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

AI is fast becoming a practical tool for Norwegian retailers to lower costs and run leaner, greener stores: Norway's strong digital infrastructure, strict regulation and public trust make it ideal for responsible AI adoption (see Business Norway's overview), and homegrown pilots show real impact - from eSmart Systems' AI that cuts helicopter trips to repair power lines to Völur and Savvie helping meat producers and bakeries squeeze more value and reduce waste.

On the shop floor, demand-forecasting and digital ordering can shrink bakery losses dramatically (Link Retail reports Meny cut bread waste by 30–50%), while industry surveys show near-universal gains in stocking and efficiency.

For retail leaders and staff looking to act now, practical upskilling - for example Nucamp's AI Essentials for Work bootcamp - translates these tools into day-to-day savings and better customer service.

Bootcamp	Length	Cost (early bird)	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work (15 Weeks)

“I believe artificial intelligence can directly promote sustainability.” - Kjell Reidar Mydske, CEO, Smart Innovation Norway

AI is making demand forecasting and inventory management far more reliable for Norwegian retailers by bringing together real-time data, machine learning demand sensing and collaborative forecasting so teams can act faster and with less guesswork - from national chains to local bakeries.

Platforms such as the RELEX unified planning platform show how unifying merchandising, replenishment and store ops on a single data core reduces siloes and improves availability (RELEX customers in Norway include Berggård Amundsen and Mesterbakeren), while AI/ML demand-sensing tools can fold in weather, social signals and supplier inputs to sharpen short-term forecasts and cut excess stock.

Independent analyses also find that augmenting store data with external signals and generative tools can boost forecast accuracy by double-digit percentage points, letting retailers avoid stockouts and shrink waste.

For Norwegian bakers and grocers this translates into fresher shelves and lower markdowns, and for planners it means moving from reactive fire-fighting to predictable, low-touch replenishment powered by explainable models and human oversight - a practical step toward leaner, greener stores.

Read more from the RELEX unified retail planning platform and the Retail TouchPoints AI-driven demand sensing article.

RELEX metric	Value
Customer NPS	61
Customer referenceability	100%
Net carbon impact (tonnes)	950,000
Food waste prevented (kg)	280,000,000+

“Without a proper and accurate forecast, we can't understand the demand… what needs to be supplied, to who, and when. If it's too much, it increases our costs and hence prices to our customers, or too little, which means we won't be able to provide the right offering to our customers. This can have a big impact on our business and the way we serve our customers”, says Peter Grimvall.

AI-driven pricing and promotions are already reshaping Norwegian grocery economics: electronic shelf labels (ESLs) and pricing platforms let chains react to competitors, demand and perishability in near real time, turning markdowns into a tool for both customer value and waste reduction.

Norway's REMA 1000 famously pairs a team of “price hunters” with centralised pricing software so prices can be adjusted across hundreds of stores in minutes, from routine parity moves to peak-event bursts (roughly 100 daily changes in normal conditions, surging to as many as 2,000 around Easter), and nightly automatic markdowns on bread and short-dated items that help cut food waste by about 40% (see NPR's Planet Money profile and Canadian Grocer's REMA write-up).

The upside is clearer stock flows, higher sell-through and smarter promotions; the downside - price wars, pantry-loading and regulatory scrutiny - means safeguards and explainable models matter.

For Norwegian retail leaders, the practical “so what?” is simple: combine ESLs, robust inventory signals and customer-first rules so prices can nudge waste down and traffic up without surprising shoppers - picture a canned-mushroom label blinking from full price to 13.70 kroner while nearby loaves drop to half-price at 22:00.

Metric	Value
Stores (REMA)	675
Price observations per week	Up to 1,000,000
Transactions per day	~700,000
Items per store	~45,000
Competitor observations per day	~150,000
Typical price changes per day	~100 (can spike to 2,000)

“While the store is open, prices shall only go down.”

Personalisation and customer-service automation are already turning routine interactions into measurable gains for Norwegian retailers: secure, source-aware chatbots can answer product and stock questions after hours, plug into inventory to recommend alternatives, and free staff for higher-value in-store advice - a shift that produces the “wow” effect once pilots go live.

Internal assistants like Frøydis show how an AI point-of-contact speeds employee searches across handbooks and SharePoint, improving response accuracy and reducing friction in large, distributed organisations (see the Computas Frøydis AI assistant case study).

Meanwhile, Oslo-focused deployments demonstrate sharp operational wins - big cuts in customer-service costs, faster response times, and notable after-hours conversion uplift when chatbots are integrated with sales and logistics systems (read the Conferbot Oslo chatbot deployment guide).

Practical next steps for retail teams include starting with a concrete use-case, securing the right data connectors and privacy guardrails, and testing empathetic, customer-first reply templates such as Nucamp's damaged-goods response to keep interactions calm and helpful; the result is fewer wasted staff hours and smoother customer journeys that keep shelves moving and shoppers returning.

