AI Adoption Strategy
Turn AI ambition into governed use cases, production patterns, and delivery decisions your team can actually execute.
The current AI landscape is noisy. Organizations are rushing into proofs of concept without clear value, evaluation discipline, or a path into existing workflows.
Relevant Writing
The Agent Security Supply Chain: What the Sysdig Intrusion Means for Australian Builders
When 97% of enterprises expect a major AI agent security incident and two-thirds have already experienced one, the question is not whether your agent toolchain will be attacked — it is whether you will see it coming.
What ASIC's AI Risk Radar Means for Your Startup
ASIC has put AI governance squarely on its 2026 enforcement radar. For Australian startups building or deploying AI in financial services, the compliance clock is already ticking — here's what you need to know and what to do about it.
APRA Wants AI Governance Evidence. CPS 230 Is Where They Will Look.
APRA's April 2026 letter demands a step-change in AI governance. For regulated entities that have deployed AI in critical operations, the evidence APRA is asking for lives inside the CPS 230 program — not beside it.
The MCP Supply Chain Crisis: Why Every CTO Needs a Gateway
200,000 vulnerable instances. 60-72% poisoning success rates. ASI04 on the OWASP Agentic Top 10. The Model Context Protocol is having its 'log4j moment' — and the response is not a patch, it is a gateway.
EU AI Act Countdown: Human Oversight Cannot Be A Checkbox
With the EU AI Act's 2 August 2026 application date approaching, fintechs need to test whether their human-in-the-loop controls are real, usable, and evidenced.
AI Washing Is The New Greenwashing
The SEC's AI-washing actions show a simple pattern for fintech leaders: if you claim AI advantage, you need evidence, governance, and disclosures that match the claim.
I Gave an AI Agent the Keys to My Life. Here Is the Trust Architecture.
My personal AI agent can read my email, manage my calendar, run scripts, and message people. Handing that much access to an autonomous system is reckless — unless you build the right boundaries first. Here is the architecture that makes it safe.
One Model Is the Wrong Default
Most people pick one AI model and route everything through it. That is like hiring one person to be your strategist, your developer, and your data-entry clerk. A well-built agent uses the right model for each job — and the cost difference is enormous.
Your AI Agent Needs a Soul File
Large language models are stateless — they wake up with no memory of who they are or who you are. The fix is not a bigger context window. It is a handful of plain text files that give an agent continuity, personality, and judgement across sessions.
MCP Tool Poisoning: The Attack Vector Nobody Is Talking About
AI agents trust their tools. That trust is now being exploited. The new attack surface is not your servers — it is the instructions your agents receive.
Who Signs the Contract When Your AI Agent Does It?
AI agents are now managing budgets, filing reports, and executing transactions. Australia's financial law has not caught up. That gap is both a risk and an opportunity.
The Docker Moment for AI Agents
AI agents are moving from clever demos to production systems. The important question is no longer only which model to use, but what scaffolding makes agents reliable, observable, and safe.
Where Are You on the AI Journey? The 4 Stages of AI Maturity
A practical maturity model for moving from scattered AI experimentation to governed, measurable, production AI adoption.
The Human-AI Partnership: A Framework for Safe Adoption
A practical framework for AI adoption that separates where AI should assist, where humans must decide, and how organisations can build trust through verification.
Beyond the Hype: A Pragmatist's Guide to AI Adoption
An optimistic but realistic guide to AI adoption: where AI creates genuine leverage, where it introduces risk, and how leaders can adopt it without losing control.
Where I help
- 01. Value first, tech second Identify high-impact use cases where AI solves a real business problem rather than becoming another isolated demo.
- 02. Security and governance Define guardrails for data privacy, human review, evaluation, and safe adoption inside enterprise constraints.
- 03. Integration, not isolation Embed AI into workflows, systems, and team habits so it improves delivery instead of creating another tool to reconcile.
What a useful engagement produces
- A ranked AI opportunity map tied to business value and delivery risk.
- Governance guardrails for data, human review, evaluation, and rollout.
- A first production candidate with success metrics and owner-ready next steps.