How AI Is Helping Government Companies in Czech Republic Cut Costs and Improve Efficiency

AI matters for Czech government companies because it's now an explicit national priority: the Ministry of Industry and Trade's National Artificial Intelligence Strategy 2030 sets seven focus areas - from public administration to education and security - to push AI into real public-sector uses that cut costs and speed services, with implementation plans envisioning roughly 19 billion CZK in project investments to seed automation and decision‑support pilots; yet adoption remains uneven (41% of large firms use AI while overall company uptake is near 11%), so practical training and rapid upskilling are essential - programs such as the AI Essentials for Work bootcamp can equip municipal and state employees with prompt‑writing and tool‑use skills in 15 weeks to turn strategy into day‑to-day savings.

Read the government strategy on the National Artificial Intelligence Strategy 2030 and an independent review of the 2025 AI landscape and adoption data for further context.

Program	Details
AI Essentials for Work	15 weeks; practical AI skills for any workplace; Courses: AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills; Early bird $3,582, regular $3,942; 18 monthly payments; AI Essentials for Work syllabus • Register for the AI Essentials for Work bootcamp

“Artificial intelligence represents a huge potential for our economy and society and can significantly improve our quality of life.”

The National Artificial Intelligence Strategy 2030, updated and approved on 24 July 2024, turns high-level ambition into a practical funding and governance roadmap: the Ministry of Industry and Trade now coordinates seven interlinked focus areas - research, education, labour‑market adjustment, ethics, security, industry and public administration - while an Action Plan within the Digital Czechia programme earmarks roughly 19 billion CZK for projects such as subsidies, retraining, test centres and support for SMEs to pilot AI in real conditions; details on the strategy and its aims are available from the Ministry's press release and the OECD policy entry.

At the same time, national implementation of the EU AI Act is being prepared with a dedicated Implementation Plan that proposes CZK 232 million (2026–2028) for enforcement, new roles and an AI Competence Centre for eGovernment, and a regulatory sandbox for safe testing, as tracked in legal analyses of Czech implementation.

The result is a tightly linked funding landscape: strategic R&D and skills investment plus targeted implementation money to help government companies move from pilots to measurable efficiencies.

Item	Amount / Lead
Action Plan project investments	~19 billion CZK
Estimated annual budget (NAIS, OECD)	€125,167,000 per year
EU AI Act implementation allocation (2026–2028)	CZK 232 million
Primary implementing ministry	Ministry of Industry and Trade (MPO)

“Artificial intelligence represents a huge potential for our economy and society and can significantly improve our quality of life.”

Digitization and automation of administrative tasks are central to the National Artificial Intelligence Strategy 2030's public‑services goal: the plan explicitly promotes automating routine forms, rolling out chatbots and shared APIs, and training officials so citizens get faster, more reliable service.

Practical pilots already show the payoff - AI chat tools cut call‑centre queues and the Ministry's pandemic chatbot was consulted by 76,000 businesspeople in its early weeks - while robotic process automation (RPA) combined with machine learning shortens the time managers spend on repetitive approvals and helps flag anomalies before they become costly errors.

The promise is clear: fewer manual clerical steps, shorter response times and a friendlier citizen experience - but the work isn't done; legacy systems, uneven internet infrastructure and low uptake of e‑services still slow scale‑up, so pilots must be paired with procurement reform and staff upskilling.

For concrete examples of the impact on workloads and user experience, see analyses of Citymind's municipal pilots and the government's NAIS announcement that connects these technical measures to training and ethical oversight.

These moves can turn small process changes into measurable savings across ministries and agencies.

“Artificial intelligence represents a huge potential for our economy and society and can significantly improve our quality of life.”

For Czech government companies, AI decision‑support and complex‑process automation promise to move beyond simple chatbots to systems that help steer multi‑step workflows - everything from automated permit triage to predictive maintenance schedules and logistics routing - so managers can focus on exceptions instead of spreadsheets.

