The Industry AI Innovation Award
Some AI only makes sense inside one industry — its data, its regulator, its failure modes. This category is for vertical AI built for banking, health, retail, telecom, manufacturing, agriculture or the public sector, and judged on what it changed there.
- Independent jury
- 250
- Innovations evaluated
- 4,500+
- Star rating
- 1–5
- Running since
- 2010
Academics, scientists, officials, investors
Across seventeen editions
Every nominee earns an AGBA Innovation Star
India’s longest-running innovation award
Three categories sit close to data. This is the one about method and evidence.
Most nominations that fail here fail because they were in the wrong category. Read across before you write anything.
Innovation in Industry AI
Vertical AI shaped by one industry’s data, regulation and failure modes.
This category
Innovation in Functional AI
AI inside a business function — sales, CX, operations, finance — across industries.
Innovation in Core AI
Foundation models, agentic systems and AI infrastructure itself.
Who should enter
- Vertical AI trained on industry data, with the domain constraints made explicit
- Regulated-sector deployments where approval, audit or explainability shaped the design
- Clinical, financial or industrial decision support with measured outcomes
- Domain models and copilots built for one profession’s workflow
- Industry data platforms where the labelling or the ontology was the hard part
What this award recognises is depth in one industry — the edge cases you handled, the regulator you satisfied, the practitioners who kept using it after the pilot ended.
If the application generalises across industries, enter Innovation in Functional AI. If the innovation is in the model or infrastructure, enter Innovation in Core AI.
What the jury looks for
- Problem definition & market opportunityWhether the innovation solves a real problem, backed by evidence and a measurable impact on its target audience.
- InnovativenessNovelty and uniqueness — new technology, functionality or business model — measured against what already exists, including IP.
- Market potential & impactSize and growth of the addressable market, competitive landscape, and scalability across geographies.
- Social impact & sustainabilityContribution to society and the environment, including social responsibility and the UN Sustainable Development Goals.
These are the same four criteria — problem definition, innovativeness, market impact and social/sustainability impact — used to assess every category. In this category the jury also weighs methodological rigour — including what you did to avoid fooling yourselves — the decision that changed as a result and what it was worth, the reproducibility and honesty of the evaluation, and the engineering quality of the data foundation underneath. Read the full evaluation criteria.
Who judges this category
Nominations are assessed by an independent jury, not by the organisers. Jurors are matched to the categories where their expertise lies. These are among those who read vertical and industry AI entries.
Prof C Krishna MohanProfessor, Computer Science and Engineering, IIT Hyderabad
Dr. Ashutosh ModiAssistant Professor, Computer Science and Engineering, IIT Kanpur
Dr. Anandhi RamachandranProfessor, International Institute of Health Management Research, Delhi
Dr. Deepankar RoyAssociate Professor, National Institute of Bank Management
Prof. Arunabha MukhopadhyayProfessor, Information Technology and Systems, IIM Lucknow
Dr. Harpreet SinghScientist and Head, Division of Biomedical Informatics, ICMR
Prof. Pabitra MitraProfessor, IIT Kharagpur
Ms. Kavita BhatiaSenior Director, Emerging Technology, MeitY, Government of IndiaEight of roughly 250. The full panel spans the IITs, the IIMs, the IIITs, the Indian School of Business, NITI Aayog and central ministries, the Department of Science & Technology, MeitY and CERT-In. See the full jury panel.
Verticals on the podium
Industry AI became its own group at the 16th edition; before that vertical work was recognised inside each sector’s category. These are the most recent.
Previous winners
Industry-specific AI has been recognised under sector labels for years and as a group of its own since the 16th edition. The complete record sits in the winners archive.
Category names have changed across editions as the field has moved; the rows above are the closest equivalents in the published record. See the winners archive for the full 1st–16th edition history.
What happens after you submit
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Stage one · nowNomination registration
Register your company and category through the nomination process, then complete the detailed nomination form and processing fee.
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Stage twoJury presentation
Present the innovation to the jury and take questions. Jurors score each entry against the four evaluation criteria and rank it within its category.
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Stage threeStar Certification
Nominees are also assessed for AGBA Innovation Star Certification, an independent maturity rating alongside the category judging.
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Stage fourFinalists announced
Once every category has been evaluated, finalists are announced on the website ahead of the ceremony.
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Stage fiveAward ceremony
Winners are honoured on 18 February 2027 at Hotel Ashoka, Chanakyapuri, New Delhi.
Before you write your nomination
What makes an entry “industry” AI rather than functional?
Domain depth. If the data, the regulator or the failure modes of one industry shaped the design, it belongs here.
How are regulated deployments assessed?
On the approval path as much as the model — explainability, audit trail, clinical or financial validation, and who signed it off.
Is a domain-specific model required?
No. Fine-tuned, retrieval-based and rules-plus-model systems are all read here, provided the domain work is visible.
How do I submit a nomination?
Nominations are submitted through the nomination process, which sets out the stages, the supporting material required and the current edition’s dates.
Put your vertical in front of the panel
Nominations for the Industry AI Innovation Award are read by an independent jury drawn from the sectors themselves. Bring the domain detail — the edge cases, the sign-offs, the practitioners who stayed.