Executive AI leadership

I build AI operating models that move enterprises from ambition to governed production delivery.

I architected Centrilogic's AI Factory and the COE, Landing Zone, and Agent Factory patterns behind it, then used those foundations to help teams deliver governed AI in regulated enterprise environments.

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20+ years

enterprise transformation

Architecture, strategy, and delivery leadership

AI Factory

AI operating model architect

COE, Landing Zone, and Agent Factory patterns

3 director roles

leadership progression

Architecture, consulting, and technical strategy

Regulated + F500

trust environments

Financial services, public sector, and enterprise delivery

Mandate

How I think, how I lead, and how I deliver.

The differentiator is not whether I have worked with AI. It is whether I can define the system that decides what gets funded, how risk is governed, how teams execute, and how results hold up under scrutiny.

Executives do not need another polished online resume. They need evidence that a leader can connect strategy, governance, architecture, delivery, and team development into a durable AI capability.

That is the role I play: translating business ambition into a governed operating model, then helping teams use that model to ship production systems with clarity, repeatability, and executive confidence.

01

Build the operating model

Design the structures that let executives fund AI with confidence and let delivery teams execute repeatedly.

AI Factory delivery modelAI COE frameworkAI Landing Zone and Agent Factory patterns

02

Lead governed production delivery

Translate architecture, governance, tooling, and business priorities into AI systems that hold up in production.

Genesys + Salesforce + Azure OpenAI contact-centre agentRole-based GenAI knowledge assistant for regulatory contentAudit-ready workflows in regulated environments

03

Grow teams executives can trust

Recruit, coach, and align architects while connecting technical depth to proposals, SOWs, and executive decisions.

Architecture practice leadershipProposal and SOW ownership on strategic accountsBusiness translation across AI, cloud, and enterprise transformation

Industries

Financial servicesPublic sectorManufacturingTechnologyEducation

Transformation patterns

AI operating modelsProduction agentsKnowledge systemsContact-centre transformationEnterprise roadmapsRFP and SOW automation

What leaders hire me for

Standing up an AI practiceMoving from pilots to governed productionBuilding executive confidence in architecture decisionsCoaching architecture teams through change

Operating Model

The system behind repeatable AI delivery.

This is the proof surface that matters most to an executive audience: not just what shipped, but the operating logic that lets teams ship under governance, budget pressure, and real stakeholder scrutiny.

Click a phase to inspect the model

Mandate and portfolio alignment

The operating model starts by deciding what deserves investment, where risk needs to be managed, and how success will be measured across a portfolio of use cases rather than a series of isolated pilots.

What executives need

  • Prioritized use-case portfolio tied to business value
  • Governance posture aligned to regulated environments
  • Roadmaps executives can sponsor, defend, and sequence

What delivery teams get

  • Bid and no-bid discipline for internal demand
  • Shared operating metrics for velocity, quality, and auditability
  • Clear language across sponsors, architects, and delivery leaders

Grounded in prior proof

  • Enterprise IT roadmaps delivered across financial services, public sector, and manufacturing
  • Technical detail translated into proposals, SOWs, and program direction

Leadership Proof

Production AI, framed as leadership proof.

Each surface goes beyond a project summary. It shows the business context, the leadership decision, the architectural choices, and the outcomes that made the work matter.

Flagship proof

The AI Factory

An enterprise AI operating model built to scale governed delivery.

Open proof surface

Architected Centrilogic's AI Factory, a production delivery model designed to move organizations beyond POC and support multiple AI use cases per month at predictable, measurable velocity.