The National Artificial Intelligence Strategy 2030 explicitly singles out public administration for pilot projects, regulatory sandboxes and shared data standards to enable trustworthy decision‑support, while on the ground Czech firms show what's possible: AI production‑planning projects have shortened cycles “from days to hours” and cut costs within months, a pattern government operators can replicate in transport, energy and state enterprises (see practical examples in Adastra's analysis).

At the same time legal and governance work is tightening: implementation of the EU AI Act and national oversight plans aim to certify high‑risk systems and create a sandboxed testing environment so complex automation scales safely (details in the NAIS press release and legal overview).

The payoff is measurable - faster, more consistent decisions and fewer costly bottlenecks when pilots are paired with good data and clear conformity checks.

“Artificial intelligence represents a huge potential for our economy and society and can significantly improve our quality of life.”

Predictive maintenance is already cutting real costs for Czech state enterprises: Prague's metro - which carries about a million passengers daily - equipped 21 escalators with 189 acoustic sensors and an nBox edge processor so neural‑network models spot anomalies, send SMS/email alerts and present technicians with component‑level recommendations via a service app, reducing downtime and service costs (see Neuron Soundware DPP predictive maintenance project).

At the fleet and logistics level, condition‑based platforms show measurable wins too: Arriva Czech Republic reported a 13.5% increase in mean time between failures, a 66% drop in tows and a net saving of roughly 2% per km after adopting a predictive solution, demonstrating how AI lets operators shift maintenance to off‑peak windows, extend asset life and plan routes around vehicle health rather than arbitrary intervals (details from Stratio predictive maintenance analysis).

For government companies, these examples point to faster, cheaper service delivery, fewer emergency repairs and logistics schedules optimised by data‑driven foresight rather than guesswork.

Project / Operator	Key result
Prague DPP (Neuron Soundware)	21 escalators, 189 sensors; edge AI with real‑time alerts and component recommendations
Arriva Czech Republic (Stratio)	+13.5% MTBF; −66% tows; ~2% net cost saving per km

“Like all machinery in the transport system, escalators can break down, which can lead to delays in a critical part of the transport infrastructure. We want to embrace digitalization in monitoring our equipment, which is why we chose a solution where sensors collect acoustic data and process it using AI. This allows us to monitor the equipment remotely and send a specialist out if our Neuron Soundware equipment warns us of a change in the condition of the escalator parts,” - Ing. Petr Vondráček, Head of Transportation System Service, Traffic Route Metro.

Cybersecurity and fraud prevention are core pillars of Czech AI policy: the National Cyber Security Strategy (2021–2025) lays out a national playbook for detection, coordinated incident response and resilient infrastructure while the updated NAIS 2030 and the government's implementation plans tie AI into concrete measures - regulatory sandboxes, conformity assessments and new oversight roles - to ensure safe deployment across public services (National Cyber Security Strategy, NAIS 2030 press release).

On the operational side, AI-driven CERT incident triage and SOC integration - workflows that can sift millions of logs and surface the first glint of malicious activity in minutes - are explicitly encouraged and can be aligned with NIS2 obligations and local SOCs (AI-driven CERT incident triage workflow).

These moves also strengthen fraud prevention: homegrown firms like Resistant AI already target financial crime with machine‑learning models, and the forthcoming sandbox, CTU/ÚNMZ roles and testing regimes are designed to let public bodies and vendors validate such systems before wide rollout - reducing false positives, accelerating response and protecting citizen data.

“Our goal is to create a transparent and quality environment in the Czech Republic that will allow only trustworthy and competent entities to certify AI systems according to the rules of the European Act on Artificial Intelligence,” - Jiří Kratochvíl, Chairman of the ÚNMZ.

Document automation, NLP and chatbots are practical levers for Czech public services to collapse paperwork bottlenecks and surface the right facts faster: academic analysis shows chatbots are already seen as a “revolutionary” tool for modernising administration under the Client‑oriented Public Administration 2030 agenda, while NLP techniques - from summarisation and named‑entity extraction to intent recognition - can turn lengthy legislation, case files and citizen messages into searchable, actionable data for frontline teams (see the scholarly review of chatbots in Czech public administration and HPE's overview of NLP in government).

