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

Denmark's retailers are moving fast from pilot projects to practical gains: one in four Danish firms with 10+ employees now use AI and 71% say it simplifies workflows, helping boost competitiveness and improve products and services (Invest in Denmark analysis of AI in Danish companies).

Global research suggests AI could lift retail efficiencies by roughly 59% by 2035, but only 28% of retailers treat AI as a top spend and 40% cite funding gaps - so Danish leaders must target high-ROI pilots like inventory optimization and demand forecasting to cut costs without chasing “vanity” projects (Knight Frank report on AI in retail).

Practical steps - for example, a 14-day SKU demand-forecasting plan across Zealand and Jutland - translate strategy into fewer stockouts and smarter markdowns (SKU demand forecasting plan for Danish retail).

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Denmark's AI infrastructure just leapt from idea to industrial-scale capability with Gefion, a national NVIDIA DGX SuperPOD that brings 1,528 H100 Tensor Core GPUs, 191 DGX H100 systems and NVIDIA Quantum‑2 InfiniBand networking to Danish researchers and retailers alike; hosted in a Digital Realty AI‑ready facility running on 100% renewable energy and assembled by Eviden, Gefion is owned and operated by the Danish Centre for AI Innovation (DCAI) after major public‑private backing from the Novo Nordisk Foundation (≈DKK 600m) and EIFO (≈DKK 100m, 15% stake) - read the operational announcement in the Novo Nordisk Foundation operational announcement about Gefion and the launch coverage on the NVIDIA blog launch coverage of Gefion for more context.

For retail leaders, that means local access to high‑throughput training, storage and tools such as NVIDIA BioNeMo and CUDA Quantum for projects that span demand forecasting, energy optimisation and multimodal data pipelines without shipping sensitive datasets overseas.

Feature	Detail
Architecture	NVIDIA DGX SuperPOD (191 DGX H100 systems)
GPUs / CPUs	1,528 NVIDIA H100 GPUs / 382 Intel Xeon Platinum CPUs
Networking	NVIDIA Quantum‑2 InfiniBand
Hosting & Energy	Digital Realty datacentre - 100% renewable energy
Operator / Owner	Danish Centre for AI Innovation (DCAI), funded by Novo Nordisk Foundation & EIFO
Rankings	IO500: 7th fastest production storage; TOP500: 21st

“Gefion is going to be a factory of intelligence. This is a new industry that never existed before. It sits on top of the IT industry. We're inventing something fundamentally new.” - Jensen Huang, NVIDIA

Demand forecasting and inventory optimisation in Denmark is moving from spreadsheet guesswork to nimble, data‑driven action as retailers capitalize on a recovering retail market and clearer economic signals: retail sales accelerated to 2.7% year‑on‑year in July 2025 (Denmark retail sales year-on-year (Trading Economics)), while the EU's spring forecast expects the economy to expand in 2025, supporting higher consumer demand (European Commission economic forecast for Denmark (2025 growth outlook)).

Practical AI pilots - such as a focused 14‑day SKU demand‑forecasting plan across Zealand and Jutland - turn those macro signals into concrete outcomes: fewer stockouts, smarter markdowns and leaner replenishment cycles by stitching POS, online clicks and weather or promotion feeds into probabilistic demand curves (14-day SKU demand forecasting plan for Danish retailers (Zealand and Jutland)).

The immediate “so what?” is operational: when forecasts become reliable at the store‑SKU level, inventory turns improve and working capital is freed for higher‑value activities like localized promotions and faster product rotation.

Indicator	Value
Retail Sales YoY (Jul 2025)	2.7%
GDP growth forecast (2025)	3.6%

“Implementing Aimplan has been a tremendous time-saver. We now have data available, and we can connect to it from virtually anywhere, further enhancing our agility and efficiency in forecasting. It has definitely been a game changer for Fellowmind Denmark, and we look forward to continuing to integrate it into our operations.” - Martin Sams, CFO, Fellowmind Denmark

Pricing and revenue optimisation in Denmark is rapidly shifting from static rules to AI‑driven, real‑time repricing that helps retailers defend margins as competition intensifies - especially with worries about Amazon's approach to low prices and its potential impact on Danish webshops (Priceshape analysis: Amazon entering Danish eCommerce market).

