Ahmed Sidky’s agile mindset model (2014) is clean, logical, and directionally correct. It is also widely taught. The model describes a continuum from mindset to values, values to principles, principles to practices, practices to frameworks such as Scrum, Kanban and SAFe. The arrow runs left to right. It teaches well and accepts easily.
Sidky’s model, however, is incomplete. A linear sequence implies causation: get the values right at the start, and the practices will follow. That logic is exactly what allows a program, a team, a project, to declare itself “agile”, install a framework, rename the roles and the meetings, and continue operating exactly as before, all while genuinely believing the transition is underway. Practitioners sometimes call this pattern “wagile” — waterfall execution dressed in agile vocabulary.
Decision speed matters more than methodology
The case for genuine agile practice is well documented.
The Standish Group’s CHAOS studies have tracked the outcomes of more than 50,000 technology projects across a quarter-century. In the 2020 dataset, agile projects achieved a 42% success rate against waterfall’s 13%. Agile projects failed outright at 11% against waterfall’s 59% (The Standish Group, 2020). The gap widens as project size increases; medium and large programs show the most dramatic divergence. A 29-point success gap and a 48-point failure gap are structural differences, not statistical noise.
McKinsey’s 2021 global survey of 2,190 respondents found that highly successful agile transformations delivered around 30% gains in efficiency, customer satisfaction, employee engagement and operational performance, and made organisations five to ten times faster (Aghina, Handscomb, Salo & Thaker, 2021).
The mechanism that converts governance structure into delivery outcome is decision latency: the time between a decision becoming necessary and the decision being made. The Standish Group’s 2018 CHAOS report identified decision latency as the single largest determinant of project success across its dataset, outweighing methodology choice. A project that generates one decision for every $1,000 in labour cost, with each decision escalated through three or four management layers, accumulates overhead that appears nowhere in the plan. It shows up as slower delivery, more rework, and teams waiting rather than working (The Standish Group, 2018). Methodology is secondary; governance conditions are primary.
The distinction the research surfaces, and that practitioners rarely make clearly, is this. The performance gap is not between agile and waterfall. It sits between genuine agile practice and everything else, including agile theatre performed with commitment.
Waterfall governance rewrites your team's values
The ZXM agile mindset model describes something more accurate than the Sidky continuum. Instead of a left-to-right flow from mindset to practices, it maps two hemispheres that must function together. The left hemisphere is logical structure: demonstrating agility through empiricism, managing the system of work rather than the people, optimising the flow of value, building alignment and synchronisation across teams, holding customer centricity at the centre of decisions. Beneath those actions sit the frameworks that give them operational form: Scrum, Kanban, Lean, XP, Nexus, Scrum@Scale, LeSS, SAFe. The right hemisphere is social-emotional. It contains the four values of the Agile Manifesto (Beck et al., 2001) and the twelve principles that follow from them, and between values and principles a layer Sidky’s model omits entirely: ethics, psychological safety, self-management, and respect for people.
The relationship between the two hemispheres runs in both directions. Festinger’s (1957) formulation of cognitive dissonance established that the relationship between held values and observable behaviour is bidirectional. People change their stated values to match their behaviour almost as readily as they change their behaviour to match their values. In organisational contexts the consequence is direct: a team operating under waterfall governance, with fixed scope, fixed budget, and escalation chains, will internalise the values that waterfall produces. The agile values on the wall will not survive contact with the operating model.
That is the structural explanation for the behaviours the original post documented precisely. “Scrum is too hard, so we’ll just say we’re being agile.” “We have a pragmatic approach so we take what works, even though we have no real experience to base that judgement on.” “Being agile is somehow superior to doing agile, so I’m better than you.” “Agile has too many meetings, so I’ll use that as an excuse not to attend Sprint Planning or the Daily Scrum.” These are not character failures in the individuals expressing them. They are the predicted output of organisations running the left hemisphere with the right hemisphere stripped out.
Eloranta, Koskimies and Mikkonen (2016), in their empirical study of Scrum anti-patterns, found that the most harmful deviations are the ones that preserve the observable form of Scrum events while removing the authority and conditions those events depend on. A Sprint Review that cannot change direction. A Sprint Retrospective whose improvement actions require approval that never arrives. The events remain on the calendar. The decision-making they were designed to enable does not.
Declaring agility stops teams becoming agile
West, Gilpin, Grant and Anderson (2011) named the dominant pattern at Forrester Research as water-Scrum-fall: agile practices layered over waterfall governance, producing neither the predictability of waterfall nor the responsiveness that agile is designed to create. Dikert, Paasivaara and Lassenius (2016) reviewed 52 publications covering 42 industrial cases of large-scale agile transformation. Their finding: reverting to old structural patterns while maintaining surface adoption was the most consistently reported failure mode, including in well-resourced transformations led by experienced practitioners.
