Today, around one in two consumers use AI platforms such as ChatGPT, Gemini and Perplexity as their primary research tool, not a search engine or a brand website, but AI. That signals something far more significant than the rise of a new technology. It reflects a fundamental shift in how customers discover, evaluate and engage with brands, one that most enterprises were not built for.
Across Singapore and the wider Asia Pacific and Japan (JAPAC) region, AI is no longer simply a question of adoption. It is increasingly becoming the environment through which customers discover, evaluate and make decisions. That fundamentally changes what it means to have a strong digital presence and raises a strategic question for every enterprise: how well are a brand’s content, data and customer experiences structured to be found, interpreted and acted upon by AI?
The gap between investment and impact
Brand visibility in AI environments is increasingly determined before a customer ever reaches a company’s owned channels. AI-powered search, conversational interfaces and multimodal experiences are reshaping the path to engagement, changing not only how businesses operate, but how demand is created and decisions are made.
By now, most large organizations have made meaningful investments in AI. Tools have been deployed, pilot programs have demonstrated tangible gains, and many functions are already seeing measurable improvements in efficiency. In fact, more than one-third (36 percent) of organizations globally consider themselves ahead of the curve in AI-enabled customer experience maturity.
Yet investment alone is no longer the differentiator.
AI capabilities built in isolation and optimized for individual functions create the appearance of progress, but that rarely changes how the business operates as a whole.
From AI capability to enterprise scale
For organizations operating across JAPAC, that challenge is complex. Almost half (46 percent) say their existing data quality and accessibility are inadequate for AI at scale. In multi-market businesses, content, data and customer workflows are often owned by different teams, with no single function holding an end-to-end view of how they connect.
Regional complexity only adds to that challenge. What works in Singapore or Australia may not translate directly to markets such as the Philippines or Vietnam, where customer behavior, digital maturity and operating environments differ significantly. The task for regional leaders is not to eliminate that variation, but to build systems that maintain strategic coherence while giving local teams the flexibility to respond to market needs.
That balance is difficult, and organizations often underestimate the time, coordination and organizational discipline it requires. The alternative, however, is far more costly. Connected systems create compounding value, while isolated systems create compounding debt.
Building this coherence is necessary, but not sufficient. There is one further condition that ultimately determines whether AI delivers value at scale: trust. The barriers to adoption are rarely technical; they stem from a lack of confidence in how AI reaches its conclusions and how much control people retain. Adobe’s research finds that trust is built when AI shows its work, when users can see the reasoning behind a recommendation and retain the ability to review or reverse it. These are the conditions under which people incorporate AI into their core workflows rather than work around it.
The discipline behind value
Ultimately, AI is only as valuable as the discipline behind it.
The conversation must move beyond the business case for AI adoption to the why: what role is AI expected to play in the business, and how does that ambition shape the way the organization is built?
Across JAPAC, we are seeing two distinct approaches emerge. Some organizations continue to treat AI as a collection of tools deployed to solve specific business problems. Others are using AI to rethink how the organization operates, connecting data, content and workflows so intelligence becomes embedded across the business rather than confined to individual functions.
Neither approach is inherently right or wrong. But the organizations seeing the greatest long-term impact tend to share one characteristic: they are clear about the outcomes they are trying to achieve and deliberate about how AI supports those outcomes.
Changi Airport Group in Singapore, for instance, has moved beyond deploying AI for isolated efficiency gains, using data and automation to reduce friction across the entire traveler journey, from pre-departure to arrival.
The goal is not to bolt solutions onto existing processes, but to redesign how those processes work. That distinction between AI as a tool and AI as an operating principle is what separates organizations building for the next phase from those still optimizing the last one. Business leaders across JAPAC are more likely to succeed in the next phase of AI adoption if they begin making deliberate organizational decisions today that allow AI to deliver value tomorrow.
Ben Goodman is President, JAPAC at Adobe.
Editor’s note: This contributed article has been lightly edited for TNGlobal style and clarity. The views and arguments expressed remain those of the author.
Share your perspective: TNGlobal welcomes contributed insights and expert commentary from across Asia’s technology and innovation ecosystem. Submit a contribution for editorial consideration, or explore more conversations in our TNGlobal INSIDER and TNGlobal Q&A and Interviews archives.
Featured image: Steve A Johnson on Unsplash