Category: AI
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Personal AI Agents at Work: The Job Shadowing Phase
Personal AI agents are becoming an employee onboarding decision. Start with job shadowing, reviewed drafts, clear permissions, and task-specific trust.
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AI Sprawl: When Successful Adoption Hides Duplicate Spending
Successful AI adoption can hide duplicate builds, processing costs, and ERP integrations. Use cross-team visibility and shared capabilities to reduce avoidable AI sprawl.
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Decision Models: Stop Using a Racecar to Pick Up Groceries
Decision models can reduce unnecessary cost and latency in agentic workflows. Learn where to use rules, structured decision scoring, and larger language models.
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AI Model Routers: Lower Costs, Faster Responses, and Better Model Choices
What is an AI model router, when should you use one, and should you build or buy? A practical guide to model selection, cost, latency, and enterprise routing.
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Reliable AI Agents Do Not Need the Same Path. They Need the Same Test.
AI agents may take different paths to solve the same problem. Enterprise reliability comes from deterministic validation, independent checks, and clear definitions of a valid result.
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AI Agent Memory Poisoning: The Security Risk Hiding in Long-Term Memory
Long-term memory makes AI agents more useful, but it can also turn unverified assumptions into durable instructions. Memory needs provenance, boundaries, expiration, and re-verification before an agent acts.
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Simple AI Software Is Becoming the New Shared Spreadsheet
AI lets business teams create useful software as easily as they once built powerful spreadsheets. The opportunity is enormous, but shared tools need a path to become owned, discoverable, and supportable.
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AI Cost per Task: What Token Prices Miss
Token prices matter, but they do not reveal the cost of an AI-enabled business outcome. Cost per successfully completed task is the metric enterprise teams need next.
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Jev Is Not Another LLM. That Is the Point.
Jev gives up text generation to make fast, bounded decisions that software can act on. For high-volume workflows, that may be more useful than another chatbot.
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AI Is Learning to Sound More Human. Humans Are Learning to Sound More Like AI.
AI-trained language is feeding back into how people communicate at work. The risk is not using the tool. It is losing the point of view behind the words.