Organisational Nihilism
Updated: Jul 28
TL;DR: Risk management only works when an organisation has a shared understanding of what matters, why it matters, and how uncertainty should be managed. When people are asked to make decisions without a clear sense of their organisation’s purpose or priority, they are left to create their own meaning. The result is often conflict, inconsistency and ineffective risk management.
If nobody can explain what the organisation really values, or why one risk matters more than another, the risk team will struggle to provide challenge. AI will make this problem worse. AI makes it easier for individuals to mimic effort, and to avoid accountability for tasks they do not value, or do not want to prioritise - it facilitates "free riding". In order to stay relevant, risk teams will need to get better at identifying and challenging how "meaning" is created, communicated and validated within the organisation.
“Nihilism may seem like a strange term at first in the context of organizations, but if nihilism refers, rather broadly, to a perceived lack of criteria on the part of knowledge workers – in other words, a failure to develop good answers to the questions why should I work, how should I work, where should I work, and particularly the development of ethical criteria, then the term organizational nihilism does not seem too far-fetched”[i] Alfonso Montouri
The views and opinions expressed on this account are my own and do not reflect the official policy or position of my employer. Any content provided is for informational purposes only and should not be considered or relied upon as professional advice.
The AI genie is out of the proverbial bottle, and risk teams must adapt. AI will create huge organisational benefits, risks, and changes to the way that people. Risk teams must anticipate both the new risks that AI will introduce, and the existing risks that AI will change, influence and catalyse.
Since ChatGPT first burst into the public consciousness in late 2022, I’ve had a nagging concern about AI’s impact on how organisations operate. I have struggled to express this feeling without coming across as a luddite, or an old man shouting at progress. After stumbling across Alfonso Montuori’s definition of organisational nihilism (see above ) I can articulate my concern.
Few organisations are entirely nihilistic, but many have pockets of nihilism: business functions or processes where employees lack a clear sense of their work's relevance to organisational goals. In my experience, organisational nihilism often arises from three main causes:
Strategic neglect: Management fails to dedicate sufficient attention to an area of the business, or fail to effectively delegate responsibility for setting strategy. This is often due to a lack of management interest and / or understanding.
Busyness: Developing strategy and meaning is deprioritised as more immediate issues take precedence.
Regulation: Regulator demands may require organisations to care about issues that management would not otherwise prioritise. Management teams can respond by a) realigning their priorities, b) admitting indifference (brave!), or c) pretending to care while informally communicating the opposite. This third option generates organisational nihilism.
Organisational Nihilism matters for risk teams because risk management depends on organisational meaning. There is no universal “correct” appetite for uncertainty. Without meaning, risk appetite becomes whatever the management team happens to prefer that day. If appetite and strategy are not clearly articulated, risk teams are left to make their own judgements, default to risk avoidance, or point out inconsistencies after they have already appeared.
How organisations create meaning
Meaning is created when organisations answer “why” questions, not just “what”, “when” and “how” . Questions like: Why are we doing this? Why does it matter? Why should one objective take priority over another? Meaning can be created through formal and informal mechanisms:
Formal meaning creation includes strategy, risk appetite, formal incentives (i.e. visible rewards explicitly tied to behaviours), and task prioritisation.
Informal meaning creation can be thought of as being created by Binding and Persuasive Precedent (with apologies to the legal profession). It is created through the organisation’s historical decisions, responses to events, resource allocation, investment and informal incentivisation (i.e. less visible rewards explicitly or implicitly tied to behaviours).
A strong sense of meaning exists when informal signals support formal ones. Meaning requires consistency: strategy, resource allocation, incentives and risk appetite need to tell the same story. When they do, employees can understand the purpose of their roles, and how to make decisions that align with the organisation’s definintion of success.
How Individuals Create Meaning
People also need room to create meaning for themselves. Role satisfaction, or meaningfulness, is driven by feelings of “autonomy (that one has choices and authority over tasks), competence (a feeling of mastery), and relatedness (connection to others)”[i].
Organisations must strike a balance. Too much top-down definition of meaning can stifle individual autonomy, damaging diversity of thought. Too little control allows each team to create its own purpose, which can lead to competing priorities and contradictory strategies.
