APAC enterprises have set their AI foundations but scaling it comes with complications

Lenovo's CIO Playbook 2026 shows near-universal intent to increase AI investment across the region. But behind the confidence, a more cautious conversation is playing out about what it actually takes to move from pilot to production.

The numbers that Lenovo brought to its Tech World eventt in Hong Kong were by any measure, bullish. Ninety-six percent of organizations across Asia Pacific plan to increase AI investment over the next 12 months. Average budgets are expected to grow by 15%. The projected return sits at roughly US$2.85 for every dollar spent, according to the company's CIO Playbook 2026, commissioned with IDC.

Matt Codrington, Vice President and General Manager for Lenovo Greater Asia Pacific, framed the moment as a pivot point. "Last year, we talked about AI economics — it was about proving return on investment, justifying early investment," he told reporters at a roundtable session during Lenovo Tech World in Hong Kong.

"But the data now tells us quite a different story. We are seeing a significant shift from building to solving, from training to inferencing,” Codrington added, noting that the initial tech foundations have been set. “CEOs are now redirecting funds into application, into usage and operations."

The shift from training to inferencing is more than a technical one. It represents a cost structure that many enterprises have not yet fully stress-tested. Lenovo has previously noted that inferencing costs can run up to 15 times higher than training over a model’s lifecycle–a figure that is beginning to land with real weight in budget conversations, particularly as agentic AI deployments multiply the number of active model calls across an organization’s workflows.

Art Hu, Lenovo’s SVP and Global CIO who also serves as chief delivery and technology officer for the Solutions and Services Group (SSG), was direct about the discipline required. “As Lenovo’s CIO, I can’t always want the most expensive model–that’s a quick way to run myself out of budget two quarters into the year,” he said. “We want to model what is going to be the right task to run at the right model, because I want the right combination of cost, latency and security.”

That pressure on cost discipline points to a broader pattern Codrington identified in the research: it is not ambition that stalls enterprise AI deployments, but foundation readiness. While 88% of organizations expect positive ROI from AI this year, only around half of proof-of-concepts successfully reach production.

The blockers, as Codrington described them, are not motivational–they are structural. Data quality, governance gaps, talent shortages in MLOps and prompt engineering, and the complexity of managing AI workloads across hybrid environments all contribute to what he called the "shadow AI function" problem: non-IT departments independently funding and deploying AI tools without the integration or controls that make those investments sustainable.

"CIOs are now becoming orchestrators," Codrington said. "They're aligning business ambition with technology platforms. They're driving governance and talent development across the enterprise. The most important asset in any organization tends to be its people–and how you bring them along, and how you get them to lead, not just participate, in this outcome, is the critical question."

On the infrastructure side, the roundtable was consistent on one point: hybrid AI is not a compromise architecture. It is the default. Linda Yao, vice president and general manager for hybrid cloud and AI solutions at Lenovo, cited data showing 86% of APAC organizations now incorporate on-premises or edge environments as part of their AI setup–driven by a combination of data privacy requirements, regulatory compliance, latency demands, and cost predictability.

In ASEAN specifically, 81% prefer hybrid models, reflecting a market where data sovereignty requirements vary significantly by country and are only becoming more stringent. The picture that emerges from Lenovo's event is of a region that has moved past the question of whether AI delivers value, but is now confronting the considerably harder engineering and organizational work of making it deliver value consistently, at scale, without blowing the budget.

The enterprises that navigate that transition successfully, as Codrington put it, will be the ones that treat AI not as a technology initiative but as a business mandate–built from the data up, governed from the start, and measured against outcomes that matter to the business, not just the IT team.