Disconnected experiments
Pilots start from tools, vendor demos, or enthusiasm. Nobody owns the baseline, so nobody can say whether the work improved the business.
AI Transition
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.
Many organizations already have disconnected AI experiments. The gap is leadership decision-making: which work deserves a pilot, which should be rejected, and what evidence would justify scaling.
Method: Baseline → Prioritize → Pilot → Measure → Scale. A valid outcome is Stop / Adjust when evidence does not justify scaling.
The problem
Experiments without a decision loop become expensive noise.
Unclear leadership decisions
Teams cannot tell which use cases to fund, which to pause, and which to reject. The portfolio grows; the operating model does not.
Governance and adoption lag the demo
Data boundaries, change ownership, and frontline workflow are treated as later problems. They are the conditions that make a pilot real.
How the work runs
Bounded pilots, measured against a baseline.
The output is a scale / adjust / stop decision. If the evidence is weak, stopping is a successful engagement.
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.
Why this offer
Enterprise credibility plus the discipline to say no.
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 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.
Related paths
Enterprise teams can start with portfolio and governance work. Québec and Canadian PME leadership can start with a practical assessment of time, service, and revenue — without buying technology they do not need.