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

AI is rapidly remaking retail in the Netherlands: EY's European AI Barometer reports that 60% of Dutch companies have saved more than €1 million through AI - and 37% of that group save over €5 million - showing clear payoffs for smarter demand planning, pricing and in-store automation; yet adoption still trails some European peers even as management reports strong productivity gains and employees push for upskilling.

Dutch retailers can turn these savings into competitive advantage by linking data-backed demand forecasting and intelligent inventory allocation with physical stores that remain central to customer discovery and loyalty; practical playbooks and local findings are summarised in the EY Netherlands coverage and in sector-focused guides on demand forecasting & intelligent inventory allocation for the Netherlands.

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“The fact that the majority of management sees positive cost effects from the use of AI is a strong signal. AI has led to cost savings or increased revenue within companies in the Netherlands. AI pays off.” - Menno Bonninga, partner at EY in the Netherlands and AI Lead

Dutch retailers are adopting AI because the business case is clear: faster processes, fewer stockouts, smarter pricing and measurable margin gains. With the Netherlands leading Europe in adoption - reports show roughly 95% of organisations running AI programmes - and a government push backed by about €276 million, the environment now favours practical automation that trims costs and protects revenue.

In retail this looks like AI-driven demand forecasting and inventory algorithms that cut markdowns, digital dynamic pricing that reduces food waste, and price-optimisation models that lift margins (see a Dutch grocery pricing optimisation case study with +0.6% like‑for‑like sales and +1.2% gross margin).

Accessible platforms are lowering the technical bar so store teams and merchandisers can pilot high-impact use cases fast, while retailers also benefit from the Netherlands' talent pool and strict privacy standards that build customer trust.

For a full picture, explore the Netherlands AI automation guide and the grocery pricing optimisation case study linked below.

Key figure	Value
Organisations running AI programmes	95% (Netherlands)
Government AI investment	€276 million
Grocery pricing results	+0.6% LFL growth, +1.2% gross margin

“Implementing the two AI models was a game-changer for our grocery retail business. Thanks to the sophisticated algorithms and machine learning capabilities of the models, we were able to gain deep insights into our customers' behaviors and preferences, and optimize our pricing strategies accordingly. We are excited to continue our partnership with RNDpoint.” - Chief Innovation Officer

Demand forecasting and inventory optimisation in the Netherlands is rapidly shifting from guesswork to a data-driven craft as retailers layer machine learning, time‑series methods and external signals (think promotions, weather and even Dutch holidays) to get the right SKUs in the right stores at the right time.

Platforms such as Manhattan Demand Forecasting software for retail supply chain planning bring automatic self‑correction, multi‑echelon inventory optimisation and better promotional modelling, while ForecastSmart's ForecastSmart AI-powered demand planning by Impact Analytics promises 5–20% uplift in forecast accuracy, big cuts in lost sales and claims toward 99%+ on‑shelf availability.

Industry reporting also shows AI helps ingest unstructured signals (social, field feedback, events) so Dutch retailers can reduce excess inventory and clearance, shorten response times and turn forecasting into a profit lever rather than a cost centre.

Metric	Reported improvement
Forecast accuracy	5–20% (ForecastSmart)
Lost sales reduction	~20% (ForecastSmart)
On‑shelf availability	99%+ (ForecastSmart)
Decrease in excess inventory	25% (o9)
Inventory / fulfillment gains	15% lower inventory, 17% higher fulfillment (McKinsey via Omniful)

“Demand is typically the most important piece of input that goes into the operations of a company.” - Rupal Deshmukh, Partner, Kearney

Pricing and promotions in the Netherlands are increasingly fused with personalization: recommendation engines and personalization platforms let retailers surface the right add‑on, coupon or time‑sensitive offer for Dutch shoppers across web, app and email, reducing wasted markdowns while lifting basket size and loyalty.

