Artificial intelligence is no longer a boardroom experiment, but is already operating inside ports, banks, fraud detection systems, logistics networks and supply chains, according to Anand Patil, Senior Director, CX Solutions at Cisco.
AI in Operational Reality
“AI has crossed the operational point of no return,” Anand Patil said. “It is not a pilot. It is not a slide in the strategy deck anymore. It is operational. It is moving cargo. It is moving money. It is moving cities.”
His comments align with one of the report’s central findings: AI has moved from experimentation to operational reality, with agentic AI systems making operational decisions once reserved for human traders.
Singapore’s Port Shows AI at Work
Anand Patil pointed to Singapore’s port ecosystem as a live example of AI operating at scale, with autonomous guided vehicles and cranes controlled through command centres, and a digital twin of the facility receiving real-time telemetry data.
This system determines which containers should move next, what route they should take and how incoming vessels should adjust their speed so that berths are ready when they arrive, Anand Patil said.
The example is important because it moves the AI discussion away from generative AI as a workplace tool and into AI as operating infrastructure, Anand Patil noted.
In trade, AI is now being used to route containers, manage port assets, improve vessel movement, detect fraud, forecast supply chains and support customer engagement, according to the report.
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AI-Related Goods Are Reshaping Trade Growth
DMCC’s report shows that AI is also changing what gets traded, with AI-related goods, including semiconductors, servers and data centre hardware, expanding at five times the rate of non-AI goods in the first half of 2025.
AI-related goods represented 15% of global trade by volume, but they accounted for 43% of trade growth during that period, the report states.
This is reshaping which countries and corridors gain influence, with Malaysia, Singapore, Thailand and Vietnam together accounting for nearly 30% of global semiconductor export growth by 2024.
For ASEAN, this creates a major opportunity, as the region is already benefiting from supply chain diversification and rising demand for electronics, semiconductors, AI infrastructure and data centre equipment.
To capture the next phase of value, economies will need to move beyond assembly and into higher-value capabilities around design, deployment, services, data infrastructure and AI governance.
One of the key challenges in achieving this is the need for artificial intelligence to be integrated into existing systems and processes, rather than being seen as a separate entity.
AI in Banking and Fraud Detection
Anand Patil also used a banking example to show how AI is already operating in real‑world financial systems, describing a Singapore doctor who was targeted by scammers posing as government investigators.
DBS detected an anomaly as the transfer was being initiated, and when the analyst could not reach the doctor directly, she contacted his wife, a joint account holder, and the transfer was stopped before any money was lost.
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“That’s the partnership in action,” Anand Patil said. “AI was able to very quickly recognise the pattern, but it took a human to understand it and take action on it.”
This is a useful reminder that the most valuable AI use cases in trade and finance may not be fully autonomous, but rather hybrid systems where AI identifies risk, detects patterns and accelerates decisions, while human operators provide judgement, accountability and intervention.
Infrastructure Readiness Is the New Bottleneck
The report and Anand Patil’s remarks both suggest that AI adoption is no longer limited by ambition, but is increasingly constrained by readiness.
The problem is “not intent or ambition”, but whether enterprise infrastructure is prepared for AI workloads, Anand Patil said.
As AI becomes embedded in supply chains, ports, banks and customs systems, weak infrastructure could become a competitive disadvantage, Anand Patil warned.
The DMCC report also points to a widening talent gap, with more than 1.6 million open AI‑related positions worldwide, but only about half a million qualified professionals to fill them.
For companies, this means AI strategy must now include workforce planning, infrastructure investment and governance design, while for governments, it means AI readiness is becoming a national competitiveness issue.
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The operationalisation of AI is creating a divide between early movers and laggards, with only 14% of businesses describing their AI use as transformational or integrated, while more than 25% report no meaningful adoption.
This gap matters because AI will increasingly influence productivity, trade efficiency, compliance capability, market intelligence and customer responsiveness, Anand Patil said.
Companies that can integrate AI into real operations will be able to forecast demand more accurately, manage disruptions faster, reduce fraud risk, optimise logistics and improve decision‑making, while those that remain stuck in experimentation may find themselves falling behind not only technologically, but commercially.
For Asia and ASEAN, AI is both a market opportunity and a structural test, as the region is already important in AI‑related goods, particularly semiconductors and hardware, and is seeing rapid growth in digital services, data infrastructure, payments and logistics technology.
However, the next phase will require more than export growth, as it will require trusted AI systems, resilient networks, skilled talent, cybersecurity capability and policies that allow AI to operate safely across borders and sectors, Anand Patil noted.
Anand Patil’s Singapore examples show what that future looks
in practice, as AI is helping ports move containers, banks stop fraud, and cities manage complex operations.
For global trade, that is the deeper shift, as AI is becoming part of the operating system of commerce, and the winners will be the businesses and economies that build the infrastructure, talent and trust needed to deploy it at scale, Anand Patil said.
