Why Trusted AI?
Somewhere in your organization, an AI system is making a decision about credit, production, hiring, treatment or price. And nobody could fully explain that decision if your board, a regulator or biggest customer asked.
That’s not a hypothetical. A 2026 survey found that 91% of organizations only discover what an AI agent did after it has already executed the action. While AI adoption has climbed to 72% of organizations, the instrumentation to understand what those systems are actually doing did not keep pace.
The upside is real. So is the downside. A biased hiring model, a hallucinating support bot or a credit decision nobody can defend can undo years of earned trust overnight. Trusted AI exists to close that gap.
This isn’t a compliance newsletter, and it isn’t AI hype. It’s a strategic resource for leaders who’ve figured out that trust, not model capability, is the actual bottleneck on scaling AI and driving business value. The question was never whether your organization needs these capabilities. It’s whether you build them on your own terms.
What You’ll Find Here
Trusted AI is organized around the two capabilities that actually determine whether an AI program holds up under pressure and scales to deliver business value.
Governance is the frameworks, ownership, and decision rights that define what your AI should do. Sizing NIST, ISO 42001, and the EU AI Act correctly to the risk you actually carry. Accountability that survives contact with a real incident, not just a board slide.
Observability is the technical discipline of knowing what your AI is actually doing once it’s live. Evaluation, monitoring, drift detection, and the incident response muscle that catches problems before a customer or regulator does.
Every issue is written for executives, board members and senior practitioners who need to move past AI hype into AI reality. Substance over buzzwords, evidence over assertions, and guidance built to be used this quarter, not filed away.
New here? Start with:
What Is Trusted AI? The foundational case for why governance and observability are two halves of one capability.
Most AI Governance Is Theater. Yours Doesn’t Have to Be. It explains why most enterprise frameworks are copied prop documents, and how to build one that isn’t.
Who writes this?
I’m Jon Knisley. I’ve spent my career at the intersection of emerging technology and business transformation, not writing about governance from the outside, but building AI with a P&L attached under deadline.
A few stops along the way:
Commercialized AI software deployed at 7 Fortune 100 companies for analyzing complex unstructured data
Served as Chief Architect for Business Process Transformation at the Department of Defense’s Joint AI Center
Built an automation Center of Excellence for a financial services firm that won the Microsoft Power Platform Partner Award
Managed digital services for an organization that won the Nobel Peace Prize
Produced a top-ranked application Wired called “state of the art”
I’ve worked inside organizations, including Procter & Gamble, Mastercard, IBM, Capital One, Pfizer, General Motors, NASA and the FDA, across financial services, healthcare, manufacturing and government. I’ve seen governance programs that actually changed what shipped, and I’ve seen the ones that exist only to be pointed at in a board deck.
That’s the difference here. Not theory about what governance should look like, but pattern recognition about what actually works, built from watching programs succeed and fail at scale.
Join me
If you’re building, funding or overseeing AI inside an organization, subscribe and join a community of people asking the same hard questions. Interested in contributing an article? Message me directly.
