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A department store carries the environmental cost of its business model on every line of its accounts: hundreds of thousands of items sourced, a store estate to heat and light, and a delivery network built to move parcels quickly. It takes title to the goods it sells and holds the estate it trades from, so the emissions of the assortment and the emissions of the asset base both affect its own accounts rather than a supplier's or a concession partner's. Both therefore belong in the capital plan. Across the sector they are argued instead in the vocabulary of impact, materiality and disclosure — a language with no line in the accounts just described. IADS found the same during its Sustainability Operation Meetings in 2022 and 2023: the strategies were sound with reporting improving every year, and the case still had to be translated before a board would fund it.  

The case has improved because two things happened: institutional investors converted a stated preference into an applied filter, and quantifying environmental exposure stopped being expensive. The obstacle was the cost of building a shared vocabulary. This has now collapsed with the ability of large language models to read a company's disclosures against its financial statements, work that was once laborious but not conceptually hard. The IADS position is that sustainability should now be run as capital allocation, subject to the same tests as anything else, rather than as a communications function with a budget attached.

The translation problem 

Sustainability proposals in retail rarely fail on merit. They fail because a lighting retrofit or a packaging redesign is described by its carbon saving rather than by its effect on gross margin, and the two are never reconciled. Falabella's 38% year-on-year fall in regional Scope 1 and 2 emissions is a case in point: the reduction is reported as an emissions figure, while the energy efficiency and logistics changes that delivered it necessarily moved an operating cost line that isn’t stated beside it. The retrofit was costed in tonnes for an audience that allocates in margin points, and nobody converted it.

Carbon Trust found that a 20% cut in energy costs delivers the same benefit to the bottom line as a 5% increase in sales. A department store — a large, old, centrally located estate trading long hours — is the most leveraged possible case in a sector where retail buildings are already the largest consumers of energy among non-residential buildings in Europe. Yet the number that would move the decision, expressed in points of like-for-like sales, is left unstated.

The reporting frameworks were not built to close this gap and do not. The four pathways by which any investment creates economic value: reducing current costs, avoiding future costs, protecting revenue and securing investor access carry unequal weight for a department store. Each is examined in turn below. Whichever the pathway, a board weighs it on return and risk alone — and a proposal reporting neither is not competing for capital.

Some effects are quantifiable; others can only be argued directionally, and forcing the second kind into a precise number produces business cases that collapse under questioning. A department store commits capital on incomplete data routinely — an autumn buy placed six months out against a weather pattern nobody has, a concession agreement signed on a brand’s own unauditable sell-through projection — quantifying what it can and bridging the rest with argument. Sustainability proposals alone are asked to prove returns to a precision no other category faces. The evidential bar should be the one the store already applies to its buying and refurbishment decisions.

There is a second, larger distortion. The less-sustainable path almost always appears cheaper because the model has not formally priced regulatory exposure, customer or talent backlash, or eventual disposal costs. Pricing those risks explicitly, whether in figures or in stated direction, and then comparing the sustainable option against the fully loaded alternative typically narrows considerably and often closes entirely.

One feature of the department store model makes this translation harder than it is elsewhere. Between 90% and 95% of a retailer's total emissions sit in Scope 3, and the upstream part of that is, in substance, the emissions its suppliers generate in their own operations and their own supply chains. A department store therefore carries, on its own disclosure, a footprint generated inside businesses it does not own — and a single store might account for only 5% of a given supplier's business, which is why no department store changes a supply chain by itself. The consequence for capital allocation is that the pathways a department store can act on alone are concentrated in the small share of emissions it does own, while the exposure an analyst will model sits in the rest. A proposal that does not say which side of that line it falls on is not yet a business case.

The pathways that pay 

Operational cost reduction is the least contestable of the four pathways: energy efficiency across large store footprints, packaging reduction across distribution networks, and waste recovery that removes landfill charges improve margins without requiring any customer to pay a premium. The magnitudes are company-specific and rarely disclosed as savings, which is itself part of the problem. Where described, the mechanism is an ordinary one: a certified zero-waste programme recovering 90% or more of store waste removes landfill charges, reduces handling cost, and turns part of the residue into a revenue line. All of it lands in operating costs in the year it happens.

Avoiding future costs means pricing regulatory schedules: exposure belongs on the balance sheet rather than in a compliance register. Chile’s Extended Producer Responsibility law imposes progressively tightening recovery targets on producers and importers of electrical and electronic goods; obligations with a timetable can be modelled as liabilities, and the question is not whether the business is compliant today but what the schedule costs over five years. The same exercise is available to every member trading in Europe on a longer schedule — five instruments with published timetables, from CSRD to France’s AGEC law, which makes the retailer accountable for green claims made by the brands it carries rather than only for its own. The French retail federation costed AGEC alone at an additional €3.5bn a year across the sector, around 35% more than retailers were then investing.

