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

Retailers in the Czech Republic should pay attention: AI isn't just a flashy upgrade - it's a practical lever to cut operating costs, speed up planning and keep shelves stocked.

Local research shows Czech adoption remains modest (around 11%), partly because firms wait for proven results and navigate EU rules like GDPR, but real wins already exist - manufacturers cut planning from days to minutes (Hyundai's 5‑minute production planning saved CZK 13 million) and Bednar FMT moved from spreadsheets to AI-driven forecasts that boosted revenue, illustrating what smarter inventory and demand forecasting can do for stores.

Industry studies also report AI can reduce operational costs by up to 30% and AI-driven inventory systems can cut stock shortages by roughly 20%, making a strong case for pilots that deliver quick, measurable wins.

For a grounded view of local barriers and success stories see the Adastra analysis on Czech AI adoption and Qeedio's roundup of AI opportunities in Czech manufacturing and services, and consider short practical training like the AI Essentials for Work bootcamp to build usable skills fast.

Bootcamp	Length	Early bird cost	Register
AI Essentials for Work	15 Weeks	$3,582	Register for the AI Essentials for Work bootcamp

Faster checkouts and a smoother in‑store experience are already practical in the Czech Republic when user data and computer vision are combined: in a pilot at Arkády Pankrác, Albert put 10 self‑service terminals under the microscope and StringData's clear heatmaps recorded 38,896 screen taps over six live days, proving the revised graphic interface was easy to use and revealing precisely where customers hesitated StringData Albert self-service checkout heatmaps case study; beyond UX testing, computer vision brings ready‑made tools for Czech stores - from crowd monitoring and targeted in‑store promos to cashierless formats and automated shelf checks - as outlined in a practical roundup of retail CV use cases Computer vision in retail: key uses, challenges, and innovations.

Pilots should be narrowly scoped and respect GDPR, since accuracy, real‑time processing and integration with staff workflows remain technical hurdles; when done well, a single heatmap can spot a noon “hotspot” and trigger an extra open lane or a restock alert, turning dozens of customer clicks into concrete improvements on the shop floor - and simple shelf‑monitoring pilots can be an easy next step for Czech chains Shelf monitoring with computer vision for Czech retail.

Metric	Value
Pilot checkouts	10
Live measurement	16–21 December 2020
Screen clicks recorded	38,896
Project duration	18 working days
Location	Arkády Pankrác Shopping Centre

Inventory and replenishment in Czech retail are moving from rule-of-thumb ordering to data-driven orchestration: homegrown forecasting engines now target short horizons (note the Rohlik sales forecasting challenge that focuses on predicting the next 14 days) and feed warehouse plans that aim to keep 17,000+ SKUs fresh while cutting waste and manual judgement calls; see how Rohlik advertises roles to “develop and fine‑tune forecasting models that drive warehouse planning, demand prediction, and supply chain efficiency” (Rohlik machine learning engineer job listing).

Czech pilots pair time‑series models with automation in fulfilment centres - case studies report dramatic productivity gains from AutoStore and partners - so replenishment can shift from reactive restocks to planned, measurable triggers that reduce stockouts and spoilage (Rohlik AutoStore automation case study).

For teams building models, public contests such as the Rohlik sales forecasting Kaggle competition dataset and challenge provide realistic datasets and evaluation against operational KPIs - one clear “so what?”: better forecasts mean fewer emergency replenishments and fresher baskets for Czech shoppers.

Metric	Source / Value
Forecast horizon	Next 14 days (Kaggle challenge)
Assortment size	17,000+ items (Rohlik)
Fulfilment productivity uplift	Reported 2x–3x (AutoStore/Rohlik case)

“There is huge demand across Europe for online groceries delivered quickly and reliably without any compromise on quality. We don't see that as a short-term phenomenon, but as a long-term opportunity around which to build a market-leading proposition.”

Logistics and warehouse teams in the Czech Republic can translate the same AI playbook used globally into real, local savings by focusing on three things: connect scattered data, predict service times, and continuously reroute with live visibility.

