This article argues that the rise of AI necessitates a shift away from consensus-based decision-making in organizations. It highlights the weaknesses of consensus, particularly its impact on speed and information distortion, and proposes structural changes like 'Autonomous Scrums' to foster agility and adaptability in the AI era.
AI is bringing about an organizational reckoning. While most leaders probably agree that their organizations will need to adapt, too few are willing to admit that this will require them to abandon one of the most pervasive management principles of the past half-century: decision-making by consensus.
The companies that survive the next decade will not be those with the best algorithms or the most data. They will be those that have the courage to abandon how decisions get made. Consensus management was, in its time, a rational response to complexity. As organizations grew larger, became global and multi-disciplinary, and as knowledge work displaced physical labor, the command-and-control structures of early industrial management gave way to distributed decision-making, stakeholder alignment, and “socialized” choice. It is not surprising that large, global companies moved to distributed decision making given the geographic breadth and the earlier, asynchronous communications of the past 50 years. Allowing far-flung units to operate with some independence was a needed consequent of a pre-digital world. Consensus became the hallmark of modern organizations. There are two important weaknesses to this approach, however. The first is speed. Decisions pass through gaggles of lawyers, marketing teams, PR, investor relations, risk managers, and compliance, each of whom is incentivized to mitigate personal and organizational risk. Audacious initiatives get their edges smoothed, reaction time slows, and the organization optimizes for defensibility rather than speed. In an era of digital transparency, excepting the CEO, spreading responsibility across committees insulates leaders from the public spectacle of failure. Consensus management is the culture of calmer waters: collegial, risk-averse, and optimized for stability rather than speed. The second is that it distorts information. In our work across dozens of corporate turnarounds, the most dangerous threat to a company was often the systematic filtering of reality as information travels upward through layers of management. At each stage of the consensus process, gatekeepers filter and interpret information, and signals degrade. By the time it reaches the C-suite, information has been curated, smoothed, and stripped of the weak signals that often harbor critical clues to the optimal strategy. Leadership then makes the compounding mistake of trusting the output. The result is what we call “Success Theater”: weekly dashboards and siloed reports crafted by middle managers whose careers depend on protecting the status quo. AI pushes us into rougher waters and turns both weaknesses into critical liabilities. On the first, because it rapidly accelerates the speed at which companies can—and must—operate. And on the second, because the more AI accelerates decision cycles, the more working from filtered, degraded information—the kinds of distortions consensus management creates—turns into a critical liability. Together, they create an organization that is both slow and blind—a dangerous combination in any era, but fatal post-AI. Looking ahead, success will depend on organizational agility: the speed at which companies identify signals, make decisions, and execute. Legacy companies need to leave consensus behind and reorganize themselves around new decision-making structures and methods that are suited for the AI era. This isn’t easy and it’s understandable that leaders feel daunted by the task. CEOs of companies such as Walmart and Coca-Cola have said that the scope of the AI transition shaped their decisions to retire and pass the reins on to the next generation of leaders. But the transformation is non-negotiable. Based on our work as operators, consultants, and investors, here’s what we’d recommend leaders do now. The Architecture of Speed Leaders need to redesign their organizations around a different decision-making architecture, and to invest in advanced AI-enabled information systems. We propose two structural changes that boards and CEOs can implement immediately. And we know they work because we have used them ourselves for clients and our own companies. The “Autonomous Scrum” Most organizations give small teams permission to recommend. High-velocity organizations give them permission to act. This, of course, requires rebalancing priorities in addition to changing the decision-making architecture. Leadership needs to be willing to tolerate more frequent missteps in favor of speed and innovation and celebrate thoughtful strategies even when they fail. It also demands that leadership cede power. In our view, these tradeoffs are essential. While forward sensors and solid information systems provide much of the solution, tight teams with narrowly defined objectives produce better outcomes. As such, scrums of six to eight people—interdisciplinary, empowered, and unshackled by bureaucratic approval chains—should become the organizational default, not the exception. This approach builds on agile methodology but departs from it in a critical respect: traditional scrum teams recommend and escalate; the Autonomous Scrum owns outcomes. The shift from advisory to decision authority is critical, because every layer of approval is a form of impedance. Leadership’s role is to provide the data environment and tools in which scrums can make good ones, and to require a time-bound, evidence-backed case for any veto. We know this works. During the 2002–2006 United Airlines bankruptcy—one of the largest and most complex corporate reorganizations in U.S. history—we organized the restructuring around exactly this architecture. We didn’t call them scrums; we called them working groups. But the structure was identical: cross-disciplinary teams of six to 10 people, drawn from creditors, management, labor, and outside professionals, each assigned a discrete and consequential task. One group was charged with renegotiating 660 aircraft leases. Another tackled labor contracts across multiple unions. A third focused on real estate. A fourth was tasked with raising $2 billion in exit financing. Six groups in total, each meeting twice a month, each given substantial latitude, with the default presumption that their recommendations would be adopted. Leadership’s role was to monitor, guide, and create conditions for success, not to second-guess every decision. What made it work was not the structure alone but the shared understanding of stakes. UAL had a 75-year history, 83,000 employees, and serviced 70 million passengers annually. Failure was not abstract. Even