By feeding live competitor feeds, inventory levels, customer behaviour and local demand signals into automated models (for example, minute‑by‑minute adjustments during a Black Friday surge), retailers can capture more value on hot SKUs while clearing slow movers without blanket markdowns - Nimble's writeup shows how real‑time pipelines power exactly these outcomes (Nimble dynamic pricing and real‑time data case study).

AI‑powered systems also enable regional and channel‑aware strategies, bundle opportunities and seamless pushes to POS or electronic shelf labels, as described in Hexaware's case for generative and optimisation engines that learn continuously (Hexaware AI‑powered dynamic pricing and optimisation).

The payoffs are higher revenue and leaner inventory, but Danish retailers should balance automation with transparency and customer fairness to avoid backlash and preserve trust.

Customer service automation in Denmark is already shifting from expensive phone queues to smart, local-first chatbots that handle routine asks, surface store‑SKU availability and free human agents for complex cases - a practical step highlighted by Elkjøp's straightforward, flexible Kindly e‑commerce chatbot implementation (Elkjøp Kindly e-commerce chatbot case study).

Retail‑focused research shows these bots win acceptance quickly (roughly 34% acceptance for online retail) and drive real operational benefits - think 24/7 order‑tracking, instant returns guidance and cart recovery - so Danish chains can reduce peak‑season headcount and cut handle times while keeping service consistent (Retail chatbot industry analysis and acceptance rates).

Integration matters: when chatbots tap live inventory and customer profiles they become virtual shop assistants - confirming in‑store stock, enabling BOPIS workflows and even multilingual replies for Denmark's diverse shoppers, a capability documented in Shopify's guide to retail chatbots and integrations (Shopify guide to retail chatbots and inventory integrations).

The “so what?” is vivid: one retailer noted nearly a third of chats happen outside opening hours, turning midnight browsers into satisfied buyers and measurable savings for service teams - making chatbots a practical, high‑ROI pilot for Danish retailers willing to pair NLP with strong escalation rules and regular training of the bot's knowledge base.

Personalisation in Denmark is shifting from one-size-fits-all email blasts to precise, data-driven touches that feel local and timely: retailers that blend behavioural RFM signals with modern clustering - think K‑Modes and ensemble methods used in recent research at SDU - can group shoppers by real purchasing patterns rather than crude demographics (K‑Modes clustering study).

Combined with predictive segmentation frameworks that integrate stated, derived and inferred data, Danish marketers can move toward near one‑to‑one experiences without drowning in noise - targeted offers, regional assortments and lifecycle nudges that feel bespoke to Copenhagen commuters or Jutland weekend shoppers (SAS on predictive segmentation).

Practical pilots - small RFM segments fed to personalised campaigns and measured for conversion - deliver the

“so what?”: fewer wasted impressions, more relevant offers, and marketing that behaves less like a mass leaflet and more like a trusted local shopkeeper remembering a favourite product;

for stepwise playbooks and use cases, Denmark‑focused guidance can be found in the Nucamp retail guide (Nucamp AI Essentials for Work syllabus).

Sentiment analysis and social listening are practical tools Danish retailers can use to turn noisy social feeds into merchandising signals: APIs that handle Danish text, audio and even video (for example, Repustate Danish sentiment analysis API) can monitor Facebook, Twitter and Instagram for product praise, recurring complaints or emerging trends, while lightweight local models like SENTIDA Danish sentiment analysis model have shown strong, domain‑independent performance in Danish (above 80% accuracy in validation tests), giving confidence that automated signals can be trusted.

Practical pipelines combine these models with Denmark‑specific corpora and labelled sets - Twitter Sentiment, LCC Sentiment and AngryTweets among them - so teams can surface regionally relevant insights, spot a sudden rise in negative posts about an SKU, and react by adjusting assortment, pricing or a targeted promo rather than mass markdowns.