Local proof points show the impact - Pets Place, the Netherlands' largest pet retailer, recorded a 15% revenue lift after rolling out tailored product recommendations, and global vendors like Dynamic Yield recommendations platform and platforms such as Retail Rocket personalization platform advertise measurable uplifts in AOV, ARPU and conversion by combining offline purchase data, loyalty signals and real‑time behavior to pick timing and channel for offers.

AIMultiple's roundup of recommendation case studies also underscores that personalized marketing is one of the clearest levers for improved customer experience and sales - a practical win for Dutch retailers balancing tight margins with high customer expectations.

Metric / Source	Reported impact
Pets Place (AIMultiple)	+15% revenue from personalized recommendations
Dynamic Yield (vendor)	+15% ARPU uplift; +62% AOV from recommendation quizzes
Retail Rocket (platform)	~18% order amount increase via product recommendations

“With Dynamic Yield, Sephora customers can seamlessly find the right products for their beauty needs. Personalisation is at the core of our eCommerce strategy and partnering with Dynamic Yield allows us to craft truly customised shopping experiences across all touch points.” - Alexis Horowitz-Burdick, Managing Director, Sephora

Customer service automation is becoming a core cost-and-retention play for Dutch retailers: with 95% of organisations already running AI programmes and a large Netherlands BPO market (projected at roughly US$7.74 billion in 2025), retailers are deploying AI agents and chatbots to deliver 24/7 answers, faster complaint resolution and smarter hand-offs to human specialists.

Advanced AI agents can resolve complex issues and recommend personalised offers that boost repeat visits, while tools that assist live agents free teams to focus on relationship-building - statistics show virtual assistants can cut call, chat and email volumes by about 70% and chatbots can automate up to 30% of routine tasks, often delivering much faster complaint resolution.

For practical vendor options and local providers, explore Zendesk's CX research on AI agents and a roundup of chatbot companies in the Netherlands to match use cases with suppliers.

Metric	Source / Value
Organisations running AI programmes (Netherlands)	95% (Lleverage)
Netherlands BPO market (2025)	~US$7.74 billion (Invensis)
Virtual assistants reduce inquiries	~70% (chatbot statistics)
Share of routine tasks chatbots can automate	Up to 30% (chatbot statistics)

“We take a fundamentally different approach compared to other AI platforms. Rather than focusing on the technology itself, we concentrate on the underlying challenge: enabling business experts to automate their knowledge without getting lost in technical complexity. With Lleverage, describing the problem is all it takes to begin solving it.” - Lleverage CEO

Logistics and last‑mile delivery in the Netherlands are prime targets for AI savings: home‑delivery grocers and e‑commerce teams can use always‑on, dynamic route planning and real‑time monitoring to cut fuel and driver costs while improving on‑time performance.

Norma Live, established as an operational entity in the Netherlands in 2021, combines AI and scientific routing algorithms to complement existing TMS and telematics, offering live re‑routing, a driver app and predictive alerts that help planners respond to traffic, cancellations and last‑minute orders; Softec's product pages describe how dynamic planning reduces operational friction and raises customer satisfaction through full visibility.

Practical vendor solutions like Norma LIVE show measurable efficiency gains - fewer vehicles, shorter distances and less driving time - which make it easier for Dutch retailers to scale same‑day and neighborhood deliveries without blowing up margins.

Explore Norma Live's Netherlands operations and the route‑planning AI case notes for concrete next steps.

Metric	Reported value
Established Netherlands entity	Norma Live operational in NL (2021)
Customers / vehicles / experience	5,000+ customers · 100,000+ vehicles managed · 15+ years
Reduction in vehicles used	Up to 35%
Reduction in kilometres driven	Up to 22%
Reduction in actual driving time	Up to 18%

In the Netherlands, in‑store automation is shifting from checkbox tech to practical shelf intelligence: computer‑vision platforms can monitor shelf conditions, track product placement and ensure accurate pricing so teams spot empty facings, misplaced SKUs or incorrect tags faster and reduce manual walk‑rounds.

