Εθνική Στρατηγική για την Τεχνητή Νοημοσύνη (ΤΝ) της Κυπριακής Δημοκρατίας

General Comments
I would like to acknowledge the considerable work that has gone into preparing the draft Cyprus National AI Strategy 2032. The Strategy is ambitious and, importantly, recognises that successful AI adoption depends not only on technology, but on the data, institutional, governance and human capabilities required to deploy AI responsibly and at scale.
Several elements are particularly strong. The emphasis on data as a strategic national asset, interoperability, secure data sharing and reusable digital infrastructure provides the right foundation for scaled adoption. The principles of “build once, reuse everywhere”, the need for measurable value, and scaling only where impact is demonstrated are also important, particularly given the Strategy’s own recognition of the risk of fragmented pilots and “AI theatre”.
The Strategy also rightly recognises the need for AI adoption with productivity, public-sector transformation, skills, research, innovation and responsible governance, rather than treating AI as a standalone technology policy.
The main challenge for the final Strategy is therefore not to add further ambition, but to translate the existing ambition into a smaller number of clearly prioritised and sequenced actions, with defined ownership, dependencies, potential funding sources, implementation milestones and practically measurable outcomes.
For Cyprus, this is particularly important. Its advantage is unlikely to come from competing with larger economies on the scale of AI investment or compute capacity. It can, however, benefit from its relatively compact ecosystem to coordinate quickly, improve interoperability, test solutions in real operating environments and scale successful applications more rapidly.
The following comments therefore focus primarily on prioritisation, implementation readiness, measurability and governance, with the objective of strengthening the Strategy’s ability to move from ambition to sustained execution.

1. Prioritise national foundations and critical domains before broader international positioning
Article / Section: Sections 1.3 “Priority Sectors for Leadership”, 1.4 “The Strategic Gap and the Implementation Approach”, 1.6 “A Narrow Window”, 2.3 “The Strategic Choice Alignment for the Way Forward”, 2.4 “The Vision: Where Cyprus Will Lead”, and 2.4.1 “Priority Sectors for Leadership”

Comment / Suggestion
The Strategy would benefit from a clearer hierarchy and sequencing of priorities.
In the first phase of implementation, priority should be given to establishing the fundamental capabilities required for effective AI adoption in Cyprus, particularly data quality and governance, interoperability, appropriate digital infrastructure, organisational capability, skills, procurement and effective implementation mechanisms.
At the same time, the definition of “priority sectors” should be reconsidered. The current approach appears to place significant emphasis on contribution to GDP and exposure to AI-driven productivity gains. These are important criteria, but they should not be the only determinants of national priority.
The Strategy should distinguish more clearly between:
1. foundational and nationally critical domains, where resilience and effective operation are prerequisites for the wider economy and society,
2. sectors where AI adoption can generate significant productivity, service-quality or societal improvements, and
3. a smaller number of areas where Cyprus has a credible basis for developing internationally competitive and exportable AI capabilities.
Under the first category, consideration should be given to areas such as water, energy and electricity, transport and mobility, and the built environment/critical infrastructure. Their importance should not be assessed primarily through their direct contribution to GDP, as their effective functioning underpins economic activity, resilience, quality of life and the operation of virtually every other priority sector.
Similarly, the ambition for Cyprus to become a trusted regional AI hub can provide useful long-term direction, but should be treated primarily as a potential outcome of successful implementation, rather than as a near-term objective that drives resource allocation.
A possible sequencing principle would therefore be: foundations and critical national needs first → proven adoption and scaling second → international positioning and specialisation third. Progression should be based on demonstrated readiness and measurable results rather than ambition alone.
If the concept of Cyprus as a “trusted regional AI hub” remains a central strategic ambition, the final Strategy should also define what this means in practical and measurable terms. The relevant capabilities and indicators may already be reflected across different parts of the Strategy, but they are not sufficiently consolidated around this central ambition. A limited set of characteristics and measurable outcomes should therefore define what would constitute success and, importantly, what would differentiate Cyprus from other European and regional jurisdictions. Such differentiation is more likely to arise from Cyprus’s ability to provide a fast, trusted and well-coordinated environment for AI adoption, validation and deployment than from competing with larger economies on absolute scale of compute, hardware or capital.