Multiple AI use cases per monthReusable governed architectureExecutive-ready delivery cadence

Leadership decision

What leadership changed

  • Defined the AI Factory service model and delivery motion
  • Designed the AI COE framework, AI Landing Zone, and AI Agent Factory
  • Connected business sponsorship, governance, architecture, and team execution

Architecture choices

How the system was shaped

  • Pro-code patterns on Azure OpenAI, Azure AI Foundry, Microsoft Fabric, and OneLake
  • Reusable controls for data access, orchestration, and auditability
  • Foundations built for parallel enterprise engagements rather than single demos

Business outcomes

What changed in practice

  • Created a repeatable path from experimentation to production
  • Raised confidence that AI delivery could be governed and measured
  • Shifted the conversation from isolated pilots to operating capability
Azure OpenAIAzure AI FoundryMicrosoft FabricOneLakeGoverned pro-code delivery

Production agent

AI-Powered Contact Centre Agent

Operational automation in a regulated customer-service environment.

Open proof surface

Delivered a production-grade AI contact-centre agent integrating Genesys Cloud, Salesforce, and Azure OpenAI to eliminate manual after-call work and create auditable CRM records at scale.

Manual after-call work removedAuditable CRM records createdContact-centre workflows accelerated

Leadership decision

What leadership changed

  • Shaped the solution around regulatory expectations and operational fit
  • Aligned platform, process, and stakeholder decisions across business and technology owners

Architecture choices

How the system was shaped

  • Integrated Genesys Cloud, Salesforce, and Azure OpenAI
  • Embedded summarization and record generation into production workflows

Business outcomes

What changed in practice

  • Automated end-to-end call summarization
  • Reduced manual effort for agents
  • Improved auditability in a regulated environment
Genesys CloudSalesforceAzure OpenAIGoverned workflow integration

Governed knowledge

GenAI Knowledge Assistant

Secure access to sensitive regulatory knowledge at enterprise scale.

Open proof surface

Delivered a secure, governed GenAI knowledge assistant with role-based access to regulatory content, reducing dependence on SMEs and accelerating compliance workflows.

Role-based access to sensitive contentSME dependence reducedCompliance workflows accelerated

Leadership decision

What leadership changed

  • Positioned governance and access control as first-class design requirements
  • Connected risk management expectations to user experience and adoption

Architecture choices

How the system was shaped

  • Designed governed retrieval and access patterns for regulated information
  • Protected content exposure while improving search and response speed

Business outcomes

What changed in practice

  • Improved access to regulatory knowledge
  • Reduced reliance on manual expert intervention
  • Managed compliance risk through governed architecture
GenAI retrievalRole-based accessGoverned content patterns

Commercial acceleration

RFP Bid/No-Bid and Response Automation

Internal agents that increase response speed without losing judgment.

Open proof surface

Built internal Copilot agents that evaluate bid opportunities against real capabilities and draft compliant responses from an evolving repository of past RFPs and Q&A.

50-70% faster response cyclesStronger bid disciplineMore consistent quality and brand tone

Leadership decision

What leadership changed

  • Applied product thinking to internal commercial operations
  • Connected capability reality, proposal quality, and executive decision-making

Architecture choices

How the system was shaped

  • Knowledge-backed automation across historical RFP content and response assets
  • Draft generation paired with gap analysis and recommendation logic

Business outcomes

What changed in practice

  • Accelerated response velocity
  • Improved consistency and compliance in outputs
  • Created leverage for presales and solution teams
Copilot agentsKnowledge indexingGap analysisResponse generation

Executive strategy

Enterprise IT Roadmaps and Cloud Transformation

Architecture strategy that gives large organizations a direction they can execute.

Open proof surface

Led IT strategy engagements for large enterprise clients, delivering roadmaps from current-state assessment through target architecture across cloud, infrastructure, and AI transformation.

Business-aligned target-state directionMulti-year transformation sequencingExecutive confidence in major change programs

Leadership decision

What leadership changed

  • Worked directly with account and executive leadership on strategic planning
  • Translated broad technical choices into business and program direction

Architecture choices

How the system was shaped

  • Defined target architectures across Azure, AWS, and hybrid environments
  • Mapped tactical migration steps from current state to target state

Business outcomes

What changed in practice

  • Enabled large-scale cloud and infrastructure transformation planning
  • Linked roadmaps to business priorities and measurable value
  • Supported major renewals, extensions, and strategic decision-making
AzureAWSHybrid architectureRoadmap designExecutive planning

Career

Leadership progression, not resume sprawl.

