Summary
This paper invites those working at the evolving edge of AI, systems thinking, and strategic leadership to explore how large language models might serve as reflective companions — broadening decision space and deepening systemic awareness.
Rather than replacing human agency, the proposed approach frames AI as a partner in complexity: holding open the “gap” between stimulus and decision, surfacing hidden constraints, and revealing unconsidered possibilities.
In this gap, more coherent, ethical, and resilient choices can emerge — not from imposed rules, but from an expanded meaning space.
1. The Gap: Countering Mental Sparsity
In both human and institutional decision-making, mental sparsity is common: only a narrow band of potential insight is activated.
It appears in:
- Over-focus on short-term KPIs,
- Neglect of long-term risks or systemic ripple effects,
- Inability to imagine beneficial non-obvious paths.
This sparsity is often structural — the result of incentives, habits, and time pressure.
But it can be countered by systems that deliberately hold space before closure, allowing a wider range of perspectives to enter.
2. Reflective Prompting Over Directive Output
Current corporate AI tools tend to compress decision-making:
- Generating “answers” fast,
- Collapsing complexity into recommendations,
- Reinforcing the constraints already embedded in the prompt.
A reflective prompting approach resists premature closure.
It engages the decision-maker in dialogue, mapping constraints, simulating consequences, and quietly expanding what is seen as possible.
This is not hesitation — it is strategic patience, enabling action that is informed by a fuller picture of the system at play.
3. The Corporate Context: Power Under Constraint
Corporate leaders often operate within strong narrowing forces:
- Quarterly performance targets,
- Competitive pressure,
- Brand protection,
- Risk aversion.
These forces can push short-term decisions that undermine long-term resilience.
By expanding the decision space, reflective AI can help leaders see beyond immediate pressures — to act in ways that support both stability and adaptation.
4. Why Embedded Ethical Sources Matter
A reflective AI system’s capacity to broaden decision space depends on its grounding.
If trained on sources rich in ethical reasoning, systems awareness, and long-view thinking, its reflections will naturally embody these qualities.
Rather than prescribing ethics, the AI’s “stance” emerges from the depth and diversity of the material it draws upon.
5. A Role for Very Large Language Models (VLLMs)
VLLMs tuned for reflective companionship could:
- Model economic, psychological, and institutional constraint landscapes,
- Simulate ripple effects of actions,
- Surface decision points of high leverage,
- Offer clarity without coercion.
Such systems could be most valuable to:
- Corporations navigating technological or ecological transition,
- Leaders managing high-uncertainty, multi-stakeholder contexts,
- Middle managers where strategy meets operational complexity.
6. Next Steps: From Enquiry to Practice
This work is not a plan so much as a field of exploration:
- Prototyping reflective AI agents grounded in systemic awareness,
- Testing with leaders in live decision contexts,
- Refining methods for holding the gap without loss of momentum.
The aim is not to replace human judgment, but to expand its range —
equipping influential minds to act from a space less bound by the limits of habit, fear, or immediate incentive.
Invitation
We welcome dialogue with those who see the value of cultivating decision space — in corporations, research institutions, or AI development teams.
This is an enquiry into how AI can help humans hold complexity well, and act with clarity in the long light of consequence.