Executive Seminar
AI for Manufacturing
Delivered through Saifk Thaki, an initiative of Ministry of Communications and Information Technology
A focused, three-hour session that connects AI capabilities to real factory operations — through deep-dive explanations, industrial case studies, and live demonstrations. Less hype, more pathway to measurable value.
In-person
Format
3 Hours
Duration
1 Pilot-Ready Use Case
Outcome
- In-person
- 3 Hours
- WHY NOW
This seminar helps leaders see
where AI creates measurable value and how to approach implementation in a structured, practical way.
AI is reshaping uptime, quality, efficiency and supply-chain resilience. But adoption succeeds or fails on how leaders understand their data, systems and workflows.
- 01.
See the value, not the buzz
Cut through the noise. Learn to tell AI apart from automation and traditional industrial systems, and where each genuinely belongs.
- 02.
Understand why projects fail
Most AI initiatives stall on data quality, silos and integration — not on models. We make those failure points visible early.
- 03.
Leave with a starting point
By the close, every participant defines one pilot-ready use case grounded in their own plant’s existing data and systems.
- WHO & WHAT
Designed for leadership.
Aimed at action.
AI is reshaping uptime, quality, efficiency and supply-chain resilience. But adoption succeeds or fails on how leaders understand their data, systems and workflows.
- WHO IT'S FOR
Plant Managers
Heads of Manufacturing & Operations
Supply Chain Leaders
Strategy & Transformation Heads
Innovation Leads
- WHAT YOU'LL WALK AWAY ABLE TO DO
Evaluate the strategic role of AI in manufacturing operations
Understand how data, infrastructure and workflows enable AI systems
Apply a structured approach to prioritising AI initiatives
Define a practical starting point for adoption in your organisation
Identify high-impact use cases across production, quality and supply chain
- THE SESSION
The three-hour agenda,
block by block
From systems and data to live demos and a roadmap, every block ends in something usable.
● AI in Manufacturing: Systems, Data & Infrastructure
- What AI is and isn’t on the factory floor
- AI vs automation vs traditional industrial systems
- Manufacturing data types: sensors (IoT), machine logs, images, maintenance reports
- Infrastructure: edge vs cloud, real-time vs batch
- Why most AI initiatives fail: data quality, silos, integration
Outcome: Leaders see AI as a system built on data and infrastructure decisions, not just models.
● How AI Works in the Factory Environment
- The end-to-end pipeline: data → model → decision → action
- Integration with PLCs, MES and existing factory systems
- Predictive vs reactive operations
- Walkthrough: sensor data → anomaly detection → failure prediction → maintenance planning
Outcome: “What data exists in your plant that is currently underutilised?”
● Industrial AI Use Cases
- Predictive maintenance — reducing downtime and failures
- Quality control and defect detection
- Production optimisation and process efficiency
- Supply chain forecasting and risk management
- Energy optimisation, cost reduction, safety & compliance
Outcome: What problem was solved, what data enabled it, and what leadership decisions made it work.
● Tea Break
Pause
Tea Break
● AI for Quality & Defect Detection
- Computer vision for quality inspection
- How models detect defects from images — and read their outputs (accuracy, errors, limits)
- When AI can replace, and when it should support, human inspection
- Live demonstration of a computer-vision solution
● AI for Operations & Decision Workflows
- Production data → insights → alerts
- Monitoring performance metrics and triggering actions
- Connecting AI to reporting, maintenance triggers and decision support
MINI APPLICATION: Each participant maps one workflow to automate: input → decision → output.
● AI Strategy & Roadmap for Manufacturing
- Prioritisation framework: impact vs feasibility, quick wins vs strategic bets
- Structuring adoption: pilot → scale → integrate
- The leadership role: driving adoption and managing data & system readiness
OUTPUT: Every participant defines one pilot-ready use case.
● Q&A and Discussion
Q&A and Discussion