Human bias, greed, and self-interest plague businesses and prevent them from rapidly growing sustainable profit or protecting workers and the climate. Until now, human decision-makers have been trusted to curb their vices to represent the best interests of all workers, investors, and customers. The main culprits of unethical business decisions have been the self-interests of decision-makers, the lack of valid, unbiased, reliable performance ratio data, and confusion over how performance is enabled, ensured, and continuously improved.

Only performance ratio data captured automatically without human involvement is valid, unbiased, and reliable enough to be predictive. That data is the “holy grail” of every business worldwide because it references the most precise performance level possible, which is the time it takes each worker to perform every step of every SOP they execute. Time and money are ratio data because they can be used in mathematical calculations, translated to money, and broadly compared across workers, SOPs, equipment, and any other important performance variables. 

Knowing the time it takes to perform every step of every SOP also informs businesses of the cost it takes to perform each step, the time and cost to perform each SOP, and ultimately the time and cost of all business activities.

For AI to equally benefit economies, businesses, and workers, companies need to a) stop relying on old ways of doing things, b) create a common language with clear goals that everyone in the company understands, c) gather and share accurate and unbiased performance data across all areas of the business, d) develop or purchase an advanced intelligence augmentation (AIA) system to collect the data needed for AI, and e) create or buy and implement a workflow for AI-driven needs analysis that the AI can follow.

Once everyone in a business is philosophically, strategically, and tactically congruent, then the business can write or optimize the algorithms and flowcharts their AI will follow. After an AI-driven needs analysis process is implemented, it can be expected to look at performance data all day, every day, without the bias, greed, or high costs that come from having humans do the analysis.

This book suggests an AI-driven needs analysis system as the solution. When provided valid, reliable performance ratio data, VURPRD, the system, can perform business-wide needs analysis 24/7/365, without the time, cost, bias, greed, or self-interest incurred by human decision-makers. The system creates and keeps an up-to-date list of actions the AI suggests and populates project plans that include details like goals, measurements, deadlines, budget, potential problems, communication strategy, and recommended team roles.