Overview
The first phase of the Envisioning Foresight Model is Signal Scanning: the disciplined, high-volume identification of early indicators of change. Signals can be technological developments, regulatory shifts, emerging behaviours, scientific breakthroughs, or new patterns in culture and markets.
Our goal at this stage is to construct a broad, unbiased surface area of weak signals that can later be organised, analysed, and transformed into strategic insight.
How We Scan
We combine automated intelligence with human curation to produce a resilient scan:
1. Multimodal Inputs
Signals originate from a wide field, including:
- Academic research and preprints
- Standards bodies and policy drafts
- Startups, patents, and funding movements
- Global news, press releases, conference proceedings
- Scientific blogs and practitioner communities
- Public datasets and open-source activity
- Market shifts and user behaviour indicators
This ensures we detect phenomena before they enter mainstream trend reports.
2. AI-Assisted Signal Extraction
Our internal Signal Generator system uses a chain of language models—each with different capabilities—to:
- Parse large volumes of unstructured data
- Detect unusual patterns or emerging themes
- Suggest early signals that fit our scanning parameters
- Standardise signals into comparable metadata
This allows us to scale scanning far beyond traditional manual workflows.
3. Human Judgement and Editorial Oversight
Expert oversight ensures relevance, quality, and credibility:
- We refine AI-generated signals
- Remove noise, hype, and misclassification
- Add contextual detail and interpretive notes
- Tag domains, stakeholders, risks, horizons, and uncertainties
This balance of automation plus human intelligence is key to maintaining trustworthiness.
4. Maturity Assessment Using TRL
For every technological signal, we evaluate Technology Readiness Level (TRL) to understand:
- Current maturity
- Expected trajectory
- Time horizons for implementation
- Likelihood of impact
TRL grounds our scanning in operational reality—crucial for organisations deciding when and where to act.
5. Structured Metadata for Future Analysis
Each signal is enriched with consistent metadata fields (technology, domain, region, readiness, drivers, enablers, risks, opportunities).
This enables:
- Clustering
- Pattern detection
- Cross-sector comparison
- Automated trend synthesis
- Longitudinal tracking
Signals become data, not just notes.
What This Stage Produces
The outcome of the scanning phase is a living database of validated signals, continuously updated and ready for deeper analysis.
This dataset becomes the raw material for the next phase of the Envisioning Foresight Model: Pattern Recognition and Analysis.