Arek Skuza recently shared something that caught my attention: "Managers must prepare for a future in which outcomes are no longer deterministic." He added that as AI becomes the new norm, results will "increasingly shift towards probabilistic outcomes," and that value will come from unstructured data that traditional tools like Excel cannot handle well.

I think he is pointing to a management shift that is bigger than picking the right model or buying another analytics platform. The real change is cultural: moving from expecting one correct answer to managing a range of plausible answers, each with uncertainty attached.

"In a world where AI is the new norm, results will increasingly shift towards probabilistic outcomes." - Arek Skuza

In this post, I want to expand on what Arek is getting at, and translate it into practical decisions managers and leadership teams can make: how to run meetings, choose workflows, hire, and stay flexible when the outputs you get are often "maybe" instead of "yes."

Deterministic management is a habit, not a law

For decades, many business processes were designed around deterministic tools:

Spreadsheets that produce one number when you enter one formula
Dashboards that show one KPI as the "truth"
Forecasts that are treated like commitments rather than estimates

Those tools are useful, but they encourage a mindset: if the inputs are correct, the output is correct. AI breaks that mental model. Even when the system is working as intended, you can see variation:

Two summaries of the same call can differ slightly
A risk model can output a probability, not a verdict
A sales forecast can be best expressed as a range

When Arek says outcomes are no longer deterministic, I hear a warning: if we keep managing as if every output is certain, we will either over-trust AI or reject it entirely the moment it disagrees with us.

Why unstructured data changes the game

Arek also notes that "Value can be extracted from unstructured data that traditional tools, such as Microsoft Excel, cannot effectively handle." This is where many organizations will feel the biggest gap between old and new capabilities.

Unstructured data includes:

Customer emails and support tickets
Sales calls and meeting recordings
Chat transcripts
Contracts, policies, and PDFs
Images, diagrams, and scanned documents

Excel can store a link to a call recording, but it cannot listen to 500 hours of calls and tell you what changed in customer objections this quarter. AI can.

The management challenge is that insights from unstructured data often arrive as interpretations, clusters, and confidence levels. You get "themes" and "signals" more than clean rows and columns. That is powerful, but it is also probabilistic by nature.

A practical example: from one metric to a distribution

Think about churn risk.

Old approach: a rules-based scorecard that outputs "high" or "low" risk
AI-enabled approach: a probability (for example, 0.72) plus drivers extracted from notes, tickets, and calls

If your team is only comfortable with deterministic decisions, they will ask: "So is this account churn or not?" The better question is: "Given a 72% risk, what intervention is worth doing, and how do we measure whether the intervention changes the odds?"

Boardroom conversations will evolve, and they must

Arek predicts that "boardroom discussions will evolve" and that it will be essential to involve "domain experts with diverse experiences" in these conversations. I agree, and I would add something specific: the boardroom will need to get comfortable debating assumptions, not just reviewing numbers.

When AI outputs multiple plausible options, the leadership team has to decide:

Which outcome matters most (cost, speed, safety, quality, reputation)
Which trade-offs are acceptable
What uncertainty is tolerable, and where you need more evidence

That cannot be delegated solely to the data team. It requires domain context: people who know the edge cases, the regulatory constraints, the customer psychology, and the operational realities.

AI does not remove judgment. It changes where judgment is applied: from producing an answer to selecting among plausible answers.

Make "challenge and calibration" a standard agenda item

One simple tactic: treat AI outputs like forecasts that must be calibrated over time.

What did the model predict last month?
What actually happened?
Where were we overconfident or underconfident?
What new data should change our belief?

This turns AI from a novelty into a measurable decision system.

Choosing workflows: where AI helps, and where it hurts

Arek highlights a "strategic focus on selecting workflows that can be enhanced with AI." That is the right lens. Not every workflow benefits from probabilistic outputs.

I like to sort workflows into three buckets:

1) High-volume, low-risk tasks (automate first)

Examples:

Drafting first-pass customer replies
Summarizing meetings
Tagging and routing inbound requests

Here, probabilistic outputs are fine because a human can review, or the cost of a small error is low.

2) Judgment-heavy tasks (augment, do not replace)

Examples:

Hiring decisions
Pricing exceptions
Compliance reviews
Safety procedures

Here, AI can surface patterns and options, but you should design the process so humans remain accountable, and you can explain why a decision was made.

3) High-stakes tasks with low tolerance for error (be cautious)

Examples:

Medical triage
Critical infrastructure operations
Financial reporting that must be exact

AI might still help, but typically through constrained use cases: extraction, reconciliation, anomaly detection, and controlled decision support.

The key management move is to match the workflow to the uncertainty you can accept.

Recruiting and team design for a probabilistic world

Arek ends with a practical leadership note: success will belong to those who "embrace external perspectives," "reevaluate their recruitment strategies," and remain "flexible" amid uncertainty.

Here is what that looks like in practice.

Hire for probabilistic thinking, not just tool fluency

Tool skills change quickly. What lasts is comfort with:

Ranges and confidence levels
Hypothesis testing
Clear communication of uncertainty
Decision-making with incomplete information

Interview for this explicitly. Ask candidates to explain a decision they made with ambiguous data, how they communicated risk, and what they did to reduce uncertainty.

Add "translator" roles to your org design

Many companies need people who can connect:

Business goals (what matters)
Domain constraints (what is allowed)
Data realities (what is measurable)
Model behavior (what is probable)

Sometimes this is a product manager, sometimes an operations leader with analytics depth, sometimes a domain expert who learned AI fundamentals. However you title it, the function matters.

Embrace external perspectives without outsourcing accountability

External experts, vendors, and advisors can widen the lens, especially in the boardroom. But the leadership team still needs to own:

The decision criteria
The risk posture
The governance process

External perspective should increase your options, not dilute responsibility.

A simple operating model for probabilistic outcomes

If you want a lightweight way to implement what Arek is talking about, try this loop:

Define the decision: what choice are we making, and by when?
Define the acceptable uncertainty: what level of confidence do we need?
Run AI as a generator of options: produce scenarios, not a single answer.
Bring domain experts into the review: stress-test assumptions and edge cases.
Decide and log rationale: capture what you believed and why.
Measure calibration: compare expected vs actual and update.

This is how you turn probabilistic outputs into deterministic execution.

The real competitive advantage: flexibility with discipline

What I appreciate about Arek Skuza's point is that it is not "AI will solve everything." It is more mature: AI introduces uncertainty, and leaders must build systems that can work with uncertainty.

Organizations that win will not be the ones with the flashiest demos. They will be the ones that:

Use unstructured data responsibly to surface real signals
Upgrade boardroom conversations from certainty to scenarios
Choose workflows where AI meaningfully augments outcomes
Recruit for probabilistic thinking and domain breadth
Stay flexible while maintaining accountability

That is management for the AI era: not certainty, but better judgment at scale.

This blog post expands on a viral LinkedIn post by Arek Skuza. View the original LinkedIn post →