Senior Director, AI Strategy, Governance and Transformation
Information Technology Senior Management Forum, Chicago, IL, United States
Executive Summary
The Senior Director AI Strategy, Governance & Transformation, converts enterprise AI ambition into prioritized use cases, governed delivery, and realized business value. This leader owns the AI portfolio, resourcing and prioritization—from idea intake and prioritization to Responsible AI oversight, operational and change management, adoption, and benefits tracking—ensuring AI becomes a repeatable engine for growth, productivity, and risk-aware innovation.
This role will partner with Corporate and Division leaders, and AI Engineering teams to align platform capabilities with sponsored, high-impact use cases, while embedding Responsible AI standards and measurable outcomes across the lifecycle.
Strategic Mandate
Build and lead an enterprise AI portfolio office, a new organization focused on AI governance, transformation, process reengineering, portfolio management and change management.
Identify & prioritize enterprise AI/GenAI use cases tied to strategic objectives and P&L.
Build and run a unified AI portfolio with clear stage-gates, funding, and ownership.
Develop a process excellence mindset and capability to ensure processes are AI ready.
Embed Responsible AI (RAI), risk controls, and regulatory compliance by design.
Drive adoption, change management, and value realization across business units.
Establish an AI operating model that scales: intake ? experiment ? pilot ? production ? value.
Key Responsibilities 1) Enterprise Use-Case Strategy & Portfolio
Stand up and lead an
enterprise AI Portfolio Office (APO)
that manages demand intake, evaluation, and cross-BU prioritization. Run regular portfolio reviews with IT, Finance and Business Unit Leaders to ensure strategic and investment alignment.
Build a
12–24-month AI roadmap
that sequences lighthouse use cases alongside platform enablers; define rubic scores across value, feasibility, risk and data readiness.
2) Governance and Responsible AI
Partner with existing data use and global compliance councils to operationalize
Responsible AI standards
(fairness, transparency, privacy, safety, explainability, human oversight) across the lifecycle.
Oversee
model risk classification , AI risk registers, human-in-the-loop controls, documentation and audit readiness across the lifecycle.
Govern
third-party AI/LLM providers
and data usage in partnership with Legal, Procurement, and Security.
Establish stage gate governance with clear exit criteria, investment thresholds and benefits tracking (discovery – pilot – scale)
3) Process Transformation & Adoption
Lead
process reengineering and design
to make workflows AI ready—defining new roles, policy guardrails, controls, and exception handling.
Build and execute a
change management and enablement
engine (communications, training, playbooks, competency development) to drive sustained adoption.
Partner with R&D to build highly technical training programs and HR/L&D to build enterprise
AI fluency programs
for executives, product owners, engineers, and end-users.
4) Value Realization & Performance Management
Design and implement
benefits tracking —from baseline to post-deployment value capture (revenue, cost, risk, CX).
Publish a
quarterly AI Value Dashboard
(adoption, time-to-value, ROI, control effectiveness, incident reporting, model performance).
Continuously improve through
post-implementation reviews
and portfolio rebalancing.
5) Organizational Leadership and Influence
Develop, and retain key talent to enable adoption of AI; foster a culture of excellence, accountability, and continuous learning
Work in partnership with business and IT to ensure
executive sponsorship
and product ownership for each use case; clarify OKRs, KPIs, and benefit hypotheses up front.
Run a
cross-functional steering forum
with Technology, Risk/Compliance, Legal, Security, to unblock, align, and accelerate.
6) Partnership with AI Engineering (Operating Model)
Co-own
AI release readiness
(security, privacy, resilience, monitoring) and handoffs from experiment ? production ? run.
Align
platform roadmaps
with prioritized use cases; ensure reusability via APIs, shared services, templates, guardrails, standardized tooling.
Success Profile (12–24 Months)
A
governed AI portfolio
is in place with clear sponsorship, funding, and stage-gates; 3–5 lighthouse use cases scaled enterprise-wide.
Responsible AI
embedded in intake, build, and run; positive outcomes in
internal audit/external exams .
Adoption and value realization metrics established and tracked
A durable
AI operating model
(APO, governance forums, dashboards, playbooks) used across business units.
High trust and alignment between
business, risk, and engineering , with demonstrable acceleration and fewer production frictions.
Process re-engineering is a key enabler for all AI transformation efforts.
Ideal Candidate Profile Experience
15+ years
in strategy, transformation, product/portfolio leadership, or risk/governance roles within complex regulated industries ( financial services, automotive, or large-scale technology );
7+ years
working with AI/ML initiatives.
Built and led an
enterprise portfolio office
or transformation program with measurable outcomes (multi-BU scale).
Hands‑on experience with
Responsible AI/Risk , regulatory engagement, and
audit‑ready controls
in complex environments.
Proven track record
driving adoption
and realizing benefits for AI‑enabled process change.
Technical & Governance Acumen
Working knowledge of
AI/ML lifecycles , GenAI (LLMs, RAG, prompt orchestration), data governance, and model monitoring.
Familiarity with
MLOps
concepts and platform‑led delivery; comfortable partnering deeply with engineering leaders.
Proficient in
risk/control frameworks , documentation (model cards, data sheets), and
policy design .
Leadership & Influence
Credible with executives and regulators;
clear, concise communicator
able to translate complex risk/tech topics into business outcomes.
Inclusive leader who
builds high performing teams both direct reports and across a matrix of teams.
Builder‑operator mindset: strategic framing with
bias for execution
and measurable results.
Education
Bachelor’s degree required (Business, Computer Science, Engineering, Data/Analytics, or related).
