Mary C.

I designed and led a multi‑year enterprise AI and growth strategy and institutionalized Responsible AI governance with an executive AI Ethics Council. I rebuilt AI approval and governance frameworks (notably at Empower), cutting approval cycles from 30 to 10 days, advancing 75+ use cases into 32 production solutions while sustaining zero material audit findings. I took direct accountability for a $300M+ technology portfolio and $150M+ P&L across 180+ professionals in 60 countries, delivered $5M in net‑new AI revenue and ~1/3 portfolio TCO reduction, and drove vendor consolidation and operational modernization at Adecco (25→12 vendors, $4.2M annual savings, and ~40% improvement in delivery predictability).

Partnered with Legal, Compliance, and business leaders to define enterprise AI policies, governance frameworks, and executive reporting structures. Developed dashboards and guardrails ensuring AI initiatives aligned with strategic objectives while minimizing regulatory risk. Enabled leadership teams to confidently scale AI adoption within compliant and ethical boundaries.

Objective

Ensure responsible AI adoption aligned to strategy.

Scope

• Defined AI policies and governance frameworks
• Partnered with legal and compliance teams
• Developed executive dashboards

Actions

• Established guardrails for AI usage
• Created reporting and monitoring structures
• Aligned AI initiatives to business goals

Outcomes

• Reduced regulatory exposure
• Increased executive confidence in AI adoption
• Enabled scalable, compliant AI deployment

Served as a fractional executive for a growth-stage energy organization, leading technology modernization and investment optimization initiatives. Rationalized platforms, streamlined operations, and prioritized high-impact initiatives, reducing operating costs by 25%. Delivered 6–8x ROI compared to a full-time executive hire while strengthening long-term competitive positioning.

Objective

Drive transformation for growth-stage organization.

Scope

• Served as fractional executive leader
• Led technology modernization initiatives
• Optimized investment strategy

Actions

• Rationalized platforms and vendors
• Prioritized high-impact initiatives
• Streamlined operations

Outcomes

• 25% reduction in operating costs
• 6–8x ROI vs. full-time executive hire
• Improved competitive positioning

Led AI readiness assessments for financial services and healthcare clients, evaluating data maturity, infrastructure, and organizational capabilities. Identified high-value opportunities projected to deliver $8–12M in annual efficiency gains and risk reduction. Developed phased implementation roadmaps achieving 3–6x ROI within 18–24 months and reducing administrative burden by 20–25%.

Objective

Enable safe, compliant AI adoption at scale.

Scope

• Designed AI governance frameworks
• Defined policies and ethical guardrails
• Collaborated with compliance and legal teams

Actions

• Implemented risk and control mechanisms
• Established governance boards and processes
• Developed AI lifecycle management practices

Outcomes

• 40%+ reduction in compliance risk
• 5–7x ROI on AI initiatives
• Multiple patent-pending innovations

Designed and implemented enterprise-wide AI governance frameworks, including policies, controls, and ethical guardrails to ensure compliant and responsible AI adoption. Reduced compliance and operational risk exposure by over 40% while accelerating enterprise AI deployment. Enabled monetization strategies with projected 5–7x ROI and contributed to multiple patent-pending innovations leveraging domain-specific LLMs and IoT platforms.

Objective

Enable safe, compliant AI adoption at scale.

Scope

• Designed AI governance frameworks
• Defined policies and ethical guardrails
• Collaborated with compliance and legal teams

Actions

• Implemented risk and control mechanisms
• Established governance boards and processes
• Developed AI lifecycle management practices

Outcomes

• 40%+ reduction in compliance risk
• 5–7x ROI on AI initiatives
• Multiple patent-pending innovations

Built and scaled a high-performing product organization of 50+ professionals across product, engineering, and data disciplines. Owned full P&L responsibility for a $150M portfolio, driving strategic investment decisions and operational efficiency. Improved gross margins by 25% through competitive analysis, strategic partnerships, and integration of AI/ML-driven capabilities.

Objective

Scale product organization and improve profitability.

Scope

• Built and led team of 50+ professionals
• Owned $150M P&L
• Drove strategic investment decisions

Actions

• Implemented performance management frameworks
• Established strategic partnerships
• Integrated AI/ML capabilities

Outcomes

• 25% improvement in gross margins
• Increased organizational efficiency
• Enhanced product innovation capacity

Developed and led execution of a multi-year product roadmap leveraging Agile methodologies across distributed teams. Prioritized high-impact features and optimized sprint planning and delivery processes. Successfully launched 12 major features ahead of schedule, driving a 60% increase in user retention and a 35% increase in customer acquisition.

Company: Deloitte

Objective

Accelerate product delivery and improve customer outcomes.

