Cyprus’ AI strategy is sound – execution is what matters

The Cyprus National AI Strategy 2032 is currently open for public consultation. The following is offered as a contribution to that process.Dr Panayiotis C. Andreou and Dr Costas Tziouvas, Cyprus University of Technology The publication of the Cyprus National AI Strategy 2032 is, in our view, an important step forward for Cyprus. Moving beyond individual applications of artificial intelligence toward a coordinated national framework matters if Cyprus is to approach one of the most significant technological transformations of our time in a systematic and deliberate way. Read through an economic and academic lens, several elements of the Strategy stand out. A FOCUSED STRATEGY First, the broad architecture is economically sound. Cyprus does not attempt to compete across every dimension of AI. The Strategy instead follows a logic of focus and differentiation, concentrating on sectors where the country already has significant economic weight, institutional capabilities or established sectoral strengths, including financial and professional services, shipping, tourism, healthcare, education and government, whilst treating data, infrastructure, skills, research, governance and trusted AI as shared enabling capabilities. This architecture is also consistent with approaches adopted by other small European economies, where emphasis is placed on coordinated adoption, shared infrastructure, human-capital development, public-sector transformation and trusted governance. STRENGTH THROUGH PARTNERSHIP The idea of sovereign capability through partnerships is likewise well suited to a small economy. Strategic autonomy does not require a country to build every AI model, semiconductor, cloud platform or technological capability at home. For Cyprus, the more useful reading is the capacity to retain enough knowledge, control, resilience and freedom of action, whilst drawing on European and international partnerships to achieve a scale that a small economy cannot generate efficiently on its own. A related point deserves attention from an economic standpoint, namely, institutional capability itself. Data governance, interoperability, procurement capability, technological assurance, cybersecurity and regulatory certainty are not merely constraints around innovation. Designed well, they form part of the infrastructure through which innovation spreads and scales. EVIDENCE OF GAINS Second, the microeconomic and firm-level evidence supporting the proposition behind such strategies has grown substantially stronger. AI can raise productivity and support innovation at the level of workers and firms and, through these and other channels, may in time expand an economy’s productive capacity. What remains considerably less certain is the magnitude and timing of these effects at the aggregate level. Three recent studies, each working at a different level of the economy—the worker, the firm and the university—illustrate different links in a possible transmission chain from individual productivity through firm growth to entrepreneurship and new economic activity. – Brynjolfsson, Li and Raymond (2025) find meaningful productivity gains after the introduction of generative AI in a real workplace. – Babina, Fedyk, He and Hodson (2024) show that firms investing in AI go on to record stronger growth in sales, employment and market value, with product innovation an important channel. – Gofman and Jin (2024) add a finding of direct relevance to national policy, whereby the loss of AI professors reduces AI startup formation among affected students, consistent with university-based AI knowledge supporting entrepreneurship. Together, these studies sit alongside a growing body of work from the OECD and IMF pointing to potentially meaningful aggregate productivity effects from AI. An open question remains, however. We may still be at an early stage in understanding the full economic model through which AI affects an economy. We are becoming better at measuring what AI can do at the level of particular tasks, workers and firms, but we know much less about how those gains eventually spread across the economy as a whole. Adoption is only the start of the transmission mechanism. Task-level productivity gains have to be matched by organisational redesign, complementary investment in technology and infrastructure, and the development of human capital before they translate into firm-level gains. Those changes can then support innovation, the creation of new firms and products, and spillovers across sectors, which in turn accumulate and, over time, raise aggregate productivity and potential output. This transmission is neither instantaneous nor automatic. It depends on skills, on data readiness, on organisational redesign, on capital investment, on how widely adoption spreads across firms, on institutional capability and, in the end, on execution. The history of earlier general-purpose technologies is instructive. Their full economic effects often became visible only after substantial complementary investment in new processes, organisational structures, skills and intangible capital, that is, the pattern Brynjolfsson, Rock and Syverson (2021) term the productivity J-curve. The economic model of AI, in other words, is still being written. This matters for any assessment of its wider value, because the effects are likely to run through several channels that need not appear at the same time or show up in a single indicator. Some benefits will register through measured productivity and output. Others may come through new firms, intellectual property, human capital, technological capability, knowledge spillovers, better public services, greater resilience and forms of economic activity that are hard to anticipate in advance. The economic impact may therefore become multidimensional before it becomes fully measurable. This brings us to the question of macroeconomic magnitude. HOW GAINS SPREAD The scale of the eventual gains clearly matters for analysis and for determining the appropriate level and allocation of investment. The Strategy refers to headline ambitions of up to 15% productivity improvement relative to the baseline trajectory and 12% GDP expansion from AI-enabled transformation. At this stage, we believe these figures are more appropriately interpreted as