The Economics of Enterprise AI
What enterprise AI is actually worth, and how to tell a real business case from an attractive one.
What this collection argues
Establish an honest economic frame: where value hides in fragmented operations, how to establish a baseline, how to count cost realistically, and why the first useful projects are usually unglamorous.
In this collection
5 articles. Each one opens with the business problem, names the executive who owns the outcome, and ends with the metrics that would show whether the work paid.
The Hidden Cost of Doing Business Through Spreadsheets, Email and Messaging Apps
Fragmented information does not merely slow work down; it converts a coordinated business into a chain of manual re-entries, and the cost of that chain is invisible until it is measured.
From AI Experiments to Business Results: How to Calculate the Value of Enterprise AI
A defensible business case for enterprise AI starts from a measured baseline, counts the full cost of delivery including adoption, and claims only what the evidence supports.
Your Company Does Not Need More AI Agents. It Needs Better Business Processes.
Automating a poorly designed process reliably amplifies its errors, because automation preserves the decisions a bad process already makes and removes the human judgement that used to catch them.
The Business Case for Fixing Small Operational Errors
Operations rarely fail catastrophically; they fail as a low rate of small errors that no one owns, and the aggregate cost of that rate is substantial enough to justify a funded programme on its own.
The CEO's Guide to Finding the First Ten High-Value AI Opportunities
A structured method for identifying, ranking and cheaply validating the first ten candidate projects, using five dimensions that can be scored honestly before any technical feasibility work begins.