Publication No. 003 | September 2026

The Cost of Solving the Wrong Problem

Why Good Solutions Fail When the Diagnosis Is Wrong

Organizations rarely suffer from a shortage of solutions. They hire, restructure, purchase software, replace managers, add policies, and launch new initiatives.

But there is a problem that occurs before any of those solutions are chosen: what if the organization has correctly solved the wrong problem?

This publication examines the difference between an observed condition, its presumed cause, and the intervention chosen to address it—and why confusing those three can cause capable organizations to spend significant time, money, and effort successfully solving a problem they never actually had.

At the center of the analysis is a simple question:

What are we trying to change, and what evidence tells us that this action will change it?

Samuel Monastra
Foundation Insights | Foundation Building Advisors

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Publication No. 002 | September 2026

The Hiring System Didn’t Break Because of AI

Growth Exposed a Weakness. Automation Scaled It.

Employers say they cannot find qualified people. Candidates say employers cannot see their capabilities. Both sides are increasingly turning to automation and artificial intelligence.

This publication examines what happens when a hiring system with an unresolved information problem is scaled through technology—connecting application volume, automated screening, skills translation, candidate adaptation, AI use, experience requirements, and declining trust.

At the center of the analysis is a simple question:

If we make this system faster, are we certain we want more of what it currently produces?

Samuel Monastra
Foundation Insights | Foundation Building Advisors

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Publication No. 001 | August 2026

AI Isn’t the Problem With Recruiting. We’re Asking It to Make the Wrong Decision.

A systems analysis of the modern hiring funnel, the constraints driving automation,
and a question that may change how we think about recruiting AI.

This publication examines application volume, automated screening, signal quality,
transferable capability, skills-based hiring, human judgment, and the distinction
between processing more candidates and making better hiring decisions.

At the center of the analysis is a simple question:

Before asking AI whether a candidate should continue, should we first ask whether
we have enough evidence to know?

Samuel Monastra
Foundation Insights | Foundation Building Advisors

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