had to log into each system in turn and run a manual export every day, across every brand.

The cost of that was not only the hours spent extracting files before anyone could look at them. Operations, finance and the brand teams each built their own consolidation from the same exports, so the same measure could read differently depending on who calculated it. Reports arrived describing a period that had already closed. And because every file was produced and combined by hand, its structure was never guaranteed to hold from one day to the next.

Where the design changed was in recognizing that the manual processes created inconsistencies and inefficiencies. TriSeed's approach aimed to automate and integrate these systems, allowing for a seamless flow of data and insights. By leveraging AI to standardize data extraction and reporting processes, TriSeed was able to eliminate the discrepancies that arose from manual handling.

This transformation not only streamlined operations but also provided the restaurant group with a clearer view of its business performance, enabling better decision-making and ultimately driving revenue growth. The integration of AI allowed the restaurant group to harness the full potential of its data, transforming it from a passive asset into a strategic advantage.

Moreover, this case illustrates a broader trend in the business landscape where companies are increasingly recognizing the importance of data integration and automation. As organisations continue to adopt AI technologies, the need for cohesive data strategies becomes paramount. Companies that fail to address these underlying issues may find themselves struggling to achieve the promised benefits of AI.

In conclusion, the journey to effective AI integration is complex, but with the right strategies and partnerships, businesses can unlock hidden revenue by turning disconnected data into actionable insights. The lessons learned from TriSeed's engagement with the restaurant group can serve as a guide for other enterprises looking to navigate the challenges of AI adoption and realize its full potential. As the landscape of business continues to evolve, those who adapt and innovate will be best positioned to thrive in an increasingly competitive environment.