{"id":150,"date":"2026-08-25T10:30:00","date_gmt":"2026-08-25T05:00:00","guid":{"rendered":"https:\/\/www.wininfosoft.com\/insights\/ai-ready-data-india-enterprises-checklist\/"},"modified":"2026-08-25T10:30:00","modified_gmt":"2026-08-25T05:00:00","slug":"ai-ready-data-india-enterprises-checklist","status":"publish","type":"post","link":"https:\/\/www.wininfosoft.com\/insights\/ai-ready-data-india-enterprises-checklist\/","title":{"rendered":"AI-Ready Data in India: Why One Survey Says 4% and Another Says 63%, and a Checklist to Find Out Where You Stand"},"content":{"rendered":"\n
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Quick Answer:<\/strong> Nobody agrees on how many Indian firms have AI-ready data, and three surveys from June to early August 2026 show why. A Dun & Bradstreet survey reported by CXOToday<\/a> puts firms with fully AI-ready data at 4%. KPMG in India<\/a> found 53% of enterprises say their data is not AI-ready, which implies about 47% are, while in SAP research reported by CIOL<\/a>, 63% of respondents describe their data as ready. The gap is mostly about what “ready” means and who does the grading. So do not pick a number. Audit your own data against the one use case you are about to fund.<\/p>\n<\/blockquote>\n\n\n\n

Anyone asking for an AI budget in India this year will have a readiness statistic quoted back at them. Which statistic depends on which report the finance committee read last. Three surveys made public between June and early August 2026 put the share of Indian organisations with AI-ready data at 4%, about 47% and 63%. They cannot all be measuring the same thing, and a close reading shows they are not.<\/p>\n\n\n\n

Putting the three figures side by side shows why they diverge, and points to something more useful than a fourth number: a self-audit you can run on your own data before any money moves. The audit is narrow on purpose. It asks whether your data is ready for one named use case, because that is the only version of the question with a checkable answer.<\/p>\n\n\n\n

Three surveys, three very different answers<\/h2>\n\n\n\n

Start with what each survey actually reported, because the detail changes the meaning.<\/p>\n\n\n\n

Dun & Bradstreet: 4%<\/h3>\n\n\n\n

A Dun & Bradstreet survey, reported by CXOToday on 4 August 2026<\/a>, found that only 4% of Indian firms have fully AI-ready data. Put the other way round, 96% do not. It is the most quotable of the three, and it arrives with almost no explanation. The report does not give a sample size, methodology, industry split or definition of “ready”.<\/p>\n\n\n\n

Most of the weight sits on the word “fully”. It sets a high bar, and it says nothing about how many of the other 96% are partly ready, or ready for one purpose and not another. Without that definition, you cannot tell whether 4% is harsh or simply precise.<\/p>\n\n\n\n

KPMG in India: about 47%<\/h3>\n\n\n\n

KPMG in India reported in July 2026<\/a> that 53% of Indian enterprises say their data is not AI-ready. KPMG counted the shortfall. Turn it round and about 47% are left, assuming every enterprise outside the 53% would call its data ready. That assumption may be generous: if some respondents were simply unsure, the ready share by KPMG’s count is below 47%.<\/p>\n\n\n\n

SAP: 63%<\/h3>\n\n\n\n

SAP’s Value of AI 2026 research<\/a> from June 2026, reported by CIOL, found that 63% of its 200 Indian respondents describe their data as ready for AI. It is the most optimistic of the three, and it states its sample, which the Dun & Bradstreet coverage does not. Describing data as ready, though, is a different act from having it inspected.<\/p>\n\n\n\n

Why the three figures sit so far apart<\/h2>\n\n\n\n

The spread from 4% to 63% looks like a contradiction. Most of it comes down to what each survey counts as ready and who is doing the counting. Timing is not the cause. All three appeared between June and early August 2026.<\/p>\n\n\n\n

Three differences account for most of the spread.<\/p>\n\n\n\n