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

AI matters for New Zealand retail because it offers practical ways to cut costs and lift efficiency while navigating a uniquely sceptical, price‑savvy market: the EY Future Consumer Index finds only 28% of Kiwis think AI's benefits outweigh the negatives and shoppers are increasingly focused on essentials, so transparency matters (EY Future Consumer Index: Winning Over New Zealand Consumers).

At the same time, NZ firms report rapid uptake and real gains - 82% using AI and 93% saying it boosts efficiency - meaning tools from chatbots and demand forecasting to dynamic pricing can trim waste and free staff for high‑value service (Kinetics report on AI‑Driven Productivity Gains in New Zealand (2025)).

Local analysis also argues NZ's strength in tangible sectors makes it well‑placed to use AI as augmentation, not replacement, so retailers can embrace planogram optimisation, tailored offers and smarter replenishment without losing the human touch (University of Auckland: New Zealand's AI advantage).

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“The future isn't AI versus humans – it's AI + humans.” - Michael Summers‑Gervai, EY

AI in New Zealand retail has moved fast from curiosity to everyday tool: national surveys show broad uptake (around 82% of organisations use AI) and big efficiency wins, with many firms reporting faster workflows, clearer forecasting and lower operating costs - precisely the outcomes retail teams need to shrink waste and free up staff for service.

Practical retail use cases are already mainstream - demand forecasting, inventory optimisation, chatbots for customer support, and targeted promotions - and specialist tools such as visual merchandising and planogram optimisation can cut the time staff spend rearranging displays while lifting shelf‑level sales.

Two independent snapshots underline the momentum: large numbers of NZ businesses report productivity gains and positive operational impact, yet firms still flag skills, scaling and governance as blockers that need practical, role‑based training and clear policies to convert pilot wins into store‑wide change.

Metric	2025 figure	Source
Organisations using AI	82%	Kinetics report: AI-driven productivity gains in New Zealand (2025)
Businesses reporting improved efficiency	93%	Kinetics report: AI-driven productivity gains in New Zealand (2025)
Organisations reporting positive operational impact	88%	Datacom State of AI Index 2025: AI insights for New Zealand

“It is encouraging to see New Zealand organisations capitalising on the benefits AI offers. We are still seeing business leaders calling for greater guidance and support around AI and 50% rank New Zealand's position in AI innovation and regulation as “lagging” compared to other countries.” - Justin Gray, Managing Director, Datacom New Zealand

Inventory and demand planning in New Zealand stores is moving from gut feel to data-driven precision as AI models pull together POS history, weather, social signals and supplier data to cut stockouts and shrink excess holding - so even small stores gain enterprise-grade foresight; Trace Consultants' primer on AI-driven demand forecasting explains how ANZ retailers can boost accuracy and responsiveness through real‑time models and integrated dashboards (Trace Consultants AI-driven demand forecasting for Australia and New Zealand), while specialist platforms like StyleMatrix show how cloud-based inventory tools let footwear and apparel teams reallocate stock across locations quickly (StyleMatrix cloud inventory management reshapes retail inventory in New Zealand).

The payoff in NZ is practical - fewer markdowns, fresher perishable aisles, and planners spending time on supplier strategy not spreadsheet firefighting - but success still hinges on training and change management so staff trust and act on the machine's recommendations.

“The era of reactive inventory management is over,” says Cookesley.

Smarter freshness management is already making a measurable difference for Kiwi supermarkets: Foodstuffs North Island's rollout of WhyWaste - an AI-driven, real-time expiry and rotation system - has helped stores such as New World Whangaparāoa and Four Square Matakana cut food waste by up to 90% in some locations, while surfacing timely markdown or donation prompts and improving forecasting for thousands of short‑life SKUs (Foodstuffs AI-powered stock management case study).

Complementary vendor platforms show how automated shelf‑life tracking, agile pricing and surprise‑bag workflows can both reclaim lost margin and free staff from repetitive expiry checks - VusionGroup reports big gains in associate time savings and value recovery when stores pair forecasting with automated markdowns (VusionGroup food waste management solutions).

The result in New Zealand terms is simple and tangible: fresher aisles, fewer customer complaints, and sustainability wins that translate into community donations and staff time reclaimed for service rather than chasing expiry stickers.

