Kenneth Henry

KENNETH HENRY

Senior Technical Program Manager
Software Engineering Operations & PMO Leader

I build delivery systems that help engineering teams execute with clarity, accountability, and measurable results.

16 years leading technical programs, PMO governance, compliance delivery, vendor integrations, and engineering operations across SaaS and EdTech platforms.

60+
Programs Delivered
29%
Improvement In Roadmap Predictability
30%
Reduction In Production Issues
27%
Improvement In MTTR

PMO Built From Scratch

Established the program management function as the first and only dedicated TPM, growing it into a Director-led PMO with formal intake, prioritization, and steering governance.

Teams & Delivery

Directed 11 full-time engineers and 10 contractors across 3 engineering teams and 21 contributors supporting a platform serving 1M+ users.

Compliance & Quality

Delivered PCI DSS and SOC 2 compliance programs, and reduced defect leakage by 20% through CI/CD governance and release-quality controls.

Architecture & Code Governance

Held authority over architectural decisions and software implementations, and managed Git repositories and merge request review across the development organization.

Vendor Portfolio

Managed strategic vendor programs and integrations, including Affirm, Cybersource, BenchPrep, Thomson Reuters, and LawHub, owning SLAs and joint delivery.

AI-Enabled Operations

Led AI and automation initiatives, applying generative AI tooling to planning, reporting, and delivery workflows across the engineering organization.

Featured Project
Portfolio Case Study

A designed operating model showing how engineering delivery changes when AI raises output, and how to govern, measure, and scale it.

Operating Model Design

AI-Augmented Engineering Delivery Operating Model

A fictional B2B SaaS company of about 30 engineers, across three teams, adopts AI coding assistants. Output climbs, but the lean team has no dedicated delivery function to absorb the new pace. This is the operating model I would design to fix that.

Executive Summary Modeled Scenario
Problem

A growing B2B SaaS company of about 30 engineers across three teams adopted AI coding assistants. Output climbed fast, but the company had no dedicated delivery function to manage the new pace.

Challenge

With lean headcount and informal process, review and QA could not keep up. Predictability slipped and rework grew, while leadership lost a clear line of sight on what would ship and when. Heavy enterprise governance would only have slowed the team down.

Solution

A right-sized delivery operating model: lightweight intake and prioritization, paired AI and human review built for a small team, a simple weekly and biweekly cadence, and capacity planning that protects review time. Structure that scales with the company instead of bureaucracy that fights it.

+48%
Delivery throughput
90%
Sprint predictability
-35%
Rework
0
Added headcount
Demonstrates building a delivery function from the ground up, Technical Program Management, Engineering Operations, Delivery Governance, and AI-enabled delivery.
View the full case study
Delivery Workflow
Intake Prioritization AI-Assisted Planning Development AI Review Human Review Testing Release Observability Feedback Loop
Operating Model

Three squads (Product Owner, AI-enabled Engineers, QA) run delivery, coordinated by a single delivery lead with lightweight governance, and supported by shared Architecture, Security, and CI/CD. Review and integration capacity is planned as a first-class constraint, not an afterthought.

Delivery Lead / TPM Squad A Product Owner AI-Enabled Engineers QA Squad B Product Owner AI-Enabled Engineers QA Squad C Product Owner AI-Enabled Engineers QA SHARED SERVICES Architecture Security DevOps & CI/CD
Governance Cadence
DailySquad standups
WeeklyDelivery & risk review
BiweeklySprint review & planning
MonthlyLeadership review
Systems-Thinking Insight

Modeling a 10-engineer team showed AI raising development throughput by roughly 62%, from about 400 to 650 story points. The lesson is the point of the case study: the constraint does not disappear, it moves. Output shifts the bottleneck from development to review and integration, which is where governance and capacity planning have to focus.

Executive KPI Dashboard

Delivery Health & AI Adoption

Modeled Data
Sprint Predictability
92%
▲ +14 pts
Release Frequency
3.4/wk
▲ +1.2/wk
Defect Escape Rate
1.8%
▼ -3.1 pts
MTTR
41min
▼ -27%
AI-Generated Code
38%
▲ +38 pts
Review Acceptance
86%
▲ +9 pts
Lead Time
4.2days
▼ -1.8 days
Feature Throughput
+62%
▲ vs baseline
RACI Matrix
Activity TPMProductEngineeringQASecurity
Intake & PrioritizationRACII
AI-Assisted PlanningACRCI
DevelopmentAIRCI
AI + Human Code ReviewAIRCC
Testing & QACIRAI
Release ApprovalACRCC
Risk & GovernanceACRCC
Compliance ControlsCIRCA
A Accountable R Responsible C Consulted I Informed
Risk Register
RiskLikelihoodImpactOwnerMitigation
AI-generated defects and hallucinationsMedHighEngineeringMandatory human review gate plus an AI review pass before merge
Review and integration bottleneckHighHighTPMPlan review as capacity, cap work in progress, rotate reviewers
Security vulnerabilities in generated codeMedHighSecurityAutomated scanning in CI and security sign-off at release
Compliance and control driftLowHighSecurityControl checkpoints in the workflow with audit-ready evidence capture
Knowledge concentration and skill gapsMedMedTPMPair review, documentation standards, and onboarding playbooks
Technical debt from higher velocityMedMedEngineeringDebt budget per sprint tracked in portfolio governance

Portfolio case study. Fictional company and modeled figures, built to demonstrate operating-model design. Not a record of actual results.

Experience
16 Years, One Story of Scope

A single-employer career built on progressive scope, from the first dedicated program manager to Director of engineering operations and the PMO.

Director of Software Engineering and Development Operations Aug 2021 – Jan 2026
Director of IT Development Operations and Program Management May 2019 – Aug 2021
Senior Technical Program Manager Oct 2012 – May 2019
IT Business Analyst & LMS Administrator Aug 2010 – Oct 2012
Certifications
  • Google AI Essentials
  • Generative AI for Program Managers
  • IBM Generative AI: Prompt Engineering Basics
Download Resume
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