NORAI project: How the Balearic Islands plan to reduce bureaucracy with AI – Revolutionizing administration with 5.45 million euros

Administration 2.0: The Balearic Islands are transforming their AI operations with €5.45 million

The future of administration in the Balearic Islands: AI as the key to efficiency

The Balearic Islands are embarking on a significant transformation of their administrative structures, driven by the use of artificial intelligence (AI). With an investment of €5.45 million, the NORAI project is being launched, aiming to create a shared AI platform for the entire autonomous administration within 24 months. The goal is not only to improve efficiency and processing times but also to enhance the quality of services for citizens and businesses. This initiative goes beyond simply digitizing existing processes, aiming to transform the fragmented administrative landscape into a networked and reusable platform for public services. However, the success of this transformation depends not only on the technology itself but also on the administration's willingness to fundamentally reorganize. Key questions include whether AI will truly enable the Balearic Islands to achieve shorter processing times, lower error rates, and increased administrative capacity, and how they will meet the challenges of a restructured public administration.

The Balearic Islands are transforming their administration into an AI platform

5.45 million euros against bureaucracy: The crucial question is not whether AI works, but whether the state can be transformed by it

The Balearic Islands are undergoing a technological transformation whose economic significance extends far beyond the introduction of a few digital assistants. With an investment of €5.45 million and a 24-month implementation period, a shared artificial intelligence infrastructure, known as NORAI, is being developed across the entire autonomous administration. It will reach all regional ministries and public institutions, supporting administrative staff in file processing, analyzing documents, preparing reports, handling citizen inquiries, and integrating with process automation, process mining, and advanced data analysis. From an economic perspective, this is therefore not a typical IT project, but rather an attempt to transform a fragmented public administration landscape into a reusable production platform for public services.

The central question is not how many tasks a language model can perform. The crucial question is whether the Balearic Islands can actually achieve shorter processing times, lower error rates, increased administrative capacity, and better services for citizens and businesses through technology. This is more challenging than simply deploying a chatbot. Public administration consists of legal frameworks, responsibilities, data repositories, exceptions, deadlines, and transparent decisions. Artificial intelligence can make this system more productive, but only if processes, data, responsibilities, and controls are modernized simultaneously.

From digitization to reorganization

Many digitization programs in the past have simply replicated analog processes electronically. A paper form became a PDF, an official letter an email, and a file folder a document management system. The process itself often remained unchanged. NORAI pursues a more comprehensive approach: A shared technological layer is designed to analyze information, prepare processes, and support recurring workflows across departmental boundaries. This shifts the focus from merely providing digital access to the intelligent organization of the underlying administrative processes.

This distinction is economically important. While a digital form reduces travel and shipping costs, it doesn't automatically eliminate media breaks, duplicate data entry, or internal queues. An AI-powered platform, on the other hand, can classify documents, extract content, identify missing information, pre-sort files, and generate standardized drafts. Combined with rule-based automation, clearly defined work steps can then be executed without manual data entry. The potential productivity gains thus arise not solely from artificial intelligence, but from the interplay of AI, automation, data access, and redesigned processes.

This is precisely where the strategic value of a central infrastructure lies. If each agency develops its own individual solutions, parallel costs arise for procurement, integration, data protection audits, operation, training, and maintenance. Furthermore, incompatible data models and differing security levels proliferate. A shared platform can provide basic components once and reuse them across multiple departments. These include identity and access management, logging, model access, document processing, search functions, quality controls, and interfaces to existing specialized applications. The more frequently these components are used, the lower the average cost of each subsequent use case can be.

5.45 million euros as a platform bet

The budget of €5.45 million equates to approximately €2.725 million per year during the planned implementation period. This is not an excessive sum for a comprehensive modernization of all ministries and institutions. Rather, it is just enough to force prioritization. The amount can establish a solid common foundation, but it is insufficient for a complete overhaul of all administrative procedures. Therefore, the economic success depends on whether those processes with particularly high volume, standardization potential, and workload are selected first.

