Nadya Shaznay Patel & Jawn Lim Tze-hin
Organisations often slip into an Anti-Futures Triangle where the push of feared tomorrows, the pull of nostalgic yesterdays, and the weight of present-day constraints trap them in regression. We model this decline as a five-layer Regression Spiral: Restorationists; Defenders; Reactionaries; Fatalists; and Dystopian Destroyers, accelerating ongoing systemic breakdown. We integrate Critical Design Futures (CDF) with generative AI (GenAI) to diagnose an organisation’s position on the Regression Spiral Scenario, disrupt it with AI-generated visual provocations, and design a preferred-futures portfolio linked to specific transformation projects. CDF question-starters are externalised as synthetic images that surface assumptions and broaden option sets. Governance then channels these insights into a portfolio of probes. The Diagnose-Disrupt-Design method can produce visual artefacts, option roadmaps, and governance that leaders can adopt to transform their businesses.
1. Introduction: The Paradox of Progress
In volatile conditions, many firms suffer from strategic myopia, a focus on near-term efficiencies that blinds them to emerging risks and opportunities (Catino, 2013). Two defensive reflexes dominate: (1) a push of feared futures (e.g., automation threats) and (2) a pull of pastoral nostalgia for legacy models, both compounded by the weight of present constraints. Coupled with the weight of the present, tight margins, rigid hierarchies, and linear KPIs, these forces create what we call the Anti-Futures Triangle. This shadow geometry mirrors, yet paralyses, Inayatullah’s (2008) Futures Triangle. Whereas the latter highlights the aspirational pull of desired futures, the Anti-Futures Triangle locks organisations into a defensive crouch and erodes their capacity for anticipatory learning. Recognising this darker triangle is a prerequisite for effective foresight.
Figure 1 – The Anti-Futures Triangle and the Regression-Spiral
This article pursues three objectives. First, we conceptualise the Anti-Futures Triangle (Figure 1), framing its dynamics as a five-layer Regression Spiral Scenario of Restorationists, Defenders, Reactionaries, Fatalists, and Destroyers. Second, it demonstrates how CDF thinking, augmented by GenAI, can turn latent fears and hopes into vivid artefacts that catalyse reflection. Third, it presents a three-stage approach, Diagnose, Disrupt, Design, that enables organisations to escape Anti-Futures inertia and build a preferred-futures portfolio. By integrating rigorous theory with actionable guidance, this paper contributes to debates on futures literacy and anticipatory governance while equipping leaders with practical tools to develop transformative strategies.
Our Diagnose–Disrupt–Design sequence stands in a long tradition. Lewin’s (1947) field theory and planned change model sought to create and then re‑stabilise disequilibrium for new learning (“unfreezing—change—refreezing”). Schein (1996) later reframed “unfreezing” as managed learning, disconfirmation, and psychological safety to reduce learning anxiety. Argyris & Schön’s (1978) double‑loop learning and Senge’s (1990) “learning organisation” emphasised surfacing governing variables and redesigning systemic structures. We treat “refreezing” pragmatically as re‑gelling, institutionalising new practices while keeping them adaptive under turbulence (Cummings et al., 2016).
2. Mapping the Territory
This paper juxtaposes two triangular heuristics that diagnose why organisations either move imaginatively forward or remain strategically stuck: Inayatullah’s (2008; 2023) Futures Triangle and the inverse, our Anti-Futures Triangle. Together, they form the conceptual model for the three-stage approach developed.
2.1 The Futures Triangle
The Futures Triangle locates change at the intersection of three forces: weight of the past, push of the present, and pull of the future (Inayatullah, 2008; 2023). The weight includes entrenched mental models, sunk investments and regulatory legacies that narrow perceived options. The push comprises immediate drivers, technological acceleration, demographic shifts, and supply-chain shocks that press systems toward adaptation. The pull is supplied by compelling visions that mobilise resources and legitimise experimentation. Mapping the three vectors reveals “corridors of possibility” where push aligns with pull, as well as friction zones where the past overwhelms aspiration, hence its popularity across policy, urban planning and corporate strategy. Milojević and Inayatullah further developed a Change Progression Scenario method along the triangle’s right flank (Milojević, 2002; Inayatullah, 2020). It presents four scenarios: no change, marginal change, adaptive change, and radical change, based on their distance from historical inertia and aspirational pull. The ladder enables decision-makers to judge how far current trajectories sit from preferred futures, a logic we mirror, in reverse, for the Anti-Futures Triangle (Figure 2).