The psychological mechanism that makes this pattern so durable comes from Gabriele Oettingen’s research on motivation. Across her studies, the more positively people imagined achieving a goal, the less effort they put in and the less they actually achieved (Oettingen, 2014). Programs tend to behave the same way. A program that believes it is agile is less likely to examine the concrete actions those practices require or the obstacles preventing them. The declaration of agility becomes a substitute for the work of becoming agile.
Alami and Krancher (2022) studied 39 Scrum practitioners across multiple organisations and published their findings in Empirical Software Engineering. The social antecedents most reliably associated with quality outcomes were collaboration, psychological safety, accountability, and transparency. These are exactly the conditions that fail to materialise when the right hemisphere is absent. Their specific failure conditions follow the same pattern: inconsistent implementations, cultural constraints, and inaccessibility of end-users. In each case the left hemisphere is in place. The right hemisphere is not.
Teams adapt when they can decide
The teams that get the results the research describes are rarely the ones with the most mature frameworks or the most comprehensive training programs. They are the ones in which both hemispheres are operating, and decision latency is where the difference shows first.
Moe, Dingsøyr and Dybå (2010), in a nine-month field study of a Scrum team published in Information and Software Technology, found that delegating decision authority to the operational level increases the speed of addressing problems and adapting to changing conditions. The mechanism is self-management, which the right hemisphere of the ZXM model names explicitly and which Sidky’s linear sequence omits. Self-management is also the layer that agile theatre most reliably destroys. When an organisation installs Scrum frameworks but routes every substantive decision upward, it has produced the left hemisphere with the right hemisphere missing. The teams become faster at executing tasks. Their capacity to adapt remains unchanged.
An executive who genuinely wants adaptive teams — teams that respond to new information rather than defending an existing plan — is not asking a methodology question. They are asking whether their governance model has placed decision authority where the information actually lives. That question has three components, each corresponding to a condition on the right hemisphere.
- Transparency: Is the work visible enough that the people doing it can see what is actually happening, rather than what the reporting cycle says is happening?
- Customer centricity: Is feedback from the people the work is for arriving in time to shape what is built, or arriving after scope is locked?
- Distributed decision-making: Do teams have the authority to act on what they learn within a Sprint, or does action require approval that arrives after the moment has passed?
These are governance decisions, and they sit with the executive rather than the delivery teams. Frameworks and practices — the left hemisphere — function as designed when the right hemisphere conditions are in place. Without those conditions, what the executive gets is water-Scrum-fall with better vocabulary: agile theatre performed with commitment, producing outcomes indistinguishable from the waterfall it replaced.
The executive who wants genuinely adaptive teams does not need to choose a better framework. They need to examine whether the governance decisions they have already made have made adaptability structurally possible.
References
- Aghina, W., Handscomb, C., Salo, O., & Thaker, S. (2021, May 25). The impact of agility: How to shape your organization to compete. McKinsey & Company.
- Alami, A., & Krancher, O. (2022). How Scrum adds value to achieving software quality? Empirical Software Engineering, 27(165).
- Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R. C., Mellor, S., Schwaber, K., Sutherland, J., & Thomas, D. (2001). Manifesto for agile software development.
- Dikert, K., Paasivaara, M., & Lassenius, C. (2016). Challenges and success factors for large-scale agile transformations: A systematic literature review. Journal of Systems and Software, 119, 87–108.
- Eloranta, V.-P., Koskimies, K., & Mikkonen, T. (2016). Exploring ScrumBut — An empirical study of Scrum anti-patterns. Information and Software Technology, 74, 194–203.\
- Festinger, L. (1957). A theory of cognitive dissonance. Row, Peterson and Company. (Reissued 1962, Stanford University Press)
- Moe, N. B., Dingsøyr, T., & Dybå, T. (2010). A teamwork model for understanding an agile team: A case study of a Scrum project. Information and Software Technology, 52(5), 480–491.
- Oettingen, G. (2014). Rethinking positive thinking: Inside the new science of motivation. Current.
- Sidky, A. (2014). The agile mindset [conference presentation]. Agile Alliance conference.
- The Standish Group. (2018). CHAOS report: Decision latency theory — it is all about the interval. The Standish Group.
- The Standish Group. (2020). CHAOS report: Beyond infinity. The Standish Group.
- West, D., Gilpin, M., Grant, T., & Anderson, A. (2011). Water-Scrum-fall is the reality of agile for most organizations today [technical report]. Forrester Research.