We’ll let AI sneak in on a small hope:
that smarter ones will make up for human foolishness.
Better models. Faster analysis. More objective recommendations.
Surely, decisions will improve.
But in reality, many organizations find something awkward instead.
AI doesn’t quietly make bad decision-making go away.
It puts it on display.
AI Doesn’t Choose What Matters — It Amplifies It
AI systems are good at spotting patterns, tweaking variables and scaling logic. What they cannot do is to determine what should matter.
They function in the limit that we impose:
- The objectives we define
- The metrics we reward
- The constraints we tolerate
- The trade-offs we won’t say aloud
When the inputs are bad, AI does not correct them — it amplifies them.
If speed is rewarded at the expense of quality, AI just accelerates bad outcomes more quickly.
When incentives are at odds, AI can “hack” one side and harm the system as a whole.
Without clear accountability, AI generates insight without action.
The technology works.
The decisions don’t.
Why AI Exposes Weak Judgment
Before AI, poor decisions typically cowered behind:
- Manual effort
- Slow feedback loops
- Diffused responsibility
Smell of doughnuts “That’s the way we’ve always done it” logic
AI removes that cover.
When an automated system repeatedly suggests actions that feel “wrong,” it is rarely the model that’s at fault. It’s not that the organization never has aligned on:
- Who owns the decision
- What outcome truly matters
- What trade-offs are acceptable
AI surfaces these gaps instantly. You might find that visibility feels like failure — but it’s actually feedback.
The True Issue: Decisions Not Designed
Numerous AI projects go off the rails when companies try to automate before they ask how decisions should be made.
Common symptoms include:
- Insights Popping Up in dashboard with Division of Responsibility is not defined
- Overridden recommendations “just to be safe”
- Teams that don’t trust the output and they don’t know why
- Escalations increasing instead of decreasing
In the midst of those spaces, AI makes clear a much larger problem:
decision-making was not optimally designed in the first instance.
Human judgment was around — but it was informal, inconsistent and based on hierarchy rather than clarity.
AI demands precision.
It’s also usually not something that organizations are prepared to offer.
AI Reveals Incentives, Not Intentions
Leaders could be seeking to maximize long-term value, customer trust or quality.
AI competes on what gets measured and rewarded.
It becomes manifest when AI is added to the mix, that space between intent and reward.
When teams say:
“The AI is encouraging the wrong behavior.”
What they often mean is:
“The AI is doing precisely what our system asked — and we don’t like what that shows,” he says.
That’s why AI adoption tends to meet with resistance. It is confronting cosy ambiguity and making explicit the contradictions that human beings have danced around.
Better AI Begins With Better Decisions
The best organizations aren’t looking at A.I. to replace judgment. They rely on it to inform judgment.
They:
- Decide who owns the decisions prior to model development
- Develop based on results, not features
- Specify the trade-offs AI can optimize
- Think of AI output as decision input — not decision replacement
In these systems, AI is not bombarding teams with insight.
It focuses the mind and accelerates action.
From Discomfort to Advantage
AI exposure is painful because it takes away excuses.
But that discomfort, for those organizations willing to learn, becomes leverage.
AI shows:
- Where accountability is unclear
- Where incentives are misaligned
- The point where decisions are made through habit rather than intent
Those signals are not failures.
They are design inputs.
Final Thought
AI doesn’t fix bad decisions.
It makes organizations deal with them.
The true source of advantage in the AI era will not be individual analytic models, but the speed at which models are improved. It will be from companies rethinking how decisions are made — and then using A.I. to carry out those decisions consistently.
At Sifars, we work with companies to go beyond applying AI towards developing systems where AI enhances decisions not just efficiencies.
If your A.I. projects are solid on the tech side but maddening on the operations side, that problem may not be about technology as much as it is about the decisions it happens to reveal.
👉 Contact Sifars to create AI solutions that turn intelligent decisions into effective actions.
🌐 www.sifars.com