AI continues to attract enterprise investment, but turning that ambition into reality is a different story. From legacy systems to security bottlenecks, organisations are running into unexpected obstacles at every turn.
In the recent “Driving Digital Transformation Journey Through Intelligent Integration & Automation” roundtable hosted by Boomi, senior IT leaders from across Asia-Pacific shared candid insights into their biggest AI implementation concerns, in the hopes of finding practical solutions to stay ahead in the digital race.
Managing expectations
A recurring issue across enterprises is the gap between AI’s promise and its actual impact. Participants agreed that managing the hype around AI is a challenge in itself. Every department wants to deploy some form of AI, even if there’s no clear operational inefficiency to solve and no robust infrastructure to support it.
One IT leader noted that their organisation had deployed around 250 AI models in recent years, but very few generated lasting value.
Modernisation also emerged as a recurring problem, with countless long-established organisations still relying on legacy systems. When such organisations attempt to embrace the cloud — and on top of that, AI — they often encounter gridlock.
Then there’s the ongoing tension between innovation and security. Business units want fast, visible results, while security teams must ensure minimal risk to the enterprise. Both sides have valid concerns; the challenge is striking the right balance.
According to Nathan Gower, Senior Director for Enterprise APAC at Boomi, starting with smaller, more targeted projects proves more successful than immediately tackling large-scale, high-concept initiatives.
“We’re seeing more organisations turn to smaller, focused projects — often delivered by niche systems integrators who can act quickly and build trust. That way, they earn the confidence to go back and ask for a budget for the next one,” he observed.
Centralisation vs decentralisation
Striking the right balance between centralisation and decentralisation remains a challenge for many enterprises. While most IT leaders acknowledge the need for a unified view of customers, employees, and operations, data often resides in systems owned by different teams, each with its own processes and reluctance to share.
“We’ve worked with organisations that favour a highly distributed model, where data is democratised across departments,” said Gower. “But you still need a centre of excellence to provide oversight. That’s true of any large organisation. The IT and integration teams want to maintain control and guardrails, but they can’t do everything themselves. Eventually, the business outpaces them, and shadow IT creeps in. So the real challenge isn’t the technology, it’s the people and processes.”
Meanwhile, in highly regulated industries, many enterprises that migrated to the cloud have found themselves repatriating assets to meet evolving data residency requirements.
According to Gower, Boomi has already addressed the technical side of the data sovereignty challenge.
“We use a decoupled architecture; data simply transits through our system. We’re not the system of record,” he explained. “We can position the data or runtime plane wherever it’s needed: on a private cloud, on-premises, or in any public cloud environment. The SaaS component is just the management plane, a single pane of glass, while everything else remains decoupled, so we can fully meet your data sovereignty requirements.”
The problem with data
AI cannot function without data. More importantly, it cannot function effectively without clean, reliable data. Throughout the roundtable, data quality consistently emerged as a major bottleneck to AI implementation. In some cases, the issue stems from ageing legacy systems; in others, it’s due to overlapping sources and unclear ownership.
To illustrate the challenge, Gower offered a retail example: a customer contacts a call centre to update their personal details. A few days later, they visit a physical store to make a purchase. The next day, they place an order through the app. Is the data captured consistently across all touchpoints, and where does it all go?
“You also have the finance department involved, with a system of record capturing accounting data,” Gower pointed out. “There are so many endpoints, and the data attached to that one person keeps changing. How do you manage all that?”
Approaches to solving this vary. Some organisations are investing in data virtualisation and quality tools. Others are embedding data stewards into teams or writing custom rules into their data pipelines. Still, few participants claimed to have a mature, enterprise-wide master data management (MDM) strategy in place.
According to Gower, this is where Boomi DataHub, one of the Boomi Enterprise Platform capabilities, comes in. DataHub is designed to ensure systems of record remain trustworthy and accurate.
“That’s the role of data management,” he said. “It’s not about creating a new silo. It’s about making sure the data flowing between systems is correct behind the scenes.”
Integration is key
AI can only deliver value if it is built on a stable, unified foundation. Yet, several IT leaders noted that integration is often treated as an afterthought, addressed only after front-end platforms are selected. This approach leads to higher costs and reduced agility.
According to Gower, Boomi’s answer to the AI-integration challenge is to apply AI itself, along with a vast amount of metadata collected over time.
“Since the early days of our platform, we’ve been capturing metadata at the management and control plane,” he said. “We now have nearly a petabyte of metadata based on what our 25,000 customers have built over more than 20 years. So if you’re creating an API or an integration between platforms like Workday and SAP, we can start recommending next-best actions, because we’ve seen these patterns before.”
Gower added that Boomi has applied machine learning to this metadata for many years. In particular, its field-to-field mapping capabilities benefit from the accumulated insight: Around 90% of the time, customers accept the system’s recommended mappings. This significantly reduces integration time. The same technology underpins Boomi’s data management offerings, making the transition from machine learning to AI a straightforward one.
Looking ahead, more organisations are beginning to treat integration, especially integration that uses cloud-native and API-led approaches, as a foundation for transformation. As this shift continues, AI adoption is likely to become faster, simpler, and more accessible across the enterprise.
Want to explore even more practical solutions to the most common challenges in AI implementation? Join us at the Boomi World Forum Singapore on Tuesday, 18 November 2025.
Whether you’re a long-time Boomi user or just starting your journey, this is your opportunity to discover what’s possible with the Boomi platform.
Register now: https://boomi.to/BWFSingapore2025