Revenue protection is the weakest of the four, depending as it does on a customer premium the survey evidence says is not there. Where it holds, it runs through the supply chain — for fashion and home, traceability of inputs. Falabella verifies material composition, certification and documentary traceability from origin for products carrying environmental attributes — a system that also maps where the business is exposed if an input fails. Its limit is the size of the set it covers: verification runs on the products carrying an attribute, while the rest of the assortment — in a department store, most of it — is estimated. Sector practice has been to hold real data for private label and apply category averages everywhere else — so the published figure is a small verified core surrounded by a large approximation. That is true of every such system now in operation, and it is where the residual analytical cost has moved.

Investor access demands demonstrable competence in identifying and managing material risk — in this sector no longer only an investor preference but a contractual term. Sustainability-linked facilities are already in use among large European department store groups, with the bulk of the margin tied to a few KPIs: meet them and the cost of debt falls, miss them and it rises. Where such a facility exists, a lender has already priced the plan and the board follows the KPIs because the financing depends on them: whether sustainability is a financial matter was settled by the loan agreement, not by the sustainability team.

The measurement problem dissolves 

Establishing the link line by line was prohibitively slow until recently, and the evidence that this has changed is still thin with one demonstration worth examining closely. It was published in Harvard Business Review as “AI Can Measure How ESG Really Impacts the Bottom Line”, by professors Robert Eccles and Shivaram Rajgopal mid-2026, applied four widely available large language models to ExxonMobil’s public disclosures. The objective was to test whether AI could take the environmental and social issues a company itself discloses as financially relevant, map them to specific income-statement, balance-sheet and cash-flow line items, and estimate the effect of performance on each. The same analysis had previously been performed by hand, at a cost of roughly 100 hours. With AI, the core work took approximately one hour, and parts of it were completed in minutes. The cost of a financially grounded sustainability analysis has fallen by roughly two orders of magnitudexxiv.

The limits are acknowledged in the work itself: the choice of model mattered, and human judgment remained essential to determine whether outputs made sensex. AI compressed the labour without removing the need for expertise. The exercise also concerns financial materiality — sustainability as value creation for the company — not a company’s total effect on the world, increasingly termed impact materiality. The reporting frameworks conflate the two into a single score, and the sustainability field has borne the credibility cost.

When this capability reaches individuals faster than ratings agencies, standard setters and fund regulators can adapt, the advantage long held by proprietary ESG scores begins to erode, and the centre of gravity shifts from arguing over whose rating to trust toward examining assumptions and dollar consequences directly. Investors, analysts and regulators can now produce a line-item sustainability risk assessment of a listed department store from its own public disclosures, without its participation. The analysis will be performed regardless; what remains open is whether it is performed internally, early enough to change anything, or read for the first time in someone else’s report.

A risk filter instead of a values mandate 

Much retail sustainability strategy still rests on the premise that a rising generation of values-driven investors will reward environmental commitment with capital. Four years of longitudinal survey evidence, covering American retail and institutional investors, indicate that this premise has decayed. In the first year, roughly 70% of younger investors expressed strong concern about climate risk against 35% of older investors. By 2025, that gap had largely disappeared and willingness to sacrifice returns fell as sharply: young investors who once claimed they would accept 6–10% lower returns now report a tolerance of around 3–4%, indistinguishable from their eldersxxx. Support for fund-manager activism has fallen to roughly one-third of young investors, again broadly equal to older cohorts. ESG support proved far more elastic than commonly assumed.

The same decay is visible on the demand side. Two thirds of Gen-Z shoppers say they are more likely to buy from a retailer with strong ethical credentials; the premium they will actually pay for a sustainable garment has been measured at around €3. A department store cannot fund a transition costed in points of turnover out of a €3 premium: stated preference is high, revealed willingness to pay is not, and a plan resting on the first will be repriced by the second. ESG still matters to capital markets, but as a risk screen rather than a values mandate — a narrower and more reliable role.