Metric	Conferbot / Computas
Average time savings on routine admin	94%
Retail customer-service cost reduction (90 days)	78%
After-hours conversion uplift	20–35%
Local deployments / success stories	52,000 (Oslo examples)

“The goal is for Frøydis to be a single point of contact for an employee who has questions.” - Per‑Andreas Drevvatne, Head of DigIT, Frøy

For Norway's small outlets and neighbourhood bakeries, AI is already a practical lever to cut COGS and food loss: consumer‑level tools like the Savvie AI app bring machine‑learning sales forecasting, automated ordering and waste tracking to a phone, helping shops balance sell‑through and leftovers so owners report waste drops of up to 75% and average profit lifts of about 20% while shaving roughly 3.6 metric tons of CO2 per store per year; learn more from the Savvie AI app for cafés and bakeries.

Those gains are vital given Nofima's finding that roughly 300,000 loaves are wasted in Norway every day and that bread alone drives a large share of retail and household waste - hence the Bread Rescuers project aiming to halve bread waste by developing rethink/reduce/repurpose strategies.

Public‑private research partnerships are scaling these approaches too: Link Retail, UiB and Meny secured significant Research Council support to develop AI‑driven waste solutions for grocery chains.

Practical first steps for retailers: connect POS and inventory, trial short‑horizon forecasts on a few SKUs, and test simple repurposing or freezing tactics before wider roll‑out - so that a busy bakery can stop seeing ocean of unsold loaves at close and instead turn smarter forecasts into steadier margins and less landfill.

Metric / Project	Value
Savvie: reported waste reduction	Up to 75%
Savvie: average profitability increase	~20%
Savvie: revenue forecast accuracy	90%
Savvie: carbon reduction per store	3.6 metric tons/year
Nofima: loaves wasted in Norway	~300,000 loaves/day
Nofima: bread waste split	35% retail/manufacturing, 54% households
Nofima: project goal	Halve bread waste (2023–2027)

“Savvie is like a brainy café assistant. It's the first tool to use machine learning to help small food businesses to thrive.” - Jessica Li, co‑founder & CEO, Savvie

Upstream supply‑chain monitoring in Norway is shifting from reactive firefighting to coordinated, AI‑driven oversight: public buyers like Sykehusinnkjøp HF have chosen Ivalua's S2P platform to consolidate category, sourcing and contract flows for a healthcare sector that spends roughly €5 billion a year, bringing standardisation, savings and clearer supplier dialogue (Ivalua S2P procurement platform for Norway healthcare - press release).

At the same time, local supplier‑intel tools are helping firms comply with the Åpenhetsloven and turn messy supplier lists into actionable insight - Ignite's due‑diligence platform, for example, helped a shipping/offshore group screen 1,500 suppliers and report double‑digit efficiency gains while delivering full spend visibility (Ignite supply chain due diligence case study).

For multi‑tier visibility and scenario planning, platforms that combine mapping, early‑warning signals and financial scoring (see o9's multi‑tier risk approach and S&P's RiskGauge capabilities) let buyers prioritise mitigation, speed up onboarding and run “what‑if” swaps before disruptions cascade - so procurement teams can replace guesswork with clear supplier scorecards and faster, lower‑risk sourcing decisions (o9 multi‑tier risk management solutions for supply chains).

Metric / Example	Value
Norwegian specialised healthcare annual purchasing	~€5 billion
Ignite: suppliers screened (Havila case)	1,500
Ignite: reported efficiency gains	+80%
Ignite: spend visibility	100%

“Ivalua's solution will allow us to manage the entire procurement process from one joint platform. The solution will allow further digitization of our processes and simplify the working dialogue with both the health trusts and suppliers. It will also contribute to more standardization of processes tied to category, sourcing and contract management processes, with an increased focus on savings, risk and ESG for both ourselves and the health trusts we serve. In addition, the solution will also support improved intake, portfolio management, and potentially spend,” said Bente Hayes, CEO of Sykehusinnkjøp HF.

Keeping lights on and temperatures steady matters for every Norwegian store - and AI is already turning bulky, costly infrastructure work into a quiet operations win: eSmart Systems' Grid Vision® uses image‑based inventories and dozens of AI models to create verified, virtual asset records and risk scores so utilities can prioritise preventive work and reduce outages that dent store sales and raise energy bills; customers have reported wins such as halving inspection time for some operators and cutting CAIDI by 28% in a field example.

That same data-driven approach supports demand‑shifting pilots (E2U) that move consumption away from grid peaks, opening practical routes for retailers to negotiate lower tariffs or avoid expensive capacity charges.