Individual “meaning-making” can be thought of as falling into one of three categories:
Constructive: aligning with organisational strategy
Destructive: conflicting with organisational strategy
Reactive: addressing a (temporary or permanent) absence of organisational strategy
Strategy is not one thing
A common mistake is to treat strategy as a single “thing”. In reality, strategy has multiple layers. Neglecting one layer can lead to a breakdown in organisational meaning.
A useful way to visualize the strategy hierarchy is:
Vision: Cultural statements and guiding principles that shape decisions. For example: “we always listen to the quietest idea”.
Grand strategy: The fundamental purpose of the organisation and the assumptions it makes about its industry, addressing big questions about business success and long-term value.
Strategy: Medium-term choices about which capabilities to build, where to invest, and how to deliver the grand strategy. Strategy supports investment decisions that will not immediately pay off.
Tactics: Short-term choices around how to win business, retain customers, protect profitability and respond to current market conditions
Operations: Tools, processes, data and systems needed to deliver the strategy and support day-to-day execution.
In practice, operations and tactics may not fit neatly into a hierarchy. They can support or constrain each other, and often influence one another.
Each strategy layer needs attention. Some companies mistakenly believe they have a strategy when they only have a one-year revenue forecast. That is not enough. A strategy based solely on tactics and operations leads to organisational nihilism.
AI as a wrecking ball
AI has no intrinsic meaning. Its output is statistical, not intentional. It enables free riding. If there is a meaning in AI output, it is derived from the human-created text the models are trained on (stole), and the questions its human operators ask it.
Despite this, AI will have a profound impact on how organisations create and maintain a coherent sense of meaning. The optimistic view is that AI frees people from drudgery, allowing them to focus on more strategic work. The pessimistic view is that we outsource the meaningful work itself, expand our to-do lists, and define productivity by the volume of output rather than its value.
How AI may be used
It is still early days, and the full impact of AI on organisations is hard to predict. What is a pretty safe bet, though, is that AI will be used in two main ways:
Constructive use: AI is used to augment processes, automate tasks, enrich data, improve information access, democratise expertise and data analysis, and reduce the tool-building costs.
(Potentially) Destructive Use: AI is used to offload the tasks that people do not care about, do not have capacity for, or cannot do.
The challenge for risk teams is that AI can make disengagement look like engagement. It will become hard to tell the difference between a team that has constructively used AI with one that has used it destructively.
Companies that already underinvest in defining strategy and appetite are unlikely to fix that problem simply by adopting AI. More likely, AI will fill the gaps and make the absence of meaning harder to see. There is an old adage that if you could apply your strategy directly to a competitor, it is not a strategy. AI will accelerate the production of these “non-strategies”.
AI and busyness
Early studies suggest that the use of AI doesn’t reduce work, it intensifies it.[ii] One eight-month study of a 200-person technology firm found that workers voluntarily took on a wider variety of unfamiliar tasks. Instead of using saved time for breaks, employees worked faster, extended their hours, broadened their responsibilities, and became exhausted.
If organisations want AI to create space for better thinking, this needs to be a conscious effort. The underlying formal and informal incentives that lead some organisations to underinvest in strategy will not disappear without intentional action.
AI and rituals of meaning
A ritual is a behaviour that carries embedded symbolism and meaning beyond its immediate output. For instance, an emerging risk paper presented to the Board does more than knowledge; it signals that the risk team is sufficiently concerned to warrant dedicated research and management consideration.
Ethan Mollick, a Wharton professor studying AI's impact on work, uses the example of a letter of recommendation to explore how AI affects workplace rituals. Crafting a strong letter takes time. The required effort signals that the professor knows the student and cares about their future career. In a post-AI world, the time cost disappears. Worse, the AI-generated letter may be better than the handwritten one, further undermining its intended signal . Mollick refers to the use of AI to offload a time consuming task as pushing “The Button”.
Organisations often use effort as a shortcut for communicating meanin, but AI weakens this shortcut. Each time “The Button” is pressed, the meaning embedded in the ritual is degraded further. This may be a good thing if it prompts organisations to develop better signals. But without a conscious rethink of how meaning is communicated and validated, AI may push organisations further towards nihilism.
