2026 Predictions Part 2: From AI ambition to operational reality in Asia

Asia’s rapid embrace of artificial intelligence has reached a more demanding phase. In 2026, the region’s challenge is not whether AI can be deployed, but whether it can be operated reliably under real-world conditions. What once appeared as momentum now meets friction. Systems that performed well in pilots are being tested at scale, under regulation, and across organisational complexity. The result is a clearer, less forgiving measure of progress.

This part of the series examines what happens inside organisations once the external forces outlined earlier collide with daily operations. The shift is not subtle. AI stops being judged by demonstration value and starts being judged by operational consequence. That change is exposing gaps that enthusiasm and early funding rounds could previously conceal.

From deployment success to operational strain

The first pressure point is execution. Over the past two years, many organisations treated AI adoption as a deployment exercise. The assumption was that once models were integrated into workflows, benefits would follow. In practice, the gap between a working demo and a durable system has proved wider than expected.

Across the Asia Pacific, AI initiatives are faltering for reasons unrelated to model performance. Data remains fragmented across regions. Ownership is unclear once systems cross departmental lines. Integrations that held together in controlled environments weaken under continuous use. These issues become acute when autonomy increases, because errors no longer pause for human review.

This strain is amplified in markets where regional teams operate under different compliance regimes. Data residency requirements, sector-specific regulations, and uneven digital maturity quickly expose weaknesses. What functions in a single market often fail when replicated across borders.

Edward Funnekotter, Chief AI Officer at Solace, describes this as the moment when assumptions meet reality. “Because AI feels easy to use, early wins can mask deeper challenges,” he says. “Real results take precision in picking a few areas where AI can deliver transformation that matters, then executing with discipline from senior leadership.”

That discipline is now being enforced. Boards are shifting their evaluation standards, with broad experimentation failing to meet expectations of progress. Funding is narrowing towards use cases that can survive operational scrutiny. In regulated industries, AI investments are increasingly evaluated alongside cyber exposure, compliance risk, and business continuity. The ambition has not disappeared, but it is being constrained by accountability.

Trust shifts from principle to prerequisite

Once AI systems begin to influence customer outcomes, trust moves from an abstract value to a hard requirement. In 2026, organisations will discover that trust cannot be retrofitted after deployment. It must be designed into systems from the outset.

This is most visible in customer-facing AI. Conversational systems and voice agents have advanced faster than public understanding. Many users struggle to distinguish between human and machine interactions. That ambiguity creates risk. Without clarity, organisations face legal exposure and reputational damage.

Nicholas Kontopoulos, Vice President of Marketing for Asia Pacific and Japan at Twilio, argues that this uncertainty will not be tolerated for long. “When the majority of customers cannot distinguish who they are speaking to, regulatory intervention is a certainty,” he says. “Transparency is shifting from an ethical talking point to an enforceable consumer right.”

The implications are practical. Disclosure mechanisms, escalation paths, and audit trails are becoming baseline requirements, reflected in AI governance guidance issued by regulators such as the Monetary Authority of Singapore. In sectors such as finance, healthcare, and telecommunications, failure to identify AI-driven interactions clearly invites sanctions. Trust has become a condition for participation rather than a differentiator.

The same logic extends inward. As agentic systems automate internal workflows, organisations must account for non-human actors in their risk models. AI agents can misconfigure systems, propagate errors, or be compromised. When governance is weak, these failures scale faster than human mistakes. Insider risk frameworks that focus only on employees are proving inadequate. Non-human actors now require the same scrutiny.

Resilience replaces reassurance

As trust becomes harder to assume, resilience becomes harder to define. Traditional measures focused on the speed of recovery. In an AI-driven environment, speed alone is insufficient. Organisations are being asked to prove that what they restore is accurate, uncompromised, and safe to use.

Digital environments have grown more complex. Multi-cloud architectures, distributed data pipelines, and automated decision systems increase the risk that corrupted data or identities re-enter production after an incident. In these conditions, restoring quickly without verification can magnify damage.

“Boards no longer accept assurances. They expect evidence. Recovery metrics, audit trails, and cleanroom validations are becoming the language of accountability,” says Martin Creighan, Vice President, Asia Pacific at Commvault.

