Enterprises across Asia Pacific, led by India, are shifting from AI experimentation to deployment. This transition focuses on moving beyond pilots to embed agentic systems and generative AI within robust governance and security frameworks.
However, India’s AI scaling ambitions are stalled by severe infrastructure, cost, and governance bottlenecks, as most IT leaders lack the real-time data streaming foundations required to move from experimental pilots to profitable production.
As AI applications and autonomous agentic systems become deeply embedded into daily business operations, enterprises are learning that a model is only as smart as the data feeding it and only as viable as the architecture supporting it.
AI Experimentation to Execution
Enterprises across Asia Pacific are accelerating their shift from AI experimentation to execution, with 96% of organizations planning to increase AI investments over the next 12 months, according to the 4th edition of the Lenovo CIO Playbook 2026 by Lenovo and IDC, The Race for Enterprise AI. On average, organizations expect AI spending to grow by 15%, spanning GenAI and Agentic AI, public cloud AI services, on-prem AI infrastructure, and AI security tools.
Read more: Decoding India’s AI Ambitions: From Sovereign Code to Tier-2 Corridors
India stands out in Asia Pacific, with 99% of organizations planning to increase AI investments over the next 12 months and the highest average year-on-year budget growth in the region at 19%. These investments prioritize deploying and supporting AI infrastructure, internal AI training including non-IT staff, and generative AI development and applications, followed by AI devices and AI security, trust and transparency tools, making AI a core enabler of enterprise efficiency, resilience, and growth.
“When 96% of organizations across Asia Pacific are planning a 15% on average increase in AI investment, it tells us that AI decisions are now being made at the core of enterprise strategy”. — Sumir Bhatia, President, Asia Pacific, ISG, Lenovo
“When 96% of organizations across Asia Pacific are planning a 15% on average increase in AI investment, it tells us that AI decisions are now being made at the core of enterprise strategy,” said Sumir Bhatia, President, Asia Pacific, ISG, Lenovo. “The differentiator will be how effectively organizations integrate AI, embedding it into infrastructure, operations, and security so value compounds over time.”
As AI becomes increasingly embedded into enterprise strategy, driving revenue growth, improving profitability, and enhancing business and customer experience have emerged as the top three business priorities for CIOs in Asia Pacific.
“Infrastructure, policy clarity and workforce readiness are becoming as critical as technical capability. Competitive advantage will accrue to firms that can translate AI capability into production-grade systems with measurable outcomes.” — Aman Singh, Co-founder, S45
Aman Singh, Co-founder, S45 says, “The real conversation is no longer about access to models or compute, but about embedding AI into core industry workflows at scale. Across regulated sectors like capital markets, the bar is materially higher. Systems must be compliant, auditable and aligned with governance frameworks from inception. Infrastructure, policy clarity and workforce readiness are becoming as critical as technical capability. Competitive advantage will accrue to firms that can translate AI capability into production-grade systems with measurable outcomes.”
In capital markets specifically, this means moving beyond analytics overlays toward AI-native operating layers that redesign document workflows, due diligence processes and transaction intelligence.
“India’s AI opportunity will be realised not through pilots, but through disciplined deployment into the economic rails that power growth,” he adds.
Nithin Reddy, Co-Founder and CGO of FinStackk, says, “AI in India and globally has crossed the threshold from innovation to infrastructure. Adoption is spreading fast not only among enterprises but among everyday users who now expect intelligent responses and instant outcomes. This is fundamentally changing how financial products are particularly built, enabling Indian startups to operate efficiently and expand seamlessly into markets like the US.
“AI in India and globally has crossed the threshold from innovation to infrastructure. Adoption is spreading fast not only among enterprises but among everyday users who now expect intelligent responses and instant outcomes. This is fundamentally changing how financial products are particularly built, enabling Indian startups to operate efficiently and expand seamlessly into markets like the US.” — Nithin Reddy, Co-Founder and CGO of FinStackk
“Costs are also falling because inefficiencies are being designed out of the system rather than patched over. Over time fewer decisions will depend on fatigue or bias and more on logic and patterns. AI is also creating new standards of transparency because actions can be tracked and refined. The most powerful shift is access as smarter automation allows services to reach more people with less friction. This transformation is enabling better outcomes by pairing human insight with intelligent automation.”
“In 2026, AI will stop being a standalone initiative and become part of the enterprise operating stack,” – Milind Shah, Managing Director, Randstad Digital India
For example, according to the 2026 Randstad Digital Industry Predictions, the technology, media and telecom (TMT) sector will move decisively from AI experimentation to enterprise-wide deployment of agentic systems in 2026, marking the end of siloed pilots. The report describes 2026 as the year of “The Great Integration,” where autonomous AI agents operate at scale within robust governance frameworks.
“In 2026, AI will stop being a standalone initiative and become part of the enterprise operating stack,” said Milind Shah, Managing Director, Randstad Digital India. “Organisations that fail to integrate AI with governance and security from the start will struggle to scale.”