Justification
The Strategy itself identifies important structural gaps that currently constrain AI adoption: fragmented institutional data and compute resources, immature data governance, limited AI-grade compute, shortages of specialised AI skills and limited applied AI experience within ministries and regulators. It also explicitly identifies the risk of “AI theatre”, where significant experimentation produces limited operational impact.
These observations suggest that Cyprus’s immediate strategic challenge is primarily one of building foundations, execution readiness and institutional capability.
At the same time, the Strategy identifies priority sectors largely on the basis of their contribution to national GDP and exposure to AI-driven transformation and productivity gains. The resulting list includes Government, Financial Services, Healthcare, Tourism, Legal Services, Education, Shipping and Entrepreneurship and Innovation. While these are clearly important areas for AI adoption, economic contribution alone does not capture national criticality or systemic dependency.
For example, water, electricity and energy systems, transport and other critical infrastructures may not necessarily be selected on the basis of their direct GDP contribution, yet weaknesses in these systems can constrain the entire economy and the wider AI ecosystem. The Strategy itself recognises, for example, that access to competitive and sustainable energy resources is important for Cyprus’s attractiveness for digital infrastructure investment.
There is also an important distinction between national priority and international leadership. A domain may be strategically important because AI can materially improve Cyprus’s resilience, productivity or quality of public services without implying that Cyprus should seek to become an international AI leader in that domain. Conversely, international specialisation should be pursued selectively where Cyprus can demonstrate a credible comparative advantage.
Against this background, statements such as that the “only strategic choice” is for Cyprus to become an Eastern Mediterranean AI hub appear stronger than the underlying analysis currently supports. Becoming a recognised regional AI hub would be a welcome consequence of successful implementation, but the more immediate objective should be to make AI work effectively for Cyprus, address critical national needs and demonstrate measurable economic and societal value.
For a country of Cyprus’s scale, disciplined sequencing can itself become a competitive advantage: establish the foundations, demonstrate successful applications, scale what works, and build international positioning on the basis of proven capability and results.

2. Strengthen prioritisation, sequencing and timeline realism through a single implementation roadmap
Article / Section: Section 1.5 “Implementation Timeline and Success Metrics”, Chapter 3 “Strategic Implementation Approach”, Chapter 4 “AI Adoption and Strategic Enablers”, and Chapter 5 “Immediate Steps for the Strategy Launch”

Comment / Suggestion
The Strategy should be complemented by a consolidated implementation roadmap that identifies, for each major initiative: accountable owner; implementation period; dependencies; indicative implementation and recurring cost; funding source; baseline; measurable target outcome; interim milestones; and criteria for continuation, redesign, scaling or termination.
The proposed implementation timeline should also be reviewed for feasibility. The use of broad phases such as 0-6 months, 6-12 months and subsequent periods provides useful direction, but several actions depend on institutional, procurement, data, infrastructure, legislative or capability prerequisites that may themselves require significant time to establish. The timeline should therefore be based on realistic delivery estimates and explicit dependencies, rather than on uniform implementation windows.
The roadmap should clearly distinguish between foundational capabilities, such as data governance, interoperability, infrastructure, procurement capability and workforce readiness, and value-generating applications. Dependencies should be explicit so that initiatives are undertaken in the correct sequence.

Justification
The Strategy includes a substantial number of proposed actions across infrastructure, governance, Centres of Excellence, data, skills, innovation, compliance mechanisms and sectoral programmes. This breadth creates execution risk if initiatives are launched concurrently without sufficient consideration of implementation capacity and critical-path dependencies.
In particular, some actions presented within relatively short implementation windows may require prior decisions on governance, staffing and funding; procurement or contracting; access to and preparation of data; integration with existing systems; development of standards; or coordination across multiple public bodies. A six-month target may therefore be realistic for initiating or designing an intervention, but not necessarily for establishing it as an operational and effective national capability.
The roadmap should therefore distinguish, where appropriate, between decision/design, establishment, pilot, operational deployment and scale-up, rather than treating an initiative as a single milestone. A consolidated and realistically phased implementation roadmap would provide the bridge between Strategy and execution and allow leadership to understand which actions are critical-path dependencies, which can proceed in parallel and which should only commence once the necessary foundations have been demonstrated.

