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India and Singapore are showing strong enterprise AI activity, and some reported measures put them ahead of global benchmarks. But the available surveys do not provide a like-for-like comparison of agentic AI adoption in the two countries. They measure different things—from exploring autonomous agents to using AI tools and redesigning workflows—so the evidence supports a qualified claim of momentum, not a definitive country ranking.
What the reported adoption figures measure
“Adoption” can describe very different stages: considering autonomous agents, testing prototypes, using agentic tools, deploying AI at scale, or letting AI coordinate an end-to-end workflow. Broad enterprise AI use is a separate measure again. The figures below are useful signals, but they should not be ranked as though every survey asked the same question of the same population.
| Country and source | Reported result | What it measures |
|---|---|---|
| Singapore — ServiceNow, 2026 Enterprise AI Maturity Index | Agentic AI tool adoption was 51%, up from 22% in the prior-year result. Autonomous end-to-end workflow use was 10%. | The index surveyed 4,500 senior leaders across 19 countries, including 200 in Singapore. Tool use and end-to-end workflow use are distinct measures. |
| India — Deloitte, 2026 report | 40% of Indian respondents reported significant or full AI use, compared with an approximately 28% global average. | This is broad enterprise AI activity, not an agentic-AI-only adoption rate. At-scale deployment varied by business function. |
| India — Deloitte, April 2025 perspective | More than 80% of Indian organisations were exploring autonomous agents. | Exploration indicates interest or investigation; it does not establish production use. |
| India — PwC India survey, 2025 | 55% of respondents were building and testing early prototypes; 14% had moved beyond early validation. | The study covered Indian domestic organisations and India-based global capability centres, not every business in India. |
| Singapore — Deloitte survey, 2025 | 14% of leaders reported mature agentic AI governance, compared with a 21% global average. | This measures governance maturity, not the rate of AI adoption. |
What does Singapore’s result say about real-world use?
ServiceNow’s index points to a sharp increase in reported use of agentic AI tools among Singapore respondents. Yet tool adoption is not the same as an organisation handing a business process to AI from beginning to end. The smaller end-to-end workflow result marks an important distinction: businesses may be using agents for particular tasks without having redesigned connected workflows across the enterprise.
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What do India’s figures show—and what do they not show?
Deloitte’s India result compares significant or full use of AI generally with a global benchmark. It is evidence of broad enterprise AI activity, but it cannot be used as India’s agentic-AI adoption rate. The report’s note that at-scale deployment differs by business function also cautions against treating enterprise use as uniform: an organisation may have mature deployment in one area and limited use in another.
The earlier Deloitte finding on autonomous-agent exploration and PwC’s snapshot of prototypes and validation add evidence of active experimentation. They describe stages of a journey, not a shared measure of production deployment. PwC’s respondent group also has a defined scope, so its results should not be generalized to every Indian company.
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Deloitte India partner Moumita Sarker noted that many Indian organisations prefer buying AI solutions rather than developing them in-house, adding that adapting purchased solutions to changing needs can be challenging. That is one practical scaling issue: adoption can move quickly while the ability to customize, integrate, and maintain systems remains a constraint.
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How much of the reported adoption is in production?
The evidence points to a mix of stages rather than a single production rate. Singapore’s tool-use figure is closer to reported use than exploration or prototyping, but it does not tell us how broadly those tools are deployed or whether they operate autonomously across whole processes. The end-to-end workflow measure is a narrower indicator of deeper operational change. In India, the exploration and prototype results explicitly sit earlier in the adoption journey, while the broad-use survey does not isolate agentic systems.
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The headline comparison that prompted this topic is attributed to Thoughtworks in Computer Weekly coverage. The underlying primary Thoughtworks report and comparable country-level figures are not available here, so that specific India-versus-Singapore ranking cannot be independently verified from the figures above. Rachel Laycock, Thoughtworks’ chief technology officer, was quoted by Computer Weekly as saying: “The organisations moving fastest are integrating AI into the core of how they operate.” The statement describes an operational direction; it does not establish a comparable adoption rate for either country.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should companies watch beyond adoption?
Adoption rates say little by themselves about whether an AI system is safe, governable, or producing durable business value. Singapore’s governance result shows that reported use and reported oversight maturity need not move together. A company assessing its own readiness should look at more than whether employees have access to agentic tools.
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- Workflow design: Identify whether AI assists an individual task or changes how work passes between teams, systems, and decision-makers.
- Governance: Define who can approve an agent’s actions, which decisions require human review, and how performance and errors are monitored.
- Data and security: Check whether agents can access reliable, appropriately permissioned information and whether sensitive actions are controlled.
- Workforce capability: Prepare employees to supervise AI-supported work, handle exceptions, and understand when to escalate a decision.
- Deployment stage: Separate experiments and pilots from sustained production use, and production use from scaled, end-to-end transformation.
These are practical tests for interpreting the surveys, not outcomes measured uniformly across the cited studies. The available evidence establishes strong activity and meaningful adoption signals, while leaving the extent of enterprise-wide transformation uneven and difficult to compare.
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