The practical payoff for stores is less reactive maintenance, more predictable energy costs and faster recovery when storms hit - imagine a field crew armed with photo‑verified asset maps and a maintenance plan instead of guesswork, so stores stay open and shelves stay cold when it matters most.

Learn more about Grid Vision® from eSmart Systems and the recent financing that's scaled these capabilities.

Metric / Example	Value
Inspected T&D lines (programs)	300,000+ km
Utilities partnered	70+
AI models powering Grid Vision®	80+
Inspection time reduced (Repower)	50%
CAIDI improvement (WEB Bonaire)	28%

“These enhanced inspections help us optimize our proactive maintenance activities, shifting work from reactive to preventive leading to improvements in reliability, resiliency, and reductions in overall cost. All of which, create value for our customers.” - Michael Lamb, Senior Vice President, Electric and Gas

Organisations across Norway are already turning AI into measurable workforce wins: Norges Bank Investment Management reclaimed roughly 213,000 hours a year - about the output of 100+ full‑time employees - by pairing mandatory AI proficiency with a small central enabler team and 40 internal ambassadors to embed tools into daily workflows (see the NBIM transformation).

Other signals show the same pattern: reported productivity uplifts (around 20% with Claude in early trials, and 15% in internal polls) have let the fund scale back hiring plans and compress work that once took days - compiling multilingual reports across 16 languages now takes minutes, not days (read the Semafor coverage).

For Norwegian retailers the lesson is practical: make adoption non‑optional but supported, link AI to core data systems, keep humans in the loop, and reskill people into AI‑assisted roles so customer‑facing staff spend more time selling and less time wrestling spreadsheets; for team leaders, Nucamp's guidance on pivoting to AI‑assisted sales roles is a handy next step.

Metric	Value / Source
Hours saved (NBIM)	213,000/year (SmithStephen analysis of NBIM hours saved by AI)
Productivity gain	~20% (Claude) / 15% (internal poll) (Ethan Hathaway report on Anthropic/Claude productivity gains, Semafor coverage of NBIM AI productivity)
NBIM employees	~676 (Semafor report on NBIM workforce and hiring)
Report compilation time	From days to ~10 minutes (Semafor report on report compilation time reduction)

“If you don't use it, you will never be promoted. You won't get a job.”

AI is turning sustainability from a compliance checkbox into a visible business asset for Norwegian retail: platforms like inoqo let grocers assess product-level carbon footprints at scale - Oda now maps emissions across a 6,600‑strong range and even prints shoppers' carbon totals on receipts - so teams can spot supplier hotspots, test lower‑impact formulations and prioritize Scope‑3 reductions (read the inoqo–Oda tie‑up).

At the same time, seafood producers such as Kvarøy combine AI, blockchain and real‑time data to give buyers and auditors end‑to‑end provenance and certification evidence, turning a QR code on a salmon pack into a trust signal rather than a marketing claim (see Kvarøy's digital aquaculture story).

New transparency rules and tools highlighted by Solita - think Digital Product Passports and stronger green‑claims scrutiny - mean AI is also the practical way to meet regulators and reassure customers: the “so what?” is clear, not only fewer fines and recalls but a shop floor where verified labels and supplier scorecards help buyers choose lower‑cost, lower‑carbon ingredients that protect margins while shrinking environmental risk.

Fact	Source / Value
Products assessed by inoqo at Oda	6,600 (inoqo–Oda partnership)
inoqo funding	Closed a 7‑figure round
Kvarøy certifications / digital tools	ASC, MSC, GLOBALG.A.P.; AI, blockchain, laser systems

“The AI powered platform is able to reengineer the composition of thousands of F&B products based on the data the retailers have available today.” - Markus Linder, Founder & CEO, inoqo

Norwegian retailers get a real leg up from three practical enablers: green, high‑capacity Nordic infrastructure, shared AI supercomputing and open Nordic models.

The region's strengths - cheap renewable power and cool climate that suit heavy AI workloads - are already drawing projects like Polar's AI‑focused data centre in Tørdal (DRA01) and larger EU resources, so stores and chains can rely on low‑carbon, resilient compute (read why the Nordics are ideal for AI infrastructure).

At the same time the LUMI AI Factory gives startups, SMEs and researchers access to world‑class GPUs, expert support, training and dataset services, and a pipeline to the upcoming LUMI‑AI supercomputer - a practical gateway for Norwegian teams wanting to train or fine‑tune models for local language and retail tasks (LUMI AI Factory access and services).

That matters because open Nordic LLMs like the Viking family were trained on LUMI and cover Norwegian - so shops, POS vendors and loyalty platforms can build Norwegian‑language assistants and demand‑sensing models without starting from scratch (Viking 13B Nordic LLM trained on LUMI).

Picture a local chain using low‑carbon datacentre capacity plus a Norway‑tuned LLM to answer customer questions in native phrasing - a small, tangible win that reduces friction, speeds service and keeps margins lean.