3. Make the National Strategic Objectives more specific and measurable
Article / Section: Sections 1.2 “The National Strategic Objectives and Priority Sectors”, 2.5 “National Strategic Objectives”, 3.11 “Measuring Impact and National KPIs”, Annex C and Annex F

Comment / Suggestion
The eight National Strategic Objectives are generally broad and directional rather than specific and measurable objectives. Concepts such as a “trusted jurisdiction”, “strong and inclusive AI ecosystem” or “sovereign capability” provide strategic direction, but do not by themselves define what success should look like by 2032.
The Strategy does provide considerably more detail elsewhere, particularly through the sector-specific initiatives and KPIs in Annex C and the measurement framework in Annex F. However, the link back to each National Strategic Objective is not always clear.
It is suggested that each objective be linked directly to a small number of defined 2032 outcomes, intermediate milestones, key initiatives, measurable KPIs and accountable owners, for example:
Strategic Objective → 2032 Outcome → Key Initiatives → Milestones → KPIs → Owner
This would not require adding further initiatives, but rather organising and, where necessary, completing the existing information into a clear delivery framework.

Justification
The National Strategic Objectives are intended to provide a common reference point for policy, investment and delivery. For this purpose, they should be sufficiently specific to allow progress and accountability to be assessed. While Annex C provides further sector-level detail and Annex F introduces an outcomes-focused measurement framework, Annex F itself is described as indicative and adaptive. The Strategy therefore contains many of the necessary components, but the roadmap connecting the high-level objectives to concrete delivery and measurable results is not sufficiently explicit. Strengthening this connection would make the Strategy more actionable and make it easier to determine, throughout implementation, whether each National Strategic Objective is actually being achieved.

4. Strengthen the KPI framework and distinguish measurable targets from ambitions and scenarios
Article / Section: Section 1.5 “Implementation Timeline and Success Metrics”, Section 2.5 “National Strategic Objectives”, Section 3.11 “Measuring Impact and National KPIs”, Annex C and Annex F “National KPIs and Measurement Framework”

Comment / Suggestion
The Strategy rightly places strong emphasis on measurable outcomes. However, the KPI framework should be strengthened to ensure that the measures presented as targets are specific, measurable and supported by a credible implementation pathway. Each core KPI should, as a minimum, specify:
baseline → target → measurement methodology/data source → accountable owner → reporting frequency → target date.
At Strategy level, priority should be given to a limited number of outcome and impact KPIs, rather than primarily measuring activities or outputs.

The final Strategy should also distinguish clearly between:
• committed and measurable targets,
• strategic ambitions, and
• scenario-based estimates or potential impacts.
Where a baseline, measurement methodology or credible attribution to the Strategy cannot yet be established, the figure should not be presented as a committed KPI.

Justification
The Strategy itself states that AI should deliver “demonstrable public and economic value, rather than activity without outcomes” and that national KPIs should be clear, comparable and actionable. This is a strong principle. At the same time, the Strategy contains ambitious quantitative statements relating, among others, to productivity improvement, AI adoption, high-skilled employment, workforce positioning and reductions in citizen waiting times. Some may be appropriate targets; others may be better understood as ambitions or potential scenarios. The distinction is important.
For example, a national productivity target requires a defined baseline, measurement methodology, expected sectoral contributions and a credible link between the interventions under the Strategy and the resulting productivity improvement. Without this, it will be difficult to determine how much of any observed change can reasonably be attributed to the Strategy.
This creates a significant implementation and accountability risk: if success is not defined and measured consistently from the outset, progress may ultimately be assessed on activity rather than results, despite the Strategy’s stated intention to do the opposite. A smaller number of robust, measurable and defensible KPIs would therefore provide a stronger basis for implementation and accountability than a larger set of ambitious but difficult-to-measure targets.

5. Significantly simplify the governance model and build implementation capacity within existing structures
Article / Section: Sections 1.4.3 “Governance and Accountability”, 3.3 “Governance and Controls”, particularly 3.3.1 “Governance Structure”, and Annex E “AI Officers”

Comment / Suggestion
The proposed governance architecture should be significantly simplified, taking into account the size of Cyprus and the need for fast and accountable implementation.
Rather than establishing multiple new permanent governance and delivery structures, consideration should be given to a leaner model centred on:
• DMRID, with a properly resourced and highly skilled central AI team responsible for coordinating implementation, monitoring progress, establishing common standards and capabilities, and reporting against the Strategy;
• the National AI Taskforce, continuing to provide expert strategic and technical advice and challenge;
• a clearly designated and appropriately skilled AI focal point/officer within each Ministry and major public organisation, responsible for local implementation and coordination with the central team; and
• existing competent regulatory and specialist authorities retaining responsibility within their established mandates.
Additional committees or structures should be created only where there is a clearly demonstrated function that cannot be performed effectively within this model.
The same principle should apply beyond governance bodies to the proposed Centres of Excellence, hubs, observatories, programmes and other new institutional mechanisms. Before establishing a new structure, the implementation plan should demonstrate the specific capability gap it addresses, why that function cannot be delivered through an existing institution or programme, the resources required to sustain it, and how its contribution to the Strategy will be measured. The objective should be to build capability, rather than institutional complexity.

