Where AI Governance Meets Platform Modernization
AI governance treated as compliance documentation fails when modernization runs on AI tooling. The concept that matters is decision auditability.
Tag: Enterprise Technology
36 articles tagged with "Enterprise Technology"
The AI Governance Market Landscape
Machine identities outnumber humans 82 to 1 in the average enterprise. Most are ungoverned. Here's the market, mapped by governance layer.
The Agent You Don't Know About
Every layer of your AI governance stack assumes the agent is visible. Shadow AI breaks that assumption. Discovery is the precondition for governance.
Nine Seconds Wasn't an AI Failure
An AI agent deleted a production database in nine seconds. The headlines blamed the agent. The actual failure was discipline that's existed for thirty years.
Proving What Happened
Logs tell you what happened. Audit trails tell you why — and whether it should have. Most organizations have one but not the other.
The Single Chokepoint
Agent gateways are the control plane for the digital workforce. Enterprises solved multi-vendor visibility before — the same pattern is forming again.
Your Agent Pipeline Isn't a Pipeline
Platforms made it easy to build a first agent. Nobody has solved how to run one in production with the same discipline we apply to software.
Your Agents Are Software. Treat Them Like It.
Every organization has change management discipline for software. Few apply it to AI agents. That gap is showing up in audits and due diligence.
Your Phishing Training Doesn't Cover This
Your phishing training doesn't cover this new attack surface. It looks like productivity. And your security posture was never built to catch it.
AI Watching AI
At scale, humans can't review every agent interaction. The case for guardian agents — and why AI overseeing AI is uncomfortable but probably inevitable.
Time to Hello World
Vibe coding made getting to a first API call table stakes. The harder question: was your documentation written for the agent reading it for a user?
Built Secure, Deployed Dangerous
An agent built correctly can still drift into dangerous territory through misconfiguration. Most organizations have no way to detect it until something breaks.
Not Every Decision Needs a Human... But Some Do
Most AI agents run at whatever autonomy level was easiest to implement, not the one that reflects actual risk. Here's how to tell the difference.
The Agent Can Think. It Just Can't Get to the Data.
The biggest barrier to real AI automation isn't the model. It's connectivity. And the protocol solving it is creating your next governance problem.
Your Digital Workforce Has a Landlord
Thousands of AI agent workflows disrupted overnight — not because agents broke, but because a vendor changed its billing. That's a governance failure.
Every Agent Needs a Badge
Most organizations badge their contractors, track their access, and revoke it when they leave. They don't do any of it for AI agents. That gap is closing fast.
Your AI Strategy Is Only as Strong as Your Inventory
You can't govern, defend, or prove value from AI systems you can't account for. Why inventory is the first place enterprise AI governance gets real.
Who Owns the Outcome? Governing the Age of AI Sprawl
AI agent sprawl is outpacing enterprise governance. Here's why that's a leadership problem — and what the governance stack actually needs to look like.
Modernization in the Age of AI: The Economics Shift, the Discipline Doesn't
AI is removing the mechanical friction of modernization. It does nothing to fix the strategic decisions that make modernization succeed or fail.
How Modernization Fails: Detecting Drift Before It's Terminal
Modernization efforts don't crash. They drift. The warning signs are visible six months before leadership notices. Here's how to spot them.
Modernization Without Destabilization: Every Step Has to Pay for Itself
Big-bang rewrites fail. The alternative is sequencing modernization as self-funding increments. Here's how to do that under PE constraints.
Reliability as the Modernization Business Case
Most modernization pitches die in the boardroom because they're framed as technology problems. The ones that get funded are framed as financial ones.
The Rise of Artificial Confidence
AI delivers answers with total certainty, even when it's wrong. How artificial confidence is quietly eroding critical thinking in technical teams.
Part 3 — Edge AI in the Developer’s Workflow
Edge AI is moving from IoT devices to developer laptops. Running models locally eliminates latency, outage risk, and cloud costs in day-to-day coding.
AI at the Crossroads (Part 2): Rising AI Costs and the Push to the Edge
Cloud AI costs are rising fast, and token economics change how teams build. Part 2 explores why edge computing is becoming the pragmatic alternative.
AI at the Crossroads (Part 1): Why the Edge Matters More Than Ever
Cloud computing democratized AI, but rising costs and latency are pushing compute back to the edge. Why the next era of AI may not live in the cloud.
A Second Set of Eyes: How AI Is Quietly Crowdsourcing Our Workflows
AI’s biggest near-term value isn’t automation — it’s acting as a second set of eyes. How personal crowdsourcing is quietly reshaping how we work.
The AI Hype Trap: Why You Should Be Skeptical of Overnight Success Stories
“Most AI success stories collapse under basic business scrutiny. How to separate real product-market fit from tech theater and weekend demos.”
The Real AI Transformation Starts When You Stop Talking About AI
The companies getting real value from AI have stopped talking about it. What post-hype AI adoption looks like when it's embedded in actual workflows.
The Developer Paradox: Why Technology Innovation and AI Keep Creating More Dev Jobs
Every productivity tool was supposed to reduce demand for developers. Instead, the industry grew from under 1M to 27M. Why AI will likely do the same.
What It Means to Be Data Intentional
Most organizations collect far more data than they use. A data intentionality strategy focuses collection on what matters and turns it into decisions.
What It Means to Be Data Intentional
Most organizations collect far more data than they use. A data intentionality strategy focuses collection on what matters and turns it into decisions.
The Healthcare Industry is Prime for Curing the Paper Problem with Context
Healthcare still runs on paper despite decades of IT investment. Semantic technology and contextual data capture can reduce the back-office burden.
Building an Empire of Knowledge with Semantic Data
Knowledge graphs and semantic data are the foundation of the autonomous enterprise. Why being data-driven isn't enough — you need to be context-driven.
Why Enterprise Survival Depends on a Context Driven Approach to Data
Enterprises invest heavily in big data and AI but struggle with ROI because most data is inaccessible. A context-driven approach unlocks the value of dark data.
Progressing the Digital Worker with Context
Most organizational data is dark and inaccessible. Enriching captured data with business context is the key to enabling digital workers and automation.