The promise of AI is undeniable: the ability to automate tasks, uncover insights, and drive innovation. Yet most enterprises struggle to turn that promise into tangible results. We have plenty of proof-of-concepts, buzzwords, and grand ambitions, but very few lasting results.
From legacy setups to ironclad security hurdles, companies are hitting roadblocks in their AI implementations. The key to breaking through to achieve tangible results is tackling the fundamentals.
Align AI Ambitions With Capabilities
There is often a significant disconnect between AI’s promised benefits and its actual performance in practice. Many teams appear eager to embrace AI, even when there are no real problems to address.
I’ve spoken with IT executives who have implemented numerous AI projects, and most of them have failed to make an impact. Modernization presents significant challenges for established businesses still reliant on outdated legacy systems. When they attempt to incorporate cloud technology, and even AI, their progress often grinds to a halt.
The ongoing conflict between the drive for bold innovation and the need for strong security often leads to a standstill. Business teams seek quick wins, while security teams focus on protecting their assets. Both perspectives are valid, but the challenge lies in finding a balance that satisfies both sides.
AI can be a faster, smarter way to connect your data, your people, and your technology from day one. Demis Hassabis, CEO and co-founder of Google DeepMind, is quoted as saying, “AI is going to be a part of our lives and will help us solve many problems, but only if we let it.” That’s too passive. We have to do more than “let” it: we have to enable it.
Bridge the Hype Gap: Start Small or Risk Everything
In my experience, the best approach is to start with small, high-impact projects that demonstrate tangible value. Achieving a small win can lead to buy-in for bigger enterprise-wide AI budgets.
Although it may not seem appealing, this approach is more effective than directly tackling large-scale changes, which often fail. I’ve watched organizations succeed by partnering with agile systems integrators who deliver fast and build credibility.
Reconcile the Centralized and Decentralized Conundrum
Balancing centralized control with decentralized freedom is a complex issue that many enterprises face. While everyone agrees on the need for a unified view of customers, staff, and operations, data tends to be scattered across siloed teams
I’ve worked with companies that advocate for democratically distributing data across departments, which can foster empowerment. However, without a central hub to provide oversight, this approach can lead to chaos.
IT and integration teams prefer to maintain tight control with appropriate guardrails, but they simply can’t manage everything on their own. When business needs exceed current capabilities, shadow IT often emerges, leading to even greater complications.
The issue lies not with technology, but with people and processes that need improvement. In regulated sectors, there’s a significant backlash against cloud migration, leading many enterprises to revert to on-premises data to comply with evolving data sovereignty regulations.
Tackle the Data Dilemma: Quality Is Non-Negotiable
Data quality is the Achilles’ heel for so many AI initiatives, because AI is only as good as the data feeding it. Poor data quality, stemming from outdated platforms, duplicative systems, or unclear ownership, severely undermines outcomes. I’ve seen retailers scramble to reconcile customer profiles across call centres, stores, and mobile apps, only to end up with chaos.
Different organizations have various approaches to solving this issue. Some are investing in data virtualization tools or embedding data stewards within teams, while others are coding custom rules into their data pipelines. However, very few have implemented a robust, company-wide master data management (MDM) system.
That’s precisely what Boomi Data Hub is: a powerhouse within the Boomi Enterprise Platform, ensuring that systems of record remain accurate and reliable. It’s not about creating another silo; it’s about seamlessly validating and correcting data flows behind the scenes.
Integration: The Foundation of AI Success
Integration is frequently overlooked in the race for AI development. Teams often prioritize front-end platforms and then scramble to connect them. This leads to escalating costs, months of custom coding, and unstable point-to-point connections.
The Boomi Enterprise Platform solves these issues by supporting low-code integration, B2B/EDI connectivity, and API Management — the critical ways you need to connect your data to become AI-ready.
We also have infused AI into our platform, backed by a treasure trove of deidentified metadata from over two decades and 25,000 customers.
We’ve amassed nearly a petabyte of insights, so when you’re building an API or linking systems like Workday to SAP, we can recommend the best next steps based on proven machine learning machine-learning-based, data-driven patterns. Customers accept our recommendations about 90% of the time, significantly reducing setup time. This same technology also drives our data management, making the transition to full AI integration seamless.
With integration as the foundation, there’s no limit to what you can achieve. The Boomi Enterprise Platform also includes Boomi Agentstudio, a full agent lifecycle management solution that helps you design, govern, and orchestrate all AI agents at scale. And it’s not just limited to Boomi AI Agents — Boomi Agentstudio supports agents from Amazon Bedrock, Salesforce Agentforce, and Microsoft Copilot, with API support to upload custom agents.
As more companies adopt cloud-native, API-led integration as the core of their transformation, AI will be deployed more smoothly, quickly, and at a larger scale.
Looking to deliver real-world fixes for these AI roadblocks in APAC? Come to one of our free Boomi World events: Boomi World Tour Sydney, 12-13 November, or Boomi World Forum Singapore on 18 November 2025.