system has so far shown considerable resilience. based on the latest data, another very small increase was recorded in the first quarter. so what will happen next? at a time like this, making predictions is even harder than usual. in april ’ s economic bulletin, in the annual report published at the end of may, and in the recently released projections conducted as part of the eurosystem coordinated exercise, the bank of italy formulated scenarios – updated as the situation evolved – based on developments in ukraine. the most optimistic scenario in april ’ s economic bulletin assumed a rapid conclusion to the conflict, which unfortunately did not materialise. the central scenario formulated at the time and its successive refinements continue to project positive growth in both 2022 and 2023. compared with the prewar forecasts, however, the new growth projections are down by almost 1 percentage point per year. a more adverse scenario takes into consideration a temporary interruption in the flow of natural gas from russia, to be offset only in part by other sources : the impact on gdp is projected to be significant, and activity in some very energy - intensive sectors could be affected by interruptions in production. the sharp acceleration in imported fuel prices has considerably widened the deficit in the energy balance of the balance of payments, which in turn led has led to a worsening in the current account balance. for now, the energy balance deficit remains smaller than in the early 1980s. 3 in any case, the strong positive net international investment position that italy has built up over the last decade, thanks to the enhanced international competitiveness of its production system, means it is less vulnerable than in that earlier period. the impact of the crisis on prices instead remains significant. inflation, measured according to the eu ’ s harmonized definition, exceeded 8 per cent in the euro area and 7 per cent in italy in may. the dynamics of energy prices directly account for about half the change in the consumer price index observed since the beginning of the year. 4 however, it also pushes the prices of other goods upwards, owing to the energy costs incurred by firms across all sectors. medium - term inflation expectations, both those obtained through surveys and those implied by market trends, are largely consistent with the eurosystem ’ s objective for the time being, but they have increased, and are the focus of intense scrutiny. italy ’ s energy supplies and the measures adopted russia is europe's main hydrocarbon provider. in 2021, the european union ’ s imports from russia accounted

complicate the conduct of surveys, but also pose serious risks to data quality and inevitably raise questions about how to use and potentially adapt sample surveys in the future. where could we go next? the digitalization of information systems, the increasing availability of administrative data, big data, and non - probabilistic and online surveys15 ( which may seem like quick, easy, and inexpensive ways to collect data ), as well as the rise of artificial intelligence ( ai ), bring potential opportunities for the development of surveys that must be carefully assessed. i see some promising directions. for example, we should take full advantage of the growing availability of administrative and other unstructured data. combining these data with methodologically sound and carefully designed sample surveys can greatly enhance the informative value of both. this integration can occur at various levels, such as sample selection, weighting, and streamlined questionnaires. in addition, we should harness the rapid advances in ai and machine learning techniques, which have potential benefits at various stages of the survey process. let me briefly highlight some avenues for further exploration and research in the latter area. survey research focuses primarily on the wording of questions and answers in an attempt to gather soft information that it is not readily available in more traditional data, a. k. a. hard information. so it seems natural, at least to me, that a first way to improve traditional surveys is to use large language models ( llms ). in the preparation phase of surveys, for example, llms could help create questions, identify inconsistencies and suggest effective response options. in data cleaning and management, llms could help detect inconsistent responses and prevent low - quality entries. in the final stage of reporting results, llms could help ensure accessible formats, whether in the form of summaries, visualizations, presentations, or written text. m. bottone, l. modugno and a. neri, ‘ response burden and data quality in business ’, journal of official statistics, 37, 4, pp. 811 - 836, 2022. for example, m. galesic and m. bosnjak, ‘ effects of questionnaire length on participation and indicators of response quality in a web survey ’, the public opinion quarterly, 73, 2, pp. 349 - 360, 2009. r. gambacorta, m. lo conte, m. murgia, a. neri, r.