Enabler	Key fact
LUMI HPL performance	379.7 petaflops
LUMI GPU count	11,912 AMD MI250X GPUs
Polar DRA01 (Tørdal, Norway)	12 MW first phase (AI‑focused)
LUMI AI Factory access	Support & free services for startups/SMEs

“The LUMI AI Factory will be a pioneering AI solution that seamlessly integrates world-class computing power, high-value data, and top-tier AI talent.”

Norwegian retailers face familiar hurdles on the path to useful AI - skills scarcity and competition for talent, patchy data accessibility and security, technical infrastructure gaps, rising compliance questions and local employee scepticism - but each barrier has a practical counter.

Start small with pilot use-cases tied to clear KPIs, pair them with cross‑functional governance and human validation to keep models explainable, and lean on reskilling and job‑protection programmes instead of purely hiring expensive specialists (both recommended in the Cognizant Nordics generative AI adoption report).

Invest to make data not just “clean” but accessible across teams, tighten privacy and cyber controls, and use partnerships or local compute (Nordic supercomputing and vendor alliances) to avoid expensive one‑off builds - steps echoed in the Gallagher 2025 AI adoption and risk benchmarking survey.

Importantly, treat AI as an ongoing programme, not a project: combine short pilots with continuous upskilling, clear procurement guardrails and vendor contracts for safety (see Nucamp's Nucamp AI procurement guide (AI Essentials for Work syllabus)).

The “so what?” is immediate - freeing a store manager from the 40% of their time spent on reports lets them focus on customers - so pairing modest tech investments with people and process change turns risk into advantage.

Barrier	Approx. share / finding
Skills shortage	30% (Gallagher)
Ethical & data privacy concerns	30% (Gallagher)
Compliance & regulatory uncertainty	27% (Gallagher); 45% expect inadequate compliance (Cognizant)
Data accessibility & security	22% rate data accessibility good; 48% say security not robust (Cognizant)

“The need to carefully manage potential risks means that a successful framework for AI integration requires more than investment in technology.” - Mark Bloom, global chief information officer at Gallagher

Case studies show fast, tangible wins for Norwegian retail: small cafés and bakeries using Savvie's phone‑based ML assistant can forecast product sales by weather, weekday and holidays, link directly to POS, and report up to 75% less food waste, ~20% higher profitability and ~90% revenue‑forecast accuracy while cutting about 3.6 metric tons of CO2 per store per year - a practical, low‑cost way to turn leftovers into margin (see the Savvie case study at Business Norway).

Complementary solutions such as Winnow's machine‑vision systems scale this approach into larger kitchens and cafeterias, driving big aggregate savings (Winnow reports over $33M saved annually and 36,000 tonnes CO2 avoided in deployments) - read more at the EIB feature on Winnow.

These vendor tools sit alongside national moves to halve food waste and smarter pricing/redistribution apps, so quick wins for retailers are clear: hook POS into forecasting, trial a few SKU forecasts, and pilot markdowns or donations to protect margin and the planet (overview at Norway Is Reducing Food Waste).

Metric / Example	Value
Savvie: reported waste reduction	Up to 75%
Savvie: profitability increase	~20%
Savvie: revenue forecast accuracy	90%
Savvie: carbon reduction per store	3.6 metric tons/year
Winnow: annual purchasing savings	$33 million+
Winnow: CO2 avoided (deployments)	36,000 tonnes/year
Norway: food waste reduction target/effort	~50% (national initiatives)

“Savvie is like a brainy café assistant. It's the first tool to use machine learning to help small food businesses to thrive.” - Jessica Li, co‑founder & CEO, Savvie

Norwegian retail leaders ready to move from pilots to scale should follow a clear, low‑risk path: use the user‑friendly NTNU guide to AI assistants as a practical checklist (the committee delivered it after a focused two‑to‑three‑month effort), start with small, measurable pilots tied to clear KPIs, and pair each pilot with the five‑step, people‑first adoption practices recommended for retail teams to avoid resistance and lock in gains.

Prioritise role‑based training and internal champions so staff see AI as an augmenting tool - not a threat - and measure ROI from day one; a phased, practical approach is the same playbook advisors recommend for private companies moving GenAI into everyday workflows.

To turn strategy into skills, consider structured upskilling like Nucamp's 15‑week AI Essentials for Work bootcamp that teaches prompt‑writing and job‑focused AI skills and links learning to on‑the‑job use.

Taken together - practical guide, people‑first rollout and targeted training - these steps translate AI from a tech project into predictable cost savings, fewer stockouts and a steadier, more sustainable retail operation in Norway.

Bootcamp	Length	Cost (early bird)	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work

“The guide serves as a ticket to start using AI and to understand how to realize its benefits.” - Professor Jon Atle Gulla