What else to take into account

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Jill Sweeney
Chief Strategist at Redboard Advisors

One additional consideration is that agentic AI requires leaders to design an operating model, not simply deploy a tool. The most important questions are: What decisions may an agent recommend, execute, or escalate? What data can it access? Who is accountable for its outcomes? And how will the organization audit its reasoning, actions, and exceptions?

In practice, effective human-agent collaboration depends on clear decision rights, guardrails, and feedback loops. For example, an AI agent may be authorized to identify supply-chain risk, model alternatives, and recommend a response, while a human leader retains authority to approve actions with financial, customer, regulatory, or workforce impact.

Leadership therefore becomes less about personally having every answer and more about creating the conditions for trustworthy, transparent, and responsible decision-making at scale. The differentiator will not be who adopts agents first, but who builds the governance, data discipline, and organizational trust needed to use them well.

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