Vendors and in‑house teams can pick between off‑the‑shelf services or custom NLP stacks that use lexicon, ML and deep‑learning approaches (see common methods described in Danish tool writeups), and then validate pilots against local datasets to reduce false alarms.

The payoff is concrete: faster reaction to real customer feelings, fewer bad buys on the floor, and merchandising that feels less like guesswork and more like listening to a trusted neighborhood shopkeeper.

Dataset	Notes
LCC Sentiment	Sentiment corpus - 10,588 words / 499 entries
Twitter Sentiment	Train: 1,215 - Test: 512 (privacy rules apply)
AngryTweets	Crowdsourced sentiment dataset - 1,266 items

Danish retailers can cut shrink, defects and energy waste by turning camera feeds and edge sensors into a practical “artificial eye” that spots anomalies before they cascade into bigger losses: local machine‑vision vendors (from IVISYS and JLI Vision to Sentispec) mean solutions can be sourced and tuned in Denmark rather than shipped abroad (Top computer vision companies in Denmark for retail AI solutions).

Proven loss‑prevention systems - like Diebold Nixdorf's Vynamic Smart Vision - show how in‑store video AI reduces human interventions, trims erroneous self‑checkout transactions from about 3% to under 1% in pilots, and can cut shoplifting sharply when deployed sensitively (AI-driven vision systems for retail shrink and self-checkout fraud).

At the same time, end‑to‑end inspection frameworks automate defect detection, augment scarce defect images with synthetic training data, and enable near‑real‑time analytics so lines stop for a fault rather than shipping faulty goods - delivering lower rework, less waste and measurable sustainability gains (Capgemini Intelligent Inspection for automated defect detection).

The upshot for Denmark: combine local vision suppliers, careful privacy and GDPR practices, and targeted pilots (self‑checkout, receiving docks, production lines) to cut losses, save staff hours and free energy and capital for growth.

Provider (Denmark)	Primary focus
IVISYS	Logistic machine vision / real‑time monitoring
JLI Vision	Advanced machine vision systems for production
Sentispec	AI computer vision for stock transparency and fill‑rate optimisation

“There are AI driven video surveillance solutions placed inside of the stores to help to analyze and decrypt suspicious actions... these technologies work, because these solutions [reduce shoplifting] by 40%.” - Hervé Grelet (RETHINK Retail)

Enterprise readiness for AI in Danish retail now hinges on three connected pillars: iron‑clad data governance, local compute that preserves sovereignty, and clear regulatory guardrails so pilots scale without legal surprise.

Denmark's new high‑performance nodes - most notably the NVIDIA DGX SuperPOD “Gefion” hosted under the Danish Centre for AI Innovation - give retailers in‑country training and inference power while explicitly supporting Danish data sovereignty (NVIDIA Gefion DGX SuperPOD Denmark operational announcement), and DTU's Computerome already offers fenced storage (50 PB) and strict access controls that log every access for auditability (DTU Computerome fenced storage and Danish data sovereignty details).

At the same time, legal and compliance teams must factor the Danish AI bill (introduced Feb 2025, with an implementation window tied to the EU AI Act) into procurement, model‑risk and vendor clauses so high‑risk systems meet transparency, human‑oversight and GDPR requirements (Bird & Bird Danish AI regulatory roundup and EU AI Act implications).

Practical next steps for retailers: treat sovereignty as a feature in vendor selection, bake governance into data pipelines, and run small, auditable pilots that prove both ROI and compliance - because in Denmark, control over data is now a competitive asset, not just a checkbox.

Item	Key detail
Gefion	NVIDIA DGX SuperPOD in Denmark; supports Danish data sovereignty
Computerome	50 PB storage; fenced Danish‑soil hosting; access logs retained (auditability)
Danish AI Law	Bill introduced Feb 26, 2025 - complements EU AI Act with national enforcement & oversight

“Every time someone accesses the data, it is recorded, and the logs are stored for 15 years.”