Scholars have long linked organisational learning and foresight. Burke (2002) fuses action learning with futures work to connect desired futures with present experiments and governance routines, arguing for iterative learning loops within organisations. Others add empirical evidence that scenario planning increases perceived organisational agility across multiple firms (Chermack et al., 2019). Mecartney (2022) demonstrates how imagination practices can be operationalised for transformation, translating imaginative exploration into strategy‑shaping tools. Farrow (2020) synthesises insights from AI‑futures workshops to derive leadership and team-adaptation principles. Yazici (2024) explicitly employs the Futures Triangle to scaffold assumptions during industrial transformation, and Abdullah et al. (2024) reflect on the usefulness and limits of scenarios in higher‑education settings for developing decision agility.
Figure 2 – The Anti-Futures Triangle and Original Futures Triangle
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- The Anti-Futures Triangle and the Regression-Spiral
Our fieldwork shows that many organisations never reach the aspirational apex, instead spiralling into a Regression Spiral Scenario, an Anti-Futures Triangle whose vertices drag them backwards rather than forward.
- Push from undesirable futures: hyper-visible threats (e.g., job-killing automation) trigger defensive postures and paralysis rather than preparedness.
- Pull toward the past: executives romanticise a “golden age” of stable business models, reinforcing restoration over innovation.
- Weight of the present: rigid KPIs, sunk costs and hierarchical routines punish radical experimentation.
Comparatively, Figure 3 shows the oppositional dynamics of the Futures Triangle and the Anti-Futures Triangle.
Figure 3 – Futures Triangle vs Anti-Futures Triangle: Key oppositional dynamics
The three forces amplify one another – fear intensifies nostalgia, which legitimises further investment in obsolete assets, and new commitments deepen the present constraints. To gauge regression depth, we invert the change progression ladder, yielding five layers that map mindsets along the triangle’s right edge (Figure 4).
Figure 4 – Regression-Spiral ladder for the Anti-Futures Triangle
This people-centred ladder grades regression from the mild Reversion of Restorationists to the terminal Doom Spiral of Destroyers, mirroring, in negative, the visionary ascent of reformists, progressives and dreamers in the Change Progression Scenario.
To illustrate, we present a hypothetical company exhibiting a Stasis behaviour.
Aegis, a regional insurer, had just weathered a minor regulatory inquiry into its AI-assisted underwriting. No fines were issued, but Compliance responded by freezing all change requests until controls are watertight. The COO quietly extended approval gates, requiring three executive signatures and a new “change impact memo” for even trivial experiments. Teams learned to protect what works: cloud-migration sprints were paused, a usage-based policy pilot was shelved, and vendor POCs expired. People started saying, “Let’s wait for next quarter.”
Here, process sandbags piled up: approval lead times for even small pilots stretched, and a monthly change board green-lit fewer requests. The result was portfolio atrophy, with 5% of pilots implemented, and about 95% of spend locked into business-as-usual. Predictably, cultural drift followed. Retrospectives slid from learning to justification, and company chatter fixated on the “risk of headlines” rather than the risk of inaction.
Yet diagnosis alone is insufficient. Therefore, locating an organisation on the ladder allows foresight teams to calibrate the required intervention intensity.
3. Unfreezing Organisational Imagination: Critical Design Futures (CDF)
Building on classic organisational learning scholarship, we use “unfreezing organisational imagination” in Schein’s (1996) sense of managed learning, reducing learning anxiety through structured critique and psychological safety, while leveraging CDF Question Starters as “cognitive prosthetics” to externalise and test assumptions before redesigning action (Patel & Lim, 2025).