traditional adversaries—creditors, labor, management, and outside professionals—took their responsibilities seriously. The working group structure ultimately produced the merger with Continental Airlines, an outcome that would have been impossible in a traditionally managed organization. The risks of this architecture producing a suboptimal decision pale against the paralysis of endless deliberation. AI agents equip the Autonomous Scrum with an army of savants, deep subject matter expertise, and almost instantaneous feedback on what actions are producing positive results. The OVIS framework for decision rights When decisions are the result of consensus, accountability is illusive. We’ve had success with a different approach: the OVIS framework. One person Owns the decision. Two or three people Veto or Influence it. Everyone else Supports the outcome. This is not a subtle refinement of consensus – it is its deliberate replacement. OVIS eliminates the ambiguity about who decides, which is the primary mechanism by which consensus culture perpetuates itself. When accountability is diffused, speed is impossible. In practice: Veto authority belongs to one or two decision-makers who can formally block a choice. Influence belongs to those whose input the Owner must consider but whose approval is not required. The distinction matters enormously; conflating the two recreates the consensus problem OVIS is designed to solve. The Owner is accountable for the outcome—and that accountability is what makes the system work. Time and again, we have seen the power of this OVIS framework to clarify roles, accelerate decision making, and eliminate—or at least materially reduce the pocket veto, which some colleague employs out of an individual belief that “this must stop.” We are fans of the management aphorism usually attributed to Jeff Bezos: “Disagree and Commit.” The OVIS framework gives a structure—defined roles and responsibilities—to disagreeing. And then hands the decision power to the “O,” compelling everyone else to commit to that decision. AI will not produce the decision, but it can marshal the information, simulate the outcome, and challenge assumptions. There must be a human in the loop to minimize mistakes, recognize hallucinations, and ensure the application of common sense. At least today. What Boards Must Demand While the CEO and her management team must execute these changes, it is unlikely to happen without a Board of true believers—devoid of the discomfort of radical mandates. Most incumbent CEOs are loath to pivot abruptly, to take on the team they painstakingly built, or to remove those that can’t adapt. It is natural to be patient, to allow time, to hope. But AI will tolerate none of this and competitors will be ruthless in seizing market share. Boards can’t continue to operate as they have in the pre-AI era. If they’re working off filtered, committee-approved reports from the C-suite they are not exercising governance—they are perpetuating the distortion field. The fiduciary duty of oversight now requires something more uncomfortable: unfiltered access to real-time signals that bypass executive summary, such as short, bounded experiments with clear success metrics and accountability. More information isn’t an invitation to meddle. The default position should be to empower the Autonomous Scrum and get out of the way, and reserve full board deliberation for major acquisitions, culture-defining hires, and ethical dilemmas. But the stakes of these decisions are incredibly high right now. At moments of industrial transformation—and AI may be the most consequential in a generation—these decisions reset organizational trajectory for years, reshaping strategy, workforce, and operations simultaneously. Get it right and it looks like genius in hindsight. Get it wrong, and without early, unfiltered signals, course corrections can come too late to matter. What boards do need to ask is: Do we have a leadership team capable of deciding and executing at this pace? This question is a survival assessment. Consensus is the hallmark of the status quo, and the status quo is the most dangerous place to stand during a period of exponential change. The executives who thrive in this environment will share a particular disposition. They will be comfortable— even excited—making consequential decisions on incomplete information. They will trust signals over instinct, speed over process, and small teams over consensus. They will demand a world of continuous feedback loops and recognize that the greatest sin is not making the wrong call but making no call at all. This is, at its core, a test of character rather than capability or intellect. We once encountered a CEO mid-turnaround who argued, with genuine conviction, that his talents were better suited for growth than crisis. “That’s like being a peacetime general,” we told him. He was replaced the next day. The anecdote is instructive not because of its outcome, but because of the psychology it reveals; even as the organization was failing, the leader’s mental model remained anchored to a world that no longer existed. The technology required to compete at AI speed already exists and will only grow more pronounced. What seems scarce is the genuine willingness to dismantle the organizational structures and leadership cultures that no longer serve. The Great AI Reorganization will not be won by companies with the most compute or the deepest pockets. It will be won by those with the clarity to see what the new environment demands, and the courage to build it.
AI Decision-Making Consensus Management Organizational Agility Autonomous Scrum
United States Latest News, United States Headlines
Similar News:You can also read news stories similar to this one that we have collected from other news sources.
Signed: Emei Inks With Atlantic/Nice Life; Sara Bareilles Jumps to CAALos Gemelos de Sinaloa inked a management deal with Street Mob Records, which will oversee the duo’s management and strategy as they enter their next career phase.
Read more »
Ripple Expands Its Digital Asset Solutions With Its Treasury Management System LaunchRipple reveals another major innovation following the launch of the first treasury management system with on-chain digital asset capabilities.
Read more »
Scientists may have found the 'holy grail' of pain management: a non-addictive opioidToday's Video Headlines: 04/09/26
Read more »
Central Group Promotes Rinascente CEO to European Role, Beefs Up Management RanksRinascente CEO Pierluigi Cocchini has been promoted to a European role by Central Group, the Thailand retail, real estate and hospitality conglomerate.
Read more »
End E-Check by end of year is Northeast Ohio lawmaker’s goalEnd E-Check by the end of the year the goal of a Northeast Ohio lawmaker, new data on Ohio’s air quality has supporters of ending e-check optimistic.
Read more »