Metric	Detail
Organization	Foodstuffs North Island
Initiative	Rollout of WhyWaste AI across 80+ supermarkets
Scope	Thousands of product lines, many with short shelf lives
Functionality	Tracks expiry dates, prompts stock rotation, suggests markdowns or donations
Integration	Connected with point-of-sale data for real‑time insights
Impact	Food waste drops up to 90% in some locations; improved forecasting and reduced complaints
Outcome	Supports goal of zero edible food waste to landfill by 2027
Stores cited	New World Whangaparāoa; Four Square Matakana

Pricing, promotions and margin optimisation in New Zealand retail is as much about trust and visible value as it is about squeezing margins, which is why AI‑driven dynamic pricing and personalised offers must be used with care: Woolworths' move to localised pricing across its 185 stores - which left some South Island shoppers paying more and saw everyday items like capsicum vary from $3.89 to $4.39 - shows how locationed price changes are noticed immediately by consumers (Newsroom article on Woolworths New Zealand localised pricing).

Data from dunnhumby underlines the point that base price shapes value perception - 89% of Kiwis check non‑promo prices, so AI should protect everyday “key value” lines while using targeted promotions and margin‑aware rules to lift conversion (Dunnhumby analysis: balancing pricing and promotion in New Zealand).

Practical guidance from pricing specialists also warns that algorithmic repricing needs human guardrails - when done well, dynamic pricing can reduce waste and recover margin; done without oversight it can erode trust, so short, transparent rules and collaboration with suppliers deliver the quickest, least‑risky wins (Simon‑Kucher guide to dynamic pricing dos and don'ts), leaving shoppers better off and retailers protecting long‑term loyalty.

“This isn't a move to lift our margins, it's about offering better value to our customers.” - Pieter De Wet, Woolworths NZ

Building on inventory and pricing improvements, supplier automation and smarter purchase‑order workflows are where New Zealand retailers can squeeze obvious, low‑risk savings: AI-powered RFQ automation for procurement and supplier selection accelerates sourcing and evaluates bids on reliability, ESG and total cost rather than price alone, while unified source‑to‑pay platforms centralise spend, automate PO creation and match invoices to receipts so teams stop chasing paperwork and start improving supplier relationships (GEP guide to AI automation in procurement evaluation).

Practical NZ deployments already show the payoff: source‑to‑pay automation shortens approval cycles, surfaces early‑payment discounts and gives suppliers a self‑service portal that reduces admin friction - Fujifilm's NZ offering describes exactly these flows, from automated PO tracking to AI invoice validation (Fujifilm Source‑to‑Pay automation in New Zealand).

The clearest gain is human: procurement staff move from firefighting to strategy, and AI can even flag a likely late supplier before the truck arrives - a tactile win that keeps shelves stocked and margins intact.

Customer service automation is already shifting the balance in New Zealand retail from queues and routine calls to smarter, faster support: ASB Bank's “Virtual Assistant” shows what's possible locally, driving a 30% lift in customer satisfaction while cutting support costs by 20% (New Zealand AI business case studies), and SME tools like Aider - paired with voice workflows used by Meebz Coffee - free owners an extra 10+ hours a week to focus on customers rather than spreadsheets.

For retailers this looks like chat and voice bots taking routine queries (order status, returns, basic refunds) while conversational AI and agent‑assist tools surface relevant product offers or next‑best actions to lift basket sizes; cloud contact‑centre platforms with predictive routing shorten handle times and let human agents concentrate on complex sales or loyalty work (Conversational AI for omnichannel customer support) and proven contact‑centre modernisations show chatbots handling the majority of web messaging with measurable CSAT gains (Genesys contact centre chatbot case study).

The net effect for NZ stores is practical: fewer on‑floor interruptions, faster service, and staff time reclaimed for selling and relationship building rather than answering repeat questions - small changes that add up to meaningful cost and efficiency wins.

