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A study of 461 senior executives points to unclear ownership, weak coordination and limited workflow integration as barriers to scaling AI – not technology alone.
Most companies have AI initiatives running across different teams and functions, with pilots launched, tools deployed and roadmaps in place. However, progress often slows when organisations try to scale these initiatives.
Research by Adobe, conducted with Incisiv, suggests that organisational readiness is a stronger predictor of whether AI adoption scales than the technology itself.
The study surveyed 461 senior executives across industries in Southeast Asia who have authority over AI investment, alongside in-depth interviews examining the opportunities and challenges of AI adoption.
While companies may use the same models, spend similar amounts and hire comparable teams, their results can differ depending on how they organise their operations. This includes who owns AI, how outcomes are measured, how business and IT teams work together, and how workflows are redesigned.
AI adoption stalls across Southeast Asia
Among the respondents in Southeast Asia, approximately 57% are stuck in what the study calls the “frozen middle”, where companies are piloting or deploying AI but lack the coordination needed to scale adoption. For agentic AI specifically, the figure stands at 40%.
Moving from experimentation to initial deployment also takes time. Six in 10 organisations need at least six months to make the transition, while almost one-third require 12 months or longer.
These extended timelines can hinder momentum, with 48% of organisations still working on their first AI use case or having stopped altogether after an unsuccessful attempt.
For many companies, AI tools are already in production and early results are emerging. However, they lack an enterprise-wide operating model to support wider adoption. As a result, functions may progress independently, while new pilots launch and existing initiatives fail to advance.
Only 6.3% of Southeast Asian companies in the dataset, representing 27 organisations, have scaled AI across multiple workflows and established an operating model to sustain it, the survey highlighted. These companies are classified as “leaders” and convert pilots into production at nearly four times the rate of other organisations.
Meanwhile, approximately 79% of companies are classified as nascent, meaning they are at an early stage of both AI deployment and operating model maturity. This is the highest proportion in the Asia Pacific region.
The findings come as 73% of companies increase their AI investment year on year, while almost 60% believe they are still underinvesting. This places greater emphasis on the organisational foundations needed to turn investment into adoption and measurable value.
Organisational readiness sets AI leaders apart
Across industries, organisations tend to score better in strategy and governance than in structure and workflows, the study found.
This may be because approving a policy or signing off on a strategy can be done in a meeting, while changing roles, redistributing decision-making authority and redesigning how teams operate require more time.
Southeast Asian Leaders recorded higher-than-average scores in structure, which covers dedicated ownership and accountability, and workflows, which measures how deeply AI is embedded in business processes.
Their maturity in these areas exceeded the regional average by 67% and 59%, respectively.
When executives were asked what they would do differently, their responses pointed to decisions they wished they had made earlier. These included setting clearer metrics, securing budgets sooner, appointing an owner, involving IT earlier and investing more in change management.
In some cases, these foundational decisions were made only after deployment had begun, by which point the opportunity to establish a baseline for measuring progress had passed.
Only 15% of AI ideas reach production
Of every 100 AI ideas proposed by Southeast Asian organisations, approximately 15% make it into production, according to the study.
About 36% of proposals do not progress beyond IT evaluation, often because business teams lack the architectural and data capabilities needed to scope implementation effectively.
Another 18% are rejected during security reviews. This rejection rate nearly doubles in highly regulated industries, including financial services, healthcare and life sciences.
The findings suggest that the challenge is not a lack of ideas, but the absence of established pathways to move them into production. These include pre-approved integration methods, standardised security reviews and established budget categories.
Without these mechanisms, proposals must navigate new or different processes, reducing their chances of progressing.
Skills shortages remain another obstacle, ranking as the third most common barrier to scaling AI. Some 81% of businesses said their skills shortfall had remained unchanged or worsened over the past 12 months.
At the same time, more than half of organisations are expanding existing roles to include AI, while 21% are introducing new skill requirements.
Five ways companies can scale AI successfully
The study identifies five practices that distinguish organisations that have successfully scaled AI from those still struggling to move beyond experimentation.
1) Assign clear ownership: Only 12% of Southeast Asian companies have a designated AI owner with budget authority and accountability for outcomes, compared with a global average of 20%. Among leaders, the figure rises to 89%.
2) Measure results from the start: Only 8% of companies measure AI return on investment comprehensively. Many track usage but do not assess output quality, rework or the actual cost of reviewing AI-generated content.
3) Bring business and IT teams together early: Some 78% of surveyed companies still operate AI through a request-and-delivery model, where business teams submit requests and IT teams deliver solutions. Leaders, however, involve both sides in shaping initiatives and agree on what success looks like before a project brief is written.
4) Treat failure as a learning opportunity: More than one-third of companies repeat the same AI mistakes because recurring problems are not identified and flagged. Leaders keep their destructive-failure rate below 1% by creating an environment where unsuccessful attempts can be discussed and used to inform future decisions.
5) Identify and empower internal champions: Among leaders, 41% consider this their single most effective move. Organisations can recognise employees who are already using AI effectively in real-world workflows, provide modest budgets to extend their work and give them the autonomy to share successful practices across teams.
AI investment alone will not close the adoption gap
Global leaders derive 1.4 to 1.6 times more value from the same AI technology as other organisations, highlighting the importance of organisational readiness.
Clear ownership, measurable outcomes and collaboration between business and IT teams can help companies learn from failures and scale successful initiatives. Without these foundations, organisations in the “frozen middle” risk falling further behind.
Read full survey findings here.
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Lead image / Adobe
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