This expectation is changing how resilience is measured. Metrics such as Mean Time to Clean Recovery are gaining prominence. Recovery environments are becoming more isolated and more automated. Validation is continuous rather than episodic. In heavily regulated sectors, demonstrable resilience is moving from an advantage to a requirement.

This shift also reflects a broader acceptance that failure is inevitable. Rather than chasing total prevention, organisations are designing systems to limit blast radius and recover with confidence. Resilience becomes an operating discipline rather than a communications exercise.

Architecture, not models, sets the pace

As execution pressure mounts, another constraint becomes clear. AI velocity is limited less by model quality and more by architectural choices. While foundation models continue to improve, inflexible infrastructure slows adaptation.

Many enterprises remain tied to legacy virtualisation stacks and tightly coupled platforms. These environments resist change. When costs shift or models evolve, organisations struggle to respond. In contrast, those that invest in modular, provider-agnostic architectures retain flexibility.

This is driving a move away from monolithic platforms. Containerisation, open orchestration, and subscription-based infrastructure are replacing long-term, capital-heavy commitments. The goal is optionality. Organisations want the ability to reconfigure without renegotiating their entire technology stack.

Energy constraints sharpen this trend. Across Southeast Asia, data centre expansion is limited by power availability and cooling costs. As AI workloads grow, efficiency becomes critical. Workloads are increasingly placed based on energy and cost profiles rather than peak performance. Architecture becomes a strategic decision, not a technical one.

Governance enters the executive core

Operational complexity brings governance into sharper focus. In 2026, cyber risk, data sovereignty, and AI accountability are firmly established as board-level concerns. They are framed as financial and reputational risks, not technical issues.

Recent incidents across the region underline this shift. Many high-impact breaches stemmed from basic failures such as misconfigured access controls or unpatched systems. In the rush to deploy AI, foundational security work was often deferred. The result was increased exposure.

Boards are now demanding clearer metrics. Cyber risk quantification, resilience testing, and sovereignty mapping are becoming standard agenda items. Organisations are prioritising operational resilience over the illusion of total security. The emphasis is on limiting impact rather than avoiding every incident.

This governance shift also changes internal dynamics. Responsibility for AI outcomes is moving upward. Accountability cannot be confined to technology teams. Decisions about autonomy, risk tolerance, and investment sit squarely within executive remit.

Commerce and experience recalibrate around friction

While infrastructure and governance mature, digital commerce is also adjusting. Agentic commerce continues to develop, but its role is becoming more precise. Rather than replacing existing channels, it assists specific moments of intent, choice, and execution.

Shane Happach, Chief Executive Officer of Coda, frames this change in competitive terms. “The winners will be those who move from thinking about payment processing to enabling seamless value exchange for end users,” he says.

This evolution is measured rather than radical. Consumers blend AI-assisted discovery with traditional search and social platforms. As discovery fragments, data quality and localisation become more important. Depth of automation matters less than relevance and reliability.

Customer experience expectations are also shifting. Speed remains valued, but not at the expense of security. Across the Asia Pacific, consumers are increasingly comfortable with intentional friction when it signals care. Verification steps and contextual explanations are accepted when aligned with risk. Trust is reinforced through transparency, not speed alone.

Discipline defines the year

Taken together, these dynamics define 2026 as a year of discipline. Asia’s digital economy continues to advance, but with sharper selection and stronger governance. Operational discipline, not novelty, now defines how AI is judged.

Organisations that succeed will treat AI as one component within a broader system. Data, infrastructure, governance, and people must align. Trade-offs will be explicit. Foundations will take precedence over rapid scaling.

Asia has long been praised for its ability to leapfrog legacy systems. In this phase, its advantage lies elsewhere. The ability to embed advanced technology into disciplined operating models, under real constraints, will separate durable operators from the rest.

Editor’s note: This article draws on insights shared by technology and business leaders from Solace, Twilio, Commvault, Coda, Pure Storage, Thales, Criteo, and other organisations as part of a multi-company contribution to Tech Edition. Some inputs have been synthesised into broader industry analysis.

2026 Predictions Part 1: The five forces reshaping Asia’s digital economy
2026 Predictions Part 2: From AI ambition to operational reality in Asia