Suitable processes involve many similarly structured documents, clear review steps, recurring queries, and significant manual search effort. These can include grant applications, approval documents, invoice audits, internal reports, information requests, or standardized citizen inquiries. Less suitable are processes where each case requires a highly individualized legal assessment or where even minor errors can have significant consequences for fundamental rights. Therefore, an economically sound approach begins with supporting and preparatory functions, not with fully automated decisions.

Furthermore, the investment should not be measured solely by saved personnel hours. Public administration does not generate profit in the traditional sense. Its economic benefits also include avoided waiting times, reduced costs due to errors, faster disbursement of benefits, quicker approvals, and improved planning for businesses. When a company receives a permit sooner, a family can access benefits more quickly, or employees waste less time resubmitting existing documents, a real economic benefit arises, even if it doesn't appear as revenue in the budget.

A purely cost-cutting approach would therefore be too narrow. The Balearic Islands are experiencing seasonal and structural growth under particular pressures: tourism, housing shortages, environmental issues, island traffic, and a geographically dispersed population generate numerous complex administrative interactions. If the workload increases faster than staffing levels, additional digital capacity becomes essential to maintaining service levels. In this context, AI can act as a productivity booster. It doesn't automatically replace jobs, but ideally increases the number and quality of processes that can be handled with existing resources.

The pilot delivers a strong but limited signal

The Balearic Islands are not starting from scratch. In a two-year automation pilot project, 432,440 files and 70,674 documents were processed. The systems ran for 8,765 hours and, according to the administration, generated a work output equivalent to 45,592 human working hours. The average processing time for each case was 1.16 minutes, corresponding to approximately 52 cases per hour. The technology was used in 16 administrative procedures, including agricultural and fishery-related subsidies, social benefits, applications for self-consumption of energy, internal financial management, and housing-related processes.

These figures are economically relevant, but should not be uncritically extrapolated to NORAI. The pilot project focused primarily on robot-assisted process automation in clearly defined workflows. Generative and analytical AI often handles less standardized tasks where results need to be verified. An automatically transmitted dataset is easier to measure than a summarized report, a researched memo, or a draft response for a citizen. The automation to date demonstrates high volumes and operational experience within the administration. However, it does not yet prove that every new AI application will achieve the same speed or the same economic return.

Nevertheless, the pilot project provides a useful point of reference. Spread over two years, 45,592 avoided human work hours equate to approximately 22,800 hours annually. Assuming a productive annual working time of roughly 1,600 hours, this would correspond to roughly 14 full-time equivalents. This figure does not mean that 14 jobs were or should be eliminated. Rather, it illustrates the capacity that can be freed up for more complex checks, citizen interactions, and backlogs when machines take over repetitive tasks.

The monetary value depends on the actual total cost of a working hour. Using a broad estimate of €35 to €60 per hour, the gross value of the time saved over the entire pilot period would be approximately €1.6 to €2.7 million. From this, development, operating, licensing, monitoring, and modification costs would have to be deducted. This calculation is therefore not proof of a return on investment, but rather a scenario. However, it demonstrates that even a limited number of well-selected processes could economically justify a substantial portion of the investment.

The greatest leverage lies in reuse

NORAI's key advantage lies not in a single spectacular use case, but in the ability to implement many medium-sized improvements on the same foundation. A document component that recognizes, classifies, and summarizes text can be deployed across multiple ministries. A secure assistant for internal policies can support employees in different agencies. A shared analytics environment can evaluate process data from several departments. This creates a portfolio effect: not every use case needs to deliver extraordinary savings, as long as the platform spreads its base costs across numerous uses.

This scalability only works, however, if reuse is already architecturally planned. If applications are built as custom solutions despite a shared platform, fragmentation returns through the back door. Uniform interfaces, standardized role models, common quality requirements, and a central catalog of approved components are necessary. Specialized agencies must be able to represent their specific needs without creating a new technical silo for every task.

From an economic perspective, every new use case should answer at least four questions: What is the case volume? How much time does the current process consume? What percentage of the work can be standardized? What costs arise from errors, queries, and delays? Only from these metrics can a reliable benefit be derived. Political visibility or technological modernity alone are poor selection criteria. An inconspicuous process with hundreds of thousands of transactions can be more valuable than a high-profile assistant with low usage.