Home‑grown document‑processing platforms and Czech‑language data services (including solutions that supply native‑speaker training data) mean chatbots and OCR pipelines can be built to respect linguistic nuance and reduce manual review cycles; the strong private‑sector activity and funding cited in national AI analyses suggest these tools can be piloted quickly and scaled once compliance and data‑governance rules are in place (read the national legal and regulatory context in Global Legal Insights and explore Czech NLP service options).

The payoff is simple and vivid: instead of staff digging through file cabinets, AI delivers one clear, short summary that lets a human make the final call.

“The advent of artificial intelligence represents a significant opportunity for the transformation and modernisation of Czech industry. That is why we at the Ministry have decided to assume the leading role in implementing AI into the Czech legal system and to actively support its development and practical application.”

Workforce retraining in Czech Republic is now a policy priority that blends national strategy, regional providers and clear funding channels: the country already boasts 69.1% coverage of basic digital skills but lags on specialist ICT roles (4.3% vs the EU 4.8%), so NAIS and education reforms push lifelong learning, micro‑credentials and targeted reskilling to close that gap - supported by the EUR 7 billion Recovery and Resilience Plan with 22% earmarked for the digital transition.

Practical delivery is local and varied: DigiKoalice coordinates over 200 members while organisations like Czechitas run educational and community centres in eight cities, universities offer lifelong‑learning courses and micro‑credentials, and national career‑guidance networks tie training to regional labour‑market forecasts.

Governance ties these measures back to the National AI Strategy and its implementation roadmap, which assigns ministries and test‑centre roles to ensure training maps to regulated AI use.

For municipal and state operators the logic is simple and vivid: turn clerical roles at risk of automation into supervised, higher‑value posts in data quality, oversight and SOC/CERT workflows so services keep running while staff move up the skills ladder (see the Czech Republic digital skills snapshot and the NAIS timeline and implementation).

More flexible, local micro‑learning and clear career guidance make reskilling realistic rather than theoretical.

Metric / Initiative	Key fact
Basic digital skills coverage	69.1% (Digital Decade 2024)
ICT specialists in employment	4.3% (below EU average 4.8%)
Recovery & Resilience Plan - digital allocation	EUR 7 billion total; 22% for digital transition
Czechitas regional footprint	Educational/community centres in 8 cities
NAIS 2030 approval	Government Resolution No. 520, 24 July 2024

The Czech AI ecosystem is a practical asset for government companies: strong incubators and hubs are seeding vendors that public procurement can tap, with Prague and Brno home to more than 80% of AI firms and research talent and CzechInvest's AI Hub and Technology Incubation drawing heavy interest - 178 project applications and dozens of incubated AI teams as of late 2024 - so municipal buyers can find tested, local suppliers for automation, NLP and predictive‑maintenance pilots; national funding streams such as the TWIST programme and OP TAK also help turn prototypes into deployable products, while nation‑wide events like AI Days (20 cities, 200+ events in 2024) speed matchmaking between startups, integrators and public buyers.

For a straight look at the AI Hub and incubator metrics see CzechInvest's AI & Digital overview and the AI Hub incubation metrics summary, and for details on TWIST and OP TAK funding calls consult the legal and policy review.

Metric	Value / Date
AI Hub project applications	178 applications
AI projects incubated in AI Hub	48 AI-based projects (Nov 2024)
Share of TI incubated startups that are AI	30%
AI Days 2024 reach	20 cities; 200+ AI events
Geographic hotspot	Prague & Brno: >80% of AI companies/researchers

“Artificial intelligence represents a huge potential for our economy and society and can significantly improve our quality of life.”

Concrete Czech examples show AI can pay for itself fast: Hyundai's Nošovice plant used an AI sequencing and optimisation system (built with Adastra) to tame a welding shop with roughly 300 robots and a paint shop that was wasting paint during colour changes, and the measured wins are striking - CZK 13 million saved per year with an investment payback in about three to four months, planning time shrunk to five minutes and paint‑grouping improved by 74% (primer) and 54% (topcoat) after a short PoC; read the detailed Adastra case study and the interview with Artur Heider for the full rollout story.