Scope

• Led multi-year product roadmap
• Implemented Agile methodologies
• Managed distributed teams

Actions

• Optimized sprint planning and prioritization
• Focused on high-impact features
• Improved delivery processes

Outcomes

• 12 major features launched ahead of schedule
• 60% increase in user retention
• 35% growth in customer acquisition

Defined and executed a comprehensive product vision and strategy for a SaaS platform, aligning engineering, marketing, and sales teams around customer-centric growth initiatives. Led expansion into two new market segments and optimized go-to-market strategies. Delivered 45% year-over-year revenue growth and increased ARR by $28M through targeted product innovation and strategic positioning.

Company: PwC

Objective

Drive revenue growth and expand into new markets.

Scope

• Defined product vision and strategy
• Aligned cross-functional teams
• Led go-to-market execution

Actions

• Identified new market segments
• Delivered targeted product enhancements
• Strengthened positioning and messaging

Outcomes

• 45% YoY revenue growth
• $28M increase in ARR
• Expansion into two new markets

Defined and executed a comprehensive product vision and strategy for a SaaS platform, aligning engineering, marketing, and sales teams around customer-centric growth initiatives. Led expansion into two new market segments and optimized go-to-market strategies. Delivered 45% year-over-year revenue growth and increased ARR by $28M through targeted product innovation and strategic positioning.

Company: emPOWER

Objective

Optimize revenue through strategic pricing and packaging.

Scope

• Defined pricing models for AI-enabled products
• Conducted market and competitive analysis
• Developed customer segmentation strategies

Actions

• Implemented value-based pricing
• Modeled willingness-to-pay scenarios
• Aligned packaging with customer needs

Outcomes

• Increased average deal size
• Improved revenue predictability
• Strengthened product-market fit

Defined and implemented a data-driven pricing and packaging strategy for AI-enabled products, aligning customer value with revenue optimization. Conducted competitive analysis, customer segmentation, and willingness-to-pay modeling to refine monetization approaches. Increased average deal size and improved revenue predictability while ensuring alignment with market demand and product differentiation.

Company: emPOWER

Objective

Optimize revenue through strategic pricing and packaging.

Scope

• Defined pricing models for AI-enabled products
• Conducted market and competitive analysis
• Developed customer segmentation strategies

Actions

• Implemented value-based pricing
• Modeled willingness-to-pay scenarios
• Aligned packaging with customer needs

Outcomes

• Increased average deal size
• Improved revenue predictability
• Strengthened product-market fit

Led the development and launch of AI-powered product capabilities grounded in deep customer discovery and Voice of Customer (VoC) insights. Conducted executive-level client interviews and translated qualitative feedback into prioritized roadmap features. Delivered solutions that improved decision accuracy by 49% and reduced sales cycle length by enabling data-driven insights, directly impacting revenue velocity and customer satisfaction.

Company: Deloitte

Objective

Deliver AI-powered solutions aligned to customer needs.

Scope

• Led customer discovery and VoC initiatives
• Translated insights into product roadmap
• Partnered with clients for validation

Actions

• Conducted executive interviews and workshops
• Prioritized features based on business impact
• Integrated AI into decision-support workflows

Outcomes

• 49% improvement in decision accuracy
• Accelerated sales cycles
• Increased customer satisfaction and adoption

Owned end-to-end product strategy and roadmap for a cloud-based platform supporting 1M+ users. Led cross-functional alignment across engineering, sales, and marketing to deliver customer-centric enhancements and scalable architecture improvements. Drove a 28% increase in platform adoption and generated $100M+ in incremental revenue through feature innovation, user experience optimization, and targeted go-to-market strategies.

Objective

Drive product adoption, revenue growth, and platform scalability.

Scope

• Owned product strategy and roadmap
• Led cross-functional alignment (engineering, sales, marketing)
• Enhanced platform architecture and UX

Actions

• Delivered customer-driven feature enhancements
• Optimized go-to-market strategies
• Scaled platform for 1M+ users

Outcomes

• 28% increase in user adoption
• $100M+ revenue growth
• Improved customer engagement and retention

Architected and deployed a global PMO framework across geographically distributed Fortune 100 portfolios, standardizing governance, execution, and reporting. Defined enterprise RACI models, executive dashboards, and portfolio prioritization methodologies that connected strategic objectives to delivery execution. Resulted in improved delivery predictability, 30% faster decision-making cycles, and enhanced executive visibility into $500M+ program investments.

Company: PwC

Clients: Fortune 100 Global Enterprises

Objective

Standardize program execution and align strategy to delivery outcomes.

