Picture an executive meeting. Everyone at the table agrees that the world is more uncertain, customers less predictable and technology faster-changing. The same concepts circulate through the slides: VUCA, BANI, sometimes TUNA, RUPT and, more recently, FLUX. Yet when the meeting ends, investment decisions rest on the same assumptions, authority stays concentrated in the same place, and the strategy is filed away once more, to be reviewed next year. This imagined scene raises an important question: is our ability to describe the world developing faster than our ability to manage it?
Each of these concepts directs our attention somewhere different. They are valuable for building a shared language, making scattered observations intelligible and asking better questions. But reading them as a ranking of fashions narrows what they can contribute. VUCA did not disappear because BANI arrived. A company can operate in a volatile market, depend on a brittle supply system and struggle with its employees’ anxiety, all at the same time. Which lens we need depends on the issue in front of us.
VUCA: Four conditions, four different responses
VUCA is formed from the initials of volatility, uncertainty, complexity and ambiguity. These four conditions may look alike, but they call for different management responses. Prices swinging rapidly and not knowing why customers don’t buy are not the same problem. The first may call for room to maneuver and flexible resources; the second for better research or an experiment. Simply saying “we are in a VUCA environment” loses its explanatory power when we fail to make this distinction.
BANI: Why do strong-looking systems break?
BANI, developed by Jamais Cascio, makes another layer visible: brittle, anxious, nonlinear and incomprehensible. In other words, systems that are fragile, that generate anxiety, that do not behave linearly and that are hard to understand. Cascio’s distinction around brittle is especially important. A system can look strong from the outside, work extremely well under normal conditions, and then suddenly break at a certain pressure threshold. That is why BANI should not be read merely as a framework describing how employees feel. It invites us to think about system design and its effect on people together.
For example, concentrating all critical knowledge in one employee, all production in one plant or all technical infrastructure with one provider can make things easier in the short term. But we also need to work out what happens if that arrangement is interrupted. The management question here cannot stop at “Can we be more efficient?” The question “Which dependencies does this efficiency rely on, and what will we do if one of them breaks?” belongs on the same table. Alternative capacity and redundancy have a cost. That cost should be weighed together with the impact of a possible disruption.
TUNA: Novel situations and scenarios
TUNA, used in the Oxford scenario planning approach, points to turbulent, uncertain, novel and ambiguous conditions. Placing novelty alongside turbulence and uncertainty matters. Sometimes the problem lies less in our inability to gather more information than in the fact that what we know cannot explain the new situation. The distribution model, customer relationship or form of expertise that brought success in the past may no longer produce the same result. Rather than tying us to a single forecast, Rafael Ramírez’s scenario approach helps us think about how different futures would change our present assumptions.
The management value of scenarios goes beyond writing interesting stories about the future. Suppose the trajectory of customer demand and the availability of a critical technology are the two major uncertainties affecting our decision. When we think through four different futures along these two axes, some investments remain meaningful under every condition, while others create value only if certain developments materialize. This lets us separate the choice we make today from the choice we will make once we see a signal later. The distinction also keeps us from spreading resources at random.
RUPT and FLUX: Paradox and experience
The Center for Creative Leadership’s RUPT framework, with the terms rapid, unpredictable, paradoxical and tangled, draws attention to speed, unpredictability, paradox and entanglement. The paradox dimension in particular sits very close to managers’ daily lives. We want speed, and we also want control. We expect local teams to take initiative, and we also want to preserve corporate consistency. When we treat these tensions as problems to be solved once and for all, we can weaken one need while meeting the other. Management skill here means recognizing the value of both sides and rebalancing between them as conditions change.
FLUX, as Timothy Tiryaki uses it, highlights speed, fluidity and unexplored territory through the terms fast, liquid and uncharted. The final component appears in different accounts as either experiential or experimental. The post by Tiryaki that I draw on here uses experiential. The management implication I take from it in this article is to combine learning from experience with running controlled experiments in uncertain territory. This is an interpretation aimed at practice, not a claim that the concept comes with a single, fixed prescription.
What changes on Monday morning?
So what can a manager who knows these five lenses do differently on Monday morning? My suggestion: put one important decision back on the table. There is no need to open the company’s entire strategy for debate at once. A pending investment, a new product, an AI application or an organizational decision is enough. First, write down explicitly the assumptions the decision rests on. Then separate which data supports those assumptions, which of them are merely expectations, and which development could invalidate the decision.
Hypothetically, consider a company thinking about expanding its use of AI in customer service. The system may lower the cost per transaction while increasing repeat contacts among some customer groups. Speed may not mean resolution from the customer’s point of view. In a decision like this, tracking the usage rate alone is not enough. Outcomes such as first-contact resolution, repeat contacts, total cost to resolve and access to human support need to be assessed together. Which result would lead us to expand the application, and which would lead us to scale it back, should be clear before the pilot begins.
AI and dynamic strategy
At this point, AI can add to the leadership team’s thinking capacity. We can ask it to raise objections to the same decision from the perspective of customers, finance, operations and employees. We can have it find the weak assumptions behind an option or suggest alternatives we have overlooked. But a well-written answer is not a verified answer. Claims about sources, numbers and causality in particular must be checked. Scenarios generated by AI do not replace an assessment of probabilities. Final judgment, and responsibility for the consequences, remain with the manager.
This is where the connection I value in my AI-Powered Dynamic Strategy approach is made. Planning organizes a chosen future. Strategy chooses which future we will pursue. AI-Powered Dynamic Strategy makes that choice continuously smarter. To do so, the choice has to be accompanied by what we give up, by timing and by a disciplined review rhythm. Changing direction with every new signal and holding on to an old decision simply because it was made earlier deserve equal scrutiny.
Not knowing, or not acting?
Of course, not every delay is caused by uncertainty. Sometimes the data is sufficient but authority is unclear. Sometimes the options are clear but no one wants to let go of a project. And sometimes the reality of budget and people does not support the priorities on paper. In such cases, preparing more scenarios can simply postpone the real issue. One of the things that makes these concepts useful is the ability to draw this distinction: are we unable to decide because we don’t know, or because we cannot act on what we do know?
That is why the record of an important decision should contain more than what we decided. It should also state the assumption behind the decision, what we postponed, who is responsible, and which finding would lead us to reopen it. The limits of the first trial, the resources it needs and when it will be evaluated should be explicit. That way, learning moves from a well-meant wish to a regular part of management.
At sea, knowing the name of the wind is valuable. But that knowledge only serves the voyage when it shapes decisions about course and sail. VUCA, BANI, TUNA, RUPT and FLUX also show us different signs. The choices we make after learning to read those signs are the real test of our leadership. Think of an important decision you are making today: do you know which new piece of information would lead you to reconsider it tomorrow?
Sources and a note on use
- Jamais Cascio, Human Responses to a BANI World (2023)
- Rafael Ramírez, Oxford Scenario Planning Approach, OpenLearn
- Center for Creative Leadership, Navigating Disruption With RUPT
- Timothy Tiryaki, post on FLUX (LinkedIn)
The management responses, decision flow and applications are a synthesis developed through Hakan Tetik’s approach. The concepts are used neither as stages that supersede one another nor as validated measurement scales. For FLUX, the use of “experiential” in the source post is followed; the controlled-experiment approach is an interpretation for practice.