Industry write‑ups note AI has moved from experimental pilots to mission‑critical systems, so Czech operators shouldn't wait to test integrations - see the case for an AI strategy for logistics - LastMileExperts.

Practical route engines combine real‑time traffic, vehicle capacity and predictive ETAs to cut fuel burn, lift first‑attempt delivery rates and reduce WISMO calls - an accessible primer is AI route optimization for delivery efficiency - Descartes.

Last‑mile tracking and unified warehouse feeds turn the final leg from a cost centre into a controlled workflow; FarEye's case studies show how live tracking plus ML ETAs and dynamic re‑routing deliver measurable gains and lower support costs - making a narrow pilot on dense Czech routes a high‑leverage next step for retailers that want faster deliveries and fewer emergency restocks.

See last-mile tracking case studies - FarEye.

Outcome	Reported improvement
First‑attempt delivery success	+22% (FarEye case)
Delivery speed (case)	+15% faster (FarEye case)
ETA accuracy (case)	97% (FarEye case)

"After adding FarEye's last mile tracking software, we recorded a 22% jump in first‑attempt success and full fleet visibility that cut unnecessary miles and reduced call center volume."

Dynamic production and merchandise planning in Czech retail is shifting from brittle spreadsheets to AI-driven orchestration that ties forecasting, factory schedules and assortment decisions into one nimble loop: proven local projects show the impact - Hyundai's AI planning cut a multi‑hour process to 5 minutes and saved CZK 13 million, while Bednar FMT moved off manual plans and added €1.75 million in annual revenue - so the “so what?” is simple: faster, repeatable plans free teams to focus on assortment and store-level promotions.

National testbeds and support (see CEITEC's AI‑MATTERS network) give SMEs access to industrial equipment and paid infrastructure for trials, lowering the barrier to deploy predictive maintenance and sequencing; at the same time smarter e‑commerce tools, like Outfindo's AI Product Guide, turn customer choice data into actionable assortment signals that lift conversions and shrink returns.

Start with narrow, measurable pilots - integrate demand forecasts with production and replenishment, measure lead‑time and spoilage reductions, and scale what pays back in months rather than years to make planning both dynamic and defensible for Czech chains.

Metric	Value / Source
Hyundai production planning	5 minutes; CZK 13 million annual savings (Adastra)
Bednar FMT revenue uplift	€1.75 million annual (Adastra)
Manufacturing capacity / inventory	Up to 20% capacity gain; inventory costs up to 15% lower (JUSDA)
CEITEC / AI‑MATTERS funding	CZK 200 million available to Czech testbeds until 2027 (CEITEC)

“Here at RICAIP Testbed Brno, we can provide companies that have an innovative idea but lack the necessary equipment with our entire scientific infrastructure to test the functionality of their concept.”

Price and promotion optimisation in the Czech Republic is already delivering measurable uplifts when e‑commerce teams combine better product feeds, segmentation and algorithmic pricing - Alza's Dotidot pilot shows a 25% jump in clicks and 20% ROI growth while saving 20 developer hours a month, and local campaigns such as Slevomat's CRM‑driven ads report a 3.2× ROI with higher CTR and lower CPC; these wins come from practical tools like automated product feed management, segmented search and shopping ad automation and dynamic price modules (Bayesian, reinforcement‑learning or decision‑tree approaches) that react to demand, competitors and inventory in real time (see Dotidot's Alza case and ROI Hunter's Czech success stories).

However, Czech retailers must balance upside with compliance: EU regulators are actively scrutinising algorithmic pricing for tacit coordination risks, so governance, documented controls and careful vendor management are non‑negotiable - start with narrow, auditable pilots that link pricing moves to conversion metrics and data governance to capture quick wins without raising antitrust exposure.

