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AI could reduce administrative effort in Flemish public services, but the official sources available do not show that government deployments have delivered net savings. For now, cost saving is a proposition to test—not a proven result. A credible path is to measure specific processes, include the full cost of operating AI, and expand only when savings and service quality hold up.
What the Flemish policy and audit record establishes
Flanders has an AI policy framework intended to help Flemish and local authorities consider AI in public processes and services while managing risks. The plan is a basis for responsible adoption, not evidence that adoption has already reduced public spending.
Audit Vlaanderen’s June 2023 thematic audit examined conditions for mature AI management within public entities and the value of a cross-entity strategy. It recognizes efficiency potential, stating that “Based on these smart and real-time links, the Flemish government can offer solutions to a range of challenges, while making internal processes more efficient.” That describes potential; the audit is a governance assessment, not a financial impact evaluation. It also stresses management and attention to risk across an AI system’s lifecycle. Audit Vlaanderen’s thematic audit and the Flemish AI strategy set out those institutional contexts.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWhy policy budgets do not prove savings
The Flanders AI Policy Plan renewed in March 2024 reports an annual investment of roughly €36 million. Its page also lists an annual implementation component of approximately €9.5 million and a separate annual SME digitalisation component of approximately €12 million. These are investment allocations, not measured returns. The SME digitalisation amount should not be read as funding solely for internal government AI deployment. The policy plan’s budget details describe the components and their purpose.
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The official sources reviewed provide no project-level figure for net savings delivered by Flemish government AI deployments. Without such evidence, neither the plan’s budgets nor general claims about productivity can answer whether AI has saved money. A valid savings claim needs deployment-specific data: baseline workload, implementation and operating costs, oversight, service outcomes, and the period over which benefits were measured.
A practical pathway to test whether AI saves money
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Pick a bounded process and set a baseline
Start with a repetitive administrative task that has a defined service problem, such as avoidable delays or rework. Record current staff time, volume, error and rework rates, and service outcomes before choosing a technology. The goal is to test a real process, not meet an AI procurement target.
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Assign ownership and assess readiness and risk
Identify who is accountable for the service, data, system, and ongoing decisions. Check data quality and access, security, human oversight, and the consequences of errors. Assess whether the process is high-impact and whether its risks can be controlled before procurement or development. Audit Vlaanderen treats mature management and lifecycle risk attention as critical conditions.
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Calculate full lifecycle cost
Count more than the initial purchase or build. Include integration and data preparation, infrastructure or vendor charges, security, oversight, staff training, monitoring, maintenance, and the cost of handling exceptions or correcting errors. Compare these costs with the measured change in workload and service outcomes; budget allocations alone are not a benefit estimate.
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Prepare staff and the organisation
Plan how responsibilities and workflows will change, and train the people who will use, supervise, or be affected by the system. Flanders’ AI support centre includes resources on training, people and organisation, and shared learning—evidence that implementation involves more than selecting a model. The Flemish AI support centre provides that institutional support.
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Pilot and compare results with the baseline
Track net cost, processing time, error and rework rates, service quality, and cases escalated to a person. Include effects on accessibility, citizens, and staff, not just throughput. Measure over a period long enough to capture operating and oversight work. These metrics are a practical evaluation approach; official sources do not establish a universal savings rate.
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Scale only when benefits persist
Expand only if measured gains remain positive after operating and governance costs, while service outcomes remain acceptable. Reassess local adaptation needs before transferring a system across agencies: reusable architecture or data may help, but does not eliminate integration and context-specific costs.
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Generative AI and regulatory checks
Flemish guidance for publicly accessible generative AI advises evaluating risks first and limiting use to situations where those risks can be managed. The page points to guidance version February 2025. That makes open-ended or public-facing use a poor candidate for assumed savings: its suitability depends on the specific task and controls. Flemish guidance on publicly accessible generative AI describes this risk-first approach.
The EU AI Act entered into force on 1 August 2024, and obligations apply in phases. The Belgian FPS Economy page, last updated 3 September 2026, reports targeted amendments introduced by the Digital Omnibus on AI of 8 July 2026. Because obligations can depend on a system’s role and use, check the current authoritative legal text and current Flemish guidance for a specific project rather than relying on a general summary. The FPS Economy’s AI Act information provides the cited update.
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