Project	Impact (reported)
ASB Bank Virtual Assistant	+30% customer satisfaction; −20% support costs (New Zealand AI case studies)
Aider / Meebz Coffee	Saved 10+ hours/week for small business owners via mobile analytics and voice workflows (New Zealand AI case studies)
Toyota Finance NZ (UiPath RPA)	Reduced manual processing time by ~70% for loan approvals and onboarding (New Zealand AI case studies)

“So fraud, for example, there's an urgency involved in it, as opposed to somebody who's just calling in to ask a question about mortgage rates in the future…So how does an agent prioritize this [against] the 10 calls that they have? Which ones should they be answering immediately? Which one is on fire? That's the way to think about it.” - Dr. Tanushree Luke, Head of AI at U.S. Bank

Personalisation in New Zealand retail is proving to be a practical, trust‑sensitive lever: Spark NZ's “Made For You” machine‑learning programme boosted customer retention by 18% and sector campaigns such as New World's “Trolley Truths” drove weekly sales up 44% by turning purchase data into timely offers, showing that relevance pays off (Marketing Association NZ: How Marketers Use Data to Make Smarter Decisions).

The playbook is clear - move from one‑size‑fits‑all blasts to AI‑driven segmentation and predictive recommendations so messages land where they matter (on the app, email, or in‑store when a shopper is near the produce aisle), while protecting privacy and keeping everyday value lines visible (Shopify NZ: Personalization Trends 2025 for Retailers).

Practical tools - AI customer segmentation, dynamic offers, and loyalty integrations - let small NZ retailers target high‑value shoppers without bloated campaigns; start with clean first‑party data, test predictive offers on a modest scale, and measure lift in retention and CLV to make personalisation a predictable, low‑risk source of margin and loyalty (Mailchimp guide: AI Customer Segmentation for Retailers).

Checkout and POS are becoming invisible in Kiwi stores as computer vision and voice tech move payments and workflows away from the counter and into shoppers' hands (or trolleys): Auckland start‑up IMAGR's SMARTCART pairs a phone to the trolley so items appear in a virtual basket and shoppers skip barcode scanning and queues - a practical example of checkout‑less retail being trialled at Four Square Ellerslie (IMAGR SMARTCART checkout-less trial at Four Square Ellerslie).

At the same time, hands‑free in‑store ops are already saving time for small businesses: Aider's mobile analytics and voice workflows, used by Meebz Coffee, reclaimed more than 10 hours a week for owners by automating sales and inventory checks, while local AI voice providers now offer natural Kiwi accents and 24/7 booking or order handling that reduce wait times and missed leads (NewZealand.AI artificial intelligence case studies featuring Aider).

These shifts matter in NZ where voice and “near‑me” queries are mainstream - optimising voice search and voice commerce turns casual local searches into foot traffic and faster checkouts (voice search adoption and optimisation guide for Kiwi businesses) - imagine the checkout that rides in the trolley and frees staff to help the customer who still wants the personal touch.

Metric	Detail / Source
SMARTCART pilot	Four Square Ellerslie trial of IMAGR checkout‑less shopping (IMAGR SMARTCART trial on EcommerceNews)
Hands‑free time savings	Meebz Coffee + Aider: saved 10+ hours/week for owners (Aider and Meebz Coffee case study on NewZealand.AI)
Voice search usage	Nearly half of Kiwis use voice search; high conversion for local queries (voice search optimisation guide for New Zealand businesses)

Back‑office automation is one of the clearest low‑risk wins for New Zealand retailers: Robotic Process Automation (RPA) can ingest invoices, match payments, reconcile bank feeds and batch payroll tasks so finance teams stop firefighting and start analysing - Canon Business Services reports RPA can lift accuracy to ~98% and be operational within weeks via an RPA‑as‑a‑Service approach (Canon Business Services Robotic Process Automation for New Zealand retailers).

Real Kiwi examples are practical and measurable: a property management group processing 400+ invoices a month templated an automated invoicing bot to cut errors and free staff for client work (OCH invoice-processing automation case study (property management)), and broader accounting analysis shows NZ firms are already shifting to cloud tools and continuous close processes so automation delivers real time insight, faster closes and fewer reconciliation headaches (Andersen insights on automation transforming accounting in New Zealand).

The payoff is vivid: a previously “45‑minute” manual payment amendment becomes a repeatable robot action, turning lost hours into strategy time and steadier cashflow for stores large and small.

