Effective communication between government institutions and citizens is essential for transparency, participation, and trust. However, many official communications remain inaccessible to a broad portion of the population due to their complexity, which hinders comprehension and reinforces informational inequality. This highlights the need for implementing plain language strategies in public administration. The CLINFO project combines Plain-Language guidelines with Natural Language Processing and Artificial Intelligence methods to facilitate the effective dissemination of public information. This paper delineates five fundamental research challenges that serve as a framework for the development of effective tools aimed at facilitating the transformation of complex Spanish text into plain language: automatic complexity assessment, explainable recommendations, web-scale collaborative integration, cross-lingual and domain adaptation, and human-centered validation. By framing these challenges, we define a research agenda for combining theory and practice in plain-language intelligence, invite collaboration across disciplines, and set milestones for advancing AI-supported clarity in institutional texts.