Embedding Trust in Innovation: AI Governance and Quality Infrastructure for Growth
Abstract
The round‑table explored how an AI‑enabled Quality Infrastructure (QI) can reinforce governance, accountability and global acceptance of AI‑driven processes. Participants examined concrete use‑cases—digital‑twin‑based inspection, sensory‑evaluation AI, and AI‑powered knowledge platforms—highlighting the productivity gains and data‑quality improvements they deliver. The discussion then turned to three systemic barriers (regulatory gaps, accreditation challenges, and international harmonisation) and proposed practical pathways: evolving accreditation frameworks, building AI‑specific standards, and fostering cross‑border cooperation. Insights from the Indian Ministry of Electronics & Information Technology on AI‑related cyber‑risk, and from the Bureau of Indian Standards on emerging AI standards, rounded out the dialogue, underscoring the need for interoperable assurance mechanisms that enable MSME competitiveness, secure cross‑border trade and position India as a leader in responsible AI‑driven digital transformation.
Detailed Summary
Speaker: Jagdheesh Manian (Bureau Veritas)
- BV’s AI engine generates a digital twin of a product or facility.
- From the twin, the system calculates all required volumetrics and presents them instantly to the customer.
- Human inspectors later review the data and issue formal reports.
- Key performance gains:
- Inspection time reduced by ~80 % (from 4–5 days to a few hours).
- Data accuracy now at 99 %, markedly higher than the 80‑90 % typical of conventional manual methods.
- Safe working conditions for inspectors (no need for physical entry).
- Scaling challenges: when extending the solution beyond Europe, three major barriers surfaced:
- Regulatory framework gaps – existing regulations are built for manual inspection; AI‑assisted inspection lacks a holistic, system‑wide view and legal certainty.
- Accreditation & conformity assessment – while ISO 42001 (Quality Management) and the emerging ISO 23894 (AI risk management) exist, they are not yet fully integrated across supply‑chains.
- Trust in AI outputs – customers ask, “Do we trust results generated by Microsoft’s AI engine?” A common language or framework to validate AI‑produced results is missing.
2. AI‑Enhanced Productivity in Knowledge Work
Speaker: Amanda Craig (Microsoft)
- Copilot integration with Microsoft 365 (Teams, PowerPoint, Word, Excel) accelerates staff productivity – faster content creation, improved document quality.
- ChatGPT‑style knowledge platform: technical teams extract complex methodology information and review scientific documents more efficiently.
3. Analytical AI & Sensory Evaluation
Speaker: Jagdheesh Manian (continued)
- Introduction of electronic nose and tongue (digital sensory laboratory).
- The electronic nose captures chromatographic fingerprints of volatile compounds. A database of ~275 k compounds enables the system to identify aromas comparable to a human panel.
- AI processes the chromatograms to:
- Produce objective sensory descriptions.
- Detect off‑odors (early spoilage or degradation).
- Enable rapid batch‑to‑batch benchmarking and statistical analysis (seconds vs. manual hours).
- Creation of a digital library of human sensory expertise that preserves knowledge across decades.
- Clarified that AI does not replace human judgement; it augments it while scientific accountability remains human.
4. Transition to “Solutions” – Role of Accreditation Bodies & Policymakers
Speaker: Moderator (introducing the next pillar)
- Explained that accreditation bodies verify conformity‑assessment organisations, ensuring they meet impartiality, competence and technical know‑how requirements.
- Invited participants to discuss how the CAPs (Conformity‑Assessment Providers) can adopt AI within a consistent regulatory environment.
5. Vision for Indian Quality Infrastructure (QCI Perspective)
Speaker: Chakravathy T Kannan (Quality Council of India)
- Global benchmarks: Germany & Japan’s quality infrastructures are ubiquitous and affordable, fostering trust.
- India faces a compressed timeline (4‑5 years) to build a comparable, democratic QI that can support rapid digital transformation.
- Emphasized the need for a leap‑frog approach (similar to UPI’s evolution) rather than incremental upgrades.
- Described four AI‑driven “agents” under development within the Digital Quality Infrastructure (DQI) programme:
- Smart‑scan agent – OCR‑style extraction of data from any document, eliminating manual entry.
- Voice‑enable agent – provides contextual best‑practice videos or courses when users encounter an “objective element” with unclear guidance.
- API‑integration agent – creates smart APIs to seamlessly link institutional systems with a national QI platform, turning audits into routine work‑management tasks.
- Knowledge‑repository agent – curates industry‑specific knowledge (e.g., tailored guidance for rural vs. advanced customers).
- Highlighted the need for interoperability, common data formats and metadata standards to scale AI with trust.
