2026 is the year where the nature of work will change. After two years of rapid breakthroughs in agentic AI, businesses have hit the inflection point: AI is no longer a tool – it is becoming a teammate, a digital colleague woven directly into every workflow.
Across conversations with technologists and industry leader, one trend is unmistakable: Enterprises will aim to embed AI agents deep into their business processes, the same way e-mail and cloud computing became the backbone of modern work.
AI agents, autonomous software systems that perceive, reason and act, will sit beside people in the flow of work, not just automate tasks behind the scenes. Employees will work manage and collaborate alongside AI one or even dozens of AI agents.
As Nesan Govender, a managing director at consulting firm Accenture, put it, workers now need to learn how to work with and lead digital teammates.
Technical savviness, along with human strengths like judgement, critical thinking, problem-solving and interpersonal skills, will define performance, he noted.
Said Dr Leslie Teo, senior director at AI Singapore: “Automation is no longer about taking tasks off human hands, “it will complement human judgement, creativity, and decision-making”.
Every industry will feel this shift, from engineering and finance to media and healthcare, he added.
Govender cautions that the human resource (HR) departments must step up. Traditional frameworks for performance, remuneration and productivity were not designed for human-AI teams and will have to be reframed.
Questions like how output will be measured, when it is co-produced, how roles are re-designed, and what “performance” even means will dominate leadership and HR agendas in 2026.
Digital stewardship
HR and workforce transformation is only part of a complex equation in the deployment of AI. An emerging critical issue is digital stewardship – how organisations govern digital assets and models, corporate and situational context that now hold competitive value.
Ben Tan, senior executive partner of Gartner Advisory in Asia-Pacific, emphasises one urgent point: Enterprises must own the entire prompt-to-outcome lifecycle.
Prompt generation has become more sophisticated, he pointed out, noting that they now incorporate internal and external knowledge bases, conversation histories, domain rules and situational context and others into prompts.
Thus every element from context to output becomes sensitive intellectual property, he added.
“If I instruct the LLM with all that internal and external context, you must own all the information, output and analyses,” he said.
“Ownership must be locked down so that no one can touch it,” he stressed. “Failing to do so risks confidential business data flowing into public models.”
The budget check
AI also upends traditional budgeting. Compute usage and therefore costs often grow with adoption. Instead of predictable annual capital expenditure for hardware and software, AI shifts organisations into operating expense model where day-to-day costs can quickly balloon quickly.
This can be mitigated if AI is governed like any strategic investment, says Peter Marrs, president of Dell Technologies, Asia-Pacific, Japan and Greater China. “Every dollar should be tied to a clear value proposition, with transparent usage, ownership, and payback expectations”.
His advice: Focus on a small number of shared AI platforms with strong governance and financial operations discipline. Make business leaders directly accountable for the compute consumed and the outcomes delivered, so that AI spend grows in line with return on investment rather than hype, he added.
Yet, for many enterprises especially small- and medium-sized ones, budgets are not the core hurdle. It is organisational readiness. They want to move beyond pilots. But infrastructure gaps, skills shortages and unclear governance slow progress, said Govender.
Globally, enterprises fall into three buckets: the front runners and fast followers reinventing themselves to be AI-first; the intermediate runners centralising data and deploying vertically like finance or HR; and the slow starters struggling with fragmented systems, unstructured data and low adoption of cloud, he explained.
Southeast Asia is likely to mirror the same pattern, he noted. However, the AI adoption momentum in the region will build up speed in the next 12 to 24 months as enterprises unify their data and plug their infrastructural gaps, he added.
He also noted that large conglomerates in the region are already deploying AI projects in various areas and exploring how their backroom services such as logistics can be turned into commercial offerings for smaller firms in their ecosystems.
One sector poised for quick adoption is the financial industry, he added. “The nature of banking is that it is very rules driven with clear policies and regulatory guidelines, so it is much easier to apply AI.”
In Singapore, banks have expressed their desire to be AI-first banks.
Singapore’s DBS Bank has been steadily building its AI capabilities and was recently named the World’s Best AI Bank by Global Finance’s inaugural AI in Finance Awards.
Other sectors that are quickly getting on the AI train are pharmaceutical firms and oil and gas companies.
To be sure, agentic AI will be the emerging force reshaping work. But the real edge for enterprises will not come from who buys the most advanced model.
It will come from who uses AI intelligently alongside human talent to elevate workers, speed up insight and redesign roles around higher-value, judgement-driven work.