The Flip Side
At the same time, India’s AI ambitions are at risk. Nearly eight out of 10 (79%) of Indian IT leaders say a lack of real-time data infrastructure is stalling their efforts to scale AI, according to a new 2026 Data Streaming Report from Confluent. The report says the challenge is no longer access to AI, but the ability to move, govern, and act on data in real time. As organisations shift from experimentation to production, data streaming is emerging as the backbone for scalable, trustworthy AI systems.
79% of Indian IT leaders say poor infrastructure is stalling AI growth. The biggest barrier to AI growth isn’t investment, but the infrastructure needed to support it. Data infrastructure and quality issues are slowing agentic AI adoption.
97% expect data streaming increases the impact of AI investments, while 94% believe DSP technology can help accelerate AI adoption, and 87% report 2-5x return on investment (ROI) for data streaming. The vibe is that DSPs help make data more trustworthy, contextualised, and discoverable. Real-time data streaming is becoming foundational to unlocking AI from pilot to production. However, fragmented data ownership and quality challenges continue to slow AI deployment. 5x ROI, faster time-to-market, and improved customer experience are cited as the top drivers.
“As AI applications and agentic systems become more deeply embedded into business operations, organisations need data that is continuously available, trusted, and discoverable.” — Rubal Sahni, AVP, India and Emerging Markets, Confluent
Rubal Sahni, AVP, India and Emerging Markets, Confluent, says, “AI adoption has reached a point where success is no longer determined by access to technology alone. As organisations look to scale AI across business functions, the ability to move, govern, and act on data in real time is becoming increasingly important. Our findings show that Indian enterprises recognise this shift, with investments in data streaming rising alongside investments in AI.”
“What’s particularly notable is that organisations are not viewing data infrastructure and AI as separate priorities. The two are becoming closely linked. As AI applications and agentic systems become more deeply embedded into business operations, organisations need data that is continuously available, trusted, and discoverable.”
“The companies making the most progress are investing not only in AI itself, but in the data foundations needed to support it. Those foundations will determine which organisations can turn AI investment into business value at scale.” — Shaun Clowes, Chief Product Officer at Confluent
Shaun Clowes, Chief Product Officer at Confluent, said: “Most organisations do not have an AI investment problem, they have a data problem. AI systems depend on fresh, accurate and contextual information, but too many are still being built on fragmented data, batch processes, and infrastructure that was not designed for continuous intelligence.
“As organisations move beyond experimentation and start deploying AI across critical business processes, those gaps become harder to ignore. Models need to be connected to the systems, events and signals that reflect what is happening across the business. The companies making the most progress are investing not only in AI itself, but in the data foundations needed to support it. Those foundations will determine which organisations can turn AI investment into business value at scale.”
AI Inferencing is Pricey
As per the Lenovo AP CIO Playbook 2026, AI inferencing becomes the value engine, since over a model’s lifecycle, inferencing costs can be up to 15 times higher than training. By 2030, 75% of AI compute will be dedicated to inferencing, with 80% of enterprises relying on distributed edge infrastructure.
Also, employee productivity rises as a strategic priority, as deploying AI devices to enhance productivity and local inferencing has climbed to the #2 IT investment priority, alongside growing adoption of AI PCs, with 50% of enterprise PC purchases expected to shift to models with on-device AI agents.
Scaling AI remains the defining challenge, because while 88% of organizations expect positive ROI, only around half of AI proof-of-concepts reach production, making scale, not ambition, the critical gap.
ROI & Governance Lag
As per an ISACA Report based on a pulse poll, India’s AI boom faces a reality check as ROI and governance lag. The growing AI momentum in India highlights an urgent need for stronger governance framework, workforce training and risk oversight.
“The thing with ROI in AI is that it doesn’t arrive on schedule; it’s not a switch that can be flipped: it’s the result of sustained investment in the people, processes, and governance structures that make intelligent systems reliable. The organizations that resist the urge to declare victory too early are the ones most likely to get there.” — Keith Bloomfield-DeWeese, Senior Manager of AI Product Development at ISACA
“There’s enormous pressure on organizations to show that AI is paying off, but most organizations aren’t yet sure whether it has,” says Keith Bloomfield-DeWeese, Senior Manager of AI Product Development at ISACA. “That uncertainty isn’t a failure of AI, but a reflection of how hard it is to build something that actually works at scale. The thing with ROI in AI is that it doesn’t arrive on schedule; it’s not a switch that can be flipped: it’s the result of sustained investment in the people, processes, and governance structures that make intelligent systems reliable. The organizations that resist the urge to declare victory too early are the ones most likely to get there.”
An Eye on Every Corner of the Tech Boom
What’s particularly notable is that organizations are no longer viewing data infrastructure, soaring operational costs, and AI execution as separate, disconnected priorities. These elements are becoming fundamentally linked. As AI applications and autonomous agentic systems become deeply embedded into daily business operations, enterprises are learning that a model is only as smart as the data feeding it and only as viable as the architecture supporting it.
Moving into 2026, the competitive advantage will no longer belong to the companies with the biggest AI budgets or the most ambitious pilots. Instead, the ultimate winners of India’s tech boom will be the firms that pause to invest in the quiet, foundational architecture, ensuring their systems are cost-effective, governed, and built on continuous data from day one.