Process mining makes bureaucracy measurable

Of particular importance is the planned integration of AI with process mining. This involves using digital event data to visualize the actual flow of procedures. In this way, it becomes possible to identify where files get stuck, which loops frequently occur, where information is missing, and which variations of a supposedly standardized process exist in practice. This provides public administration not only with an automation tool, but also with an instrument for empirical organizational analysis.

This is a crucial distinction. Simply automating an inefficient process without modification may only accelerate unnecessary steps. Process mining, on the other hand, can reveal which steps should be eliminated, standardized, or rearranged. The greatest benefit often arises not from automating an existing check, but from eliminating duplicate checks altogether, directly connecting a data source, or avoiding unnecessary queries.

For this approach to work, event data must be complete and consistent. Timestamps, processing status, handovers, and decisions must be reliably recorded. If such data is missing, the analysis only provides a partial picture. Furthermore, there is an organizational hurdle: transparency regarding processing times and detours can trigger conflicts of responsibility. Process mining is therefore not neutral. It reveals where structures are not functioning and thus creates pressure for change. The technology provides the findings; political and administrative leaders must draw the necessary conclusions.

Intelligent assistants are changing everyday work

AI assistants can support administrative staff in searching for regulations, summarizing extensive files, comparing documents, and creating initial drafts. When designed effectively, they not only shorten individual tasks but also reduce the need to switch between specialized applications, drives, emails, and knowledge databases. This search and coordination effort, in particular, represents a significant and often underestimated cost factor in knowledge-intensive organizations.

However, the assistant must not be presented as an infallible source of knowledge. Language models generate plausible formulations, but can confuse facts, use outdated rules, or fill gaps with fabricated information. Therefore, a controlled approach is necessary for administration. Answers should, where possible, be based on approved datasets, clearly identify the documents used, and indicate uncertainty when information is insufficient. Employees must be able to identify which statement was derived from which administrative regulation or file.

A phased approach is particularly useful. In the first phase, AI supports purely internal tasks without direct external impact. In the second phase, it creates drafts that are reviewed and approved by qualified employees. Only in narrowly defined, low-risk standard cases can largely automated processing be considered. This sequence reduces risk, increases the organization's learning capacity, and builds trust among employees and the public.

For citizens and businesses, assistants can provide information around the clock, explain responsibilities, point out missing documents, and guide them through digital processes. Their value lies not only in faster responses but also in preventing failed applications. If users know which documents are missing or what deadlines apply before submitting an application, internal follow-up work is also reduced. Therefore, good citizen assistance and internal efficiency are not opposites but can reinforce each other.

Data quality determines the yield

The greatest technical strength of an AI system cannot compensate for poor administrative data. Inconsistent labels, duplicate records, scanned documents without reliable text recognition, conflicting registers, and unclear access rights limit its usefulness. In many organizations, therefore, a significant portion of the work is not on the model itself, but on data cleansing, metadata, interfaces, and permissions.

NORAI requires a clear data architecture. It must be defined which sources may be used for which purpose, who is responsible for their quality, and how updates are performed. Personal and particularly sensitive information may only be processed to the extent necessary. At the same time, employees must have access to the information they need for their specific case. A constant tension exists between maximizing data usage and consistently adhering to purpose limitation, a tension that cannot be resolved with a one-time technical configuration.

From an economic perspective, data quality is a capital asset. Investments in clean registers, standardized formats, and interoperable interfaces generate benefits beyond a single use case. They improve the foundation for analytics, automation, statistics, and future digital services. Conversely, an excessive focus on visible AI applications leads to the underfunding of less glamorous data work. This results in impressive demonstrations, but not reliable administrative output.

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The EU AI Act will become an architectural principle

Public authorities bear a special responsibility because their decisions affect people's services, rights, and obligations. The European legal framework differentiates between risks and sets strict requirements for high-risk systems regarding risk management, data quality, documentation, logging, human oversight, robustness, and cybersecurity. Public operators of relevant systems must comply with additional obligations, including registration, monitoring, and, in certain cases, an impact assessment on fundamental rights.

