2025 was the year of AI experimentation. Everyone, from startups to global enterprises, has been testing, trialing, and piloting new tools in search of productivity gains and creative breakthroughs. Asia Pacific (APAC) in particular is at the top of this list as the region continues to lead the world in AI adoption.
However, as 2026 begins, businesses will be asking a different question – no longer “What can AI do?” but “How will AI actually fit into the rhythm of everyday work?” With the focus moving away from dazzling demos toward practical, dependable systems that deliver value consistently, organisations need to realise that speed is no longer the currency of AI prowess – intention is.
So what are some top priorities businesses in APAC need to consider as they look to transition from trying AI to truly using it?
Align AI Tools with Organizational Priorities
In APAC, many organizations are still figuring out their AI posture. 90 percent of APAC companies are at the earlier principles and program stages, with less than 1 percent at the pioneer stage where AI is fully embedded and scaled across the organisation. What is stalling progress is not a lack of interest, but a significant surge, as most knowledge workers now face tool fatigue by constantly switching between the overwhelming number of tools, features, and “could-do” use cases that make it difficult to determine what truly matters.
Breaking out of this cycle means taking a more deliberate approach to AI procurement. That starts with understanding real business needs, the tools already in use, and whether new systems should be deployed organisation-wide or tailored to specific teams. Large organisations, for example, rarely function as monoliths. Not everyone needs the same tools — nor will the same tools deliver the same value across functions.
When companies shift away from one-size-fits-all deployments and instead match tools to the specific needs of each business unit, full-scale implementation becomes easier. But tailored does not have to mean fragmented. A unifying layer — a platform that connects different tools, enables search across systems, and keeps work organized — becomes essential for ensuring teams are not burdened by a heavier tech stack than before.
Bridge the Gap Between Personal and Organizational AI
Most people in APAC are already comfortable using AI personally — whether it is summarizing emails, generating content, or automating small tasks. Yet inside many workplaces, AI policies, governance structures, and integration plans are still being debated. The result is a growing gap between how individuals want to use AI and how their organisations enable them to.
This mirrors what we saw during the early cloud era. Back then, individuals started bringing cloud tools to work because they made life easier. Adoption spread bottom-up, not top-down. Today, we are seeing the same pattern with AI. People are learning how to use it on their own terms first — and organisations that can harness that grassroots momentum, rather than restrict it, will move faster and further.
For businesses looking to integrate AI thoughtfully in 2026, acknowledging and embracing this gap will be essential.
Context-aware AI becomes essential
So far, most AI systems understand individual context well. They know what you are working on, what you wrote last, or what information you need summarised. But the next breakthrough will be AI that understands context at the team and organisational level.
This is a much harder challenge. Drafting an email is simple; understanding that your team’s project archive, shared folder, and recent discussions all ladder up to a strategic business priority is not. When AI can connect these dots — securely, and without people having to manually feed it information — that is when AI becomes truly integrated into work.
This year, organizations will need systems that do more than handle tasks. They will need AI that understands their work environment, their shared information, and their collective goal.
Invest in skills to separate the best from the rest
Technology alone will not deliver the next wave of AI-driven productivity. Skills will. And not only technical skills, but human ones. Curiosity, adaptability, and prompt literacy are quickly becoming as essential as communication or analytical thinking.
Organizations that invest in teaching people how to think with AI, not just how to operate it, will build a more confident and more capable workforce. This goes beyond standard training sessions. It requires cultivating a culture where experimentation is encouraged, where employees can test new tools safely, and where insights are shared openly across teams.
A workplace like this also strengthens the talent pipeline. Increasingly, candidates evaluate employers based on their AI maturity and how effectively they empower teams with modern tools. AI will not replace human judgment or creativity, but people who know how to collaborate with AI will increasingly outperform those who do not.
Measure productivity differently in 2026
For decades, productivity has been measured in output: more files created, more emails sent, more meetings attended. But AI challenges that definition. If a system can draft ten reports in seconds, output alone is no longer a measure of value.
The next phase of productivity will be measured in impact. How effectively can teams turn information into insight? How quickly can they make decisions? How smoothly can they move from idea to execution? AI can help answer these questions by removing friction — automating repetitive tasks, surfacing relevant information, and giving people more time to focus on meaningful work.
For leaders, this will require treating AI not as a standalone project, but as part of the organisation’s core infrastructure — embedded into how teams search, organise, and collaborate. The most successful companies will combine technical innovation with cultural intelligence, understanding not only what AI can do, but how people in this region want to interact with it.
If 2025 was the year of experimentation, 2026 will be the year of real adoption – the year AI becomes part of everyday work.
Matthew Hong is Asia GTM lead at Dropbox, responsible for building and scaling partner ecosystems across the region. He works with distributors, Tier 1 partners and internal teams to drive co-selling, joint go-to-market initiatives and meaningful customer outcomes.
With a background spanning SaaS, enterprise technology and consulting, Matthew has led APAC partner strategies at companies including Dropbox and Procore, and previously founded his own consulting firm. He is particularly focused on helping organisations move from technology investment to real, everyday value.
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Featured image: Igor Omilaev on Unsplash
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