Tag: Automation
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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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Agent-First Design: Enterprise Software Has a Second User
As AI agents begin doing work on behalf of employees, enterprise software will be evaluated not only for people, but for agents that need governed access to data, actions, and workflows.
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Sharing AI Assets: The New Spreadsheet Problem
AI makes it easy for employees to build skills, MCPs, agents, and workflows. Without a shared capability layer, enterprises recreate the spreadsheet era at agent speed.
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Are You Using AI, Building With It, or Redesigning Work Around It?
Saying an organization “uses AI” no longer says much. The useful question is whether AI improves individual productivity, expands team capability, or changes how work gets done.
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Your AI System Has a Middle-Management Problem
Multi-agent AI systems do not fail only because the agents are not smart enough. They fail because nobody designed the management layer: routing, squads, tools, workflow graphs, and parallel execution.
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Prompts vs. Loops: Why Enterprise AI Needs Better Definitions of Done
Prompts still matter, but they are no longer the primary unit of work. Enterprise AI needs loops: outcome-driven systems with success criteria, verification, retry logic, escalation, and clear definitions of done.
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The Hidden Cost of AI Democratization Is Agent Sprawl
AI democratization is powerful, but without visibility it creates agent sprawl: duplicate tools, unclear ownership, inconsistent outputs, and hidden governance risk.
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MCP Is for the AI. MCP Apps Are for the User.
MCP lets AI connect to tools and workflows. MCP apps create the human interaction layer for verification, review, approval, and trust.