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AI is changing leadership less by making managers unnecessary than by changing who can access information and act on it. Dr. Matt Offord argues that when data and computing are more widely available, teams closer to the work can make more decisions themselves. That can improve responsiveness, but it does not guarantee better decisions or make hierarchy obsolete: leaders still need to build trust, check information, assign accountability and choose how authority should work in each situation.
What does AI have to do with leadership?
Leadership has always involved coordinating people who hold different knowledge. In information-scarce organizations, senior managers often gathered reports, made decisions and sent instructions back down the hierarchy. Offord’s argument is that modern information technology—and AI in particular—can make more information available across an organization, reducing the need for every decision to travel upward and back.
That creates an opportunity for people nearer to a problem to respond without waiting for a central decision-maker. AI can help organize information and make patterns easier to examine, but it does not determine what matters, whether an output is true, or who should be accountable for acting on it. Offord puts the distinction this way: “Since only humans can create knowledge, leaders allow AI to organise data and generate information, while supporting humans to assign meaning and test for truth.” This is his conceptual position, not a settled scientific finding.
The practical question is therefore not whether AI can replace leadership. It is how leaders should organize decisions when information is more widely available and some work can be assisted by AI.
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Is AI already changing how organizations operate?
Stanford HAI’s 2026 AI Index reports that 88% of surveyed organizations adopted AI in 2025, while 70% used generative AI in at least one business function. These are different measures: broad organizational AI adoption is not the same as generative AI use in a business function. The report also says AI-agent use remained in the single digits across nearly all business functions. The figures indicate substantial adoption, but not widespread autonomous operation or a wholesale shift in leadership structures. Stanford HAI’s 2026 AI Index, Economy chapter
Offord suggests that more accessible information could contribute to flatter, more distributed organizations over time. That is a possibility in his argument, not an established outcome of current adoption data. AI tools can be added to an organization without changing who has decision rights, how work is coordinated or how responsibility is assigned.
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- Author: Bungay Stanier, Michael.
- Publisher: Page Two
- Pages: 244
- Publication Date: 2016-02-29
- Edition: 1
When should decisions be distributed—and when should they stay centralized?
Distributed decision-making can bring authority closer to people with current, local information. It may help teams respond quickly, particularly when conditions change and a central manager cannot see every relevant detail. But a network of decision-makers can also create noise, inconsistent choices and coordination fatigue. Centralized command can be clearer when consequences are high or a coordinated response matters more than local discretion.
Offord uses the Battle of Trafalgar to illustrate decentralized command and describes research he conducted in the Royal Navy, where teams used formal and informal networks in decision-making. His article does not give enough detail about the underlying study’s methods, date or findings to treat those examples as independently verified evidence. They illustrate his argument rather than prove that one structure works best.
Leaders can assess the choice against the situation rather than treating “flat” or “hierarchical” as universal goals:
- Speed and proximity: Does the person closest to the work have the information needed to act, or does the decision depend on organization-wide coordination?
- Consequences: Could an error cause serious harm? Higher-consequence decisions may require defined approvals, review or established command procedures.
- Verifiability: Can people check the data and AI-assisted output before relying on it?
- Coordination load: Will distributing authority reduce delays, or create competing decisions and extra communication?
- Readiness: Do staff have the skills, data foundations, governance ownership and budget to take responsibility for the decision?
These are practical considerations drawn from Offord’s argument and from organizational-transformation themes in the World Economic Forum’s 2026 report; they are not a tested scoring system. The WEF report draws on more than 450 executives in its AI Transformation of Industries community. World Economic Forum, Organizational Transformation in the Age of AI
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What responsibilities do leaders retain when AI is involved?
Build trust without treating AI outputs as facts
People need a reason to trust the data, the tools and the process for using their outputs. That trust is not the same as accepting every AI-generated answer. Offord warns that generative AI is “not designed to answer questions accurately, just plausibly”; where this distinction matters, outputs need human checking against reliable evidence.
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Keep accountability attached to decisions
When an AI system informs a choice, leaders should make clear who reviews its contribution, who can challenge it and who owns the final decision. Delegating analysis to a tool does not transfer responsibility for the outcome.
Develop skills and redesign work
Adoption alone does not ensure that AI improves an organization. The WEF’s transformation report emphasizes pairing AI use with operating-model and workflow redesign, talent development, transparency and disciplined experimentation. In practice, leaders need to help staff understand where a tool fits, what it cannot establish and how its output should be checked before it affects work.
Address governance and constraints
Stanford HAI reports that AI-specific governance roles grew 17% in 2025 and that the share of businesses reporting no responsible-AI policies fell from 24% to 11%. The same report identifies knowledge gaps, budget constraints and regulatory uncertainty among reported barriers to responsible-AI implementation. These findings suggest governance is becoming more visible, while capability and implementation constraints remain. They do not show that every organization has effective oversight. Stanford HAI’s 2026 AI Index, Responsible AI chapter
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What remains uncertain about AI and the workforce?
Workforce figures in Stanford HAI’s 2026 AI Index are expectations, not observed future outcomes. The report says 32% of surveyed organizations expected AI to reduce their workforce in the coming year; 43% expected little or no change, 13% expected an increase and 12% did not know. These results describe a range of organizational expectations, not the number of jobs already lost or a forecast that applies equally to every region, industry or employer. Stanford HAI’s 2026 AI Index, Economy chapter
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsOfford also raises concerns about unequal access to AI and differences in people’s ability to use it, as well as bias, misinformation and environmental costs. His article identifies these as ethical challenges but does not quantify their scale. For leaders, they are reasons to consider who benefits, who may be disadvantaged and what checks are needed—not evidence that a specific level of harm has been measured.
What should leaders take from the shift?
AI changes the information environment in which leadership happens; it does not remove the need for judgment, trust or accountability. Leaders can use wider access to information to give teams more room to act when local knowledge and speed matter, while retaining clear authority and review where consequences or coordination demands are high. The work is to match decision rights to the situation and ensure people can test AI-assisted information before it becomes organizational action.
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