Imagine it is 2026, a semiconductor plant in Penang, Malaysia, is running at peak efficiency—not because of more workers, but because of agentic AI.
Autonomous digital agents, each with its own goals, context, and decision rights, now orchestrate production lines, dynamically rerouting workflows when a machine malfunctions or a shipment is delayed. These aren’t rule-based bots—they reason, collaborate, and learn.
In Vietnam, agentic AI forecasts monsoon-driven port congestion weeks in advance, renegotiating logistics routes with shipping partners via API, without human intervention(but with human oversight). Across ASEAN, supply chains are no longer merely reactive; they are now anticipatory.
For COOs, this is transformative. Agentic AI slashes unplanned downtime by up to 50%, optimises inventory in real time, and ensures compliance across fragmented regional regulations. Imagine AI agents acting as autonomous supply chain managers—balancing cost, carbon, and speed across Thailand, Indonesia, and India.
The future isn’t just automation—it’s intelligent agency. Leading manufacturers are already piloting multi-agent systems that simulate, make decisions, and take action. The question is no longer if you adopt agentic AI, but how fast you can scale it. The next competitive edge is self-driving operations.
Beyond the hype of agentic AI
Gartner’s recent forecasts serve as a sobering reality check amidst the growing enthusiasm surrounding agentic AI. Over 40% of agentic AI projects are projected to be cancelled by the end of 2027, driven by accelerating project costs, unclear business value, and insufficient risk controls.
Anushree Verma
“Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied. This can blind organisations to the real cost and complexity of deploying AI agents at scale, stalling projects from moving into production,” says Anushree Verma, senior director analyst at Gartner.
A January 2025 Gartner poll revealed only 19% of surveyed organisations had made significant investments in agentic AI, with a further 42% limiting their exposure to conservative pilot projects.
Alarmingly, “agent washing”—where vendors rebrand conventional bots or digital assistants as ‘agentic AI’—is rife, with Gartner estimating only about 130 genuine agentic AI vendors among thousands. As COOs, cutting through this noise is paramount.
Embrace no-code agent platforms
“You can get started with agentic AI today, and you don’t need to code everything manually. There are no-code platforms that allow product teams and operating teams to create, manage and deploy agents simply by setting parameters and objectives.” Tony Tay
Tony Tay, founder and CEO ofAgileAlgo, emphasises that no-code agent platformsare lowering the barrier for adoption. For COOs under pressure to digitalise without ballooning IT headcount, this represents a seismic shift.
By democratising access, business and ops teams can prototype, refine and launch AI agents targeted at operational pain points—without waiting for scarce data scientists.
Prioritise business value delivery
Gartner cautions against falling for vendor hyperbole or applying agentic AI indiscriminately. Significant returns are reserved for projects that clearly “enhance resource efficiency, automate complex tasks and introduce new business innovations, beyond the capabilities of scripted automation bots and virtual assistants.”
Tony Tay
“Chief operating officers care about business outcomes. They want to see ROI quickly: faster order fulfilment, fewer manual errors, lower costs, 24x7 customer support, those kinds of measurable results.” Tony Tay
Aligning agent use cases with real business challenges—such as supply chain disruptions, regulatory compliance, or scaling customer service—will determine project success.
Gartner recommends deploying agentic AI only where a clear ROI or competitive advantage is evident, especially given the risks of scope creep and costly system integrations.
Evolution, not replacement, of workflow
“Agentic AI doesn’t just automate tasks—it reasons, plans, and adapts to goals. If you drop agents into a legacy process, you end up with more headaches. You need to reimagine how things are done from the ground up.” Tony Tay
Integrating AI agents with legacy environments can disrupt existing workflows and necessitate substantial system modifications. The most successful implementations involve rethinking processes to capitalise on agentic autonomy, from dynamic routing in logistics to real-time inventory optimisation.
Build Developer and Domain-Specialised Agents
While no-code platforms are essential for rapid operational deployment, COOs also need flexibility for complex, enterprise-specific scenarios.
