Agentic AI is being talked about as the next major wave of artificial intelligence, but its meaning for enterprises remains to be settled. Capgemini Research Institute estimates agentic AI could unlock as much as US$450 billion in economic value by 2028. Yet adoption is still limited: only 2% of organisations have scaled its use, and trust in AI agents is already starting to slip.
That tension – high potential but low deployment – is what Capgemini’s new research explores. Based on an April 2025 survey of 1,500 executives at large organisations in 14 countries, including Singapore, the report highlights trust and oversight as important factors in realising value. Nearly three-quarters of executives said the benefits of human involvement in AI workflows outweigh the costs. Nine out of ten described oversight as either positive or at least cost-neutral.
The message is clear: AI agents work best when paired with people, not left on autopilot.
Early steps, slow progress
Roughly a quarter have launched agentic AI pilots, while only 14% have moved into implementation. For the majority, deployment is still in the planning stage. The report describes this as a widening gap between intent and readiness, now one of the main barriers to capturing economic value.
The technology is not just theoretical – real-world applications are starting to emerge, and one example is a personal shopping assistant that can search for items based on specific requests, generate product descriptions, answer questions, and place items in a cart using voice or text commands. While these tools typically stop short of completing financial transactions for security reasons, they already replicate many of the functions of a human assistant.
This raises bigger questions about the role of traditional websites. If AI can handle tasks like searching, comparing, and preparing purchases, will people still need to navigate online stores directly? For those who find busy websites overwhelming or difficult to navigate, an AI-driven interface may offer a simpler, more accessible option.
Defining agentic AI
To cut through the hype, AI News spoke with Jason Hardy, chief technology officer for artificial intelligence at Hitachi Vantara, about how enterprises in Asia-Pacific should think about the technology.
“Agentic AI is software that can decide, act, and refine its strategy on its own,” Hardy said. “Think of it as a team of domain experts that can learn from experience, coordinate tasks, and operate in real time. Generative AI creates content and is usually reactive to prompts. Agentic AI may use GenAI inside it, but its job is to pursue objectives and take action in dynamic environments.”
The distinction – between producing outputs and driving outcomes – captures the meaning of agentic AI for enterprise IT.
Why adoption is accelerating
According to Hardy, adoption is being driven by scale and complexity. “Enterprises are drowning in complexity, risk, and scale. Agentic AI is catching on because it does more than analyse. It optimises storage and capacity on the fly, automates governance and compliance, anticipates failures before they occur, and responds to security threats in real time. That shift from ‘insight’ to ‘autonomous action’ is why adoption is accelerating,” he explained.
Capgemini’s research supports this. The study found that while confidence in agentic AI is uneven, early deployments are proving useful when the technology takes on routine but essential IT tasks.
Where value is emerging
Hardy pointed to IT operations as the strongest use case so far. “Automated data classification, proactive storage optimisation, and compliance reporting save teams hours each day, while predictive maintenance and real-time cybersecurity responses reduce downtime and risk,” he said.
The impact goes beyond efficiency. The capabilities mean systems can detect problems before they escalate, allocate resources more effectively, and contain security incidents more quickly. “Early users are already using agentic AI to remediate incidents proactively before they escalate, strengthening reliability and performance in hybrid environments,” Hardy added.
For now, IT remains the most practical starting point: its deployment offers measurable results and is central to how enterprises manage both costs and risk, showing the meaning of agentic AI in operations.
Southeast Asia’s starting point
For Southeast Asian organisations, Hardy said the first priority is getting the data right. “Agentic AI delivers value only when enterprise data is properly classified, secured, and governed,” he explained.
Infrastructure also matters, meaning that agentic AI requires systems that can support multi-agent orchestration, persistent memory, and dynamic resource allocation. Without this foundation, adoption will be limited in scope.
Many enterprises may choose to begin with IT operations, where agentic AI can pre-empt outages and optimise performance before rolling out to wider business functions.
Reshaping core workflows
Hardy expects agentic AI to reshape workflows in IT, supply chain management, and customer service. “In IT operations, agentic AI can anticipate capacity needs, rebalance workloads, and reallocate resources in real time. It can also automate predictive maintenance, preventing hardware failures before they occur,” he said.
Cybersecurity is another area of promise. “In cybersecurity, agentic AI is able to detect anomalies, isolate affected systems, and trigger immutable backups in seconds, reducing response times and mitigating potential damage,” Hardy noted.
The capabilities are not limited to proof-of-concept trials. Early deployments already show how agentic AI can strengthen reliability and resilience in hybrid environments.
Skills and leadership
Adoption will also require new human skills. “Agentic AI will shift the human role from execution to oversight and orchestration,” Hardy said. Leaders will need to set boundaries and monitor autonomous systems, ensuring they stay in ethical and organisational limits.
For managers, the change means less focus on administrative tasks and more on mentoring, innovation, and strategy. HR teams will need to build governance skills like auditing readiness and create new structures for integrating agentic AI effectively.
The workforce impact will be uneven. The World Economic Forum predicts that AI could create 11 million jobs in Southeast Asia by 2030 and displace nine million. Women and Gen Z are expected to face the sharpest disruptions, with more than 70% of women and up to 76% of younger workers in roles vulnerable to AI.
This highlights the urgency of reskilling, and major investments are already underway, with Microsoft committing $1.7 billion in Indonesia and rolling out training programmes in Malaysia and the wider region. Hardy stressed that capacity building must be inclusive, rapid, and strategic.
What comes next
Looking three years ahead, Hardy believes many leaders will underestimate the pace of change. “The first wave of benefits is already visible in IT operations: agentic AI is automating tasks like data classification, storage optimisation, predictive maintenance, and cybersecurity response, freeing teams to focus on higher-level strategic work,” he said.
But the larger surprise may be at the economic and business model level. IDC projects AI and generative AI could add around US$120 billion to the GDP of the ASEAN-6 by 2027. Hardy sees the implications as broader and faster than many expect. “The suggests the impact will be much faster and more material than many leaders currently anticipate,” he said.
In Indonesia, more than 57% of job roles are expected to be augmented or disrupted by AI, a reminder that transformation will not be limited to IT. It will cut in how businesses are structured, how they manage risk, and how they create value.
Balancing autonomy with oversight
The Capgemini findings and Hardy’s insights converge on the same theme: agentic AI holds huge promise, but its meaning in practice depends on balancing autonomy with trust and human oversight.
The technology may help enterprises lower costs, improve reliability, and unlock new revenue streams. But without a focus on governance, reskilling, and infrastructure readiness, adoption risks stalling.
For Southeast Asia, the question is not whether agentic AI will take hold, but how quickly – and whether enterprises can balance autonomy with accountability as machines begin to take on more responsibility for business decisions.
(Photo by Igor Omilaev)
See also: Beyond acceleration: the rise of agentic AI
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