Business AI adoption in Asia Pacific is rising rapidly as enterprises accelerate investments in GenAI and agentic AI.
Tim Dillon, Chief Analyst, Omdia, said partners must address escalating cost pressures as AI increases cloud workloads and complexity. He noted that the majority of customers rely on partners for agentic AI and that the AI partner opportunity is projected to reach $267 billion. Partners can deliver high-value AI and agentic AI services as enterprises seek clarity on deployment, cost forecasting, and cloud optimization.
During the Cloud Cost Meets GenAI panel at the Canalys Forums APAC 2025 in Vietnam on 3 December, Tim Dillon discussed cost management techniques, AI-driven forecasting, and new models for optimizing cloud spending.
Deepak Waghmare, CTO – APJC, Dell Technologies, said the industry has shifted from asking what ChatGPT is to understanding the value of AI and its real use cases. He said customers initially implement pilots assuming AI will solve issues, but these pilots typically do not involve CXOs. He added that when enterprises begin large scale deployment, CXOs focus on total cost of ownership. He said prioritizing AI deployment helps CXOs save money and noted that AI on cloud platforms is costly.
Amir Bukan, ANZ Commercial Channel Lead, Qualcomm, said significant capital expenditure is being allocated to AI infrastructure. He said there will be more scrutiny on AI cost efficiency because the industry is witnessing unprecedented investment in building AI systems. He said customers should understand what can be executed on cloud and what can be localized, as many workloads do not require full cloud dependency.
Sachin Dubey, Vice President Cloud, Redington, said AI funding is increasing while the number of proofs of concept is declining. He said customers are asking more questions about the return on investment from AI deployments and that the nature of AI implementation will determine RoI. He added that agentic AI refers to purpose built enterprise solutions and that most AI adoption in the market will move in this direction. He cited an example in which the CIO of a large company asked developers not to send simple greetings to AI systems because AI usage costs were high. He emphasized that decisions on the correct adoption model are essential for cost reduction and said the cost burden driven by NVIDIA infrastructure is significant for customers.
Leo Raikhman, Director, C4X Technology Architects, said challenges are emerging because businesses are undertaking too many AI projects at once, creating strain on budgets and execution teams.
Shih Huei Tan, Partner and Alliances Manager, Hewlett Packard Enterprise (HPE), said customers need a clear understanding of the right AI solution required to solve each business problem.
A recent Canalys report highlights how accelerating GenAI adoption is reshaping global cloud spending and enterprise priorities. Global cloud infrastructure services spending reached about $95 billion in the second quarter of 2025, increasing 22 percent year on year. This expansion is being driven by GenAI workloads, renewed legacy migrations, and cloud native companies scaling operations. Enterprises now view cloud as the foundation for deploying AI and GenAI, driving long term investments across IaaS, PaaS, serverless, and high performance compute services.
The report outlined infrastructure pressures. Limited power availability and semiconductor constraints are slowing the expansion of cloud capacity. This has resulted in major increases in capital expenditure, with each hyperscaler committing tens of billions of dollars to expand data centers, custom silicon, and AI optimized clusters in 2025.
For enterprises, rapidly increasing GenAI cloud usage has created the challenge of balancing fast paced innovation with effective cost management. AI workloads generate sizable infrastructure and inference costs, pushing organizations to reassess cloud consumption, workload placement, and vendor strategies. Multi cloud and hybrid deployments are becoming more attractive as customers seek flexibility in choosing AI models, performance levels, and cost structures.
With GenAI workloads accelerating, cloud consumption has become more unpredictable and difficult for customers to manage. Canalys forecasts that cloud optimization services will become a top revenue driver for partners in 2025. AI workloads are increasing storage, network, and compute spending, prompting partners to build new cost governance frameworks.
Vendors including Dell Technologies, Qualcomm, and HPE are supporting partners with optimization tools, FinOps capabilities, and hybrid cloud strategies. Canalys said partners that help customers balance innovation with cost control will strengthen long term relationships and maintain account stickiness.
Baburajan Kizhakedath
