What was once the stuff of science fiction has become our everyday reality. Today, generative pre-trained transformers and large language models (LLMs) are woven into the fabric of our daily lives and business operations, helping to optimise logistics, generate targeted marketing recommendations, and power chatbots, among many use cases.
The next frontier of AI innovation is agentic AI, where AI agents will possess the intelligence and judgement to autonomously plan and execute intricate workflows. In Asia-Pacific (APAC), a TechTarget study found that 70% of organisations are already planning to integrate or consider the use of AI agents. As AI agents can independently perform complex tasks and adapt to new situations with minimal human intervention, they have the potential to significantly redefine how we approach work across critical sectors such as healthcare and finance, and transform economies.
A new work paradigm typified by agentic AI and automation
Agentic AI leverages the power of LLMs, machine-learning techniques, and enterprise integration to accelerate decision-making and boost efficiency across organisations. AI agents can now handle complex, dynamic tasks that previously required human intelligence.
They can set goals, design, and optimise processes to get work done. These agents can autonomously decide on actions or direct other agents to initiate processes, continuously learning from experience to enhance their performance over time.
For example, agentic AI can help insurance companies automate the entire claims process, from initial filing to final payout. An agentic-AI-powered assistant can quickly evaluate the validity of a claim, gather relevant information from multiple sources, and communicate clearly and empathetically with customers. This results in faster processing times, reduced errors, and a vastly improved customer experience. It can speed up the claims process and also reduce the workload for human adjusters, allowing them to concentrate on more complex cases and deliver a higher level of personalised service.
Preparing for a robotic and agentic future
The agentic future is upon us, but a gap exists between vision and reality. Beyond simply implementing AI and automation, organisations must rethink their workflows, decision-making processes, and structures to build an operating model that aligns capabilities with practical, day-to-day operations to capitalise on agentic AI.
Just as effective employees struggle to thrive in chaotic workplaces, AI agents also require a well-organised infrastructure to perform at their best, whether they are specialised agents focused on specific tasks like invoice processing or more versatile ones. The key to success lies in orchestration, which creates the framework for directing multiple agents. While AI agents excel at executing tasks across systems, this complexity can lead to confusion — especially with other elements, such as robots and generative AI, in the mix. A central coordinator ensures that all agents collaborate effectively toward business goals. It establishes governance and facilitates human oversight, striking the right balance between granting agents the autonomy they need and providing people with the control they require.
Additionally, organisations can build on existing processes instead of starting from scratch to transition smoothly into agentic automation. For instance, a company may already be using advanced intelligent document processing to extract insights from documents. Agentic automation can take it a step further by automating the subsequent actions.
An agentic future also means that the workforce needs to adapt to new roles and skills. Democratising innovation and fostering a culture of continuous learning will empower workers at all skill levels to embrace digital transformation. By equipping employees with the skills to harness new technologies like agentic AI and automation, businesses can optimise operations and set in motion a cycle of continuous innovation within the organisation as employees become more productive. Many organisations initiate citizen developer programs where employees identify personal pain points they experience daily at work. With basic training, these employees can create automation solutions to address those issues.
As organisations in Singapore explore more use cases, their AI strategy should include initiatives to deepen employees’ understanding of the benefits and limitations of AI and automation. Centres of excellence can play a pivotal role in this process. They provide ongoing support to employees by offering expert guidance, addressing questions, and sharing best practices that bridge knowledge gaps. For instance, Singapore’s new Sectoral AI Centre of Excellence for Manufacturing empowers organisations to boldly experiment with AI solutions and learn from failures in areas like quality assurance, operations optimisation, predictive maintenance, product design, and industrial automation.
The human agent won’t be going away
Ultimately, even as agentic AI assumes more roles across industries, the human worker retains a crucial and enduring role in an increasingly AI-first workplace. While AI can learn from data, it will still require human input to adjust learning algorithms, interpret results, and refine models. Human workers will remain the real agents of change, guiding the development and implementation of an organisation’s agentic AI vision and leveraging the power of AI to drive sustainable business results.
