Strategy + Engineering in One Leader
Unblock teams fast. I present to leadership and can ship the demo that moves the investment forward when it matters: narrative and CI/CD pipeline in the same week. I've been in both and lead teams that deliver.
Senior Director, Advisory, PwC
Andrew Dean Martin
Turning AI Ambition into
What Actually Ships
I help leaders turn AI investment into solutions that work in their context.
I speak strategy and code, unblock teams, build when it matters, and enable others, so ideas become real, fast.
About
The Short Version
I build something most days. I'm the person teams call when AI ambition collides with delivery reality: unclear scope, legacy systems, compliance constraints, and a deadline that won't move.
18 years in tech, 15 in consulting (PwC since 2010). Startups to Fortune 1; I've worked across the Americas, Asia, and Europe. I help turn roadmaps into working outcomes (demos, prototypes, use cases), often in the same week. Strategy, architecture, hands-on when it unblocks. The real challenge isn't having good ideas; it's making them real, governed, and scalable. The demos below show how.
What Sets Me Apart
How I Work
Prototype-Led Leadership
I lead with working demos (built in days, by me or the team) so stakeholders react to something real instead of debating abstractions. A mediocre prototype beats a perfect deck.
AI as Method, Not Magic
One-off experiments don't scale. Repeatable playbooks, prompt catalogs, and agent libraries so teams can adopt, adapt, and run without me.
Governance Without Bureaucracy
Compliance blocks shipping unless it's built in from day one. Human-in-the-loop controls, audit trails, trace links. Solutions that survive production.
Startups to Fortune 1
I've worked across the full spectrum: early-stage and the largest enterprises, in the Americas, Asia, and Europe. I've been there before—so I ramp fast.
See It In Action
Demos
One example run: 847 items, 612 auto-processed, 1 human checkpoint. Replay it below, then see the methodology, the assessment lens, and the reliability math. Four tools; built to be explored, not just watched.
From a real multi-agent run (anonymized). AI agents analyzed two schemas, hit a human checkpoint, produced a deliverable. Watch it replay.
Input: agent-pipeline-run/00-input-schemas.json (Legacy Order System → Target Commerce Platform).
Flow: Source Analyzer → Recommendation Engine → Human → Risk & Compliance → Validation → Documentation
Audit Trail
Vague requests → inconsistent output. Transform "I need X" into prompts that actually work. Role → Task → Context → Requirements → Output. Consistent, useful results.
Your rough prompt: drag or tap into the Optimizer →
Typical vague ask from a delivery lead
update leadership on the project
1
Prompt Optimizer Step 1: Refine
Prompt Engineer
Meta-Prompt Assistant
2
ChatGPT / Claude Step 2: Use in ChatGPT
ChatGPT
GPT-4
Copied! ✓
Why Structured Prompts Matter
The problem: Vague prompts like "update leadership on the project" produce inconsistent, low-quality outputs. No repeatability, no audit trail, no governance.
The solution: Structured prompts using Role → Task → Context → Requirements → Output eliminate ambiguity and produce consistent, professional results. Part of the methodology I use with delivery teams and AI Champions.
Governance: Structured prompts enable traceability and reuse. In delivery we pair this with change tracking (who refined what, when). Teams adopt templates that survive production.
Representative of engagements across enterprise codebases. Before an acquisition or integration, get clarity on what needs fixing and how long it'll take. 8 workstreams: architecture, security, testing, performance, and more.
8 Workstreams
Architecture, Code Quality, Security, Dependencies, Testing, Performance, Technical Debt, Documentation. Each evaluates specific dimensions and produces findings with severity and remediation estimates.
Sample findings
High
Architecture: Monolithic backend with 47 direct database connections across 12 services. No API gateway or service mesh. Any schema change risks cascading failures.
Introduce API gateway. Prioritize extraction of highest-traffic services. Estimated effort: 120 hours.
Critical
Security: 3 API endpoints accept unvalidated user input passed directly to database queries. SQL injection confirmed in staging.
Parameterized queries, input validation middleware, penetration test. Estimated effort: 24 hours.
Medium
Testing: 23% overall coverage. Integration tests absent for 8 of 12 API endpoints. 14 flaky tests.
Prioritize integration tests for payment and auth. Target 60% coverage for critical paths. Estimated effort: 80 hours.
Executive Summary
Overall Health Score6.4 / 10
Critical Findings3
High Priority7
Medium Priority12
Estimated Remediation8–12 weeks (phased)
Every agent you add multiplies risk. A 5-agent pipeline at 95% per-agent reliability succeeds only 77% of the time. The same mitigations we use to harden pipelines like the one in the first tab. Explore the math, then toggle production mitigations to see how engineering closes the gap.
The Reliability Paradox
Add agents, watch reliability degrade, then apply mitigations to see the difference.
77%
If each agent succeeds 95% of the time, a 5-agent pipeline succeeds only 77% of the time. At 10,000 daily runs, that's 2,262 failures per day.
Pipeline Reliability
95.0%
Cost per Run
$5
Latency
2.0s
Daily Failures (10K runs)
500
Monthly Cost (10K/day)
$1,500,000
Why This Matters
The compound reliability formula (Pn) illustrates why multi-agent architectures need production engineering (retries, circuit breakers, fallbacks), not just prompt engineering. The gap between "works in demo" and "works at scale" is where most AI initiatives stall.
This model uses simplified compound reliability (Pn) to illustrate the principle. Real systems have retries, parallel paths, and correlated failures. Adjust the slider to explore different baselines.
Impact
Results That Speak
This way of working has produced measurable results.
Three things that matter most: full cycle from weeks to hours; cycle time cut by orders of magnitude; and demos that move decisions.
From assessment through recommendations to something you can run. What used to take weeks now happens in hours. Real artifact, not just a deck.
Built a GenAI-powered tool that compressed survey and assessment cycles by up to 90%.
Since January 2026. Working demos that accelerated investment decisions and unblocked client conversations.