Artificial intelligence (AI) is rapidly becoming embedded across Singapore’s economy, with growing adoption across sectors. This momentum aligns with the ambitions outlined in the National AI Strategy 2.0, further supported by expanded funding and upskilling initiatives announced in Budget 2026.
However, while funding and training are essential enablers, they do not by themselves address a more fundamental issue: why enterprise AI initiatives often struggle to scale.
Nearly half (48%) of Singapore companies now report using AI, up from around 40% last year. Across boardrooms, organisations are no longer debating whether to adopt AI. Instead, they are confronting a more practical challenge — how to translate early experimentation into measurable, repeatable enterprise-wide impact.
The constraint is rarely the algorithm itself. It is the environment in which the algorithm operates.
The hidden cost of fragmented enterprise environments
Many enterprises approach AI initiatives with strong intent, only to encounter friction once deployment begins. Organisations running disconnected ERP, supply chain, manufacturing, workforce and customer systems frequently experience delayed insights, inconsistent outputs and rising integration costs.
See also: OpenAI, Anthropic, Google unite to combat model copying in China
This challenge is particularly pronounced in hybrid environments that combine legacy infrastructure with newer cloud applications. While businesses respond to national calls to accelerate AI adoption, their underlying systems are often not designed for seamless interoperability.
In such environments, AI becomes an additional layer placed on top of operational silos. Instead of enabling transformation, it exposes structural complexity.
Without addressing this architectural fragmentation, AI ambition risks outpacing enterprise readiness.
See also: OpenAI advocates electric grid, safety net spending for new AI era
Architecture, not algorithms, determines scalability
Enterprise AI success depends less on model sophistication and more on the quality, consistency and governance of data feeding it.
Layering AI onto disconnected systems forces organisations to invest significant effort in data reconciliation and integration. Over time, this increases costs and reduces confidence in AI-driven decisions.
A different approach is required. Rather than overlaying intelligence onto existing silos, organisations must embed AI directly into a unified ecosystem designed around how industries operate. When ERP, supply chain, manufacturing and workforce systems share a common data fabric, AI operates on governed, near real-time information across financials, production, quality, logistics and customer interactions.
This ensures that insights are grounded in operational reality rather than partial datasets.
Industry context as a differentiator
Generic AI solutions may demonstrate technical capability, but operational environments demand industry precision.
To stay ahead of the latest tech trends, click here for DigitalEdge Section
A food and beverage manufacturer managing compliance and traceability, an automotive supplier coordinating complex production schedules, or a healthcare provider balancing workforce constraints requires AI that understands the processes defining their industry.
Industry-specific cloud platforms, such as Infor's Industry Cloud Platform, provide this depth. By embedding preconfigured industry processes into the core system, they allow AI to operate within the workflows that drive performance.
When intelligence is built into the system of record rather than added externally, organisations unlock cleaner signals, faster deployment cycles and more reliable outcomes.
From experimentation to measurable value
For Singapore businesses under pressure to improve productivity and manage costs, the test of enterprise AI is measurable operational impact.
According to Nucleus Research, organisations consolidating onto unified platforms report integration and maintenance costs falling by as much as 37%, with AI deployment cycles up to 30% faster and measurable improvements in predictive accuracy.
These gains translate into tangible business outcomes: improved forecasting accuracy, more efficient production planning, better working capital management and faster decision-making.
Embedding AI into operational workflows through industry-specific agents
As AI capabilities mature, the next phase of value creation lies in embedding intelligence directly into day-to-day operations.
Industry-specific AI agents designed for workflows such as project management, production planning, maintenance and supplier collaboration operate within the same data environment that supports core enterprise processes. Unlike general-purpose AI assistants, industry AI agents are purpose-built for specific operational workflows such as project management, production planning, maintenance and supplier collaboration. Because they operate within the same data environment that supports daily operations, they can move beyond surfacing insights and into execution, acting with the speed and relevance that enterprise workflows demand."
Enterprise AI success is not defined by isolated breakthroughs. It is defined by how consistently organisations can extend value across functions and over time.
Singapore’s Budget 2026 strengthens financial and skills support for AI adoption. These initiatives are important accelerators. However, without addressing structural integration challenges, many organisations will struggle to translate AI ambition into enterprise-wide impact.
As AI moves from experimentation to execution, simplification will define sustainable return on investment. Enterprises that unify data, workflows and user context on cloud-native platforms will be better positioned to scale AI responsibly and repeatedly.
Victor Ng is the vice president and managing director for SENA at Infor