For Czech government companies, these outcomes are highly relevant: scheduling logic and digital twins that cut idle time on a car line can similarly optimise transit fleets, waste collection routes or maintenance windows in state enterprises, turning a tricky procurement into predictable, auditable savings rather than a speculative gamble.

The practical lesson is concrete - start with a tight PoC, measure material or service‑hour reductions, and scale what demonstrably returns money in months, not years; Hyundai's example gives procurement teams a clear template to demand ROI and fast deployment from vendors.

Project / Metric	Result
Hyundai Nošovice - annual savings	CZK 13 million (~USD 540k)
ROI	About 3–4 months
Planner time to optimise sequence	≈5 minutes
Paint shop grouping improvements	Primer +74%, Topcoat +54%
PoC / test phase	~22 days (2–3 weeks)

“We calculated the savings to be around CZK 13 million per year. I think that's a very strong result. The return on investment was about three to four months.” - Artur Heider, EM Production Planning Specialist, Hyundai (interview with Adastra)

Start small, measure fast, and use local funding and vendors: pick a tightly scoped proof‑of‑concept that targets one clear KPI (reduced integration time, fewer technician visits or faster permit throughput), partner with a Czech AI vendor or incubator, and then scale once the PoC proves net savings - Filuta AI's TWIST‑backed project is a good model because it explicitly aims to cut integration time “from one to two weeks to one to two days,” turning long rollouts into near‑plug‑and‑play wins; teams should also pursue the Ministry's TWIST grants (public call details and application rules are posted by the MPO and covered in the TWIST announcement) since projects can receive up to CZK 30 million and co‑funding up to 70%, but must meet timing rules and submission windows, so engage your Project Support Office early.

Pair procurement teams with technical leads to write outcome‑focused requirements, demand short test phases and ROI metrics, and use local startup talent and incubators (who've raised significant rounds in H1 2025) to keep costs and delivery time down - this sequence turns national strategy funding into on‑the‑ground automation that saves money within months rather than years.

Item	Detail
TWIST max grant	CZK 30 million (up to 70% of eligible costs)
Project window / duration	Start between 1 Mar–1 Sep 2025; max 24 months
Application deadlines (example call)	Contact Project Support Office by 24 Jan 2025; submit by 12 Feb 2025
Example supported project	Filuta AI - CZK 30 million for self‑service planning agents

“The aim of the project is to create friendlier conditions for the use of the agents we have developed directly by clients. We want to reduce the high demands on the expertise of the people who will work with our solution and enable them to use autonomous planning agents completely independently. This will dramatically improve and streamline the scalability of Filuta AI products. The outcome of the applied research of the TWIST project will be, in the case of Filuta AI, a reduction of the integration time of our solution from one to two weeks to one to two days, where the customer will do most of the integration himself, which represents an incredible competitive advantage for them,” - Filip Dvořák, founder of Filuta AI.

The future of AI for Czech government companies looks pragmatic and fund-backed: the National Artificial Intelligence Strategy 2030 gives a clear playbook - from research and skills to ethics and e‑government - and the Action Plan earmarks roughly 19 billion CZK to seed pilots, test centres and retraining so smart automation can move from lab demos to measured savings across ministries (see the Ministry's NAIS press release).

At the same time, EU‑level rules are being translated into national steps (with an implementation allocation and new enforcement roles planned), which means procurement, conformity checks and regulatory sandboxes will soon make it safer to scale high‑impact projects like predictive maintenance and permit triage.

That pathway only works if people can use the tools: short, outcome‑focused training and tight PoCs that prove ROI - examples show payback in months - are the fastest route to cut costs and keep services running.

For teams ready to upskill quickly, practical courses such as the AI Essentials for Work bootcamp turn strategy into everyday competence so local operators, procurement leads and vendors can translate national funding into measurable efficiency gains.

Program	Key facts
AI Essentials for Work bootcamp	15 weeks; Courses: AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills; Early bird $3,582 / Regular $3,942; AI Essentials for Work bootcamp syllabus

“Artificial intelligence represents a huge potential for our economy and society and can significantly improve our quality of life.”