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What does AI have to do with leadership?
Leadership has always involved coordinating people who hold different information and expertise. Dr Matt Offord, writing for The AI Journal, argues that traditional hierarchy partly addressed information scarcity: senior managers gathered information, made decisions and passed direction down through the organization. Modern information technology—and increasingly AI—can make information more accessible to people closer to the work.
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That creates an opportunity, not an automatic transformation. Teams with timely, useful information may be able to make some decisions faster and with less escalation. But AI adoption alone does not prove that organizations are becoming flatter, that decisions are better, or that managers are no longer needed. Offord’s article offers a conceptual argument about changing conditions for leadership, not a measured demonstration that AI improves organizational performance.
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Stanford HAI’s 2026 AI Index, Economy chapter, reports that 88% of surveyed organizations adopted AI in 2025 and 70% used generative AI in at least one business function. These are different measures: the first concerns organization-level AI adoption; the second specifically concerns generative AI use in a business function. The report also says AI-agent use remained in the single digits across nearly all business functions.
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The figures show widespread adoption alongside limited agent deployment; they do not establish that decision authority has shifted across organizations. They are report-level snapshots, not universal rates for every employer, sector or region. The available summary does not provide enough sampling detail to support fine-grained comparisons.
Does AI make leadership less hierarchical?
Not necessarily. If teams can access relevant information and understand how to use it, leaders may delegate more decisions to the people closest to a problem. That can improve proximity to frontline knowledge and reduce delays caused by repeated escalation. It can also increase the number of people involved in coordination, making communication noisier and more tiring.
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- Author: Bungay Stanier, Michael.
- Publisher: Page Two
- Pages: 244
- Publication Date: 2016-02-29
- Edition: 1
Offord does not argue that every organization should become flat. He says command-and-control still has a place in high-consequence, dynamic situations. A useful choice is not “hierarchy or networks” for the whole organization, but which decision structure fits a particular task and its risks.
- Distribute decisions when local knowledge matters, teams have the skills and information to act, and the cost of waiting for approval is meaningful.
- Retain clear command procedures when rapid coordination, consistent direction or high-consequence accountability outweighs the benefits of local discretion.
- Use networks with care when collaboration can help solve a problem, while assigning someone responsibility for resolving disagreement and making the final call.
Offord invokes the Battle of Trafalgar and his research in the Royal Navy as examples of decentralized command and formal and informal decision networks. His article does not provide the underlying study’s methods, date or findings in enough detail to treat these accounts as independently verified empirical evidence.
What should leaders remain responsible for?
AI can organize data and generate information, but leaders still need people to decide what that information means, whether it is trustworthy and what action is justified. 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.
That distinction matters because a fluent AI response is not the same as a verified answer. Offord warns that generative AI is “not designed to answer questions accurately, just plausibly.” Leaders should ensure that outputs are checked against reliable evidence, especially before they inform consequential decisions. Accountability should remain with identifiable people, rather than being treated as something delegated to a tool.
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How can an organization adopt AI without losing accountability?
AI adoption needs to be paired with changes to workflows, decision rights and staff capability. The World Economic Forum’s 2026 report on organizational transformation in the age of AI, drawing on more than 450 executives in its AI Transformation of Industries community, emphasizes operating-model and workflow redesign, human accountability, talent development, transparency and disciplined experimentation.
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- Map decisions before automating them. Identify who currently supplies information, who decides, who reviews the result and who bears responsibility if it is wrong.
- Set decision boundaries. Specify which tasks AI may support, which require human review and which remain reserved for an accountable decision-maker.
- Build verification into the workflow. Make it practical to inspect sources, challenge outputs and correct errors instead of assuming that a plausible response is reliable.
- Develop staff capability. Give people the skills to use tools, assess information and recognize when a decision needs escalation.
- Experiment with limits. Test a defined use case, monitor its effects on work and coordination, and adjust the process before expanding it.
Governance is not a future concern. Stanford HAI’s 2026 AI Index, Responsible AI chapter, reports that AI-specific governance roles grew 17% in 2025, while the share of businesses reporting no responsible-AI policies fell from 24% to 11%. The report also identifies knowledge gaps, budget constraints and regulatory uncertainty among reported barriers to responsible-AI implementation. These findings indicate organizational activity and obstacles, not that every business has an effective governance system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What risks should leaders weigh?
Offord identifies unequal access to AI and unequal skill in using it, bias, misinformation and environmental costs as ethical challenges. His article raises these concerns but does not quantify their scale. In practice, leaders should consider whether access and training are uneven, whether outputs can be checked for bias or error, and whether a proposed use has a clear owner and purpose.
Workforce effects are also uncertain. Stanford HAI’s 2026 AI Index, Economy chapter, reports that 32% of surveyed organizations expected AI to reduce their workforce in the coming year. Another 43% expected little or no change, 13% expected an increase and 12% did not know. These are expectations reported by organizations, not observed job losses or a prediction that applies to every workforce.
How should leaders decide between centralized and distributed decisions?
There is no universally superior structure. Offord’s argument, alongside the organizational transformation and governance themes in the World Economic Forum and Stanford HAI reports, points to practical questions rather than a single model:
- Decision speed: Would moving authority closer to the work meaningfully reduce delay?
- Information quality: Do the people acting have access to dependable data and the ability to verify AI-generated information?
- Consequences: How much harm could a mistaken decision cause, and what level of review is warranted?
- Coordination: Can teams work through a network without creating confusion about who decides?
- Readiness: Are skills, governance ownership, data foundations and budget adequate for the intended use?
These are decision considerations, not a validated scoring framework. The central leadership task is to choose who decides, with what information and under what safeguards—then revisit that arrangement as the work or risks change.
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