Overhauling strategies for AI leadership in 2026

Over three years after ChatGPT's arrival, the way we see AI continues to evolve. What enterprises want to know now is what to do beyond AI proofs of concept. 

Source: Zebra
Technologies.
Suryadarma.

"In APAC, rapid AI adoption is driven by structural challenges, labour shortages, cost pressures, and rising customer expectations. Multi-agent systems are already delivering up to 50% efficiency gains in sectors like finance, IT, and customer service," observed Christanto Suryadarma, VP for Southeast Asia (SEA), South Korea and Channel APJeC, Zebra Technologies.

"Research by Zebra Technologies and Oxford Economics reveals that top companies in retail, manufacturing, and logistics could unlock an average of US$3 B in additional revenue and US$120 M in profit by improving frontline workflows with AI, highlighting the transformative potential of AI-led automation. Examples like OpenAI’s collaboration with Spotify demonstrate how AI enhances discovery, personalisation, and conversational interactions." 

"As APAC advances in AI maturity, industries should explore similar innovations to boost productivity and user experience," Suryadarma suggested.

Ed Keisling, Chief AI Officer, Progress Software said businesses have to update their thinking, for one. "As the models, frameworks, and standards change, many solutions that have already been built will quickly become outdated, unsupported, or insecure. There will be a significant effort to refactor these pilots which will impede new AI development," he predicted.

"Organisations will need to pause to work on the plumbing to define and build the new frameworks and standards needed for their organisations that are scalable, flexible, and secure."

"The enterprises that break out (in 2026) will operationalise three disciplines:

- Productising AI use cases with clear owners and SLAs;

- Connecting unstructured and structured knowledge so agents operate in context; and

- Making governance a feature that accelerates—not slows—delivery," said Philip Miller, AI Strategist, Progress Software.

"McKinsey’s 2025 State of AI shows adoption is high but scaling practices (KPIs, roadmaps, robust data foundations) are still rare; organisations that do adopt them capture more value. Practically, that means investing in retrieval and semantics, lineage and policy enforcement, and 'platform-not-project' thinking so every new assistant compounds prior knowledge rather than spawning bespoke silos." 

Source: Kyndryl. Lim.

Andrew Lim, MD, Kyndryl ASEAN & Korea said that as agentic AI adoption grows, so will expectations around governance and accountability. "Structured approaches — like Kyndryl’s Agentic AI Framework — will help organisations determine which tasks can be automated, where human judgment must remain essential, and which hybrid roles must be created for safe and responsible deployment. In 2026, AI transformation becomes as much about organisation and workforce design as it is about algorithms," he said.

"As AI becomes deeply embedded in enterprise infrastructure, organisations will need to secure models, data pipelines, training datasets, orchestration systems and inference environments, not just traditional networks. Boards will treat AI security as a strategic priority."

 

Source: Qlik.
Garavello.

Scaling up

Maurizio Garavello, SVP for Asia Pacific & Japan, Qlik said that 2026 will be the year Asia-Pacific (APAC) enterprises "shift from experimenting with AI to operationalising it at scale". "From my conversations with leaders across the region, it’s clear they are no longer asking how to pilot generative or agentic AI. They’re asking me how to embed it into decision cycles, workflows, and customer-facing processes with confidence. This shift will reshape data architectures, governance models, and the skills enterprises prioritise in the year ahead," he predicted.

"Enterprises will judge AI not by prototypes but by measurable business outcomes in the new year. Agentic AI systems capable of planning, executing, and adapting tasks will become more common in operations, risk management, and supply chain optimisation. But adoption will only accelerate if organisations close the trust gap." 

I see APAC enterprises increasingly demanding explainability, transparent lineage, and governed data to ensure these autonomous systems act reliably. Black-box models will be sidelined in favour of architectures that support traceability and compliance. This makes open, interoperable data foundations not just desirable but essential."

Integration

Yuval Fernbach, VP & CTO of MLOPs, JFrog, identified the real value of AI adoption as coming from how well the AI model is connected to internal enterprise systems. "In 2026, the focus will demonstrate how AI enhances discovery, personalisation, and conversational interaction," he said, suggesting companies move away from “build your own” models and toward deploying AI that natively integrates with internal assets: data sources, tools, APIs, operational workflows, and governance layers. 