The progression matters because it shows a steady increase in accountability for operating models, commercial outcomes, and team leadership, not just increasingly complex technical systems.

20+ years

enterprise transformation and architecture leadership

3

director-level leadership roles

$110M

portfolio value in IBM hosting leadership scope

180 / 800 / 5

applications, servers, and data centres supported

20+ / 200+

architects led and delivery professionals influenced

2021 - Present

Director of Architecture

Centrilogic / WatServ

View scope

Leads and grows a high-performing architecture practice while driving AI capability from strategy through production delivery.

Current role spans AI, cloud, and enterprise architecture with direct responsibility for reusable delivery models and team development.

Core responsibilities

  • Architected and launched the AI Factory as a production and delivery model for agentic AI.
  • Designed the AI COE framework, AI Landing Zone, and AI Agent Factory on the Microsoft stack.
  • Delivered enterprise AI solutions including contact-centre automation and governed knowledge assistants.
  • Works with account leadership and executives on proposals, SOWs, planning, and strategic client direction.

Signals that still matter now

  • Architecture team leadership across AI, cloud, and enterprise domains
  • Executive translation of technical strategy into business action
  • Operational focus on shipping governed AI, not just demos

2015 - 2021

Director, Consulting - Enterprise Architecture, Technical Strategy, Innovation

CGI Inc.

View scope

Led architecture consulting teams, advised executives, and translated broad transformation goals into practical architecture and migration plans.

Scope included strategic public-sector and enterprise work, proposal leadership, and account-facing technical strategy.

Core responsibilities

  • Provided counsel to account leadership and executives, turning technical complexity into business decisions.
  • Supported multi-million dollar renewals and extensions through strong technical leadership and contract development.
  • Built tactical migration plans and target architectures aligned to client business goals.
  • Developed and sold proposals to senior management and technical stakeholders.

Signals that still matter now

  • Executive-facing enterprise architecture
  • Renewal and growth support on strategic accounts
  • Roadmaps tied to business goals and operational realities

2008 - 2015

Senior IT Architect

IBM Canada - Custom Hosting Services

View scope

Operated as part of the senior team managing a profitable hosting business with significant technical and commercial scope.

The portfolio covered 180 applications on 800 servers across 5 data centres with total contract value of $110M.

Core responsibilities

  • Led 20+ architects globally and governed end-to-end technical hosting solutions from engagement through production.
  • Created common architecture and transition deliverables that improved consistency and standards alignment.
  • Drove automation and process change that generated annual profits of up to $4M and cost savings of $2M.
  • Provided thought leadership to 200+ service delivery professionals across multiple countries.

Signals that still matter now

  • Large-scale operational complexity
  • Global leadership and standards governance
  • Commercial accountability alongside architecture depth

2002 - 2008

Technical Lead and Architect

IBM Canada - Common Development and Test Centre

View scope

Led major release and upgrade work while automating operations and improving platform reliability.

Early leadership scope combined technical decision-making, automation, and cross-team coordination across large server estates.

Core responsibilities

  • Delivered 100 percent successful upgrades during tenure with no failed changes.
  • Automated build and upgrade processes for 300+ servers and created monitoring tooling.
  • Chaired capacity planning and performance monitoring work to keep environments aligned to service levels.
  • Coordinated with global architects to improve best-practice adherence.

Signals that still matter now

  • Strong delivery discipline
  • Automation mindset before AI became the focus
  • Foundational credibility in large-scale technical operations

Contact

Bring me in when the mandate is larger than a pilot.

The best-fit conversations are about standing up an AI capability, fixing the gap between strategy and delivery, or evaluating whether executive AI leadership is needed now.

  • Standing up an AI practice or operating model
  • Moving enterprise AI from pilots to governed production delivery
  • Hiring a strategic leader for architecture, AI, or transformation
  • Connecting executive sponsorship to technical execution and measurable outcomes

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