Process	Example / Impact
Invoice processing	400+ invoices/month automated for an Auckland property manager; fewer errors and repeatable rollout (OCH case study)
Scheduled payments / cash allocation	Automated a complicated 45‑minute manual task, enabling reuse across cash processes (Canon Business Services)
Payroll & HR tasks	RPA reduces manual data issues and speeds onboarding, reconciliations and reporting (Zalaris / Andersen insights)

“The robot amends scheduled payments for customers and ensures they are contacted appropriately based on the new payment schedule. Based on this schedule, we're taking our learnings from this robot and applying it to other cash allocation processes.” - Matt Devine, CFO, Allied Credit

Supply‑chain and logistics optimisation across New Zealand is shifting from paper notes and ad‑hoc routing to simulation, digitised dispatch and driver apps that turn fuel and time into measurable savings: researchers used simulation tools to generate a low‑carbon design for a future NZ freight system (AnyLogic case study: Optimizing freight transport systems in New Zealand), while operators on the ground are cutting kilometres and friction with cloud platforms - Temuka Transport digitised its 90+ truck fleet and now uses a driver mobile app to optimise routes (Temuka Transport digitised fleet and driver app case study (M2X)), and partners such as M2X report customers like Silver Fern Farms shaving over 1 million kilometres a year from their network.

Simulation‑enabled dynamic routing also shows the scale of potential gains: a last‑mile Simio project quantified $12.8M in annual operating reductions and a $66M NPV in a complex distribution network, illustrating how modelling plus simple digitisation (slot booking, route optimisation, carbon visibility) gives NZ retailers faster turnarounds, lower transport costs and clearer decisions about consolidation versus service levels (Simio last‑mile delivery network optimisation case study).

Project / Operator	Highlight
University of Canterbury (AnyLogic)	Simulation to design a low‑carbon future freight system
Temuka Transport (M2X)	90+ trucks; digitised supply chain and driver app for route optimisation
Silver Fern Farms (M2X)	Transport optimisation reduced kilometres by over 1,000,000 km/year
Ballance Agri‑nutrients (M2X)	100% visibility on freight carbon emissions; improved site turnaround
Simio last‑mile project	$12.8M annual operating cost reduction; $66M NPV over 10 years

“M2X ticked all the boxes for us... It's very easy. It's a very user-friendly system. It's not over complicated.” - Muzza Prentice, Bulk Truck Dispatcher, Temuka Transport

Loss prevention in New Zealand retail is moving into an AI-first era - but only where strong governance, transparency and human oversight come first. Major chains have signed up to carefully scoped facial recognition trials after the Privacy Commissioner gave a conditional “cautious tick”, with Foodstuffs' pilot covering 25 supermarkets and processing more than 225 million facial scans (linked to over 130 prevented serious incidents and 1,742 alerts, 1,208 confirmed matches) - a vivid reminder that these systems can be powerful, and also require strict controls around retention, signage, immediate deletion of non‑matches and limits on watchlists (BiometricUpdate report on New Zealand retailers adopting facial recognition technology).

At the same time, payments and fraud teams are fighting back with AI: Visa's NZ roadmap highlights AI‑driven real‑time analytics and biometric checks after AI‑enabled scams helped push 2024 losses toward NZ$194m and follows earlier claims that AI systems stopped NZ$273m in fraud in 2023 - illustrating why fraud detection, clear data‑governance and SMB toolkits must sit alongside surveillance tech (Finextra coverage of Visa's AI security roadmap in New Zealand).

Policy work - a Biometric Processing Privacy Code and calls for independent bias testing (especially for Māori and Pacific peoples) - plus role‑based training and white‑box audit trails turn legal permissions into trustworthy practice, so AI protects staff and customers without eroding community trust.

Metric	Value / Detail	Source
FRT pilot scope	25 supermarkets; >225 million facial scans; 1,742 alerts (1,208 matches); ~130 serious incidents prevented	ID TechWire summary of New Zealand retail facial recognition pilot
Fraud prevented (AI)	Visa reported AI stopped NZ$273M in fraud in 2023	Finextra report on Visa preventing fraud with AI in New Zealand
National fraud losses (2024)	NZ$194M in scam and card fraud losses	Fintech.Global coverage of NZ scam surge and Visa security action
Regulatory action	Biometric Processing Privacy Code & OPC guidance; code due mid‑2025	BiometricUpdate coverage of OPC guidance on biometric processing in New Zealand

“FRT will only be acceptable if the use is necessary and the privacy risks are successfully managed.” - Michael Webster, New Zealand Privacy Commissioner

New Zealand retailers are already seeing the human upside of AI: demand for customer‑facing roles remains strong (SEEK lists thousands of Customer Experience vacancies across Aotearoa, showing retailers still need people to turn efficiency into service), while targeted upskilling - think planogram optimisation and computer‑vision merchandising - lets teams shave hours from routine shelf resets and spend more time selling and advising shoppers (SEEK New Zealand customer experience job listings; Nucamp AI Essentials for Work syllabus: visual merchandising and planogram optimisation).

