Top 10 AI Prompts and Use Cases and in the Government Industry in Turkey

Türkiye's governments have moved fast from digital services to ambitious national coordination: the Türkiye National Artificial Intelligence Strategy (NAIS) sets concrete targets - raising AI's contribution to GDP to 5% and growing an AI workforce to 50,000 - while building shared infrastructure like a Public Sector Data Space to unlock public data for service improvements (Türkiye National Artificial Intelligence Strategy 2021–2025).

Progress shows real wins - tax audits using AI have uncovered nearly US$700M in underreported revenue - and wide adoption across health, finance and transport, yet high‑risk uses (for example, judiciary tools that could pre‑tag terrorism links) underline why transparency, accountability and clear guardrails are essential (DTO progress report on AI adoption in Türkiye).

For public servants aiming to steer or operate these systems, practical training such as the Nucamp AI Essentials for Work bootcamp registration can turn policy goals into safer, smarter deployments - because strategy succeeds only when people know how to use it.

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Details for the AI Essentials for Work bootcamp	Description: Gain practical AI skills for any workplace. Learn how to use AI tools, write effective prompts, and apply AI across key business functions, no technical background needed. Build real-world AI skills for work. Learn to use AI tools, write prompts, and boost productivity in any business role.
Length	15 Weeks
Courses included	AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills
Cost	$3,582 during early bird period, $3,942 afterwards. Paid in 18 monthly payments, first payment due at registration.
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Registration Link	Register for AI Essentials for Work (Nucamp)

“This should not be perceived as a new strategy, but rather as a refinement of the previous year's planning.”

Selection of the top 10 prompts and use cases began with clear, Turkey‑specific filters: alignment with the Türkiye National Artificial Intelligence Strategy (so the prompts advance measurable public priorities such as health, transport and finance), exposure to regulatory or rights risks (KVKK guidance and the pending AI Bill), and technical feasibility around data access and shared infrastructure like the Public Sector Data Space - plus a pragmatic eye for short‑term wins that deliver public value (for example, tax audits using AI already flagged close to US$700M in underreported revenue).

Each candidate use case was scored against those criteria, checked for ethical and operational safeguards called for by the Digital Transformation Office, and prioritised where local capacity or domestic R&D (e.g., plans for a Turkish LLM) could reduce dependency on external providers; cases that promised strong citizen benefit with manageable legal risk rose to the top.

The result is a list tailored to Türkiye's strategic goals, realistic data constraints, and the national push for trustworthy, domestically anchored AI. Read the national strategy for context and DTO progress for implementation details, and consult the regulatory tracker for the evolving legal landscape.

Methodology Filter	Applied Reason	Source
Policy alignment	Prioritise cases that advance NAIS targets	Türkiye National Artificial Intelligence Strategy 2021–2025
Regulatory & rights risk	Screen for KVKK/AI Bill exposure	Turkey AI regulatory tracker - KVKK and AI Bill
Data & feasibility	Ensure usable public data via PSDS and local capacity	Turkiye Digital Transformation Office progress on AI and Public Sector Data Space

“This should not be perceived as a new strategy, but rather as a refinement of the previous year's planning.”

Smart city upgrades in Istanbul work best when traffic management is treated as part of the city's wider utility and IoT fabric: digitization and near‑real‑time analytics that Waltero highlights for gas and energy networks - where Istanbul's scale (over 6.5 million natural gas subscribers and utilities handling hundreds of thousands of emergency calls annually) already demands faster, predictive response - translate directly into smarter signal timing, predictive maintenance for roadside assets, and dynamic rerouting during incidents (Waltero Utility Asset Management in Turkey 2025 report).

Practical building blocks include city‑grade IoT, smart‑city furniture and integrated sensor networks to feed Edge AI and SCADA systems that spot faults before people do and feed traffic control centres with live operational data (Istanbul IoT and Smart City services - IT Security Solutions).

Any rollout must pair these technical gains with Turkey's emerging governance and privacy rules - KVKK and the draft AI framework call for transparency, risk assessments and registration for high‑impact systems - so planners can unlock congestion reductions without trading away citizen trust (AI regulation in Turkey: KVKK and draft AI framework overview).

The memorable payoff: one well‑placed sensor can turn months of reactive repairs into minutes of coordinated, citywide action.

Izmir's 2020 earthquake response shows how GIS-led coordination and fast data collection can transform disaster response: Needs Map teamed with the Izmir mayor's office and Esri Turkey to spin up an earthquake map and housing workflow in 48 hours, using ArcGIS Online, Survey123 and Dashboards to collect data from over 8,400 displaced families, verify donated homes, and route volunteers in real time with ArcGIS Workforce; the effort delivered rent support to 4,643 people, matched 230 shared homes, and raised about 40 million TRY while volunteers and local donors - from restaurants to a nine‑year‑old selling bracelets - filled urgent needs (a vivid reminder that digital tools amplify public solidarity).