Source: JFrog. Fernbach.

"Models and agents will increasingly use MCP-like connectors to enrich prompts with internal organisational context, retrieve real-time business data, and perform actions across existing enterprise systems. This shift turns AI from a static text generator into an operational participant — one that queries, validates, updates, and orchestrates tasks based on live internal information," he said. 

"As a result, companies will reduce drift, improve reliability, and unlock far faster time-to-value. Instead of experimenting in isolation, enterprises will rely on integrated, governed, production-ready AI systems that understand their business, operate within their environment, and continuously stay aligned with their internal truth."

Source: MongoDB. Walther.

Modernisation

Thorsten Walther, MD, CXO Advisory Asia at MongoDB, said that ASEAN’s digital transformation plans in 2026 will shift to modernising legacy systems, not just maintaining them. "Many leaders will realise that ambitious AI initiatives aren’t stalling due to technology limitations, but because legacy architectures cannot support the speed, scalability, and flexibility AI demands," he explained. 

"This recognition will redefine modernisation from just cleaning up technical debt to enabling workforce optimisation. By moving to modern, cloud-native architectures, enterprises can free their technical teams from the complexities of maintaining outdated systems and redeploy them toward innovation, AI adoption, and customer-facing projects."

"AI is putting real pressure on organisations to rethink how their systems are built, and leaders are realising that they cannot unlock meaningful value from AI while operating with rigid or outdated core architectures reliant on fragmented integration methods," Walther elaborated. 

"Instead of running isolated experiments, more enterprises are beginning to modernise entire stacks - from data, applications, and infrastructure. The goal is no longer to 'try something new', but to remove the blockers that make AI adoption slow and risky."

Speed

Megan Hughes, MD, JAPAC, HubSpot, said digitalisation and AI adoption are the JAPAC region's greatest strengths as well as defining challenges. "The competitive advantage in JAPAC's next decade won't be determined by who has the most sophisticated AI tools, it will be defined by who transforms their organisation fastest. Leadership alignment, integrated systems, and skilled teams matter more than technology selection," she said. 

"For businesses in the JAPAC region, the time for piloting AI is over. The age of AI-first operations is here. Those who invest now in empowered leadership, unified systems, and upskilled talent will not just keep pace—they'll define the next decade of growth in Asia's intelligent economy."

Managing unpredictability

Nathan Hall, GM and VP, Asia Pacific and Japan, Pure Storage, said companies would turn to subscriptions and diversify to manage unpredictability.

"In 2026, subscription overtakes ownership as the dominant model for how organisations fund and deploy AI and digital infrastructure. Faced with economic uncertainty, rapid AI capability changes, and unpredictable compute demand, enterprises will no longer commit to large capex-based or multiyear infrastructure bets," he said.

"Instead, they will construct modular, subscription-driven stacks where compute, AI models, storage, cybersecurity, and even industry-specific capabilities can be scaled up or down monthly. This shift allows organisations to redirect workloads, budgets, and markets at the pace of change, making subscription-based architecture a core enabler of resilience and growth."

"In 2026, diversification becomes a non-negotiable priority for APJ leadership. Escalating geopolitical tension, concentrated digital infrastructure risk, and rising regulatory fragmentation push enterprises to abandon single-market and single-provider dependencies," Hall added.

"C-suites will actively redesign their operating models around multimarket supply chains, multicloud and multi-AI-provider architectures, and multichannel GTM strategies. The goal is explicit: reduce vulnerability to regional shocks while positioning the business to capture growth across both emerging and mature APJ economies.

"By broadening their operational, digital, and commercial footprints, APJ enterprises will replace
the old 'optimise for efficiency' playbook with a new one built on structural resilience," Hall concluded.

APAC is an abbreviation of 'Asia-Pacific region' while APJ refers to Asia Pacific and Japan. JAPAC refers to 'Japan and Asia-Pacific'. GTM is an acronym for go to market. MCP stands for Model Context Protocol.

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