Generative AI & Innovation Foresight
Faster, cheaper, better? What Generative AI really delivers in innovation foresight
Why innovation foresight matters more than ever
Organizations today operate in increasingly volatile and uncertain environments. Markets shift rapidly, technologies evolve continuously, and customer expectations change faster than traditional planning cycles can handle.
Innovation foresight helps organizations:
- Detect emerging trends early
- Explore future market developments
- Build strategic resilience
- Identify new innovation opportunities
Yet despite its importance, many companies, especially SMEs, avoid structured foresight because it is resource-intensive, expensive, and time-consuming.
Generative AI appears to offer a solution. But can it truly deliver strategic-quality foresight?
The research setup
To answer this question, the authors Niclas Kröger, Dorothée Stadler, Antje Wild, Hanna Brehm und Carina Volk-Schor conducted a comparative innovation foresight project for a multinational garden equipment manufacturer.
We compared:
- A professional human expert foresight process
- AI-supported foresight processes using:
- ChatGPT
- Claude
- Microsoft Copilot Premium
- Microsoft Copilot Standard
The research evaluated four stages of innovation foresight:
- Trend Identification
- Scenario Axis Selection
- Scenario Development
- Strategic Innovation Field Generation
Independent foresight experts evaluated all outputs blindly using standardized quality criteria.
Key finding #1: AI performs surprisingly well in trend identification
One of the clearest findings from the study was that AI achieved quality parity with human experts during trend identification.
AI-generated trends scored similarly on:
- Strategic relevance
- Uncertainty
- Trend maturity
- Dissemination speed
This makes sense because trend identification is highly analytical and pattern-based, areas where large language models excel.
What this means for companies
Organizations can use AI to:
- Scan large information volumes rapidly
- Accelerate horizon scanning
- Reduce manual research effort
- Lower foresight costs dramatically
For companies with limited foresight resources, this can fundamentally democratize access to strategic innovation work.
Key finding #2: Humans still dominate scenario development
The most significant differences appeared during scenario development.
Human experts strongly outperformed AI in:
- Novelty
- Organizational specificity
- Strategic differentiation
- Comprehensibility
- Visualization quality
The strongest gap emerged around originality and contextual relevance.
Why AI struggles here
Generative AI systems are trained on existing data and dominant patterns. As a result, they tend to:
- Produce plausible but conventional futures
- Reinforce existing industry assumptions
- Generate less differentiated scenarios
Human experts, in contrast, bring:
- Tacit organizational knowledge
- Strategic intuition
- Creative reframing
- Cross-functional judgment
This confirms the idea of a “jagged technological frontier”: AI performs exceptionally well on some tasks while underperforming on others that appear similarly complex.
Key finding #3: AI again reaches parity in innovation field generation
Interestingly, AI regained parity during the final stage: deriving strategic innovation fields.
Despite weaker scenario development, AI-generated innovation fields were evaluated similarly to human outputs regarding:
- Strategic business fit
- Consumer relevance
- Innovation potential
- Novelty
This suggests that AI-generated scenarios may already be “good enough” to support downstream innovation opportunity identification.
The most striking result: massive efficiency gains
Beyond output quality, the efficiency gains were extraordinary.
AI reduced:
- Time by 98.70%
- Cost by 99.27%
This has major implications for:
- SMEs
- Innovation teams with limited budgets
- Organizations lacking dedicated foresight functions
AI can make strategic foresight significantly more accessible.
The emerging role of human-AI collaboration
The study does not suggest that AI should replace foresight experts.
Instead, it points toward a hybrid collaboration model.
AI is strongest at:
- Data-heavy analysis
- Pattern recognition
- Trend clustering
- Rapid synthesis
Humans remain essential for:
- Creative provocation
- Strategic interpretation
- Organizational alignment
- Sense-making
- Scenario storytelling
The future of innovation foresight is likely neither fully human nor fully AI-driven.
It is collaborative.
What this means for innovation leaders
Organizations should rethink how they structure foresight activities.
Recommended approach
Use AI for:
- Early-stage trend scanning
- Signal detection
- Initial opportunity mapping
- Research acceleration
Use human experts for:
- Scenario workshops
- Strategic interpretation
- Creative reframing
- Decision alignment
- Organizational adaptation
This creates both efficiency and strategic depth.
Final takeaway
For us at HYVE, the key question is therefore not whether AI will replace foresight experts, but how human expertise and applied AI can complement each other most effectively.
While AI can accelerate research, pattern recognition, and synthesis, successful foresight still depends on collective sense-making, stakeholder alignment, and the ability to translate future insights into strategic action.
This is where human facilitation, creativity, and organizational understanding remain critical, and where we see the future of foresight practice evolving.