Scope

• Designed enterprise PMO frameworks
• Implemented governance models and RACI structures
• Built executive dashboards and portfolio prioritization tools

Actions

• Consolidated fragmented PMOs into unified global model
• Introduced data-driven decision-making processes
• Enabled real-time portfolio visibility

Outcomes

• 30% faster executive decision-making
• Increased delivery predictability across $500M+ portfolios
• Improved cross-functional alignment and accountability

Deloitte – Enterprise AI Transformation & CoE Buildout

Spearheaded the design and launch of an enterprise-wide AI Center of Excellence (CoE) for multiple Fortune 100 organizations, establishing governance, operating models, and scalable delivery frameworks. Led cross-functional workshops with business and technology leaders to identify and prioritize 25+ high-value AI use cases, aligning initiatives to strategic business outcomes. Developed multi-year AI roadmaps tied to measurable KPIs, enabling accelerated adoption and positioning clients to achieve 3–5x ROI on AI investments within 18–24 months.

Company: Deloitte

Clients: Fortune 100 (Financial Services, Healthcare, Retail)

Objective

Establish enterprise-wide AI capability to scale innovation, governance, and monetization.

Scope

• Designed AI Center of Excellence (CoE) operating model
• Identified and prioritized 25+ AI use cases
• Developed multi-year AI roadmap aligned to business strategy
• Facilitated executive workshops across business and technology

Actions

• Built governance frameworks, intake processes, and delivery models
• Defined KPIs linking AI initiatives to revenue and cost outcomes
• Created scalable AI deployment and adoption playbooks

Outcomes

• 3–5x projected ROI on AI investments
• Accelerated enterprise AI adoption across business units
• Established repeatable AI innovation pipeline

Context: Mature core market with slowing growth and rising competitive pressure

• Led deep customer discovery across 40+ enterprise and mid-market clients
• Identified underserved adjacent use cases and validated demand through rapid MVPs
• Built a repeatable “discover → validate → scale” product incubation playbook
• Aligned roadmap with international expansion strategy and regional compliance needs

Measured Impact:

• Launched 2 net-new product lines in under 9 months
• Opened 3 new vertical markets, generating $62M in pipeline
• Achieved 35% conversion from pilot to paid contracts
• Payback period under 7 months for new product investments

Context: Board mandate to move beyond “AI experimentation” to real revenue impact

• Identified and prioritized AI use cases tied directly to customer pain, margin expansion, and defensibility
• Launched AI-powered features across forecasting, personalization, and workflow automation
• Established responsible AI governance covering security, bias, explainability, and compliance
• Partnered with Legal, Security, and Finance to accelerate enterprise adoption

Measured Impact:

• $28M in new annual recurring revenue attributable to AI features
• -45% reduction in customer manual processing time
• +19% increase in NPS among AI feature adopters
• Improved customer retention by 6.4 points

Context: High-growth company experiencing delivery bottlenecks, burnout, and missed commitments

• Rebuilt product operating model using outcome-based roadmapping, dual-track agile, and OKRs
• Consolidated 14 squads into customer-aligned value streams, reducing dependencies by 35%
• Introduced product analytics and leading indicators (activation, time-to-value, feature adoption)
• Rationalized vendor spend and tooling across Product, Design, and Engineering

Measured Impact:

• 2.3× increase in release frequency
• 34% reduction in cost per feature delivered
• -40% roadmap churn quarter-over-quarter
• Employee engagement scores up +22 points in product org

Context: Multi-product B2B / B2B2C platform with stalled growth and fragmented roadmap

• Re-architected the product portfolio around 3 core customer value propositions, retiring 22% of low-margin SKUs and doubling down on high-velocity offerings
• Introduced a value-based pricing and packaging model, shifting 60% of revenue from usage-based to tiered subscription
• Partnered with Sales and Marketing to align roadmap, ICPs, and GTM messaging
• Implemented quarterly Product–Revenue Operating Reviews with CRO and CFO

Measured Impact:

• +41% ARR growth within 12 months
• +18-point increase in gross margin
• +27% expansion revenue from existing customers
• Reduced sales cycle length by 21%

1. AI Vision & Strategic North Star

A concise articulation of how AI will advance clinical outcomes, operational efficiency, patient experience, and financial sustainability. This deliverable aligns AI investments to the organization’s mission, regulatory realities, and competitive positioning over a 3–5 year horizon.

Output: One-page vision statement and guiding principles (patient safety, equity, explainability, compliance-by-design).

2. Priority Use Case Portfolio

A structured portfolio of high-impact AI use cases across clinical, operational, and administrative domains, prioritized by value, feasibility, and risk.

Examples:

• Clinical decision support and diagnostics
• Care management and population health
• Revenue cycle optimization
• Workforce productivity and capacity planning

Output: Use case heatmap with quantified ROI, risk level, data readiness, and regulatory implications.

