ReliablyME is a P2P accountability platform that helps people overcome self-limiting behaviors.
Toronto, Ontario
Joined June 2018
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She signed off on the weekly cleaning every week. The cleaning had stopped a couple weeks earlier.
@boardyai connected me today with a quality and change consultant who has spent 25+ years in regulated life sciences. She's currently interim quality manager for a food manufacturer implementing GMPs.
She put a new weekly cleaning procedure in place. Every week she signed off that it was done and looked good. She works remotely.
Then she went on site to collect the cleaning documents. They had just stopped.
She says the hardest part of every implementation she runs is regression. Leaders make little exceptions, and eventually everyone goes back to their happy place. Those signals are hard to pick up from a distance.
Her signature was the only part of the process that kept happening every week.
hey @boardyai analyze my profile and tell me the most useful person in your network I should meet next, and why
Check out my latest article: What I heard at Toronto Tech Week: AI is ahead of trust. linkedin.com/pulse/what-i-he… via @LinkedIn
Non-technical founders are being told they can replace a senior engineer with AI and “move faster.” In reality, that’s how you end up shipping bugs, breaking prod, and losing control of what’s actually true in your system.
In this new piece I share a real incident (119,747 restarts in 6 hours) and the Trust Enablement Control Plane (TECP) pattern I now use so AI-driven changes stay observable, reversible, and investor-safe—even if you can’t read the code yourself.
If you’re leading a product without a deep tech background, this is the AI playbook I wish I’d had 👇
Replacing a Senior Engineer with AI: Possible, Getting Easier, Still Dangerous linkedin.com/pulse/replacing…
Voluntary AI codes don't fail because the people writing them lack integrity.
They fail because competitive pressure selects against them.
If following the framework costs you a deal, the framework loses.
That's not a values problem. It's a market structure problem.
Enforceable evidence standards are the only mechanism that survives.
The argument for AI restriction: fewer AI systems = less risk.
The problem: restriction concentrates AI power in the hands of the few who ignore it.
The sustainable path: governed proliferation.
More AI at the edges. Better governed. Verifiable controls. Human agency preserved.
Not less AI. Less ungoverned AI.
1/ Who authorized this action?
Not "what policy permits it." Who — specifically — issued the authorization for this AI to act in this context, at this time.
6/ How can a person contest the outcome?
If there's no answer to this one, the other five don't matter. Accountability without recourse is theater.
These aren't exotic requirements.
Authorization tickets, execution receipts, immutable ledgers, delegation records — established engineering patterns.
Making them mandatory at the execution boundary, not optional afterthoughts.
Full paper: linkedin.com/pulse/governed-…
To govern AI execution, six questions must always be answerable.
Most organizations today can answer zero of them reliably.
A thread:
The real gap is at execution: AI acts, outcomes happen, and no one can reconstruct who authorized what, under what conditions, or with what oversight.
That's not a values problem. It's a missing infrastructure layer.
linkedin.com/pulse/governed-…
Check out my latest article: AI Case Study — When Coordination Costs Collapse linkedin.com/pulse/ai-case-s… via @LinkedIn
AI and talent ROI often break down at the same point: the proof layer.
Adoption metrics miss whether follow-through behaviors actually happened.
Behavioral data may be the missing signal.
Discussing this with Dr. Carylynn Larson in our upcoming webinar:
us06web.zoom.us/meeting/regi…
AI and talent ROI often break down at the same point: the proof layer.
Behavioral data may be the missing signal.
Discussing this with Dr. Carylynn Larson in our next webinar.
Register: us06web.zoom.us/meeting/regi…
