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Abstract The volume of published academic research is growing rapidly and this new era of “big literature” poses new challenges to evidence synthesis, pushing traditional, manual methods of evidence synthesis to their limits. New technology developments, including machine learning, are likely to provide solutions to the problem of information overload and allow scaling of systematic maps to large and even vast literatures. In this paper, we outline how systematic maps lend themselves well to automation and computer‐assistance. We believe that it is a major priority to consolidate efforts to develop and validate efficient, rigorous and robust applications of these novel technologies, ensuring the challenges of big literature do not prevent the future production of systematic maps.