Local vendors and platforms - from Vision Platform's retail tools that promise quick shelf and product monitoring to Prime Vision's Product Recognition & Quality Vision modules and Smart Analytics - let retailers tie image data to inventory and loss‑prevention workflows, helping stores react to shrink and pricing errors without adding headcount.

Homegrown specialists such as Object Vision and VicarVision round out a vendor ecosystem that supports real‑time alerts, on‑shelf proofing and automated planogram checks; the result is a more accurate store floor that protects margin and keeps customers finding what they expect.

For Dutch teams balancing tight labour markets and high service expectations, shelf AI turns visual noise into a clear to‑do list for faster restock, fewer markdowns and cleaner audits.

Use case	Example vendor / product
Shelf monitoring & pricing verification	Vision Platform - retail and e‑commerce shelf monitoring tools
Product recognition & quality checks	Prime Vision - Product Recognition, Quality Vision, and Smart Analytics solutions
Local CV integrators for in‑store apps	Object Vision, VicarVision (Netherlands listings)

“For us, being at the forefront of innovation, continuity and sustainability is in our DNA. Prime Vision's solution fits perfectly with that.” - Henk Timmer, Managing Director

For Dutch retailers, procurement and sourcing are moving from opaque spreadsheets to clear, AI‑enabled threads that reveal risk, compliance and opportunity across multi‑tier suppliers: traceability platforms like Circularise multi‑tier traceability platform simplify data collection beyond Tier‑1 and deliver “audit‑ready” insights that make supplier claims verifiable, while conference forums such as the DPW Amsterdam AI sourcing conference are sharpening the sector's focus on putting AI to work across sourcing, spend analytics and tail‑spend automation; analysts at Exiger argue that this multi‑tier visibility can be value‑creating (for instance, the research notes scenarios where firms could save ~10% on materials and reinvest ~2% into sustainability) and that procurement teams who stitch together data assets and generative AI will be able to consolidate spend, spot alternative suppliers and turn risk into measurable savings.

The practical payoff for Dutch retail: faster supplier audits, clearer compliance trails and a data backbone that turns sourcing choices into margin‑protecting decisions rather than guesswork - think audit‑ready proofs instead of months of chasing documents.

Metric	Source / Value
DPW Amsterdam - dates & venue	Oct 7–9, 2025 · Beurs van Berlage
DPW attendee reach	14,000+ attendees; 98.7% recommend
Projected AI adoption uplift (procurement)	187% increase predicted (DPW/Exiger)
Current teams using AI at scale	~20% (Exiger)
Traceability capability	Circularise - audit‑ready multi‑tier traceability

“What we're able to do is extract out of our supply chains the visibility we need to start to see the underlying areas where we can consolidate spend, consolidate supply, and actually achieve change.” - Brandon Daniels, CEO, Exiger

Dutch retailers can cut both latency and cloud bills by moving inference closer to the aisle: locally hosted AI handles vision, POS checks and real‑time personalization without constant uplink to remote datacentres, preserving customer privacy while slashing bandwidth costs.

Home‑grown edge innovators such as Eindhoven‑based Axelera AI offer compact AIPUs (the Metis family) that deliver class‑leading throughput - 214 TOPS - while using just 11.5W, enabling tasks like 24 concurrent 1080p video feeds and self‑checkout monitoring without a data‑centre price tag (Axelera Metis retail AI accelerator for in-store computer vision).

For IT teams managing hundreds of stores, hyperconverged edge platforms like Scale Computing's SC//Platform simplify rollouts, cut TCO and keep stores online with self‑healing automation (Scale reports up to 40% lower TCO and dramatic uptime gains), making pilot‑to‑production a practical path for Dutch chains (Scale Computing retail edge HCI platform for stores).

Hardware partners such as AAEON supply rugged, camera‑ready appliances that slot into existing store networks so deployments scale without forklift upgrades (AAEON rugged edge appliances for smart retail).