Municipal planners and emergency units can replicate that playbook by combining geospatial situational awareness with automated resource-allocation processes and back-office automation to speed procurement and casework (Needs Map Izmir earthquake response and ArcGIS Online case study) and by following KVKK guidance and operational checklists as they scale (Back-office automation for Turkish public agencies and KVKK compliance).

Metric	Value
Displaced families (data collected)	8,400+
People receiving rent support	4,643
Shared homes allocated	230
Funds raised	40 million TRY (≈ US$5.7M)
Volunteers with ArcGIS access	1,000+
Key supplies distributed	4M COVID packs; 2M hygiene; 400k kitchen kits; 11k food parcels; 10k blankets

“It was a mass individual giving campaign,” said Ali Ercan Özgür.

Ankara's hospitals can move from ad‑hoc bed juggling to proactive capacity management by adopting models and tooling proven elsewhere: systematic reviews map a wide toolbox of statistical and simulation approaches for setting optimal bed numbers (BMC Health Services Research: models and methods for determining optimal hospital bed numbers), recent machine‑learning work demonstrates reliable weekly inpatient demand forecasts, and a new single‑centre study shows how static and dynamic inputs can feed a web-based ward‑level dashboard so administrators grasp bed‑occupancy rates (BOR) in real time (BMC Medical Informatics and Decision Making: machine-learning forecasts for inpatient bed demand, JMIR Medical Informatics: web-based ward and room occupancy tool for real-time BOR monitoring).

For Ankara this means smoother patient flows during seasonal surges, fewer emergency diversions, and faster, data‑driven staffing or elective‑surgery decisions - turning days of manual estimates into actionable, ward‑by‑ward forecasts visible on a single dashboard.

Implementations should pair predictive models with KVKK‑aware governance and local validation before scaling across city hospitals.

Study	Year	Key point
Farmer & Emami - Models for forecasting hospital bed requirements	1990	Early time‑series and regression approaches for acute bed forecasting (foundational methods)
BMC Health Services Research - Scoping review	2020	Catalogue of models and methods to determine optimal bed numbers at hospital and regional levels
BMC Med Inform Decis Mak - ML forecast for inpatient bed demand	2022	Machine‑learning strategy to predict weekly inpatient bed demand
JMIR Medical Informatics - Ward & room occupancy tool	2024	Developed a web‑based support tool combining static and dynamic data so admins can grasp BOR per ward/room

Türkiye's Financial Crimes Investigation Board (MASAK) and procurement auditors are prime beneficiaries of mature AI techniques that combine transaction monitoring, KYC/KYB automation and anomaly detection to cut fraud and strengthen AML workflows: vendors like Faceki AI and ML AML compliance solutions for Turkey highlight ML models that turn historical patterns into high‑quality risk scores and automated reporting, while new rules on crypto‑asset providers - requiring identification for transactions above 15,000 TRY from Feb 25, 2025 - sharpen the data inputs that power those models (Turkey 2025 crypto AML rules for crypto-asset providers).

Homegrown and sector‑specific work also matters: Turkish research shows SVM/ML can flag procurement and retail purchasing fraud, and insurance industry deployments demonstrate the practical payoff - Aksigorta used hybrid analytics and social‑network scoring to boost fraud detection by 66% and move from months‑long investigations to near‑instant triage (Aksigorta fraud detection case study with SAS).

For public procurement and finance teams, the lesson is clear: pair MASAK's legal backbone (Law No. 5549) with targeted ML models, good data hygiene and KVKK‑aware governance to turn scattered red flags into prioritized investigations - sometimes in seconds, not months.

Item	Value / Source
Primary AML law	Law No. 5549 (MASAK enforcement)
Crypto AML threshold (CASPs)	Identification required for transactions > 15,000 TRY (implementation by 25 Feb 2025)
Aksigorta - fraud detection improvement	66% increase; 8 seconds to flag cases; 3,000,000 customers

“It used to take our investigators six months to expose cases of organized fraud. SAS allows us to do it in 30 seconds.”

In Istanbul's commercial courts the shift to e‑justice is already concrete: filings, e‑hearing requests and documents flow through UYAP's Avukat Portal, judges verify attorneys by e‑signature and photo before hearings start, and SEGBİS or the court's own streaming feeds let minutes and exhibits appear on courtroom screens in real time - shortening what used to be a two‑to‑three hour waiting ordeal into a focused, minutes‑long exchange (and making the uploaded hearing minutes immediately visible to all parties) (Virtual Justice overview and UYAP e‑hearing rules).

