Internal audit re-architected around AI analytics and process mining across the manufacturing value chain.

The starting point.

At the Eicher Group, the internal audit function was structured around traditional sample-based coverage — periodic reviews of transactional and operational data across a large automotive and manufacturing value chain. As transaction volumes and process complexity grew, sampling was leaving blind spots between audit cycles and diluting the function's assurance value.

How the engagement was structured.

• Re-architected the internal audit function around AI analytics and process mining.
• Replaced sample-based audit coverage with continuous, full-population monitoring across transactional and operational data.
• Instrumented the manufacturing value chain so process breaks and control exceptions surfaced in near-real-time.
• Upskilled the internal audit team to own the analytics stack rather than depending on external tooling.

What changed.

• Internal audit moved from periodic sampling to continuous, full-population monitoring.
• Process mining exposed operational bottlenecks alongside financial control exceptions.
• Assurance coverage expanded across the manufacturing value chain without proportional headcount growth.