That shift raises practical people issues too: restructuring or role changes must be handled carefully because New Zealand case law and guidance increasingly require employers to consider redeployment and provide broad information during consultation - so training programmes should be paired with clear consultation and redeployment planning to protect staff and preserve trust (Buddle Findlay legal update on restructuring and redeployment processes).

The result is tangible: better‑trained retail teams, fewer low‑value tasks, and more time on the shop floor where human judgment still wins the sale.

Metric	Detail	Source
Customer experience roles	Thousands of vacancies across New Zealand	SEEK New Zealand customer experience job listings
Upskilling use case	Visual merchandising & planogram optimisation to reduce manual shelf work	Nucamp AI Essentials for Work syllabus: visual merchandising and planogram optimisation
Employment law implication	Duty to consider redeployment and wide disclosure obligations during restructures	Buddle Findlay legal update on restructuring and redeployment processes

New Zealand's retail sector faces familiar barriers - regulatory uncertainty, perceived complexity, limited understanding of AI's value and a skills gap - yet the new national AI Strategy and companion “Responsible AI Guidance for Businesses” aim to turn those hurdles into a practical playbook for adoption by clarifying how existing laws and light‑touch, OECD‑aligned principles apply to everyday systems (New Zealand AI strategy and guidance for business - Digital.govt.nz).

The strategy explicitly favours buying and adapting proven tools rather than chasing costly, home‑grown models, points to incentives such as the RDTI and data‑centre investment to lower infrastructure friction, and targets education and public‑sector leadership to lift confidence - a notable pivot given New Zealand was the last OECD member to publish a national AI strategy and the paper estimates AI could add NZ$76bn to GDP by 2038 (DLA Piper analysis of New Zealand's strategic approach to artificial intelligence).

For retailers the practical message is clear: use the Guidance, start small with governed pilots, pair tools with role‑based training, and lean on public‑sector sandboxes and university upskilling so AI becomes augmentation, not a risky leap.

“The time has come for New Zealand to get moving on AI.” - Shane Reti, Minister of Science, Innovation and Technology

Start small, move fast: New Zealand SMEs get the biggest wins by following a problem‑first roadmap - do a short readiness check, pick one high‑value, repeatable task, run a tightly governed pilot, and pair it with role‑based training and clear rules for data and privacy.

Practical guides recommend exactly this phased approach: use a readiness assessment and infrastructure plan from an implementation roadmap to scope what's needed, adopt the Government's Responsible AI Guidance to set legal and ethical guardrails, and apply the “problem‑first, governance‑enabled” playbook that other Kiwi firms are using to convert pilots into recurring savings (HP AI implementation roadmap for New Zealand; Duncan Cotterill Responsible AI guidance for New Zealand businesses; NSP SME AI playbook for New Zealand businesses).

Quick wins often look mundane but add up fast - automating a 4‑hour weekly invoice job down to 30 minutes or piloting an AI chatbot for order status both free staff to sell and serve, build confidence, and create the data that powers the next, bigger rollout.

Phase	Typical timeline
Strategic alignment & readiness	2–3 months
Infrastructure & scalability planning	3–4 months
Data strategy & governance	4–6 months
Model development & integration	6–9 months
Deployment & MLOps	3–4 months
Governance & optimisation	Ongoing

“The time has come for New Zealand to get moving on AI.” - Shane Reti, Minister of Science, Innovation and Technology

Measuring ROI for AI in New Zealand retail means tracking both the hard dollars and the everyday efficiencies: benchmark headline numbers first - the AI Forum found 91% of businesses report efficiency improvements and more than a quarter saw financial benefits topping NZ$50,000 a year, while Snowflake's research puts ANZ's average AI ROI at about 44% - then layer in operational KPIs that retailers can act on: percentage time saved on repeat tasks, reduction in operating expenses (77% of firms reported cost savings), uplift in forecasting accuracy, and the share of processes moved from pilot to enterprise scale (Datacom shows many projects remain departmental).

