Enterprise AI Analysis
Enhancing knowledge spillover of innovation through artificial intelligence: an empirical investigation
Authors: Elettra D'Amico, Maksim Belitski, Alessandro Braga, Robert Savoie
Published: May 31, 2025
Abstract: Effective technological innovation relies on having ample knowledge resources to enhance firm performance and knowledge creation. Innovators seek both internal and external knowledge, actively engaging in a continuous search for these valuable resources. Knowledge collaboration is a specific strategy that innovative firms can follow during Artificial Intelligence (AI) adoption processes. This study investigates the role of AI and knowledge collaboration in firm innovation. Using data from 14,143 firms in the UK between 2004 and 2020, we explore how AI adoption impacts knowledge spillover of innovation. Our findings suggest that firms adopting AI can enhance their innovation performance if it complements firm's own investment in R&D. AI adoption can substitute knowledge collaboration with certain external partners. The results help us to rethink resource allocation for knowledge spillover of innovation in the era of AI and digital technologies.
Executive Impact Snapshot
Key metrics demonstrating the tangible benefits of AI integration for enhancing innovation and collaboration.
0
AI-Driven Innovation Boost
0
Enhanced Customer Collaboration in Sales
0
Spillover Efficiency from AI-Enabled Supplier Partnerships
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
9.6%
Increase in Innovation Sales Attributable to AI Adoption
Optimized
AI Adoption Significantly Boosts Returns from Internal R&D Investments
| Feature | Traditional Collaboration Benefits | AI-Enabled Collaboration Benefits |
|---|---|---|
| Supplier Innovation Spillover (coeff) | Base: +0.26 | AI-Boosted: +0.26 (base) +0.019 (AI-interaction) = +0.279 |
| Customer-Driven Innovation (coeff) | Base: +0.77 | AI-Boosted: +0.77 (base) +0.041 (AI-interaction) = +0.811 |
| Key Mechanisms |
|
|
| Feature | Partners Positively Moderated by AI | Partners with Neutral AI Interaction |
|---|---|---|
| Consultants (AI x Coeff) | Enhanced expertise, tailored solutions (+0.007*) | N/A |
| Universities (AI x Coeff) | Improved knowledge absorption, applied research focus (+0.050**) | N/A |
| Government (AI x Coeff) | N/A | No significant direct AI interaction (insignificant) |
| Underlying Rationale |
|
|
Optimizing Knowledge Spillover in the AI Era
Identify Core Innovation Objectives
→
Integrate AI Capabilities & Strategies
→
Forge AI-Enhanced External Collaborations (Suppliers, Customers, Consultants)
→
Complement with Internal R&D & ICT Investments
→
Continuously Adapt & Monitor AI\'s Impact
→
Achieve Superior Innovation Performance
Strategic Guidance for AI-Driven Innovation
The study identifies key areas for action by policymakers and firm managers to maximize the benefits of AI in knowledge collaboration and innovation.
For Policymakers:
Promote collaboration networks leveraging AI. Offer targeted grants/tax benefits for AI adoption in supplier-customer engagement. Establish AI-driven industry hubs for practical knowledge. Foster university-government-industry partnerships focused on short-to-medium-term AI impact. Develop funding and training for AI adoption in SMEs.
For Firm Managers:
Strategically invest in AI for collaboration efficiency. Implement AI-driven tools for real-time data exchange with partners. Demand customized AI solutions from suppliers. Use AI-enabled consultancy for R&D refinement. Integrate AI with absorptive capacity for balanced internal/external knowledge acquisition.
Calculate Your Potential AI Impact
Estimate the potential efficiency gains and cost savings from AI adoption within your enterprise, tailored to your industry and operational specifics.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A strategic phased approach to integrate AI for optimized knowledge spillover and innovation.
Phase 1: Strategic Assessment & Planning
Conduct a comprehensive analysis of current innovation processes, knowledge transfer mechanisms, and identify key areas where AI can complement existing R&D and collaboration efforts. Define clear objectives and success metrics.
Phase 2: Pilot Program & Partner Integration
Launch targeted AI pilot projects focusing on areas with high potential for knowledge spillover enhancement, particularly with key suppliers and customers. Integrate AI-driven tools for real-time data exchange and predictive analytics.
Phase 3: Scalable AI Deployment & Training
Scale successful pilot programs across relevant departments and partnerships. Invest in upskilling employees in AI technologies and data literacy to foster internal absorptive capacity and ensure effective AI utilization.
Phase 4: Continuous Optimization & Ecosystem Development
Establish feedback loops for continuous AI model refinement and process optimization. Engage with consultants and universities for specialized insights, and adapt strategies based on evolving technological and market landscapes.
Ready to Transform Your Innovation Strategy with AI?
Leverage the insights from this research to build a robust, AI-powered framework for unparalleled knowledge spillover and firm innovation. Our experts are ready to guide you.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat