This session marks the official launch of the Ministry of Electronics and Information Technology's IndiaAI study on "Advancing AI Readiness and Adoption in Manufacturing MSMEs." Bringing together senior government leaders and ecosystem partners, it will examine how India's MSME sector can prepare for AI at scale—strengthening competitiveness, regulatory compliance, traceability, and sustainable growth in an increasingly demanding global market.
VIDEO RECORDING
Advancing AI Readiness and Adoption in Manufacturing MSMEs: Official Launch
Abstract
The event marked the formal launch of the Ministry of Electronics & Information Technology’s India AI study titled “Advancing AI Readiness and Adoption in Manufacturing MSMEs.” Senior officials from the central government, the National Institute of Smart Governance (NISG) and the consulting firm Athena Infonomics outlined the strategic importance of scaling artificial‑intelligence (AI) solutions across India’s micro‑, small‑ and medium‑size manufacturing enterprises (MSMEs). The session highlighted three focus sectors—textiles, electronics, and pharmaceuticals—and described a multi‑phase, on‑the‑ground “discovery” approach (≈ 350 factory immersions). Speakers stressed the need for affordable, “shelf‑ready” AI services, policy enablement, data‑governance, and measurable impact tracking. A brief Q&A addressed concerns around labour impact, explainability, “machinery‑as‑a‑service,” and upcoming government programmes such as the TEAM (Textiles Employment and Expansion Program) and other PLI‑type incentives.
Detailed Summary
Host/Moderator opened with a rapid series of acknowledgments (“Thank you…”) before handing the floor to Smt. Deepa Karthykeyan (Athena Infonomics).
Deepa thanked the India AI Mission for its seven‑pillar framework and for democratizing AI‑impact activities. She described the launch as the “official inception of the study” and reminded the audience that printed copies of the inception report were available.
Key Points from Deepa’s Address
Aspect
Detail
Why MSMEs matter
MSMEs constitute the bulk of India’s manufacturing output and employment. AI must reach them to lift the nation’s GDP to the third‑largest economy rank.
Three priority sectors
Textiles, Electronics, Pharmaceuticals – selected for their growth rates (e.g., electronics + 35 % YoY) and export relevance.
Study objectives
1. Map AI readiness of MSMEs; 2. Identify affordable AI solutions; 3. Create a “shelf” of modular AI services (ERP, CRM, production‑line tools).
Policy ambition
Potential for government cost‑sharing on AI adoption to keep MSMEs competitive; eventual creation of public‑good data assets (security, ownership, transparency).
Call to ecosystem
Invite AI‑solution providers, start‑ups, and industry bodies to submit use‑case proposals and pricing models.
2. Government Vision – Secretary S.C.L. Das (Ministry of MSME)
2.1 Economic Context
India has 76 million digitally registered MSMEs (target > 80 million by March).
Over 20 % of the registered MSMEs are in manufacturing; many “service‑registered” firms also have upstream/downstream production activities.
2.2 Role of AI
AI is framed as a force‑multiplier for achieving the “Aatma Nirbhar Bharat” and “Viksit Bharat” goals.
Emphasis on industry‑led transformation supported by public‑good interventions (policy tweaking, data‑security frameworks, financing).
2.3 Government Commitments
Commitment
Explanation
Policy & Regulation
Streamline regulations, improve ease of doing business, reduce “government‑to‑business” friction.
Leverage existing sector‑wise clustering (geographic and product‑based) to aggregate demand, lower transaction costs for MSMEs.
Capacity Building
Encourage youth talent, academia, and private sector to co‑create solutions; promote “frugal innovation” to keep costs low.
2.4 Call to Action
Industry: Submit ready‑to‑deploy AI use‑cases; help define cost‑effective models.
Academia: Provide research on impact measurement, data standards, and ethical AI.
Policy Makers: Identify existing policy gaps (e.g., procurement, financing, data‑ownership) and design new guidelines.
3. Methodology – CEO Bhuvnesh Kumar (NISG)
3.1 Phased Blueprint
Discovery Phase – > 350 factory immersions across textiles, electronics, pharma. Field teams will interview factory owners, shop‑floor staff, and AI‑service vendors to capture real‑world pain points.
Design Phase – Define high‑ROI archetypes (people‑process‑data‑technology bundles). Build use‑case libraries with clear operational, technical, and financial metrics.
Implementation & Diffusion – Translate archetypes into service‑delivery models (e.g., AI‑as‑a‑service, equipment‑as‑a‑service). Develop a public‑facing knowledge portal and policy briefings for rapid scaling.
3.2 Market‑Intelligence Gap
Demand‑side questions: “What is the true ROI?”, “What budget is realistic?”, “How to compare competing AI solutions?”