3.1 CDF mindset and its three capacities
CDF reframes design as a problem-finding practice that deliberately unsettles taken-for-granted assumptions. Patel and Lim (2025) formalise the approach around three mutually reinforcing capacities: critical competence, design dexterity, and futures flexibility. Together, they equip teams to interrogate power and premises (critical), translate insights into provocative prototypes (design), and cycle nimbly across multiple scenarios (futures).
- Critical competence trains attention on why things are the way they are and who benefits. Question Starters such as “What is assumed?” or “Who is excluded?” foreground hidden interests and cognitive biases, reducing the risk of path-dependent solutions.
- Design dexterity mobilises an iterative make-test-learn rhythm. Prompts like “How might we…?” or “What if we looked at this differently?” stimulate divergent ideation before converging on refinements, echoing the well-documented power of “How might we” in design thinking practice.
- Futures flexibility stretches imagination beyond baseline forecasts toward wildcards and weak signals, asking “What surprises?” and “Why now?” to expand the cone of possibility.
By cycling through these capacities, CDF offers a disciplined way to unfreeze organisations stuck in the Anti-Futures Triangle: critical competence counters nostalgia, design dexterity lightens the weight of present structures, and futures flexibility converts fear of disruption into optionality.
3.2 Question Starters as Cognitive Prosthetics
Patel and Lim characterise their curated library of Question Starters as “cognitive prosthetics” that extend human sense-making. The library of three CDF mnemonic ladders provides purpose-built prompts, such as “What gives?” to delve into context, “What matters?” to explore repercussions, and “What trends?” to surface emerging issues (Patel & Lim, 2025). The Question Starters are deployed in workshop sprints or asynchronous whiteboards, each prompt seeds a text-to-image brief that GenAI will visualise in the next stage. The resulting images externalise latent possibilities, furnishing concrete reference points for critique and iteration.
To illustrate, we present a project in which stakeholders envision a 2075 scenario in which a bio-adaptive, non-invasive fetal interface enables multisensory, womb-based learning co-created with science centres, balanced by a dystopian counter-image that surfaces inequality, surveillance, and parental pressure. Figure 5 shows how structured questions scaffolded sense-making and led to image-based artefacts:
Figure 5 – Question starters scaffolded sense-making and image generation
4. Generative AI as a Foresight Accelerator
4.1 Added value for horizon-scanning and scenario work
Unlike earlier tools, Generative AI (GenAI) produces novel artefacts rather than classifying existing data. Text-to-image generation models deliver what Chen et al. (2024) term synthetic creativity.
For foresight teams, this matters in two ways. First, vivid images reduce the cognitive effort required to grasp complex futures: experimental neuroscience shows that pictorial stimulus accelerates pattern recognition and recall relative to text-only inputs. Second, GenAI shrinks the lag between environmental scanning and scenario prototyping from weeks to minutes, allowing facilitators to explore many more “what-ifs” with participants.
4.2 Iterative Co-Imagination Loops: Human Prompts, AI Visuals and Reflexive Reframing
Ravetz (2020) argues that post-normal conditions necessitate extended peer review, where knowledge emerges through an iterative process of critique. Mercer (2024) adds that GenAI hallucinations can serve as creative foils, luring participants into the liminal space between certainty and speculation. Combined, these insights underpin our three-step approach.
(i) Human prompt (problem frame): Facilitators translate CDF Question Starters into concise image briefs, e.g., “What hurts? – A metropolis where algorithmic bias governs access to healthcare.”
(ii) AI provocation (visual synthesis). The generated image offers emotional cues that surface implicit assumptions: gated clinics, biometric checkpoints, and segregated crowds.
(iii) Reflection & Re-prompting (meaning-making): Participants interrogate the image (“Whose interests dominate?”), then re-prompt or craft counter-visions such as “patient-owned data commons,” generating new visuals that shift discourse toward preferred futures.
Within the Anti-Futures Triangle, these images puncture nostalgia by exposing risks to the status quo, lighten the present-day weight by illustrating resource re-combinations, and recast feared futures as design challenges. GenAI visualisation, therefore, accelerates both cognitive and affective labour, thereby making strategic imagination a repeatable organisational capability.
5. An Integrated Process Model
Our proposed method orchestrates CDF and GenAI text-to-image into a repeatable three-step cycle that moves an organisation from Anti-Futures paralysis toward an actionable transformation roadmap.