Justification
The Strategy currently envisages a National AI Authority, Interministerial AI Council, National AI Taskforce, National AI Infrastructure Council, National Ethics and Values Committee, National AI Misinformation and Security Council, National AI Skills Observatory, Government Innovation Hub, Industrial Centre of Excellence, National AI Cybersecurity Centre of Excellence, AI Officers and AI Champions.
While the functions addressed are individually relevant, the combined architecture appears disproportionately complex for the scale of the Cypriot public administration. There is a risk that the governance mechanism itself becomes an implementation challenge, creating overlapping mandates, additional interfaces, diffused accountability and slower decision-making.
Moreover, the Strategy already provides much of the basis for a simpler model. It states that ministries and public bodies should retain responsibility for AI adoption and that initiatives should be embedded within existing policy, operational and accountability structures. The objective should therefore be to strengthen implementation capacity within existing institutional structures wherever possible, rather than create a parallel, potentially disconnected AI administration.
For a small country, simplicity, clarity of ownership and speed of decision-making should themselves be considered governance design principles.

6. Prioritise practical public-sector capability and reusable foundations before advanced or “moonshot” use cases
Article / Section: Sections 3.4 “AI Adoption Across Government”, 4.2 “AI Infrastructure Enablers”, 4.4 “AI Adoption Enablers”, and relevant use cases in Annex C

Comment / Suggestion
The public-sector AI programme should initially concentrate on a small number of high-value, feasible and reusable applications, supported by common data and technology foundations, before progressing to more advanced or speculative applications.
Priority should be given to use cases that: address a clearly defined existing problem; use data that is already available or can realistically be made available; can demonstrate measurable improvements in cost, processing time, service quality or productivity; have manageable implementation and regulatory risk; and create capabilities that can subsequently be reused across government.
Advanced “moonshots” should remain part of the longer-term innovation pipeline but should not compete for implementation resources with the foundational transformation required first.

Justification
The Strategy already contains the very strong principles of “Build Once, Reuse Everywhere” and a “Value-First Portfolio”, and proposes selecting six moonshots from sixteen flagship transformation programmes.
These principles should be applied even more rigorously. Given the uneven level of organisational and data maturity identified elsewhere in the Strategy, early success is likely to come from solving concrete operational problems and creating reusable capabilities rather than pursuing the most technologically sophisticated applications first. The objective during the initial implementation period should be to demonstrate that AI can deliver tangible, measurable improvements in actual government operations. Successful solutions can then be scaled and more ambitious use cases pursued as the underlying capability matures.
This would also provide practical evidence, institutional experience and public confidence upon which the Strategy’s longer-term ambitions can be built.

7. Use needs-based terminology for vulnerable groups rather than demographic proxies
Article / Section: Relevant references in the Strategy to pensioners, older persons or other demographic groups in the context of digital inclusion, AI literacy, accessibility and citizen services; Section 3.6 “Talent, Skills and Workforce Transformation” where applicable.

Comment / Suggestion
Where the Strategy seeks to identify citizens who may require additional support to access AI-enabled or digital services, it is suggested that it consistently use terminology such as “vulnerable groups”, “persons at risk of digital exclusion” or “persons requiring additional accessibility or digital support”, rather than referring to pensioners or people with disabilities alone.
Where useful, the Strategy can identify specific factors contributing to vulnerability or exclusion, such as digital literacy, disability, socioeconomic circumstances, language barriers or limited access to technology.

Justification
Age or pensioner status does not, by itself, determine digital capability or vulnerability. Conversely, people in younger age groups may face significant barriers to accessing digital or AI-enabled services. A needs-based definition is therefore more accurate, inclusive and useful for policy design.
Indeed, the Strategy itself already uses the broader formulation “citizens in vulnerable or disadvantaged circumstances” in the FutureAI CY programme. Applying this approach consistently would avoid unnecessary demographic assumptions and allow interventions to be targeted according to actual need.
It would also reinforce an important principle for the digitalisation of public services: AI-enabled channels should improve accessibility and inclusion, while appropriate alternative channels and support should remain available for people who require them.