You’ve probably heard the terms “deterministic” and “probabilistic” used in discussions about artificial intelligence, but what do they really mean? What does this mean for your business?
On the one hand, we have deterministic models. This is how we’ve built our software and our business for 50 years. Every piece of software you buy, from a CRM system to a basic spreadsheet, is precise, rule-governed, and has no tolerance for error. Input A plus input B always equals output C. If they are not equal, there is a bug that needs to be fixed.
Our generative AI, on the other hand, breaks this rule. It is probabilistic, creative and context dependent. The same input can produce different outputs. It’s an inference engine, not a calculator.
This allows you to ask questions that deterministic systems cannot answer. How will tariffs affect my income this year? How will conflict in the Taiwan Strait affect the pricing of my products? There are no definitive answers to questions like these, but basic AI models can analyze large amounts of data and model multiple outcomes to inform your decisions.
The friction you feel in your operating model now—from compliance to quality control—is because we built business systems to seek out and eliminate uncertainty. However, you cannot force a probabilistic engine into a deterministic operating model. To fully harness the power of generative AI, leaders must stop thinking of AI as a faster spreadsheet.
The winners in this new era will be the companies that stop trying to suppress uncertainty and start putting it into practice. Here are three shifts you need to make to reintegrate your business and make the most of an AI future.
Measure autonomy, not just efficiency
In a deterministic world, software value is measured by access (seats) and efficiency (human work speed). We see software as a tool to empower our employees.
Generative AI turns this paradigm upside down. We are moving from software as a service to “software as a service,” where the value is in the results, not the tools. If an AI agent drafts a legal brief or resolves a customer ticket, the measurement will no longer be how much time humans save by using software, but rather whether humans need to be involved.
This requires different metrics. We need to stop measuring effort and start measuring autonomy. Do AI agents consistently reflect the truth? Does it shorten decision-making time? What is the task completion rate? The most important metric for scaling profits is: Did the AI agent solve the problem without human intervention? The goal is not to increase labor speed, but to increase productivity. This is an infinitely scalable workforce because the bottleneck (people) is removed from the cycle.
Manage uncertainty, not eliminate it
Most companies are trying to incorporate probabilistic AI into deterministic, rules-bound operating models. This doesn’t work. Traditional leaders panic when they see AI models producing hallucinations. They want to shut it down until it’s “100% accurate.”
But 100% accuracy is just an illusion of certainty. The right approach is to wrap the probabilistic engine in guardrails that manage uncertainty. exist Googlewe talk about “base” and confidence scores. Leadership teams need to stop asking “Is this answer correct?” and start asking “How confident am I in this output?” At Google, we tell employees that AI agents are not designed to generate answers, but to generate inferences.
To avoid this, we need to build systems where AI can operate autonomously when confidence is high and gracefully accept review by human experts when confidence drops.
Just like Google’s AlphaFold provides confidence levels for its protein structure predictions, your business AI needs to provide leaders with a score they can react to. These interventions become training models and drive feedback loops of continuous improvement.
Turn data into feedback, not just facts
This technology will not replace humans, but it will shift humans’ primary function from execution to expertise. “
In a deterministic system, the data is the real ledger used for reporting and historical analysis. With generative AI, data becomes instant feedback and action. Your historical data can train your future workforce—your team of autonomous AI agents. Chaotic data creates an incompetent digital workforce.
This requires an evolution of the human role. In a deterministic world, we hire legions of junior employees to perform tasks by rote. In a probabilistic world, AI does the polishing work. It instantly generates first drafts, initial code, and baseline analysis.
We are now seeing an evolution unfolding. Initially, humans do the work and AI assists. The AI then completes the work while humans supervise, stepping in when needed. Ultimately, AI operates independently while humans perform regular audits. If a human needs to approve every decision an AI agent makes, you’ve just created an expensive spell checker.
This resulted in a massive talent transfer. We don’t need people who can just execute; we need people who can audit. Artificial intelligence can generate average workloads instantly. You need someone who is expert enough to identify “great” from “good” in a matter of seconds. We need an editor-in-chief. We need people with the expertise to look at AI output and immediately differentiate between “reasonable” and “excellent.” Gone is the apprenticeship of toil; we need to develop the ability to judge.
Sailing boats and trains
The greatest competitive advantage will belong to leaders who can tolerate ambiguity in exchange for exponential speed.
Think of it this way: We’ve been building faster trains for decades. Trains run on railroad tracks (rules). It’s efficient, predictable, and works exactly as you planned. Today we are building a sailboat. They rely on wind (probabilistic data) and can reach places where rails cannot. But without a rudder (guardrail) and a compass (ground truth), you’re going to capsize.
Leaders who demand 100% certainty will continue to be stuck in the past, perfecting the efficiency of a dying model. The future belongs to those who learn to embrace and harness probability.
The views expressed in Fortune opinion pieces are solely those of the author and do not necessarily reflect the views and beliefs of: wealth.