5.1 Step 1: Diagnose the Anti-Futures Triangle
Facilitators begin by surfacing fears, nostalgia and constraints. Participants individually post sticky notes that answer calibrated Question Starters drawn from the CDF library (Patel & Lim, 2025):
- What future hurts? – captures the push of undesirable futures.
- What more of the past do we desire? – reveals the pull to the past.
- What else make change feel impossible? – maps the weight of the present.
The notes are clustered on a triangular canvas, creating a heat map of anxieties and attachments. This diagnostic mirrors sense-making routines in risk management while framing them in foresight language, making the Anti-Futures pattern visible and discussable.
5.2 Step 2: Disrupt with CDF and GenAI
Next, teams destabilise the triangle by pairing provocative prompts with generative images:
- Re-frame: Using the highest-tension clusters, facilitators craft 2-3 short prompts.
- Visualise: Text-to-image tools translate each prompt into scenes within seconds.
- Critique: Participants annotate the images, asking, What assumptions are embedded here? And Whose voice is missing?, activating critical competence.
- Prototype: Teams remix or counter-prompt images toward preferred directions, embodying design dexterity and futures flexibility.
The rapid human-AI-human loop functions as an iterative co-imagination cycle, not a source of truth, as it can hallucinate and encode bias, so it needs to be paired with human verification and counter-checks.
5.3 Step 3: Design Preferred Futures and a Transformation Roadmap
When organisational imagination is unfrozen, critical attention shifts to road-mapping:
- Envision: Select the most compelling preferred-future images and expand them into narrative user journeys.
- Back-cast: Identify milestones, capability gaps and policy enablers working backwards from the preferred future to the present.
- Portfolio build: Cluster actions into no-regret moves, flexible options and bold bets to balance certainty with experimentation.
- Governance fit: Assign owners, KPIs and review cadences, integrating change-management principles.
6. From Insight to Impact: Embedding in Business Transformation
Translating foresight into bottom-line value demands two mutually reinforcing conditions: visible executive sponsorship and measurable psychological safety. Senior leaders must weave a narrative that links confronting the Anti-Futures Triangle to competitive advantage, release discretionary funds for exploratory work, and signal curiosity rather than defensive certainty. Meanwhile, teams are encouraged to “speak the unspeakable”. To operationalise safety, leaders can administer Edmondson’s (1999) 7-item Team Psychological Safety Scale (7-point Likert; e.g., “It is safe to take a risk on this team”), taken as a baseline before the first Imagination Council and repeated after two council cycles (≈6–12 months). We expect a ≥ 0.3 SD uplift and team means trending to ≥ 5.0/7, reported at the aggregated team level to protect anonymity and used to trigger targeted coaching where scores lag. This will show that such safety is the essential precursor to collective learning and innovation.
We operationalise the indicators by illustrating them in a KPI Operations Framework that specifies definitions, collection instruments, scoring thresholds, and accountable owners to enable adoption (see Figure 6).
Figure 6 – A KPI Operations Framework
Hypothetically, a C-suite council chaired by the CFO meets quarterly. It reviews a short pack: three GenAI artefacts with one-page briefs and the current Regression-Spiral level. Each proposal gets a brief pitch, then a live score on five simple tests: strategic relevance, learning value, reversibility, ethics or safety, and portfolio fit. The council greenlights only time-boxed, reversible probes with pre-agreed scale criteria, e.g., Option-Sprint Engine to cut pilot lead time to ≤6 weeks. Results and reasons are published as one-page learning memos. By the next cycle, the council expects IQ to go up, signalling movement out of Stasis.
7. Conclusion
We name the Anti-Futures Triangle with a Regression Spiral to map organisational backsliding, where fear, nostalgia, and present-day inertia reinforce one another. Our Diagnose–Disrupt–Design method uses CDF + GenAI image artefacts to surface assumptions and convert foresight into time-boxed portfolio probes, tracked by IQ, OPD, and a cultural-shift index. While comparative studies and longitudinal metrics will refine validity, the approach is ready for piloting: a practical, repeatable cadence to prevent regression and embed futures literacy in turbulent times.
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