Messages I took away from Harari & WEF Davos regarding C-Level

In the age of artificial intelligence, it's not speed but dominance that brings victory.

My main message is: The goal should be to integrate AI into the organization not for speed, but for value, trust, and sustainable competitiveness .

Much of the AI discussion today boils down to the question of "how fast are we?"

However, the main message emerging from Davos is this: speed is no longer a competitive advantage, but a hygiene factor.

The real difference lies in which decisions AI is used in, within what limits, and with what governance.

1) Two Crises = Two Management Questions

A) Identity crisis (values and roles)

• Does the organization's "value creation mechanism" rely on human intelligence or on system and process design ?

• How do we shift people from a "implementer" role to a role that enhances decision quality (validating, framing, generating options)?

This question isn't really about competence, it's about identity. Because in the age of AI, competition comes not from organizations that "know the most," but from those that "can ask the right questions."

The role of humanity doesn't need to diminish; it needs to be elevated to a higher cognitive level.

B) Migration crisis (AI workers / AI agents)

• A new “workforce type” is entering the organization: AI agents, co-pilots, and automations .

• Management question: Who will get involved, with what authority, who will supervise, and when will they be removed from the system?

Think of it like human migration: "Skilled migration" boosts a country's growth; "uncontrolled migration" strains the system. AI is the same.

The critical difference here is that while human migration has speed limits, AI migration has no limits. You can integrate hundreds of "digital workers" into the system in a single day. Therefore, the issue isn't the technology itself; it's who you bring in, how much capacity, and under what rules.

2) Management Principles (5 rules)

1. Trust comes first, then speed. (Speeding up is easy; losing reputation is expensive.)

An AI-driven error differs from a classic operational error; it is more visible, spreads faster, and is more difficult to reverse. Therefore, trust is not only an ethical issue but also a key aspect of financial risk management.

2. Human + AI = hybrid team standard. (Not AI alone or human alone.)

The best performance is seen in models where decision-making is supported by AI but responsibility remains with humans. When this balance is disrupted, either speed is gained and control is lost, or control is maintained but value cannot be generated.

3. Authority = data + decision-making + money. Access to all three should be tiered .

What an AI can do is just as critical as what it can access. Authority should be defined not solely by "function," but by the level of risk involved.

4. Undoing is mandatory. Every critical AI deployment should have a "stop/undo button" .

Every automation you can't undo is a blind spot from a governance perspective. Good governance requires designing not only how to start it, but also how to stop it.

5. Scale it by proving it. Pilot → measurement → inspection → dissemination.

In AI projects, "early scaling" is one of the most costly mistakes. What needs to be scaled is not the technology; it's the proven value and control mechanisms.

3) “AI Migration Policy”: 10 critical questions for the Board of Directors

1. What will this AI do? Content, analysis, decision suggestions, automated processes?

2. What resources will it use? Public data / internal data / customer data — which ones?

3. What is the reach level of AI ? ( Reads / suggests / implements )

4. If AI can perform operations independently , at what threshold is double verification mandatory?

5. In which processes is its use prohibited ? (Law, pricing, recruitment, credit, healthcare, etc.)

6. Are the "red zones" clear? (Legislation, discrimination, information security, reputation)

7. Are the AI outputs being monitored ? (Logs, instructions, data source, version control)

8. If they produce something wrong , who is the "business owner , " who is the "risk bearer," and who is the "technology owner" ?

9. What are the safety triggers ? (Complaints, failure rate, security incident, media/reputational risk)

10. Can the suspension/reversal procedure be completed in one day ?

    
    

4) What should we measure? (C-Level indicators)

The fate of AI investments is determined by whether or not they are monitored with the right KPIs. The wrong metrics can cause even the right technology to fail.

• Value: time saving → cost/speed; revenue impact (prospective customer, conversion, abandonment)

• Quality: error rate, rework, verification success

• Trust: number of complaints, reputational risk, number of “fake production” (hallucination) incidents.

• Adoption: active user rate, weekly usage, training completion

• Governance: registration coverage (%), number of unauthorized uses, policy violations

These indicators tell us that AI success is not a technical achievement, but a managerial outcome. No matter how good the model is, if value, trust, and governance are not monitored simultaneously, the organization unknowingly accumulates risk.

For C-level officials, the real question isn't "Does AI work?"; it's "Is AI working in the right place, within the right limits, and with the right impact?"

5) Those who gain control, not speed, will survive.

The common message emerging from Davos and Harari's framework is clear: In the age of AI, the winners will not be the fastest, but the most informed.

Organizations that view artificial intelligence solely as a productivity tool may gain momentum in the short term. But those that address it as a matter of decision-making, authority, and governance will build trust, reputation, and sustainable competitiveness in the long run.

So the real question isn't "How quickly did we integrate AI?"

The real question is: "On whose behalf, within what limits, and at what cost is AI making decisions within our organization?"

Organizations that can provide a clear answer to this question can manage not only the present but also the uncertain future.