That convenience comes with strict evidentiary and privacy disciplines: Turkish courts admit digital material only when relevance, lawful collection and authenticity are proven, so commercial litigants should notarize or timestamp key exports, preserve chain‑of‑custody records, and bring court‑certified digital‑forensics reports to validate metadata and timestamps (Digital evidence admissibility in Turkish courts).

Balancing transparency and data protection remains essential - design e‑hearing packages so judges can evaluate evidence confidently while guarding sensitive commercial data under Turkey's data‑security guidance.

Attribute	Value / Source
UYAP total users	4,780,430
E‑hearing availability (nationwide)	260 courts in 30 cities
Istanbul courts with early e‑hearing access	Çağlayan, Istanbul Anatolian, Bakırköy

Automating municipal permit workflows with conversational agents can cut queues and speed decisions, but Türkiye's evolving rulebook makes design choices just as important as technical ones: the Personal Data Protection Authority issued a focused information note on chatbots that flags disclosure, accuracy and logging duties, so any permit bot must be wired for KVKK compliance (KVKK information note on chatbots); controllers running these services should also confirm VERBIS registration and local representation rules because Turkish authorities have begun enforcing registration and penalties for gaps (VERBIS registration and enforcement).

At the same time the Digital Transformation Office's risk‑based approach and the draft AI bill mean municipalities should treat permit chatbots as moderate‑to‑high risk systems - documenting purpose, human‑in‑the‑loop handoffs, transparency labels and retention limits in line with Turkey's broader AI guidance (AI regulation in Turkey: KVKK and risk-based rules).

The practical payoff is tangible: a well‑audited agent can answer common permit queries in seconds while preserving appeal rights and data subject access - whereas an unlogged automated reply can turn a routine permit into a protracted legal and reputational headache.

“I'm Grok, created by xAI. The Turkish court's decision to block access to some of my content is a bummer, but I'm not sweating it too much.”

For Türkiye's National Artificial Intelligence Strategy, digital‑twin modelling offers a pragmatic way to turn high‑level goals into testable, risk‑free policy experiments: a national “budget twin” can run millions of what‑if transactions to compare spending mixes, stress‑test social transfers under different economic shocks, and recommend optimized allocations before a single lira is committed.

Platforms that let analysts construct, experiment and deploy whole policy scenarios - then compare champion/challenger strategies - make tradeoffs visible across dozens of KPIs, not just headline savings (FICO digital twins and simulation platform).

Tactical benefits include faster, cheaper validation of reforms and a lifecycle sandbox for rollout, training and contingency planning (so simulations pay for themselves by avoiding costly mistakes) (Control Engineering guide to asset lifecycle optimization with digital twins); optimization engines can even prescribe the best courses of action from among thousands of scenarios (Simio digital twin simulation guide for beginners).

Implementation needs realistic budgeting - a minimum investment threshold is often cited (~$50,000) and careful data governance - but the payoff can be dramatic: higher service reliability, lower reactive costs, and policy choices vetted in a virtual, reversible laboratory rather than on citizens' backs.

The memorable upside is simple: a well‑crafted twin can reveal the one modest budget tweak that prevents months of bottlenecks and political pain.

Metric	Value / Source
Global digital twin market (2024)	$23.4 billion (Simio)
Projected market (2033)	$219.6 billion (Simio)
Projected CAGR	25.08% (Simio)
Minimum implementation threshold	≈ $50,000 (Simio)
Simulation scale	Hundreds of millions of transactions per scenario (FICO)
Reported operational gains	93–99.49% increased reliability; 40% reduced reactive maintenance (Simio/GE examples)

“AI-powered digital twins can predict equipment failures and recommend corrective actions before issues arise.” - Santosh Kumar Bhoda

Automating IP workflows at the Turkish Patent and Trademark Office can cut prosecution bottlenecks and sharpen novelty checks by embedding best practices from modern prior‑art searching into repeatable pipelines: start with an invention disclosure form and VED‑style feature prioritisation, feed calibrated search queries (including native‑language keywords and family‑member lookups), and run parallel patent and non‑patent literature sweeps so examiners and applicants see a focused set of relevant hits rather than pages of noise - approaches detailed in Sagacious's guides on conducting comprehensive prior‑art searches (Comprehensive Mechanical Prior-Art Search Best Practices - Sagacious guide and the Prior-Art Search Overview Guide - Sagacious).