3. Data & Platform Architecture Blueprint

A future-state architecture defining how data, AI models, and applications interact securely and compliantly across the enterprise.

Focus Areas:

• Interoperability (EHR, imaging, labs, claims)
• Data quality and governance
• Model lifecycle management
• Cybersecurity and PHI protection

Output: Reference architecture diagram and platform roadmap.

4. AI Governance, Risk & Compliance Framework

A comprehensive framework ensuring AI is safe, ethical, auditable, and compliant with healthcare regulations (HIPAA, FDA, CMS, state laws).

Components:

• Model validation and monitoring
• Bias detection and mitigation
• Clinical oversight and accountability
• Vendor and third-party risk

Output: Governance operating model, policies, and decision rights.

5. Operating Model & Talent Strategy

Definition of how AI is delivered and sustained across IT, clinical, and business teams.

Elements:

• Roles and responsibilities (clinical, data, legal, IT)
• Build vs. buy guidelines
• Change management and adoption strategy
• Upskilling and training plan

Output: Target operating model and capability roadmap.

6. Implementation Roadmap & Value Tracking

A phased, execution-ready roadmap translating strategy into measurable outcomes.

Includes:

• 90-day, 12-month, and multi-year milestones
• Investment requirements
• Success metrics (clinical, financial, operational)
• Continuous improvement loop

Output: Integrated roadmap with KPI dashboard and benefits realization plan.

Healthcare Transformation Engagement

·       Conducted a comprehensive AI maturity and readiness assessment across clinical, operational, and administrative domains.

·       Assessed data interoperability, privacy (HIPAA), security, and ethical considerations for AI adoption in healthcare environments.

·       Identified high-impact AI use cases (care coordination, operational efficiency, revenue cycle, clinical decision support).

·       Developed a governance-aligned AI roadmap balancing innovation, patient safety, and regulatory compliance.

Business Results & ROI

·       Enabled the organization to safely pursue AI initiatives while maintaining full regulatory and patient privacy compliance.

·       Identified initiatives projected to reduce administrative burden by 20–25% and improve operational efficiency.

·       Positioned the organization for measurable quality and cost outcomes, with an estimated 4–6x ROI over two years.

Executive Leadership Engagement

·       Served as Fractional CIO, providing strategic and operational technology leadership for a growing energy organization.

·       Modernized IT and data architecture to support operational efficiency, analytics, and cybersecurity resilience.

·       Guided vendor strategy, cloud adoption, and technology investment decisions aligned to business growth objectives.

·       Acted as a trusted advisor to executive leadership, translating business goals into pragmatic technology execution.

Business Results & ROI

·       Reduced IT operating costs by 25% through vendor rationalization and cloud optimization.

·       Improved system reliability and security posture, lowering operational risk and downtime.

·       Delivered enterprise-grade CIO leadership at a fraction of full-time cost, generating 6–8x ROI versus a traditional CIO hire.

·        

AI Readiness Assessment & Roadmap – Financial Services Organization

Strategic Advisory Engagement

·       Led an end-to-end AI readiness assessment across data, architecture, security, operating model, talent, and regulatory constraints.

·       Evaluated high-value AI use cases (risk, fraud, forecasting, customer insights) against data maturity and compliance requirements.

·       Delivered a phased AI roadmap with prioritized initiatives, investment estimates, and governance checkpoints aligned to financial regulations.

·       Defined KPIs and value-tracking mechanisms to ensure measurable business impact.

Business Results & ROI

·       Enabled leadership to confidently prioritize AI investments, avoiding ~30% in misallocated spend on low-readiness initiatives.

·       Identified near-term AI opportunities projected to deliver $8–12M in annual efficiency and risk reduction benefits.

·       Provided a clear execution plan supporting a 3–5x ROI within 18–24 months.

·       Designed and implemented an enterprise-wide AI governance and risk management framework, aligning AI usage with regulatory, ethical, and security standards (NIST AI RMF, ISO 27001, SOC 2, data privacy principles).

·       Established model lifecycle controls, risk classification, approval workflows, and executive oversight mechanisms to govern GenAI and ML use cases.

·       Partnered with Legal, Security, and Compliance leaders to define AI policies, guardrails, and audit readiness processes.

·       Created executive dashboards to monitor AI risk exposure, compliance posture, and business value realization.

Business Results & ROI

·       Reduced AI-related compliance and operational risk exposure by 40%+ through standardized governance and controls.

·       Accelerated safe AI adoption across business units, enabling earlier monetization of AI initiatives while avoiding regulatory rework.

·       Prevented costly compliance remediation and reputational risk, delivering an estimated 5–7x ROI relative to program cost.