Organisational Nihilism

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 to produce content, and for individuals to avoid accountability for tasks they do not value, or do not want to prioritise - it facilitates "free riding".  In response, risk teams will need to find new methods for confronting and addressing organisational nihilism.

Tin Man - in need of a heart

“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 so risk teams must adapt.  AI has the potential for huge organisational benefits, huge risks, and huge disruptions to the way that people go about their work.  It is the role of risk teams to anticipate the 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 towards the end of 2022, I’ve had a nagging concern about AI’s impact on how organisations operate.  I have struggled to articulate 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 quote) I can finally explain my concern.

Few organisations are entirely nihilistic. But many have pockets of nihilism: areas of the business where people do not have a clear sense of why their work matters, or what organisational purpose they serve.  In my experience, organisational nihilism usually has three main causes:

1. Strategic neglect: the management team has failed to dedicate sufficient attention, or delegation, to an area of the business.  This is often due to a lack of management interest and / or understanding of the business area.

2. Busyness: Developing strategy and meaning is deprioritised – there are always more immediate problems to solve.

3. Regulation: Regulators typically demand that organisations care about more things that the management team genuinely cares about.  When regulation requires new behaviours, management can a) realign their priorities, b) be honest that they don’t care (brave, but carries its own risks), or c) outwardly pretend that they care, whilst informally communicating to the business that they do not care.  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 the “what”, “when” and “how” . Why are we doing this? Why does it matter? Why should one objective take priority over another?

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 history of decisions, response to events and issues, 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 the formal ones. Meaning requires consistency: strategy, resource allocation, incentives and risk appetite need to tell the same story. When they do, employees can understand what they are doing, why it matters, how to make good decisions, and how their work contributes to the organisation’s 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 therefore need to strike a balance. Too much top-down definition of meaning leaves little room for 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:

1. Constructive:  aligning with organisational strategy

2. Destructive:  conflicting with organisational strategy

3. 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 different layers. If one layer is neglected, organisational meaning can start to break down.

One useful way to think about the strategy hierarchy is as follows:

• Vision: the 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. These are the big questions about what business the company is really in and where it believes long-term value will come from.

• Strategy: the 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: the shorter-term choices around how to win business, retain customers, protect profitability and respond to current market conditions

• Operations: the tools, processes, data and systems needed to deliver the strategy and support day-to-day execution.

In practice, operations and tactics do not always sit neatly in a hierarchy. Sometimes operations support tactics. Sometimes tactics are constrained by operational capability. Often, they influence each other.

Each strategy layer needs attention. Some companies think they have a strategy when they really have only a one-year revenue forecast. That is not enough. A strategy made only of tactics and operations is a path towards organisational nihilism.

AI as a wrecking ball

AI does has no intrinsic meaning. Its output is statistical, not intentional. It is fundamentally, and structurally a nihilistic technology. 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, the use of AI will have a profound impact on how organisations create meaning, and on their ability to maintain a coherent sense of meaning.  The optimistic view is that AI frees people from drudgery so they can focus on more strategic and meaningful work. The pessimistic view is that we outsource the meaningful work itself, increase the length of our to-do lists, and start defining 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:

1. Constructive use: AI is used to augment processes, automate tasks, enrich data, improve access to information, democratise expertise, democratise data analysis, and reduce the cost of building useful tools.

2. (Potentially) Destructive Use: AI is used to offload the tasks that people do not care about, do not have capacity for, or cannot do.  This will not always be harmful: if the task was not worth doing in the first place, the impact will be limited.

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 real strategy harder to see. There is an old truism 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 time saved for breaks, employees worked faster, extended their hours, broadened their responsibilities, and become exhausted.

If organisations want AI to create space for better thinking, this needs to be a conscious act.  The underlying formal and informal incentives that lead some organisations to underinvest in strategy will not disappear without effort.

AI and rituals of meaning

A ritual is a behaviour that has some embedded symbolism and meaning beyond its immediate output.  An emerging risk paper presented to the Board, for example, does more than share information on the risk. It signals that the risk team considers the issue important enough to deserve dedicated research time, and for management to spend time considering its conclusions.

Ethan Mollick, a Wharton professor who studies the impact of AI on work, uses the example of a letter of recommendation to explore how AI impacts workplace rituals. Creating a strong letter takes time. The effort required signals that the professor knows the student and cares about their future career. In a post-AI world, the time cost largely disappears. Worse, the AI-written letter may be better than the handwritten one, further undermining the signal that the letter was supposed to represent. Mollick refers to the use of AI to offload a time consuming task as pushing “The Button”. 

Organisations often use effort as a (often ineffective) shortcut for meaning. AI weakens that shortcut. Each time “The Button” is pressed, the meaning embedded in the ritual is degraded further. This may be a good thing if it forces organisations to develop better signals. But without a conscious rethink of how meaning is communicated, protected, validated, and governed, 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 our logic, confront weaknesses in our argument, research other people’s 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 by the audience to summarise what has been written).  Healthy organisational dialogue has been destroyed.

There is a growing body of evidence that suggests the use of AI can create a sort of intellectual atrophy: the use of AI “comes at the expense of mental development. One study in Brazil determined that undergraduates who used AI for studying performed significantly worse on a surprise test than those who studied without AI. The students trailed their peers even on questions that demanded reflection and effort instead of specific knowledge. Another study of hundreds of individuals in Britain found that frequent AI use for cognitive tasks is negatively associated with critical-thinking abilities.”[iv]

The challenge for risk teams

Before AI, the absence of meaning was often visible, even if it was politically difficult to challenge. In an AI-enabled organisation, it may be much harder to detect. 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 does not have to be intentionally fraudulent: for instance, 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 that are not seen as valuable / not seen as having a sufficiently high prioritisation.  For instance, questions put to the business on emerging risks that would previously have required a thoughtful answer can now be offloaded to AI.

• Undermine organisational rituals that previously relied on effort as a signal of attention and importance.  This will make it harder to develop useful controls that historically monitored effort as a proxy for meaningful risk management activity.

How risk teams need to respond

Most 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 often identified through strategic risk reviews, thematic reviews and deep dives.

What risk teams may need to do more deliberately in an AI world is to identify and catalogue the informal ways meaning is created. These informal mechanisms should then 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. The difference is that AI makes it easier to create a polished performance of compliance. Risk teams can no longer rely on effort or volume of output as evidence that meaningful work has taken place, because AI has removed the cost of creating well-presented, well-written content.

In response, risk teams will need new tools and better stakeholder engagement to help them understand how meaning is created, understood and supported across the all levels of the organisation. 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.

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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!

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[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/