For NORAI, this means that regulations cannot be retrofitted to finished applications. Logging, role distribution, traceability, and human intervention options must be integral parts of the platform. A central catalog of all deployed AI systems is particularly valuable in this regard. It provides an overview of purpose, operator, data sources, model versions, risk class, performance indicators, and responsible parties. Without such an inventory, a large administration cannot effectively manage either costs or risks.

Human oversight must not be reduced to a mere formality. When employees are expected to approve hundreds of machine-generated proposals under time pressure, an automation bias arises: people tend to follow the seemingly objective machine. Effective oversight therefore requires sufficient time, expertise, and a clear explanation of the decision-making criteria. Furthermore, it must be clear when a proposal should be rejected, escalated, or processed entirely manually.

The associated monitoring costs are not an argument against AI. They are part of the production costs of a digital administration based on the rule of law. A seemingly cheaper solution without reliable documentation can become more expensive in the long run if incorrect decisions are challenged, data leaks occur, or trust is lost. Therefore, quality, legal certainty, and security must be given the same weight as speed in cost-benefit analyses.

Digital sovereignty needs realistic limits

The Balearic strategy emphasizes public control and digital sovereignty. This is understandable, but it should not be confused with complete technological self-sufficiency. A regional administration will neither develop its own basic models on a global scale nor produce all infrastructure components itself. Rather, sovereignty means preserving choices and the ability to act: controlling data, being able to switch providers, using open interfaces, evaluating model performance, and, if necessary, continuing to operate critical functions independently.

A significant risk is vendor lock-in. Tightly tying data formats, workflows, and business applications to proprietary services increases switching costs later on. The organization can then become technically dependent, even if the initial price was attractive. Contracts and architecture should therefore allow for the export of data, logs, configurations, and knowledge bases. Separating application, model access, and data storage also improves the negotiating position.

At the same time, an excessive demand for sovereignty can increase the cost of innovation. Not every component needs to be operated in-house. Risk-based differentiation is crucial. Strictly controlled operating models are necessary for highly sensitive data and central decision-making processes. For low-risk support functions, standardized external services can be economically viable, provided data protection, security, and interchangeability are guaranteed. True sovereignty does not mean owning everything, but rather consciously managing dependencies.

Employees are not a marginal issue

The introduction of AI is changing job profiles. Routine tasks are decreasing, while testing, exception handling, communication, and quality control are becoming more important. This can enhance the value of work if employees are indeed relieved of monotonous tasks. However, it can also increase workload if only simple tasks are automated and the remaining cases are particularly complex, without any adjustments to time constraints.

Training is therefore a core component of the investment. Employees need not only general AI basics but also role-specific skills. Administrative staff must be able to review results and identify limitations. Managers must be able to manage benefits, risks, and process changes. IT and data teams need expertise in integration, monitoring, security, and model evaluation. Data protection, legal, and HR departments must be involved early on, rather than approving finished applications retrospectively.

The Balearic Islands have already significantly increased the number of AI training courses for employees. This signals demand and creates a better starting point. However, the crucial factor is the connection between training and the workplace. A one-off course achieves little if there are no reliable tools, clear rules, and supervised use cases afterward. Conversely, providing powerful systems without training leads to shadow use, misuse, and mistrust.

Employee involvement is also economically sound. They are familiar with the actual detours, special cases, and information gaps of a process. If applications are planned solely from a technical perspective, they often automate an idealized process model. Therefore, skilled workers should be involved in the selection, design, and testing. This not only increases acceptance but also reduces costly errors.

Citizen engagement must not shrink to a chatbot

Fast digital responses are valuable, but not every citizen can or wants to communicate exclusively digitally. Older people, people with disabilities, people with limited language skills, or those without a secure digital identity must not be disadvantaged. Therefore, a smart administration must maintain multiple access channels and use AI to support personal consultations, not to eliminate them across the board.