AI and the end of writing as thinking
George Orwell compared writing to a “long bout of some painful illness.”[iii] That is part of its value. Writing forces us to find gaps in logic, confront weaknesses in arguments, research ideas, and imagine how a sceptical audience might respond.
The use of AI can destroy this meaning, both in its production (by pushing “The Button” and bypassing the difficult work of thinking), and in its consumption (where AI is used to summarise written content). Healthy organisational dialogue has been destroyed.
Evidence suggests that AI use can lead to intellectual atrophy. For instance, one study in Brazil found that undergraduates using AI for studying performed worse on surprise tests than those who did not use AI, even on reflective questions. Another study in Britain indicated that frequent AI use for cognitive tasks negatively correlates with critical-thinking abilities.[iv]
The challenge for risk teams
Before AI, the absence of meaning was often visible, even if politically difficult to challenge. In an AI-enabled organisation, detecting this absence may become much harder. AI can create the appearance of engagement, compliance and coherence. For example, it can help teams:
Manufacture evidence of control performance or decision-making. This doesn't need to be intentionally fraudulent: a team may justify flawed AI use as "better than nothing". Pre-AI, the evidence would just not be produced - a meaningful data point. Post-AI it is produced in a flawed way, disguising meaning.
Dodge constructive engagement with tasks deemed less valuable or low organisational prioritisation. For instance, questions about emerging risks that previously required thoughtful engagement can now be offloaded to AI.
Undermine organisational rituals that relied on effort as a signal of importance, making it harder to develop useful controls that historically monitored effort as a proxy for meaningfull activity.
How risk teams need to respond
Risk teams already understand that strategy and appetite are only useful if they guide decisions and support effective delegation. Weaknesses in strategy and appetite are already identified through the standard risk management toolset: strategic risk reviews, thematic reviews and deep dives.
In an AI world, risk teams may need to more deliberately identify and catalog informal ways of creating meaning. These informal mechanisms should be tested against formal strategy and risk appetite to identify contradictions.
AI does not create a new problem so much as intensify an old one. People have always been able to decouple from official strategy and policy, but AI makes it easier to create a polished performance of compliance. Risk teams can no longer rely on effort or output volume as evidence of meaningful work, as AI has removed the cost of producing well-presented, well-written content.
In response, risk teams will require new tools and improved stakeholder engagement to understand how meaning is created and supported at all organisational levels. Practical steps include:
Incorporate informal meaning creation activities into risk assessments.
Use thematic reviews and post-mortems to examine the quality of decision-making.
Create decision exception logs to identify repeated inconsistencies between strategy, appetite and decisions.
Treat AI-generated evidence as a prompt for deeper challenge.
Categorise control strength based on how embedded each control is in business processes, monitoring and reporting. Adapt business engagement strategies to prioritise those teams with less embedded controls.
Identify governance and oversight processes that may lose meaning as AI becomes more embedded in the organisation.
The goal for risk teams is simple: we need to help organisations to create meaning. Attaining that goals is difficult, and AI will make it harder.
I hope this blog sparks ideas and discussion. If you found it interesting, please share or connect with me on LinkedIn to contribute or provide feedback!
[i] Montuori, Alfonso. Complexity, Epistemology, and the Challenge of the Future.
[ii] Milena Nikolova, Femke Cnossen, What makes work meaningful and why economists should care about it, Labour Economics, Volume 65, 2020, 101847, ISSN 0927-5371, https://doi.org/10.1016/j.labeco.2020.101847. (https://www.sciencedirect.com/science/article/pii/S0927537120300518)
[iii] AI Doesn’t Reduce Work—It Intensifies It, by Aruna Ranganathan and Xingqi Maggie Ye, Harvard Business Review, February 9, 2026
[iv] George Orwell, Why I Write, Gangrel, No. 4, Summer 1946, https://www.orwellfoundation.com/the-orwell-foundation/orwell/essays-and-other-works/why-i-write/
[v] The End of Reading Is Here, Rose Horowitch, The Atlantic, https://www.theatlantic.com/magazine/2026/08/reading-crisis-postliterate-age/687618/
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