An action‑focused roadmap for Danish retailers starts with tight, measurable pilots: run a 14‑day SKU demand‑forecasting pilot across Zealand and Jutland to prove near‑term inventory turns and reduced markdowns (see the 14‑day 14-day SKU demand forecasting plan for Denmark retail), and in parallel launch a small responsible AI assistant pilot using Securiti's practical nine‑step checklist - define the use case, map and minimise data, use RAG to reduce hallucinations, instrument logging and QA, and plan escalation paths (Securiti responsible AI assistant nine-step checklist for Denmark).

Make governance non‑negotiable by naming an executive owner, embedding EY's responsible‑AI principles into procurement and pilots, and treating auditability as a success metric so pilots can scale without regulatory surprise (EY guidance: How Nordic leaders can drive responsible AI in Denmark).

The payoff is concrete: short, auditable experiments that free working capital, trim service costs and create a repeatable path from pilot to production.

Quick‑win pilot	Scope	Core governance action
SKU demand‑forecasting	14‑day, Zealand & Jutland	Define success metric; data lineage & access controls
Responsible AI assistant	Pilot with limited scope & RAG	Follow Securiti's 9‑step QA, logging & compliance checklist
Continuous audit loop	Monitoring & anomaly detection	Executive owner + audit trail for model changes

“AI ownership is everyone's job - and no one's mandate.”

Beginner-friendly case studies from Denmark show how simple, focused pilots deliver real wins: Ento's AI has already scanned meters and logs across Salling Group's 700+ Danish grocery stores to flag buildings with energy waste, creating a short list of actionable fixes that can be implemented quickly and - based on other operators' experience - often cut electricity use by more than 15% (Ento AI energy waste solution at Salling Group); meanwhile, compact operational pilots like a 14‑day SKU demand‑forecasting plan across Zealand and Jutland translate directly into fewer stockouts and smarter markdowns for store teams (14‑day SKU demand‑forecasting pilot in Denmark (Zealand & Jutland)).

Denmark's market context encourages rapid experimentation - 81% of Danish execs expect GenAI to help their business, yet only a sliver move past pilots - so beginners should pick one measurable KPI, run a tight 2‑week or 30‑day loop, and prove ROI before scaling (BCG State of Generative AI in Denmark report (2024)).

The memorable takeaway: modest pilots turn existing data into an operations to‑do list, so teams can document quick wins and build momentum without heavy upfront spend.

Case study	What it did	Quick win
Salling Group (Ento)	AI identified buildings with energy waste across 700+ Danish stores	Documented energy savings; potential >15% electricity reduction in some cases
14‑day SKU demand forecast (pilot)	Short, focused forecasting across Zealand & Jutland	Fewer stockouts, smarter markdowns, improved inventory turns

“We are already collecting a lot of data from our buildings, which we use in our work to optimize energy consumption. Ento Labs' tool helps us to analyze all this data and find the best cases where we can quickly achieve savings.” - Martin Kortegaard, Energy Manager, Salling Group

For retail leaders in Denmark the path forward is pragmatic: pick two measurable, short pilots - start with a 14‑day SKU demand‑forecasting trial across Zealand and Jutland to cut stockouts and markdowns, pair that with an AI‑pricing pilot to defend margins and react to real‑time competitor signals, and add a focused chatbot rollout to shave service costs and capture off‑hours sales; practical playbooks and use cases for the forecasting pilot are available in the Denmark retail guide (Denmark SKU demand forecasting plan and retail use cases), while recent industry writing shows how AI Pricing turns price pressure into margin opportunity (AI pricing strategies and use cases for retailers) and AI customer‑service automation reduces routine workload so staff can focus on complex cases (AI customer service automation in retail).

Pair pilots with clear KPIs, data‑access rules and a two‑week learning loop, and invest in team capability - Nucamp's AI Essentials for Work helps nontechnical staff learn practical AI tools and prompting methods to run these pilots effectively (AI Essentials for Work syllabus and registration).

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