strategic scenarios or targets than as empirically established forecasts. In the next phase, these headline figures could be further underpinned by a Cyprus-specific scenario and impact-assessment framework, making explicit the assumptions, baseline trajectory, transmission channels and time horizon from which they arise. Such a framework could then be progressively calibrated and refined as Cyprus-specific evidence from implementation becomes available. This uncertainty over magnitude does not, however, invalidate the strategic direction. It does mean that the scale, sequencing and prioritisation of investment should evolve with the evidence. Even more moderate but sustained improvements in productivity, innovation, public-sector efficiency, human capital and new economic activity can accumulate into substantial value over time. The strategic case for AI therefore need not rest on any single GDP or productivity estimate; it rests on whether AI can materially strengthen productivity, innovation and economic capability, and the evidence increasingly suggests that it can. The Strategy gives Cyprus a framework within which to build capability, accelerate adoption, strengthen institutions, experiment, learn and gradually identify the applications that generate the most economic and societal value. As implementation advances, if accompanied by rigorous evaluation, it can also generate something just as valuable—Cyprus-specific evidence—allowing effects to be measured more accurately, assumptions to be refined and resources to be steered toward what works. MEASURING THE IMPACT A final consideration is particularly relevant for Cyprus. It is a small, highly service-oriented and internationally connected economy. Financial services, professional services, shipping, tourism, healthcare, education and public administration are precisely the information- and knowledge-intensive activities in which AI can change how work is done, how information is processed, how decisions are made and how services are delivered. Standing still whilst that transformation unfolds would itself be a strategic choice. The evidence is strong enough to justify acting; at the same time, much remains to be learned about the magnitude, timing and distribution of the eventual gains. These two observations are not in tension. Taken together, they make the case for combining strategic ambition with implementation, measurement, learning and continuous economic calibration, which is exactly the process the Strategy allows Cyprus to pursue. Setting the direction is an important first step. WHY CYPRUS MATTERS The task now is to propel that direction into measurable economic and societal value. The Strategy provides a credible foundation, and its emphasis on selected sectors, trusted AI, human capital, shared infrastructure and sovereign capability through European partnerships is well aligned with Cyprus’ economic structure and strategic position. Its next iteration can build on this foundation by further strengthening the link between strategic architecture and implementation discipline. FROM PLAN TO PRACTICE With the EU AI Act now generally applicable, aligning the Strategy’s implementation framework with its evolving obligations can strengthen this framework further. By connecting governance, procurement, risk classification, transparency, human oversight, AI literacy, regulatory sandboxes and post-market monitoring to Regulation (EU) 2024/1689, as amended, Cyprus can build on the Strategy’s existing emphasis on trusted and responsible AI. Keeping the implementation roadmap in step with the evolving EU regulatory timetable could enhance legal certainty, support consistent implementation across the public and private sectors, and reinforce institutional credibility and investor confidence. Regulatory readiness, on this view, can serve not only as a compliance foundation but also as an enabling condition for translating AI adoption into economic and societal value. REGULATORY READINESS A useful next step is to translate its high-level objectives into an increasingly measurable investment and delivery framework, supported by: (i) clearly allocated institutional responsibilities; (ii) a multiannual, prioritised funding envelope; (iii) common data and compute infrastructure; (iv) an AI-ready procurement framework; (v) measurable sector-level adoption and productivity indicators; (vi) mechanisms for diffusion to SMEs; and (vii) rigorous evaluation of economic and societal outcomes. The case for action is therefore strong and does not rest on any single headline GDP or productivity figure. The Strategy provides Cyprus with a clear framework for progressively translating AI into higher productivity, stronger firms, better public services, new knowledge-intensive activity, improved human capital and greater institutional capability. Its emphasis on implementation, measurement and evidence-based prioritisation can help ensure that resources are directed towards interventions that generate the greatest economic and societal value. In this sense, the Strategy establishes both a credible direction and a practical basis for sustained learning and delivery as Cyprus develops its AI capabilities through 2032. Sources [1] Brynjolfsson, E., Li, D. & Raymond, L. (2025). “Generative AI at Work.” Quarterly Journal of Economics, 140(2), 889–942. [2] Babina, T., Fedyk, A., He, A. & Hodson, J. (2024). “Artificial Intelligence, Firm Growth, and Product Innovation.” Journal of Financial Economics, 151, 103745. [3] Gofman, M. & Jin, Z. (2024). “Artificial Intelligence, Education, and Entrepreneurship.” Journal of Finance, 79(1), 631–667. [4] Brynjolfsson, E., Rock, D. & Syverson, C. (2021). “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies.” American Economic Journal: Macroeconomics, 13(1), 333–372. [5] Filippucci, F., Gal, P. & Schief, M. (2024). “Miracle or Myth? Assessing the Macroeconomic Productivity Gains from Artificial Intelligence.” OECD Artificial Intelligence Papers, No. 29. [6] Misch, F., Park, B., Pizzinelli, C. & Sher, G. (2025). “Artificial Intelligence and Productivity in Europe.” IMF Working Paper 25/67. [7] Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act), as amended. #ArtificialIntelligence #NationalAiStrategy #CyprusVision2035 #Cyprus #AI #Productivity #EconomicGrowth #Innovation #EconomicPolicy