Tay comments that for more complex problems—like fraud detection or dynamic pricing—"you need developer agent platforms. They let you build deep, custom agents that integrate with your systems and data sources.”
He cites the example of within the sectors like banking or healthcare, domain-specialised agents are critical. "They understand the regulations, the data formats, the nuances unique to that industry,” he observes.
This dual approach of balancing ease of use for most business needs with deep customisability for mission-critical processes empowers COOs to deliver consistent performance improvements across all layers of their operations.
Agentic AI drives autonomy and efficiency
By 2028, Gartner predicts that at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024. A third of enterprise software applications will include agentic AI capabilities, radically shifting how companies operate and innovate.
Tay believes that the difference is autonomy. "Traditional automation follows rules. Agentic AI pursues outcomes. It makes choices along the way, even in situations it hasn’t seen before," he concludes.
This transition from rigid automation to autonomous, outcome-driven agents gives COOs unparalleled levers to simultaneously reduce costs, enhance service quality, accelerate cycle speed, and maximise operational scalability.
Prepare for enterprise-scale risks and rewards
Despite the immense potential, Gartner warns that delivering enterprise value from agentic AI requires a strategic approach.
“To get real value from agentic AI, organisations must focus on enterprise productivity, rather than just individual task augmentation,” says Verma.
For COOs, this means:
Start with high-ROI domains, such as logistics, compliance, or customer engagement.
Invest in skills and governance to manage AI risk, costs and outcomes.
Demand transparency from vendors about what ‘agentic’ means, avoiding solution rebranding and hype.
Build agile pilot teams to iterate, assess and scale successful agentic use cases rapidly.
Agentic AI survival blueprint for COOs
In summary, agentic AI is fast becoming not just a competitive advantage but a necessity for business survival in Asia’s digital race.
COOs who champion a transparent, disciplined approach—balancing innovation with ROI discipline, starting small yet aiming for scale, and building both business-user and developer agent capabilities—will win.
“In this AI race, the only path to survival is to embrace agentic capabilities. You cannot afford to wait. The window to get ahead is now.” Tony Tay
Click on the PodChats player to discover Tay’s approach to harnessing the power of agentic AI to drive your next-generation automation strategies.
Please give us a brief about AgileAlgo.
To what extent is agentic AI a good fit for process operations?
Which functions or workflows are the biggest candidates for autonomous agent-driven transformation?
What new skills or roles should we develop among our workforce to maximise human-AI collaboration?
What measurable ROI have early adopters in Asia achieved after implementing agentic automation?
What human oversight is necessary to maintain trust and accountability in automated decisions?
Gartner predicts that by 2027, 40% of organisations will have failed Agentic AI projects. Other analysts estimate that up to 70% of current systems are hybrid and may be tied to a legacy system. How do we ensure agentic AI solutions integrate smoothly with existing IT and business systems?
With technology refreshes occurring faster than in the past, how do you envision agentic AI reshaping core operational processes over the next 12–24 months?
What is your advice for COOs and other functional leaders, who may work closely with the CIO and IT teams, on optimising and improving their use of agentic AI technologies?
As for CFOs, how should they approach the adoption of agentic AI?
Allan is Group Editor-in-Chief for CXOCIETY writing for FutureIoT, FutureCIO and FutureCFO. He supports content marketing engagements for CXOCIETY clients, as well as moderates senior-level discussions and speaks at events.
Previous Roles
He served as Group Editor-in-Chief for Questex Asia concurrent to the Regional Content and Strategy Director role.
He was the Director of Technology Practice at Hill+Knowlton in Hong Kong and Director of Client Services at EBA Communications.
He also served as Marketing Director for Asia at Hitachi Data Systems and served as Country Sales Manager for HDS’ Philippine. Other sales roles include Encore Computer and First International Computer.
He was a Senior Industry Analyst at Dataquest (Gartner Group) covering IT Professional Services for Asia-Pacific.
He moved to Hong Kong as a Network Specialist and later MIS Manager at Imagineering/Tech Pacific.
He holds a Bachelor of Science in Electronics and Communications Engineering degree and is a certified PICK programmer.