Advanced degree (MBA, MS in Data/AI with tech focus) or relevant certifications in
governance/risk/compliance
preferred.
The base pay for this position is $190,000.00 – $380,000.00. In specific locations, the pay range may vary from the range posted.
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This role will partner with Corporate and Division leaders, and AI Engineering teams to align platform capabilities with sponsored, high-impact use cases, while embedding Responsible AI standards and measurable outcomes across the lifecycle.
Strategic Mandate
Build and lead an enterprise AI portfolio office, a new organization focused on AI governance, transformation, process reengineering, portfolio management and change management.
Identify & prioritize enterprise AI/GenAI use cases tied to strategic objectives and P&L.
Build and run a unified AI portfolio with clear stage-gates, funding, and ownership.
Develop a process excellence mindset and capability to ensure processes are AI ready.
Embed Responsible AI (RAI), risk controls, and regulatory compliance by design.
Drive adoption, change management, and value realization across business units.
Establish an AI operating model that scales: intake ? experiment ? pilot ? production ? value.
Key Responsibilities 1) Enterprise Use-Case Strategy & Portfolio
Stand up and lead an
enterprise AI Portfolio Office (APO)
that manages demand intake, evaluation, and cross-BU prioritization. Run regular portfolio reviews with IT, Finance and Business Unit Leaders to ensure strategic and investment alignment.
Build a
12–24-month AI roadmap
that sequences lighthouse use cases alongside platform enablers; define rubic scores across value, feasibility, risk and data readiness.
2) Governance and Responsible AI
Partner with existing data use and global compliance councils to operationalize
Responsible AI standards
(fairness, transparency, privacy, safety, explainability, human oversight) across the lifecycle.
Oversee
model risk classification , AI risk registers, human-in-the-loop controls, documentation and audit readiness across the lifecycle.
Govern
third-party AI/LLM providers
and data usage in partnership with Legal, Procurement, and Security.
Establish stage gate governance with clear exit criteria, investment thresholds and benefits tracking (discovery – pilot – scale)
3) Process Transformation & Adoption
Lead
process reengineering and design
to make workflows AI ready—defining new roles, policy guardrails, controls, and exception handling.
Build and execute a
change management and enablement
engine (communications, training, playbooks, competency development) to drive sustained adoption.
Partner with R&D to build highly technical training programs and HR/L&D to build enterprise
AI fluency programs
for executives, product owners, engineers, and end-users.
4) Value Realization & Performance Management
Design and implement
benefits tracking —from baseline to post-deployment value capture (revenue, cost, risk, CX).
Publish a
quarterly AI Value Dashboard
(adoption, time-to-value, ROI, control effectiveness, incident reporting, model performance).
Continuously improve through
post-implementation reviews
and portfolio rebalancing.
5) Organizational Leadership and Influence
Develop, and retain key talent to enable adoption of AI; foster a culture of excellence, accountability, and continuous learning
Work in partnership with business and IT to ensure
executive sponsorship
and product ownership for each use case; clarify OKRs, KPIs, and benefit hypotheses up front.
Run a
cross-functional steering forum
with Technology, Risk/Compliance, Legal, Security, to unblock, align, and accelerate.
6) Partnership with AI Engineering (Operating Model)
Co-own
AI release readiness
(security, privacy, resilience, monitoring) and handoffs from experiment ? production ? run.
Align
platform roadmaps
with prioritized use cases; ensure reusability via APIs, shared services, templates, guardrails, standardized tooling.
Success Profile (12–24 Months)
A
governed AI portfolio
is in place with clear sponsorship, funding, and stage-gates; 3–5 lighthouse use cases scaled enterprise-wide.
Responsible AI
embedded in intake, build, and run; positive outcomes in
internal audit/external exams .
Adoption and value realization metrics established and tracked
A durable
AI operating model
(APO, governance forums, dashboards, playbooks) used across business units.
High trust and alignment between
business, risk, and engineering , with demonstrable acceleration and fewer production frictions.
Process re-engineering is a key enabler for all AI transformation efforts.
Ideal Candidate Profile Experience
15+ years
in strategy, transformation, product/portfolio leadership, or risk/governance roles within complex regulated industries ( financial services, automotive, or large-scale technology );
7+ years
working with AI/ML initiatives.
Built and led an
enterprise portfolio office
or transformation program with measurable outcomes (multi-BU scale).
Hands‑on experience with
Responsible AI/Risk , regulatory engagement, and
audit‑ready controls
in complex environments.
Proven track record
driving adoption
and realizing benefits for AI‑enabled process change.
Technical & Governance Acumen
Working knowledge of
AI/ML lifecycles , GenAI (LLMs, RAG, prompt orchestration), data governance, and model monitoring.
Familiarity with
MLOps
concepts and platform‑led delivery; comfortable partnering deeply with engineering leaders.
Proficient in
risk/control frameworks , documentation (model cards, data sheets), and
policy design .
Leadership & Influence
Credible with executives and regulators;
clear, concise communicator
able to translate complex risk/tech topics into business outcomes.
Inclusive leader who
builds high performing teams both direct reports and across a matrix of teams.
Builder‑operator mindset: strategic framing with
bias for execution
and measurable results.
Education
Bachelor’s degree required (Business, Computer Science, Engineering, Data/Analytics, or related).
Advanced degree (MBA, MS in Data/AI with tech focus) or relevant certifications in
governance/risk/compliance
preferred.
The base pay for this position is $190,000.00 – $380,000.00. In specific locations, the pay range may vary from the range posted.
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