Metric / Programme	Result (source)
Alza - feed & ads automation	+25% clicks; +20% ROI; 20 hours/month saved (Dotidot)
Slevomat - CRM‑personalised ads	3.2× ROI; +20% CTR; −40% CPC (ROI Hunter)

“We use the feed export tool to advertise products on comparison sites not only in the Czech market but also abroad. It has everything we need to modify feeds with hundreds of thousands of products…” - Jan Michálek, PPC Specialist (Dotidot case study)

Automating back‑office work is proven to free Czech retail teams from repetitive tasks while tightening controls: Prague‑born Rossum's IDP platform has customers reporting dramatic reductions - think 35 seconds per invoice (down from two minutes) across hundreds of thousands of documents - and local partners like Czech accounting firm This One accounting firm customer story say clients save about 75% of their document processing time.

For Czech retailers that run SAP, Rossum's SAP S/4HANA integration not only speeds AP workflows but also validates extracted fields against master data and flags duplicates, helping reduce document‑based fraud risks while keeping processes auditable and EU‑privacy compliant; see Rossum customer stories and metrics and the SAP listing for the Czech subsidiary.

These capabilities make narrow pilots - start with invoices and orders - a quick, measurable win: fewer overtime hours, fewer paper piles, and more reliable data feeding merchandising and cash‑flow decisions, all anchored in enterprise security and compliance standards.

Metric	Value / Source
Time per invoice	35 seconds (down from 2 minutes) - Rossum
This One - time saved	75% average time saved for clients - This One / Rossum
Processing volume example	50,000 invoices/month; 60% STP - Rossum customer metrics

“Rossum helped our clients' accounting departments eliminate manual tasks with the help of its AI and automation. Crucially, we were also able to stop using paper invoices, which is a more sustainable option moving forward.” - Denisa Zdarska, Transition & Innovation Manager, This One

Energy and store‑operations teams across the Czech Republic can turn AI from a cautious idea into concrete savings by starting with tight pilots: national analysis highlights energy as a top AI opportunity, from smart consumption management to failure prediction and renewables balancing (Adastra report on Czech enterprise AI adoption and priorities).

Practical tools already in market show how: Danfoss's Alsense IoT cloud ties refrigeration, HVAC and alarms into dashboards and automated hubs (TempHub, AlarmHub, ServiceHub) so teams can prioritize fixes, maintain food‑safe temps and run continuous commissioning across stores (Danfoss Alsense Food Retail IoT energy and operations monitoring solution).

Real results are tangible - an Axiom Cloud deployment flagged 43 efficiency anomalies and cut 755,000 kWh while delivering $158,600 in annual savings - a vivid reminder that small controller drifts add up fast and that predictive maintenance plus automated setpoint control pay back quickly (Axiom Cloud case study: specialty grocery retailer energy savings in 100 stores).

The pragmatic route for Czech retailers: pick one asset class (refrigeration or HVAC), run an AI Days–style workshop, then launch a monitored pilot that measures kWh, alarms and avoided service calls.

Metric	Value (source)
Energy anomalies identified	43 (Axiom)
Electricity reduction	755,000 kWh (Axiom)
Energy reduction from predictive HVAC control	~28% (Recogizer)
Annual cost savings	$158,600 (Axiom)

“Although I can't control the prices our store pays for energy, I can for sure help them minimize energy waste” - Tom, Energy Manager (Alsense)

Adoption pathways for Czech retailers now run through a clear national scaffold: the National Artificial Intelligence Strategy 2030 (NAIS) ties research, skills and business support into an Action Plan that earmarks roughly 19 billion Czech crowns for project investments - subsidies, retraining courses, manuals and pilots - and explicitly targets SME uptake via incubators, regulatory sandboxes and digital innovation hubs; see the Ministry of Industry and Trade: Czech National AI Strategy 2030 summary Ministry of Industry and Trade: Czech National AI Strategy 2030 summary.

Practical entry points for retailers include applying to European Digital Innovation Hubs (EDIHs) and national testbeds that connect firms to equipment and expertise - Cedefop notes Czechia already lists multiple EDIHs and links NAIS to regional innovation and VET measures Cedefop: NAIS timeline and implementation (EDIHs & VET measures).