6. Cyber‑Security & AI Assurance (CERT‑In Perspective)
Speaker: Ashutosh Bahuguna (CERT‑In)
- Presented a three‑dimensional AI‑cyber‑risk framework:
- AI for cyber‑incident response – case study from the World Economic Forum on rapid AI‑driven response.
- AI as an attack vector – AI lowers barriers for malware creation, sophisticated phishing; therefore AI‑aware SOPs are required.
- Attacks on AI systems themselves – poisoning, model extraction, evasion.
- Identified four priority actions to secure AI deployments:
- Dynamic autonomy adjustment based on cyber‑risk assessment.
- Secure‑by‑design development of AI applications.
- Explainability & auditability – transparent models enable root‑cause analysis.
- AI Bill of Materials (AI‑BoM) – a transparent inventory of AI components (datasets, models, libraries) to assure supply‑chain integrity.
- Discussed international collaboration: joint guidelines with the US, EU, and French authorities; a technical document on AI‑BoM already published.
- Mentioned alignment with emerging NIST “DoD‑Astra” test‑bed for AI vulnerability assessment; India plans to host a similar quality‑infrastructure test‑bed.
7. Standards Landscape (AIQI Consortium & BIS Perspective)
Speaker: Richard Skalt (AIQI Consortium)
- Reiterated that standards tie together regulators, accreditation bodies and industry; they must keep pace with AI evolution.
- Cited ISO 42001 (Quality‑Management) and ISO 23894 (AI Risk Management) as foundational but insufficient alone.
- Introduced the ISO 42119 series (AI testing):
- Part 1 – overall framework;
- Part 2 – aligns AI testing with ISO 29119 software‑testing standards (currently in draft).
- Part 3 – verification and validation of AI systems (being led by India).
- Upcoming parts on red‑team exercises, hallucination metrics, synthetic‑data validation.
- Emphasised the need for India‑specific standards to address:
- Startup ecosystem – rapid prototyping and scaling.
- Synthetic‑data governance (in line with the Personal Data Protection Act).
- Multilingual AI – testing for 22 official Indian languages to mitigate bias.
- Proposed interoperable data formats and metadata structures (as with UPI) to enable trusted AI model exchange.
- Mentioned smart standards that embed AI‑driven compliance checks directly into the standard itself (to be elaborated by a colleague, “Tim”).
8. Closing Remarks & Call for Collaboration
Speaker: Moderator (summarising)
- Emphasised that standards, accreditation, and regulation must converge to create a trusted AI assurance ecosystem.
- Highlighted remote audits as a climate‑friendly, efficient method suitable for AI‑driven assessments.
- Noted EU and UK regulatory approaches: the EU’s AI Act is operationalised through standards (e.g., EN‑SEN, EN‑SEN‑LEK); the UK leans on standards and assurance as a pragmatic bridge.
- Consensus: sustained, multi‑stakeholder collaboration is essential to embed AI within conformity assessment and to extend the framework to other emerging technologies.
9. Post‑Session Logistics
- Participants were invited to read the published round‑table paper (linked earlier in the session).
- A group photograph was taken with all speakers.
Key Takeaways
- AI‑enabled digital twins can slash inspection times by ~80 % and raise data accuracy to 99 %, but regulatory and accreditation frameworks must evolve to legitimize AI‑generated reports.
- AI‑driven sensory evaluation (electronic nose/tongue) converts complex chromatographic data into objective, rapid aroma profiles, preserving human expertise in a digital library.
- Three systemic barriers to scaling AI in QI: (1) outdated manual‑inspection‑centric regulations, (2) fragmented accreditation standards, (3) lack of a universal trust framework for AI outputs.
- India’s QI roadmap calls for a leap‑frog digital infrastructure (smart‑scan, voice‑assist, API integration, knowledge‑repository agents) to match the rapidity of UPI‑style transformation.
- CERT‑In’s AI‑cyber‑risk model stresses secure‑by‑design AI, dynamic autonomy, explainability, and an AI Bill of Materials for supply‑chain transparency.
- Emerging ISO 42119 series (AI testing) plus ISO 42001/23894 provide a standards backbone; India is leading parts on verification, red‑team testing, and hallucination metrics.
- Standardisation gaps specific to India include synthetic‑data validation and multilingual AI testing for 22 official languages.
- Interoperable data formats and metadata (mirroring UPI success) are critical for scalable, trustworthy AI model exchange across borders.
- Remote AI‑driven audits offer efficiency, reduced carbon footprint, and align with the evolving nature of AI‑based assessments.
- Continuous multi‑stakeholder collaboration (industry, regulators, accreditation bodies, standards organisations) is essential to embed AI responsibly within conformity assessment and to extend these practices to future emerging technologies.
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