QAD, Inc.
Manufacturing
SeniorManager-EnterpriseAITransformation
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“Senior Manager - Enterprise AI Transformation at QAD, Inc.. Skills: Enterprise AI transformation, AI governance, AI roadmap. Own enterprise internal AI leverage roadmap. Prioritize use cases”
What You'll Achieve.
Translate productivity goals into roadmap; Ensure AI moves into scalable execution; Achieve measurable productivity; Achieve resilience; Achieve growth
Industry & Context.
Value capture; Risk assessment
What They're Looking For.
Must Have
8-15+ years digital transformation, 8-15+ years AI transformation, 8-15+ years enterprise automation, 8-15+ years product management, 8-15+ years technology strategy, 8-15+ years operations transformation, 8-15+ years consulting, Experience working across business and technology teams, Understanding of enterprise systems, Understanding of data architecture, Understanding of workflow automation, Understanding of AI/agentic tools, Able to work directly with CFO, CHRO, COO, CRO, Engineering, IT, and functional leaders, Pragmatic build-vs-buy mindset, Comfortable with ambiguity, Comfortable with executive communication, Comfortable with measurable value capture
What You'll Do.
Own enterprise internal AI leverage roadmap
Translate executive priorities
Maintain view of ongoing initiatives
Define internal AI governance model
Establish use-case intake processes
Establish prioritization processes
Establish approval processes
Establish escalation processes
Create risk-tiered governance approach
Enable adoption without bureaucracy
Coordinate internal AI reference architecture
Define standard patterns for connecting AI tools
Define when QAD should buy
Define when QAD should configure
Define when QAD should integrate
Define when QAD should build
Prevent fragmented functional AI stacks
Prevent unmanaged shadow AI deployments
Define operating controls for tools
Establish usage tracking
Manage license allocation
Ensure access rights align
Ensure data permissions align
Ensure restrictions align
Track delivery progress
Track productivity impact
Track financial value
Define standard metrics
Prepare leadership updates
Prepare decision materials
Prepare board-ready summaries
Ensure pilots have clear success criteria
Lead Functional AI Enablement Leads
Lead AI/Data Integration Engineers
Set standards for workflow design
Set standards for agent requirements
Set standards for documentation
Set standards for testing
Set standards for adoption
Set standards for value tracking
Coach team as execution partners
How You'll Work.
Team & Collaboration
Partner with IT; Partner with Data; Partner with Engineering; Partner with InfoSec; Partner with Legal; Partner with Procurement; Partner with functional leaders; Partner with Finance; Work with IT/Data/Engineering
Communication Scope
Executive communication; Leadership communication
Process & Methodology
Roadmap planning, Prioritization
Full Job Description
QAD is a leading provider of adaptive, cloud-based enterprise software and services for global manufacturing companies. At QAD, we help customers transform and innovate through modern cloud platforms, data-driven insights, and AI-enabled solutions. Our Engineering organization plays a critical role in delivering resilient, scalable, and high-quality platforms that power customer success worldwide. The Senior Manager - 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 * Coordina
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