Coupling automated classification, claim‑mapping and threat‑perception summaries with Turkey‑specific prosecution rules (see the Turkey chapter on worldwide patents for local context) helps turn a sprawling global search into an actionable exam file, meaning a single calibrated search can reveal whether an application faces a clear blocker or a path to freedom‑to‑operate - an operational detail that can save applicants time and reduce downstream litigation risk (Patents Throughout the World: Turkey - country patent prosecution guide).

The memorable payoff: a smart prior‑art engine doesn't just list documents - it highlights the one family member that matters for a rejection or a filing strategy.

Public safety and misinformation monitoring in Türkiye now operate under one of the world's most interventionist frameworks: Law No. 5651 (reinforced by 2020–22 reforms and further 2025 updates) forces social network providers with over one million daily Turkish users to appoint a local representative, answer privacy and personal‑rights complaints within tight windows (often 48 hours) and obey court or BTK takedown/blocking orders that can arrive even faster; failure risks steep fines, ad bans and bandwidth throttling of up to 90% - a sanction that can render a service effectively unusable (Turkey's content‑moderation rules).

Newer reforms layer in algorithm‑transparency, data‑localization and faster judicial response obligations that shift monitoring burdens onto platforms and raise legal exposure for publishers and intermediaries (2025 Internet Law amendments and compliance summary).

The practical upshot for public servants and platform teams: moderation systems must be auditable, KVKK‑aligned and capable of rapid legal triage, because takedown orders and criminalised disinformation provisions can turn routine content decisions into high‑stakes enforcement in hours, not weeks.

Obligation / Rule	Key detail
Local representative	Required for SNPs >1M daily users
Response time	Complaints: ~48 hours; some orders require 24–4 hours
Sanctions	Fines, ad revenue bans, bandwidth throttling up to 90%
Disinformation penalty	Criminalises “public dissemination of misleading information” (prison sentences applied)

“public dissemination of misleading information”

Maintaining continuity on the busy Bursa–Istanbul rail corridor means shifting from reactive repairs to predictive, data‑driven upkeep: the Bandırma‑Bursa‑Yenişehir‑Osmaneli higher‑speed project (201 km) pairs over 60 Volvo machines on site with telematics‑led maintenance - ActiveCare alerts and MATRIS reports track fuel use, idle time, operator behaviour and enable oil sampling to detect wear before failure - so a single alert can mobilise a technician in minutes and avoid days of costly downtime; this hands‑on predictive model is the operational backbone that keeps the line on schedule as Türkiye scales rail capacity and resilience (Bandırma‑Bursa higher‑speed rail project, Volvo CE telematics and MATRIS).

Complementary industry best practices - fiber‑optic sensing, video inspections, RFID tagging and drone patrols - are being discussed in TCDD and UIC fora as Türkiye pairs big‑ticket investments with smarter asset‑management to protect both passenger timetables and freight corridors.

Metric	Value / Source
Line length	201 km
Istanbul–Bursa travel time	1 hour 15 minutes
Annual capacity	30 million passengers; 59 million tonnes cargo
On‑site machinery	60+ Volvo machines
Predictive tools	ActiveCare alerts; MATRIS telematics; regular oil sampling

“ActiveCare alerts our nearest service engineers the moment a technical issue arises, enabling them to intervene immediately and prevent costly delays.” - Nihat Tekin, Ascendum Makina

The next steps for AI in Türkiye's public sector are pragmatic: treat the new risk‑based framework as a roadmap rather than a roadblock, pair mandatory privacy duties with strong documentation, and build governance into day‑to‑day operations so every classifier, chatbot or scheduling model has an owner, an audit trail and a human‑in‑the‑loop escape hatch.

Practical guardrails start with KVKK compliance and VERBİS registration for systems that touch personal data, clear transparency and explainability protocols for automated decisions, and registration or certification pathways for high‑risk systems as the draft AI bill and Turkey's emerging rules align with the EU approach (Complete guide to AI regulation in Turkey, risk‑based, registration and sandbox mechanisms).

Operators should bake privacy‑by‑design into pipelines and maintain bias‑testing, logging and incident playbooks so regulators can verify controls without disrupting services - simple steps that turn regulatory scrutiny into operational resilience (Turkey data protection laws & KVKK practical requirements).

Finally, invest in people: short, practical upskilling such as the Nucamp AI Essentials for Work course helps civil servants translate policy into safer deployments and makes the difference between a contested automated decision and a transparent, defensible public service (Register for Nucamp AI Essentials for Work bootcamp).

In short: govern early, document constantly, train broadly - and use sandboxes and certification to turn pilots into trusted, scalable public services.