The result: faster model updates at store level, fewer cloud costs, and real‑time actions - so inventory gets restocked before a customer even reaches the shelf, not days later.

Solution	Key metric / benefit
Axelera Metis (edge AIPU)	214 TOPS · 11.5W · 24×1080p streams
Scale Computing SC//Platform	~40% lower TCO · self‑healing automation · fast store rollouts
AAEON edge appliances	Integration with camera fleets, Movidius/Jetson options for in‑store CV

“The exceptional performance and accuracy of the Axelera AI Acceleration platform have significantly fueled our collaborative efforts. Its unmatched performance-to-price ratio, surpassing traditional GPUs and dedicated AI Processing units, has been critical in our selection process. We are confident that leveraging their state-of-the-art YOLO performances will empower us to tackle new challenges in our current and future video analysis applications.” - Alexandre Perez, R&D Director at XXII

Netherlands retailers are turning sustainability into savings by using AI to shrink fresh‑produce waste, tighten expiry management and close the loop between supplier, DC and store; home‑grown players are leading the charge - OneThird's AI‑powered NIR scanners and cloud platform can predict shelf‑life to within a day, giving buyers and store teams real‑time signals to reroute, reprice or repurpose stock and reportedly cut fresh‑produce waste by up to 25% while slashing quality‑control labour by as much as 50% (OneThird AI-powered near-infrared shelf-life prediction solution).

Complementary systems such as VusionGroup's food‑waste management layer AI forecasting with automated markdowns and expiry tracking to boost profitability and recover value on near‑expiry goods (VusionGroup AI food-waste management platform).

The impact is tangible in Dutch contexts - from bakery pilots that aim to curb spoilage to the sobering fact that roughly 700,000 loaves are discarded daily in the Netherlands - so AI isn't abstract greenwash but a practical tool that turns expiry dates into decisions and prevents bins from overflowing with edible product.

Metric	Reported value / source
Fresh‑produce waste reduction	Up to 25% (OneThird)
Quality‑control labour savings	Up to 50% (OneThird / FreshPlaza)
Profit & category waste gains	+21% profit, up to 56% category waste reduction (VusionGroup)
Netherlands bakery waste	≈700,000 loaves discarded daily (IOPlus)

“An extra day of shelf-life would save us millions” - Major UK retailer

Dutch retailers operate in a uniquely proactive ethical and regulatory environment: the Netherlands ranks top on the Global Index for responsible AI and the national AI coalition pushes human‑centred deployment, so compliance is as much about trust as it is about avoiding fines (see the Netherlands AI Coalition guidance on responsible AI deployment).

The EU AI Act (in force since 1 August 2024) and strict GDPR requirements mean automated pricing, recommendation engines and workforce analytics need documented governance, DPIAs for high‑risk use and clear consumer notices; the Dutch Data Protection Authority (AP) now coordinates algorithm oversight and keeps a live algorithm register of public‑sector models, while sector supervisors (ACM, DNB/AFM) are primed to review market and financial risks.

The childcare benefits scandal remains a vivid reminder that opaque systems can devastate lives, so retailers should pair technical controls with explainability, bias testing and a training pipeline (national reskilling initiatives and AINED/STAP programmes) to shift jobs from routine tasks to customer experience and AI supervision roles.

Practical steps: register high‑risk deployments, build ethical checks into procurement, and invest in staff re‑skilling to protect margin and reputation.

