Job Description

The Director - Enterprise AI Transformation will lead the internal AI leverage agenda across QAD by translating company-level productivity goals into a governed, prioritized, and measurable AI transformation roadmap. The person will own the overall operating model for internal AI adoption: prioritization, governance, reference architecture coordination, usage controls, value tracking, and execution cadence. They are not a PMO lead. They are the person who ensures AI moves from fragmented functional experimentation into scalable, governed execution.

Key responsibilities

Enterprise AI leverage roadmap

- Own the enterprise internal AI leverage roadmap across functions and BUs.

- Prioritize use cases based on value, feasibility, functional readiness, data readiness, and risk.

- Translate executive priorities and external diagnostic outputs into executable implementation waves.

- Maintain a clear view of what is already underway, what should be accelerated, what should be stopped, and what requires leadership decision.

Governance and operating model

- Define the internal AI governance model in partnership with IT, Data, Engineering, InfoSec, Legal, Procurement, and functional leaders.

- Establish use-case intake, prioritization, approval, and escalation processes.

- Create a risk-tiered governance approach: fast-track low-risk use cases, structured review for medium-risk use cases, and formal approval for high-risk / sensitive-data use cases.

- Ensure the team enables adoption without becoming a bureaucratic PMO.

Reference architecture and AI stack coordination

- Coordinate the internal AI reference architecture across approved tools, data sources,



enterprise systems, workflow layers, and governance controls.

- Work with IT/Data/Engineering to define standard patterns for connecting AI tools to systems and datasets.

- Help define when QAD should buy, configure, integrate, or selectively build.

- Prevent fragmented functional AI stacks and unmanaged shadow AI deployments.

Usage, access, and spend controls

- Define operating controls for tools such as Claude, Gemini, ChatGPT Enterprise, BigQuery, Workday AI, Salesforce/Agentforce, Glean, and other internal AI capabilities.

- Establish usage tracking across users, functions, use cases, tokens/credits, spend, and adoption.

- Partner with Finance and IT to manage spend, license allocation, and value-for-money.

- Ensure access rights, data permissions, and restrictions are aligned with InfoSec and data governance requirements.

Delivery and value capture

- Track delivery progress, adoption, productivity impact, financial value, and risks across the AI leverage portfolio.

- Define standard metrics for each initiative: baseline, target, adoption, usage, productivity, quality, cycle time, and financial impact.

- Prepare leadership updates, decision materials, and board-ready summaries where needed.

- Ensure pilots have explicit success criteria and can either scale, pivot, or be shut down quickly.

Team leadership

- Lead Functional AI Enablement Leads and AI/Data Integration Engineers.

- Set standards for workflow design, agent requirements, documentation, testing, adoption, and value tracking.

- Coach the team to operate as hands-on execution partners, not meeting schedulers or project-plan chasers.

📌 Director - Enterprise AI Transformation (Pune)
🏢 QAD
📍 Pune