Multilingualism is particularly relevant in the Balearic Islands. The administration operates in both Catalan and Spanish, while tourism and international immigration bring additional languages ​​into everyday life. AI can facilitate translation and the use of clear language, but legally relevant information must remain terminologically accurate. Therefore, automatic translations should be reviewed based on risk and clearly distinguished from legally binding administrative acts.

Citizen-centricity also means transparency. People should know when they are interacting with an automated system, what role it plays, and how to contact a human. Even if an AI only prepares a decision, it must still be clear who is responsible. The more sensitive a process, the more important justification, the possibility of appeal, and human review become.

Success requires measurable goals

The formulas "more efficiency" and "better services" are insufficient for a program of this scale. NORAI requires reliable baseline measurements before the start of each use case. These include processing time, throughput time, number of queries, error rate, abandonment rate, cost per process, backlog, and user satisfaction. Only a comparison with these baseline values ​​reveals whether an application actually improves processes or merely generates activity.

It's crucial to distinguish between processing time and throughput time. AI can reduce the active work on a case from 30 to 15 minutes, while the file still sits in queues for three weeks. For citizens, the effect would be barely noticeable. Therefore, end-to-end metrics should be the primary focus. Quality must also be measured: faster responses are not progress if corrections, complaints, or appeals increase.

Suitable key performance indicators (KPIs) would include the proportion of automatically prepared processes, the acceptance rate of machine suggestions, the average correction time, the number of critical errors, usage by department, and the actual freed-up work capacity. For citizen services, comprehensibility, first-call resolution rate, waiting time, and accessibility are also relevant. Security incidents, data protection breaches, and model errors must also be factored into performance management.

A central cost-benefit analysis should separate shared platform costs from the costs of individual use cases. Otherwise, early projects appear artificially expensive because they finance the development of the entire infrastructure. Conversely, later projects appear too cheap if their infrastructure usage is not taken into account. A transparent cost model enables better decisions about which applications should be further developed, discontinued, or scaled.

The underestimated follow-up costs

The €5.45 million represents only the initial investment. After the 24-month period, ongoing costs will arise for computing power, model access, storage, maintenance, security monitoring, support, training, and adaptation to new legal requirements. Models and software components change rapidly. A system that is efficient today may be obsolete, more expensive, or no longer available tomorrow. Without a long-term operating model, a costly upgrade cycle is likely after the initial setup phase.

Even successful automation generates follow-up costs. If more processes reach a technical review more quickly, the bottleneck may simply shift elsewhere. An assistant can complete applications, but this increases the number of cases requiring review. If downstream capacities are not adjusted accordingly, the queue there will grow. Process control must therefore always consider the entire value chain.

Another risk is uncontrolled expansion. As soon as initial applications are functional, demand increases from all departments. Without clear prioritization, too many parallel projects emerge, overloading specialists and technical teams. A central steering mechanism should evaluate projects based on benefit, risk, data maturity, and reusability. Not every good idea needs to be implemented immediately.

What companies gain from intelligent management

For businesses, administration is a key factor in their location decisions. Processing times for permits, subsidies, energy projects, construction projects, or notifications influence investment decisions and liquidity. Small and medium-sized enterprises (SMEs) in particular suffer from complex documentation requirements because they have fewer staff available for administrative tasks. Faster, more transparent, and more predictable administration reduces indirect business costs.

The benefits can outweigh the agency's internal time savings. For example, if a permit is granted a few weeks earlier, a company can implement investments sooner, generate revenue, or hire employees. Faster processing of grant applications improves financing. Such effects are more difficult to measure but are significant for the economy as a whole. NORAI should therefore monitor not only internal productivity but also the impact on applicants.

A shared platform can also enable more consistent information. Currently, companies sometimes receive conflicting information from different sources during complex processes. Controlled access to knowledge can reduce inconsistencies and clarify responsibilities. This requires that the assistant accesses current, legally binding content and does not create a false sense of legal certainty where discretionary leeway exists.

A regional market for GovTech is emerging

Public AI investments also impact the technology market. They create demand for integration, data management, cybersecurity, process consulting, model validation, and training. This can lead to references and new expertise for regional and Spanish providers. A shared platform generally facilitates access for smaller specialists, provided interfaces are open and modules are interchangeable.