Supply‑side challenges: High customer‑acquisition cost for AI vendors; need for demand aggregation to achieve economies of scale.
Textiles are an “age‑old capital‑intensive” sector; AI can protect export competitiveness and sustainability (traceability of water/energy usage).
Cluster‑level deployment
Emphasized shared digital infrastructure and common data standards to reduce per‑MSME cost.
Policy linkage
Referenced the TEAM (Textiles Employment & Expansion Programme) which includes a machinery‑upgradation component; an upcoming national consultation (19 Feb, Vani Bhawan) on “machinery‑as‑a‑service”.
Labour perspective
AI is presented as productivity‑enhancing, not labour‑displacing; the goal is to make workers more efficient.
4.2 Pharmaceuticals – Joint Secretary Aman Sharma
Issue
Insight
Cost & scale
MSME pharma units face high per‑unit manufacturing cost due to limited scale.
Quality & compliance
Mandatory WHO‑GMP / revised Schedule M compliance pushes need for heavy capital investment (hardware).
AI role
AI can close feedback loops (raw‑material validation, batch‑testing, traceability) and reduce post‑mortem investigations.
Infrastructure gap
Current government funds focus on hardware (parks, factories); Sharma urges inclusion of AI software tools in common‑facility grants.
Strategic recommendation
Position AI as mandatory component of GMP to incentivize adoption and lower operating costs.
4.3 Electronics – (Reference)
Mentioned in opening remarks that electronics manufacturing has shown 35 % YoY growth, making it a prime candidate for AI‑driven productivity and quality upgrades.
5. Interactive Q&A (Highlights)
Question
Respondent(s)
Core Answer
AI for micro‑industries – will it replace labour?
Panel (primarily Rohit Kansal)
AI is expected to augment labour, not replace it; emphasis on “frugal innovation” and new job categories.
Explainability & auditability of AI decisions (e.g., credit scoring)
Shri Bhuvnesh Kumar
Working on benchmarking frameworks for model risk, safety, relevance; internal effort to draft standards for auditability.
Machinery‑as‑a‑Service for MSMEs
Rohit Kansal
Government’s TEAM scheme includes a machinery‑upgradation line; upcoming national consultation will detail policy support.
Instrumentation layer vs. model layer
Bhuvnesh Kumar
Instrumentation (sensors, high‑res cameras, loggers) is prerequisite; details to be discovered during factory immersions.
Skilling & labour‑impact concerns
Panel (general)
Acknowledged as a critical focus; AI adoption will be intentional about labour impact from the start.
Sector‑specific request – textile AI & sustainability
Rohit Kansal
Sustainability (traceability of emissions, water) will be a core KPI in the textile archetypes.
Policy gaps – procurement & financing
Bhuvnesh Kumar
In design phase, will produce guidelines on procurement models (equipment‑as‑a‑service, unbundling) and financing mechanisms.
6. Closing Remarks
Secretary S.C.L. Das thanked all participants, reiterated the government’s unwavering commitment to a vibrant, competitive MSME ecosystem, and emphasized the need for policymakers, industry, and academia to cooperate.
Deepa Karthykeyan invited attendees to collect the printed inception report and scan the QR code for the full study design.
The session concluded with a brief thank‑you from the host, followed by a recognition of the organizing teams (NISG, Athena Infonomics, Ministry officials).
Key Takeaways
AI adoption in MSMEs is a national priority: the government views AI as a crucial “force‑multiplier” for achieving Aatma Nirbhar Bharat and moving India into the top‑three global economies.
Three pilot sectors—textiles, electronics, pharmaceuticals—will be studied through > 350 factory immersions to develop high‑ROI AI archetypes.
The study will produce a “shelf” of modular AI services (ERP, CRM, production‑line analytics) that can be customised and cost‑shared with the government to keep solutions affordable for MSMEs.
Policy agenda includes: simplifying procurement, providing targeted fiscal incentives, creating public‑good data assets, and establishing impact‑tracking dashboards to ensure money is spent effectively.
Labor impact is explicitly framed as productivity‑enhancing, not job‑displacing; special attention will be paid to skill‑building and new job categories.
Explainability, auditability, and model‑risk governance are recognised as critical; a benchmarking framework is under development.
Sector‑specific programmes: the TEAM scheme (Textiles Employment & Expansion Programme) will fund machinery upgrades and support “machinery‑as‑a‑service” models; pharma parks are urged to embed AI tools alongside hardware.
Collaborative ecosystem: the government calls on AI start‑ups, solution providers, academia, and industry bodies to submit use‑cases, pricing models, and to engage in the upcoming national consultation (19 Feb, Vani Bhawan).
Immediate next step: dissemination of the inception report (both physical copies and QR‑code‑linked digital version) and the commencement of the discovery phase on the ground.