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Optimizing Evidence Synthesis with AI: Practical Implications for Governments and Businesses

Updated: Aug 19

In an era of information overload, systematic reviews remain essential for informed decision‑making, but traditional methods are increasingly impractical. Fabiano and colleagues (2024) highlight how artificial intelligence (AI), including large language models, can drastically enhance efficiency and quality across key stages: framing the question, screening the studies for relevance, conducting a quality assessment, gathering the data, and interpreting it for recommendations and actionable steps.


Because evidence continually evolves, there is growing adoption of living evidence - systematic reviews that are continuously updated to incorporate new and relevant information as it emerges.


The review process enabled by select AI tools:


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Note: These tools are not exhaustive and may not be relevant for all reviews.


Practical use for governments:

  • Faster policy cycles: AI-accelerated reviews enable near-real-time synthesis, which is critical for agile, evidence-informed policy development.

  • Transparency and reproducibility: Clear documentation of AI’s role in methods improves accountability.

  • Cost-effective governance: Reducing manual labor in evidence synthesis accelerates evaluation of policy options, leading to better budget use.


Practical use for businesses:

  • Strategic market insights: AI-powered tools can distill vast research landscapes, helping firms identify evidence-based best practices or emerging trends more quickly.

  • Informed innovation: AI-supported reviews reduce the gap between academic breakthroughs and operational application.

  • Standardizing processes: Embedding AI tools into R&D or due-diligence workflows ensures consistency and minimizes human bias.


Key Considerations:

  • Human oversight remains vital: AI should augment expert judgment with rigorous validation and expert review methods being key.

  • Document AI workflows: To ensure legitimacy and reproducibility, organizations must transparently report how AI tools are used at each stage.


The structured integration of AI into evidence synthesis closes a critical gap, enabling faster, data-driven decisions and unlocking the full value of research across public and private sectors.


The W&W Research Team


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