However, the economic policy potential depends on market design. A monolithic system favors large, general-purpose providers and can stifle local innovation. A modular architecture, on the other hand, allows for the integration of specialized solutions without relinquishing overall control. Competition should not only occur during the initial setup but should remain possible throughout the entire lifespan of the system.

Regional value creation must not be sacrificed for quality. Public administration needs robust, secure, and scalable systems. The most sensible approach combines central standards with an open ecosystem in which different providers can offer clearly defined services. In this way, NORAI can simultaneously modernize public administration and foster a more demanding market for responsible AI.

Spain provides a boost, but no guarantee of success

NORAI fits into the broader Spanish strategy of expanding artificial intelligence in business and public administration. At the governmental level, significant resources have been mobilized for infrastructure, models, data, talent, and public use cases. Plans include shared data and corpus models, innovation labs, and centrally accessible AI services. The Balearic Islands are therefore not moving in isolation, but rather as part of a national and European modernization drive.

Spain is in a comparatively good position regarding digital citizen services. A large portion of the population already uses online services offered by public bodies, and digital services for citizens are above the European average. However, the picture is more mixed when it comes to business services. This suggests that further progress depends less on mere online access than on quality, integration, and ease of use.

A solid digital foundation reduces barriers to implementation, but it doesn't guarantee a productivity boost. Many AI projects in the public sector remain stuck in pilot phases due to a lack of benefit measurement, data access, skilled personnel, or organizational accountability. NORAI will therefore have to be judged on its ability to successfully transition from experimentation to stable, regular operation. This transition is usually more expensive and difficult than the technical demonstration itself.

Three conditions for economic breakthrough

First, the platform must initially focus on a few, measurable, high-volume use cases. Rapid learning is important, but it shouldn't be achieved through trivial demonstrations. Suitable processes are those whose current costs and quality issues are known and where human oversight remains practical.

Secondly, NORAI needs strong, centralized governance coupled with genuine responsibility within the specialist departments. Central governance should define the architecture, security, legal framework, catalog, model access, and measurement methods. The specialist departments must be accountable for process design, data quality, and functional results. If responsibility rests solely with IT, organizational change will stagnate. If each department makes decisions independently, the shared platform will disintegrate.

Thirdly, the administration must deal openly with errors and limitations. AI systems will generate incorrect suggestions. The crucial factor is whether these are identified, documented, and systematically reduced. A culture that only allows success stories encourages the concealment of problems. In contrast, a learning administration treats deviations as data for improving models, rules, and processes.

The real return is the state's ability to act

The immediate benefits of NORAI will be measured in hours saved, processing times reduced, and errors avoided. However, the greater return lies in the administration's ability to handle increasing and more complex demands without a proportional increase in bureaucracy. Climate incentives, housing issues, social services, energy transitions, and business services generate additional processes. Without productivity gains, longer waiting times or rising personnel costs are likely.

A functioning AI platform can mitigate this development. It creates reusable capabilities, makes processes measurable, and enables new services to be launched faster. At the same time, it increases the demands on leadership, data management, legal compliance, and qualifications. Technology does not automatically reduce complexity; it shifts some of it to architecture and governance.

The investment of €5.45 million is therefore neither a guaranteed breakthrough nor merely an expensive digital experiment. It is a strategic bet that a regional administration can understand and manage its processes as a cohesive production system. If this succeeds, shorter processing times and better information will only be the most visible results. The deeper transformation would consist of an administration that utilizes knowledge, data, and automation across departmental boundaries without relinquishing responsibility to algorithms.

NORAI wouldn't fail primarily because of the occasional incorrect AI response. It would fail if old processes remain untouched, data responsibility is unclear, savings aren't measured, and every agency builds its own proprietary solution. Success, therefore, depends less on the latest model than on disciplined organizational policy. With NORAI, the Balearic Islands can become a robust model for intelligent regional governance. To achieve this, they must demonstrate that the state can not only purchase AI but also reorganize itself.

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