Public programs and agency grants have real weight: the Technology Agency has supported AI projects (about EUR 120 million to date) and the Action Plan will be updated annually to stay aligned with rules such as the EU AI Act, making a staged pilot→measure→scale path practical for retailers that want funded, low‑risk ways to test shelf‑monitoring, demand forecasting or energy pilots before rolling out nationwide; see the Digital Skills & Jobs: Czech National AI Strategy overview Digital Skills & Jobs: Czech National AI Strategy overview.

Program / Metric	Value / Source
Planned Action Plan investment	≈ 19 billion CZK (MPO NAIS)
Technology Agency AI support	~ EUR 120 million (Digital Skills page)
European Digital Innovation Hubs (Czechia)	6 entities listed (Cedefop)

“According to our vision, the Czech Republic should be not only a user, but also a creator of advanced artificial intelligence technologies.” - Minister of Industry and Trade Jozef Síkela

Retailers in the Czech Republic face a mix of practical barriers and rising legal obligations as national rules lag behind a fast‑moving EU framework: there are currently no Czech laws that specifically regulate AI, so companies must treat the EU AI Act as the primary rulebook while watching national implementation steps and enforcement planning (see the White & Case AI regulatory tracker for Czech Republic).

That matters on the shop floor - missing documentation, unclear vendor contracts or undocumented model use can move a pilot from

low risk

to a costly compliance problem because the Act's staged obligations kick in quickly (prohibitions begin in early 2025 and later waves cover GPAI transparency and notifying bodies); retailers should note the second wave of obligations affecting general‑purpose models and sanctions starts in August 2025 (Peyton Legal overview of AI Act obligations in Czech Republic).

The practical upshot: build an AI inventory, tighten data governance, and choose vendors that can produce auditable evidence - noncompliance carries heavy penalties (up to €35m or 7% of global turnover for banned practices, and up to €15m or 3% for other breaches), so a single undocumented pricing algorithm or poorly logged customer‑service AI can become an existential risk rather than a pilot project (summary and compliance steps explained in PwC's EU AI Act guide).

Issue	Key fact / date
National AI laws	No specific Czech AI laws yet; focus on EU AI Act (White & Case)
EU AI Act milestones	Prohibitions early 2025; second wave (GPAI, notifying bodies, sanctions) 2 Aug 2025 (DLA Piper / Peyton Legal)
Sanctions	Up to €35M or 7% turnover (prohibited practices); up to €15M or 3% turnover (other breaches) (PwC / Peyton Legal)
Practical compliance steps	AI inventory, data governance, auditable vendor contracts, use sandboxes (PwC / White & Case)

Start small, document everything, and use regulatory sandboxes and national guidance to keep pilots both useful and defensible.

Practical pilots in the Czech Republic should follow a clear, low‑risk roadmap: start with an AI Days workshop to align leadership and frontline teams, then pick one narrowly scoped pilot (invoices, shelf monitoring or HVAC controls) with a single KPI and a 3–6 month payback horizon so the business sees value fast - Adastra's guidance shows this bottom‑up + top‑down mix accelerates adoption and helps teams move beyond Excel to operational AI (Adastra's report on Czech AI adoption).

Use national entry points - EDIHs and the National AI Strategy 2030 action plan - to access expertise and co‑funding (see the OECD overview of NAIS), and shore up basics first: clean data, documented processes and a named owner for scaling.

For teams needing practical, work‑ready skills, consider a focused course such as the AI Essentials for Work bootcamp to learn prompting, tool use and immediate on‑the‑job applications that make pilots operational instead of experimental.

Metric	Value / Source
Czech AI adoption	~11% (Adastra / Eurostat)
EU average	~14% (Eurostat via Adastra)
NAIS estimated annual budget	€125,167,000 (OECD NAIS overview)

“We see the results of our survey as a relatively positive surprise because we follow surveys from institutions like Eurostat or the Czech Statistical Office, and our survey showed slightly better results.” - Roman Renda, Czech Chamber of Commerce (Czech Radio)