Start with short, repeatable measures (hours saved per week, markdowns avoided, shrink reduction, incremental sales from targeted offers) and pair them with a simple ROI formula that counts setup cost (three‑quarters of adopters reported setups under $5,000) against annualised savings and revenue uplift; two thirds of early adopters are already quantifying generative‑AI returns, so make measurement routine, visible and comparable across stores to turn one‑off pilots into predictable value.

For context and benchmarks, see the AI Forum New Zealand productivity findings, the Snowflake ANZ AI ROI analysis, and the Datacom State of AI Index reporting.

Metric	NZ figure	Source
Businesses reporting efficiency improvements	91%	AI Forum
Organisations reporting operational cost savings	77%	AI Forum
ANZ average AI ROI	≈44%	Snowflake research
Productivity gains distribution	20% significant; 28% moderate; 35% minor	Datacom / ItBrief

“The business case for AI is increasingly clear and it is encouraging to see New Zealand organisations capitalising on the benefits AI offers.” - Justin Gray, Datacom New Zealand

Practical Kiwi success stories show how retailers and partners are turning waste into wins: community rescuers and supermarket partners reclaim stock and create real social value (Deloitte's profile of Hawke's Bay charity Nourished for Nil shows the charity generated $8.80 of social value for every $1 invested and helped supermarkets meet waste‑reduction goals - a vivid reminder that rescued food becomes both community benefit and shelf‑level impact; see Deloitte's From waste to worth), while national programmes and funders are backing measurement and redistribution - the Ministry for the Environment notes KiwiHarvest has rescued over 6,000 tonnes and the government is building a national baseline so retailers can target reductions tied to emissions (food waste accounts for ~9% of biogenic methane and 4% of total GHG in NZ).

At store level simple tactics work: past Otago research and local case studies show supermarkets and cafés cutting avoidable waste (supermarkets were estimated at 60,500 tonnes pa, 66% avoidable) by using discounts, repurposing, staff training and tighter forecasting - practical, low‑cost moves that shrink waste, save money and free staff to serve customers better (more on what's known about quantities at Love Food Hate Waste).

Metric	Value / Detail
Nourished for Nil social return	$8.80 social value per $1 invested - Deloitte case study (Deloitte From Waste to Worth New Zealand 2025 report)
KiwiHarvest rescued	Over 6,000 tonnes redistributed (Ministry for the Environment - national support)
Supermarket food waste (retail)	Estimated 60,500 tonnes/year; 66% avoidable (Love Food Hate Waste)
Household landfill food waste (2018)	298,246 tonnes disposed to landfill; 52% avoidable (Love Food Hate Waste)

New Zealand retail leaders should treat the next 12–24 months as a practical roll‑out window: start with tightly governed pilots on repeatable tasks, measure recallable wins, and scale only once trust and controls are proven.

The numbers make the priorities clear - surveys show broad uptake (around 82% of ANZ retailers are using AI) yet only 9% trust AI agents to run customer interactions end‑to‑end, so transparency and human‑in‑the‑loop design must come first (IT Brief 2025 ANZ retailers AI trust and adoption survey).

Pair this with deliberate governance: organisations with stronger AI governance deploy AI more widely and realise measurable business upside - more staff using tools and nearly 5% higher revenue growth - so invest early in simple policies, reporting and bias checks (Deloitte 2025 analysis of trustworthy AI governance in consumer industries).

Small retailers face real cost and skills gaps, so prioritise off‑the‑shelf, low‑cost pilots that free hours for selling, then close the skills gap with focused courses - for example, Nucamp's 15‑week AI Essentials for Work offers role‑based prompt & tool training to turn pilots into predictable value (Nucamp AI Essentials for Work 15‑week bootcamp registration).

Do this and AI becomes augmentation, not risk: better forecasting, fresher shelves and staff time reclaimed for customer service will follow.

Metric	Value	Source
Organisations using AI	≈82%	Kinetics 2025 AI-driven productivity gains report (New Zealand)
Trust AI agents for full customer journeys	9%	IT Brief 2025 ANZ retailers AI trust survey
Businesses reporting improved efficiency	93%	Kinetics 2025 AI-driven productivity gains report (New Zealand)
Governance benefits	~28% more staff using AI; ≈5% higher revenue	Deloitte 2025 analysis of trustworthy AI governance

“AI bias is something organisations need to be particularly mindful of. While AI can be used to support and drive diversity and inclusion initiatives, without the right checks and balances, it has the potential to do the opposite.” - Justin Gray, Datacom