Client
Liminal Discovery / L-I-F-T
L-I-F-T
Liminal integral foresight tool
Role
Co-founder and head of strategic design and foresight systems, responsible for the conceptual architecture, foresight methodology, human-AI collaboration model, decision infrastructure, and product strategy behind L-I-F-T.
Led the translation of futures thinking into an operational, AI-assisted platform combining signal detection, structured sense-making, scenario exploration, and traceable strategic decision support.
Worked across strategic design, service design, workflow architecture, research synthesis, and AI-enabled systems orchestration.
Context & problem
Organisations increasingly invest in foresight, AI-assisted intelligence, and trend analysis, yet struggle to transform fragmented information into shared strategic action.
Most existing approaches remain episodic: consultant reports, workshops, dashboards, or disconnected AI prompts that fail to create continuity, organisational memory, or transparent decision rationale.
Research with foresight practitioners, strategists, NGOs, innovation teams, and transformation leaders revealed recurring problems around information overload, weak traceability, fragmented workflows, and overreliance on external consultants.
Approach
Led qualitative research, competitive analysis, and strategic framing across foresight systems, AI-assisted decision tools, and organisational workflows.
Designed the core L-I-F-T methodology combining:
- purpose and hypothesis definition
- STEEP-based signal gathering
- AI-assisted signal detection
- structured sense-making
- human-reviewed interpretation
- collaborative scenario exploration
- traceable strategic decision-making
Co-developed a modular foresight workflow architecture integrating live ingestion pipelines, review systems, signal memory, observatory views, and human-in-the-loop AI reasoning.
Defined the product's responsible technology principles around transparency, interpretability, human agency, and collaborative intelligence.
Outcome
Developed L-I-F-T into a fully testable working prototype for AI-assisted foresight and strategic sense-making.
The system now supports live signal ingestion, structured review workflows, cloud-based deployment, search-based intelligence gathering, observatory interfaces, and longitudinal signal analysis.
Positioned L-I-F-T as a hybrid alternative to consultant-led foresight, generic AI chat interfaces, and backward-looking BI systems by combining automation with human-centred interpretation and decision traceability.
Early validation research showed strong resonance around transparency, continuity, collaborative intelligence, and traceable decision-making across public, nonprofit, and commercial sectors.