Introduction to Agentic AI
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.
The Importance of Trust and Oversight
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.
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.
Defining Agentic AI
To cut through the hype, Jason Hardy, chief technology officer for artificial intelligence at Hitachi Vantara, defines agentic AI as software that can decide, act, and refine its strategy on its own. “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.”
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.
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.
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.
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.
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.
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.
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.
Conclusion
Agentic AI is a rapidly evolving field that has the potential to unlock significant economic value for enterprises. However, its adoption is still limited, and trust in AI agents is starting to slip. To realise the benefits of agentic AI, enterprises must focus on trust, oversight, and human involvement in AI workflows. They must also develop new skills and leadership capabilities to manage autonomous systems and ensure they stay within ethical and organisational limits.
FAQs
Q: What is agentic AI?
A: Agentic AI is software that can decide, act, and refine its strategy on its own, operating in real time and pursuing objectives in dynamic environments.
Q: What are the benefits of agentic AI?
A: Agentic AI can help enterprises lower costs, improve reliability, and unlock new revenue streams by automating tasks, optimising storage and capacity, and responding to security threats in real time.
Q: What are the challenges of adopting agentic AI?
A: The challenges of adopting agentic AI include developing trust in AI agents, ensuring human oversight and involvement in AI workflows, and developing new skills and leadership capabilities to manage autonomous systems.
Q: What is the future of agentic AI?
A: The future of agentic AI is promising, with potential applications in IT operations, supply chain management, and customer service. However, its adoption will depend on balancing autonomy with trust and human oversight.
