30+ years of enterprise delivery
Former Deloitte Canada Team Lead / Architect for SAP delivery. The consulting work inherits operating discipline from long-form enterprise programs, not from a tool-first pitch.
AI Transition Consultant · Montréal / Canada
Patrick Duchesneau
Turn AI experiments into measurable business results.
I help leadership teams select the right AI opportunities, set governance and adoption conditions, run a controlled pilot, and measure whether the work should scale, adjust, or stop.
Method: Baseline → Prioritize → Pilot → Measure → Scale. A valid outcome is Stop / Adjust when evidence does not justify scaling.
Transition method
01 · Baseline
then
02 · Prioritize
then
03 · Pilot
then
04 · Measure
then
05 · Scale
decision
A valid outcome is Stop / Adjust when evidence does not justify scaling.
Enterprise delivery
30+ years · Deloitte SAP lead
Method
Baseline → Pilot → Measure
Decision
Scale, Adjust, or Stop
Applied proof
Selected Bookiji product work
Location
Montréal / Canada
Enterprise credibility
Thirty years of delivery discipline, applied to AI transition work.
The consulting offer is grounded in enterprise programs, SAP delivery leadership, and a willingness to reject weak AI use cases.
AI orchestration and product ownership
I architect AI-enabled systems and direct delivery with coding agents while remaining accountable for integration, verification, security, and release decisions.
Governance and adoption discipline
Use-case selection, data boundaries, change ownership, and operating conditions are part of the work. Advisory language is operational, not legal advice.
Measurable baseline and post-pilot outcomes
Every serious initiative starts with a current-process baseline and ends with a comparison. No guaranteed ROI and no invented client outcomes.
Willingness to reject weak AI use cases
Not every experiment deserves a pilot. A rejected-use-case list protects time, budget, and credibility.
AI transition method
A decision loop, not a tool rollout.
Leadership teams get a scored opportunity set, a bounded pilot, and a measurement plan. Scaling is optional and evidence-based.
01
Baseline
Document the current process, volume, cost, quality, and risk before any tool conversation. Without a baseline, AI results are stories rather than evidence.
02
Prioritize
Score opportunities on value, feasibility, risk, and adoption. Keep a rejected-use-case list so weak ideas do not consume budget.
03
Pilot
Run one bounded pilot with an integration boundary, named owner, and a stop condition. The goal is a decision, not a demo.
04
Measure
Compare baseline against post-pilot outcomes with a measurement plan agreed in advance. Directional ROI hypotheses stay hypotheses until measured.
05
Scale
Scale only what the evidence supports. If the pilot is weak, Stop or Adjust is the professional outcome — not a delayed rollout.
Applied proof
Selected product work as portfolio evidence.
Selected product work is applied proof of architecture, orchestration, and delivery discipline. Consulting buyers are not purchasing these consumer products. Maturity labels stay exact: beta, preview, internal, or live only where the canonical product status supports it.
MyChessCoach
BetaAI-assisted chess analysis and coaching
chessAI-assistedStockfishbeta
Stockfish integration with analysis pipelines and model-assisted coaching flows.
JobHuntrX
BetaAI-assisted career-search dashboard
AI-assistedcareerflagship
Structured job-search workflows, tracking, and resume tooling — the flagship Bookiji career demo.
Kinetix
BetaBiomechanics-aware run analysis and coaching
fitnesswatchOScoaching
Cross-platform watchOS, iOS, and web coaching with scoring and analytics primitives.
See the full studio range on Projects. Maturity labels remain beta, preview, internal, or live only where canonical status supports them.
Selected case studies
Architecture decisions and operating constraints, not vanity metrics.
These studies show how delivery, boundaries, and measurement work in practice. They are evidence of method, not a product catalog for consulting buyers.
Case Study
MyAssist: Live Operational Window, Not a Sync Engine
Designed as a provider-canonical control layer that reasons across systems without data mirroring.
Problem: Most personal productivity tooling drifts from source systems and becomes fragile when data ownership is unclear.
Maturity: Strategic near-term launch path with production-ready boundaries.
Case Study
Kinetix: Biomechanics-Driven Coaching Across Watch, Phone, and Web
Architected as a cross-platform running system where data integrity and interpretable performance scoring matter more than novelty.
Problem: Most running tools optimize for passive tracking, not actionable coaching tied to form quality and execution context.
Maturity: Advanced product prototype with strong systems-level depth.
Case Study
Bookiji: Deterministic Scheduling Spine for a Larger Marketplace Vision
Architected and directed delivery of the scheduling core as a governance-first system before expanding toward universal booking.
Problem: Booking products break trust when state transitions, payments, and availability rules are inconsistent under real-world load.
Maturity: Production scheduling foundation with roadmap-ready architecture.
Consulting
Start with an AI initiative conversation or a paid opportunity assessment.
The assessment produces a baseline, an opportunity register, rejected use cases, a recommended first pilot, and a 30 / 60 / 90-day roadmap. ROI language stays directional until measured.
Hiring teams
Recruiters and hiring managers
The recruiter briefing, résumé options, guided tour, and experience timeline remain available. They are a secondary path from this consulting homepage.