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Latest articles
Most “AI Agents” Are Two Parts Out of Nine
A production agent has nine components. The typical demo ships with two of them, which is exactly why it dazzles in the room and often struggles when it reaches production.
Read articleThe Window Is Closing Faster Than You Can Build It
For a mid-market company, building your own agentic AI is a bet that you can out-engineer the problem before your competitors out-execute you. That is a hard bet to win, because the clock is not standing still.
Read articleYour Best Operations Person Is a Single Point of Failure
The most valuable knowledge in your company isn’t written down anywhere, and it leaves every time someone quits.
Read article AI GOVERNANCEYour AI policy isn't governing anything
A document reviewed once a quarter cannot govern a system that takes thousands of actions a day.
Read articleMost AI Pilots Don’t Fail. They Succeed at the Wrong Thing.
A pilot proves the technology can work. Production asks a different question, and it’s the one almost no one budgets for.
Read articleYour Best Leader Can Become the Biggest Blind Spot in Your AI Transformation
The confidence that built the company is the same instrument that can’t read the new terrain, and you can’t feel the difference from the inside.
Read articleAn AI Agent Does a Task. A Digital Worker Owns the Job.
The label you use isn’t marketing. It’s the promise you’ll be held to, and “agent” and “worker” promise very different things.
Read articleA Model on Its Own Is Not an Operation
A public LLM is good enough to produce a demo, but on its own it is nowhere near enough to run your operation. The gap is not model accuracy. It is everything needed to turn intelligence into a dependable operation: the security, SLAs, governance, integration, transaction control, and auditability that almost everyone underestimates.
Read articleDon’t Marry the Model
A new “best” LLM ships every few months. If your architecture has a favorite, you’ve turned a temporary advantage into permanent debt. Model neutrality is the strategy.
Read articleYou Weren’t Wrong About RPA. Or the AI Pilot.
Automation has moved through three generations. Each one fixed the last one’s problem and created a new one. Each also created the impression that deployment had become easier than it really was. Knowing the pattern is how you stop paying for it again.
Read articleOnly Two of the Eight Ways AI Projects Fail Are About AI
When a deployment dies, everyone blames the model. Score yourself against the eight patterns that actually kill AI projects, and notice how few of them have anything to do with AI. The framework comes from a simple observation across our deployments: the technology is rarely the primary reason projects stall.
Read articleAn Engine Doesn’t Win Races. A Team Does.
Everyone in enterprise AI is shopping for the fastest engine. Championships go to whoever fields the best car and runs the best team, lap after lap.
Read article“Our Data Can’t Leave the Building.” Good. Your AI Doesn’t Need It To.
The reason your compliance team keeps blocking AI isn’t AI. It’s often a cloud-only assumption that was never a requirement.
Read articleThe License Is the Entry Fee. The Team Is the Real Bill.
In enterprise AI, the sticker price is the smallest number you’ll pay. Here’s where the rest of the money actually goes.
Read articleAI Isn’t Software You Deploy. It’s an Operation You Run.
Traditional software is designed to remain stable until the business deliberately changes it. AI starts drifting the day you stop watching it, which is exactly why it can’t be deployed and abandoned.
Read articleYou Can Finally Calculate AI’s ROI, Because You’re Comparing It to a Role, Not a Feature
For years the honest answer to “what was the return on that AI?” was a shrug. That changes the moment AI stops doing tasks and starts owning roles.
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