How agentic AI can boost engagement and ease staff burnout

Time of India

11-06-2025

By Mahesh Raja
AI agents are rapidly advancing, with Southeast Asia emerging as a key region to watch.
Asia is racing to adopt GenAI at a pace that surpasses other regions, with APAC now second only to North America in the adoption of the
technology
.
An
April 2025 report
from
Boston Consulting Group
surveyed over 200 companies in the ASEAN-6 (Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam). The report noted that the combination of predictive AI, GenAI, and
Agentic AI
is contributing to enterprise innovation by enabling intelligent and personalised engagements at scale.
Going deeper, the report found that 92% of knowledge workers in Indonesia are already using GenAI tools at work, surpassing global (75%) and Asia Pacific (83%) averages. Agentic AI promises to push the gains of GenAI to new heights, and countries with high penetration rates will benefit more quickly.
The transition from passive '
Information Generators
' to 'Active Completion Agents' is expanding across various business applications. These autonomous systems can now plan and execute multi-step tasks beyond chatbot capabilities.
What's more? Agentic AI comes at a time when enterprise organisations are facing significant problems across the workforce.
For Southeast Asia, with employee burnout at a record high of
83%
, emerging technology solutions like Agentic AI could help in two main ways. First, it can ease the pressure on employees by sourcing tasks and actions to AI. Secondly, it can be used to support adaptive employee services, products, and experiences.
In summary, Agentic AI has the potential to support individual executives across each department of the enterprise, boosting productivity and engagement while easing the critical levels of
staff burnout
seen in recent years.
With high levels of AI adoption already present, the switch to Agentic AI is the next natural stage of evolution and one set to bring transformative benefits to the satisfaction of team members.
The move from AI as a tool to AI as autonomous assistance
Enterprise teams are likely to have a strong relationship with AI tools already for certain tasks and workflows. For instance, most recruitment platforms already use AI to filter through resumes, while GenAI is used to draft job adverts and emails. AI can also help with professional development coursework and screen requests coming through to HR.
This means that the shift to Agentic AI will be an evolution. As Agentic AI solutions begin to emerge, workplace tools are beginning to act independently, make decisions, and execute complex workflows with minimal human input.
Agentic AI will likely change how companies and HR teams
manage the workforce
and productivity, talent acquisition (TA), learning and development (L&D), and even employee engagement.
HCM platforms, such as Oracle and
Workday
last month or
SAP
late last year, have announced product developments and changes that incorporate agentic systems inside their platforms.
Take talent acquisition and recruiting as an example. Agentic AI can autonomously source candidates, assess résumés, schedule interviews, and even conduct initial screenings, all while ensuring legal compliance and adherence to company hiring and diversity policies. Unlike traditional
AI-powered ATS tools
, which require recruiter intervention, an Agentic AI hiring system could refine job descriptions, adjust outreach strategies based on candidate response rates, and handle logistics end-to-end.
In summary, Agentic AI promises to relieve the burden on hiring teams so that they can focus on recruitment strategy and company culture initiatives. However, cross-organisational buy-in will be key.
Work across departments to deploy Agentic AI
While an estimated
25% of enterprises
are planning to deploy AI agent technology in 2025, this figure means that the vast majority of large organisations are yet to recognize the potential of this tool.
For decision-makers still on the fence, it's important to temper the benefits of agents, such as reducing unnecessary costs arising from process inefficiencies, human errors, and manual processes, against potential risk factors to move forward with the decision. For large enterprises, concerns typically relate to integration complexities and legacy dependencies. Internal data silos, ethical considerations and governance challenges.
These are all valid challenges that need a clear plan of action. For this reason, cross-functional teams are the ideal candidates to spearhead the adoption of AI agents in 2025. This is because they have the clearest insights into where cross-functional handoffs might create friction, the departments with the most pressing need, and the occasions where high-value activities may stall due to fragmentation.
A sensible starting point lies with high-performing employees with a strong mastery of cross-functional processes. These individuals can use these to create pilot projects that allow AI agents to learn how to handle complex organisational workflows and the tasks within that deliver toward the set goals.
Another sensible strategy is to deploy agents in clusters so that they can learn from each other and make decisions that drive efficiency and productivity. It's also important to remember that these don't exist in a vacuum. While AI agents represent a leap forward from existing co-pilot tools, even the actions of a human co-worker need to consider colleagues and other contributions.
This means AI adoption needs to involve the employees who will work alongside these intelligent digital assistants to monitor progress, create performance feedback loops, reward good performance, and tackle friction points head-on.
With these pilot projects underway, enterprise organisations can move off the starting block in 2025 without delay and prove the business value of AI agents needed to justify further usage.
Governance of AI Agents within the Enterprise Model
The demand for AI agents will rise further as the competitive advantages of their autonomous actions become clear. For enterprise organisations, this will demand strong governance.
As with all forms of AI, the capabilities of AI agents directly correlate to the data at their disposal.
This has a major impact on how quickly an ROI can be generated.
Although the excitement around AI means that two-thirds of the respondents to Forrester Research's
2024 State of AI Survey
believe their organisations would require less than 50% return on investment to consider their AI initiatives successful, this percentage doesn't need to be the standard. While some degree of error has been accepted as standard within AI, there are strategies available to reduce the associated risks that pose a concern for business leaders.
Leaders need to agree in advance on quantifying what leaders class as 'good enough' in terms of results and agreeing on acceptable error rates. Research from Ness
Digital Engineering
found that 71% of high-performing systems for industry can achieve autonomy without compromising reliability. Thanks to clear requirements for the AI agents and a modular design system.
This shows that enterprises don't need to strive for immediate perfection here. Moving beyond pilot projects toward organisation-wide implementation can take a measured approach with both data and strategy.
Here, leaders can begin to implement AI agents in the places where data is the strongest. This is also true when it comes to deciding how to roll out the technology. These don't need to stretch across every system and process, especially not in the earliest stages. As with human co-workers, AI agents can be applied to the pressure points most in need of support or which deliver the highest-impact activities.
From here, the technology can be trained and optimised to perform autonomously within this area, rather than striving for enterprise-wide performance and excellence, which will require significant time and resource investment. With these hotspots clearly identified, guardrails and governance can be applied to allow these agents to work autonomously with appropriate management.
Agentic AI represents a transformative shift for enterprises, transcending basic automation to introduce systems capable of planning, decision-making, and execution with minimal human intervention.
About the author
Mahesh Raja
is the Chief Growth Officer at
Ness Digital Engineering
.