Fact	Detail
Responsible AI rank	Netherlands - #1 (Global Index) - AIC4NL
EU AI Act	In force 1 Aug 2024 (transition provisions apply)
Algorithm register	700+ algorithms recorded (government register)

“We are driving the responsible AI transition. Together with government, companies and education, we are accelerating AI applications in the Netherlands.” - AI Coalition 4 NL

Beginners building an AI shortlist in the Netherlands can start with practical, local case studies and a few global vendor roundups: the Jumbo Supermarkten story shows how the country's second‑largest grocer (100,000+ employees) used Bloomreach Discovery to rebuild product search and personalization across web and app (Bloomreach Jumbo supermarket case study on product search and personalization); Amsterdam's Lleverage offers a lighter, natural‑language approach to automation (recent funding and fast client wins make it a pragmatic entry point for pilots) and its guide captures why the Netherlands now runs AI programmes at scale; and Google Cloud's expansive catalogue of 601 real‑world generative AI use cases is a handy way to match business problems with vendors and proven patterns.

Together these sources give retail teams concrete pilots to emulate - demanding little custom ML plumbing at first but delivering measurable wins on search, automation and customer experience.

Case / Resource	Snapshot
Jumbo + Bloomreach	Product discovery revamp for NL grocer; web & app personalization
Lleverage (Amsterdam)	Accessible AI automation; notable funding and client pilots
Google Cloud roundup	601 real‑world generative AI use cases to explore

“We take a fundamentally different approach compared to other AI platforms. Rather than focusing on the technology itself, we concentrate on the underlying challenge: enabling business experts to automate their knowledge without getting lost in technical complexity. With Lleverage, describing the problem is all it takes to begin solving it.” - Lleverage CEO

Start pragmatically: pick one high-impact retail use case (demand forecasting, dynamic pricing or customer-service automation), run a tight micro‑experiment, and expand only after clear KPIs prove savings - this stepwise approach is what Publicis Sapient calls “micro‑experiments” to expose data gaps before costly rollouts (Publicis Sapient generative AI retail use cases).

Anchor pilots to business metrics that matter in the Netherlands - reduced markdowns, fewer stockouts, faster complaint resolution - and track outcomes closely: EY reports 60% of Dutch firms now save more than €1 million from AI, so start small to capture a slice of that value (EY European AI Barometer on AI savings).

Invest early in data quality and governance, document DPIAs where needed, and pair pilots with staff reskilling so gains stick; Bain's analysis shows AI can materially trim support costs and shave COGS, making measured pilots a fast path to margin improvement (Bain analysis on AI-driven retail efficiency).

The practical roadmap: prioritise use cases, fix data, run micro‑experiments, measure ROI, govern ethically, and scale winners while retraining teams.

Metric	Value / Source
Dutch firms saving >€1M via AI	60% (EY)
Employees pursuing AI education	53% (EY)
Potential support‑function cost cut	Up to ~20% (Bain)

“The fact that the majority of management sees positive cost effects from the use of AI is a strong signal. AI has led to cost savings or increased revenue within companies in the Netherlands. AI pays off.” - Menno Bonninga, partner at EY in the Netherlands and AI Lead

Conclusion - next steps for retail teams in the Netherlands: treat AI as a staged journey, not a one‑time project - start with one measurable pilot (demand forecasting, dynamic pricing or customer‑service automation), define clear KPIs, and expand only when results prove out; this pragmatic route is reinforced by local data that shows 95% of Dutch organisations are already running AI programmes and nearly one in six Dutch adults now use AI daily, while government backing (≈€276M) and accessible platforms make pilots low‑risk and high‑reward.

Pair pilots with stronger data governance and targeted reskilling so staff move from routine tasks into oversight and value‑added roles, lean on AI‑native tools that let business experts automate workflows (see the Lleverage guide to AI automation in the Netherlands) and consider practical courses like Nucamp's Nucamp AI Essentials for Work course syllabus to build prompt‑writing and workplace AI skills fast; the real payoffs come from disciplined pilots, ethical governance and steady upskilling so Dutch retailers capture the savings and service gains others already report.

“We take a fundamentally different approach compared to other AI platforms. Rather than focusing on the technology itself, we concentrate on the underlying challenge: enabling business experts to automate their knowledge without getting lost in technical complexity. With Lleverage, describing the problem is all it takes to begin solving it.” - Lleverage CEO