Most consequential is the asymmetry in how institutions apply the filter. Poor ESG characteristics can disqualify an investment with otherwise strong fundamentals, while strong ESG credentials rarely compensate for weak financials. For a department store the implication is a spending rule: if the upside of an excellent sustainability narrative is bounded and the downside of an incoherent risk position is not, then money spent on communicating sustainability is money spent against a capped return, while money spent on identifying and managing exposure is money spent against an uncapped loss. The budget should follow that asymmetry, which in most retailers means less reporting and more analysis. Programmes built on stakeholder goodwill are fragile, because goodwill is procyclical and the survey data show it contracting under economic pressure. The likely consequence is not a loss of access to capital but a slow repricing of it. McKinsey and EuroCommerce put the cost of the sustainable transformation at 0.4% to 0.9% of European retailers’ turnover, inside a total transformation requirement of 4.4% to 5.2% against the 3.6% the sector was then investing — €315bn to €615bn across Europe by 2030, in an industry where turnover per square metre has been falling. At that scale the allocation question is not whether to spend but on which side of the asymmetry, and there is no version of the answer in which the communications line grows.

Producing sustainability performance 

Amazon’s packaging programme in Australia shows AI applied not to measuring sustainability performance but to producing it. Shipping goods without additional packaging was constrained by whether a product could travel safely in its own retail packaging, requiring manual review item by item. Amazon deployed a tool that makes that judgement from product data, and reports adding 12,000 products in a single month, that took manual work almost 18 months to accomplish. These are unaudited company-reported figures but the mechanism remains transferable.

The saving is genuine on two counts: the tool determines the minimum packaging consistent with undamaged delivery, so it is not damage cost shifted onto customers, and eliminating an outer box removes material, weight and handling cost — the saving lands in materials and freight whether or not the carbon is ever priced. AI did not invent the packaging strategy; it removed the assessment bottleneck that had kept it sub-scale.

The objection is that Amazon owns the product, the product data and the logistics, and can therefore decide on its own that an item ships in the box it arrived in. A store trading concessions and third-party brands holds no such authority over most of what it sells, and for that part of the assortment the mechanism does not transfer at all. It transfers to private label — up to 30% of turnover in some houses and considerably less in most — the single category in which the product specification, the supplier relationship and the packaging decision sit in the same hands, and where department stores have already done this work by hand, at the pace hand-work allows. Macy’s has reported cutting box volume, waste and virgin plastic use by half on the same logic.

Falabella: from principle to practice 

The most detailed department store case available is also an unfinished one. Falabella Retail’s 2025 sustainability reporting — still in draft, with its regional policy awaiting board approval — makes environmental performance a condition of its omnichannel strategy, over the period of which the company reports moving from a US$200 million operating loss to a US$200 million profit. Falabella frames sustainability operationally in its sustainability report, which is not a neutral venue, and which earns its credibility from the  numbers rather than from the framing.

The link runs through physical infrastructure. Falabella’s regional network — 290,000 square metres of logistics estate moving 30.9 million e-commerce parcels a year — is built for service speed and is also where the environmental gains are won or lost. Regional Scope 1 and 2 emissions fell 38% year on year, achieved through energy efficiency, renewable transition and changes to how stores and logistics centres are run, against a Net Zero 2035 commitment at group levelxlv. The same assets carry the service proposition and the intensity improvement, which is why this part of the investment case requires no customer premium: faster fulfilment and lower emissions per square metre are outputs of the same operational discipline.

While intensity per square metre fell 13%, Falabella’s total emissions rose 19% against 2024 — driven mainly by higher commercial activity feeding through to emissions from the purchase and use of products sold. This is the more useful disclosure of the two, and by some distance: it states a problem no growing retailer has solved, in that intensity and absolutes move in opposite directions because the efficiency gains land on owned assets while the emissions land on sold products. Falabella’s method — targets, measurement and scalable decisions rather than an accumulation of initiatives — does not resolve this divergence: a target on intensity and a target on absolutes cannot both be met by a growing business.

The IADS position is that the two numbers are not both achievable on the current department store model, and that saying so is more useful to a board than another target. If the great majority of emissions is generated by the goods sold, absolute emissions are a function of volume, and a growing store cannot reduce them by running its own assets better. Efficiency lands on the estate; emissions land on the assortment. Falabella’s disclosure makes that visible. The exit usually proposed is circularity, and its most ambitious form is untested. Selfridges’ target of 45% of turnover through rented, refurbished, recycled or resold goods by 2030, from 1% when it was announced, is the right bet even if it is missed. It has not cleared the margin test: at that share of turnover the mix effect on group gross margin is material, and it has not been published. Until then, the defensible position is to be judged on intensity, to disclose absolutes without softening them, and to say which of the two the business is running. A board that has chosen is in a better position than one that publishes both and commits to neither.

The tools required to quantify exposure and construct a case a CFO will accept now run on commercially available models applied to disclosures the company already publishes, at a cost measured in analyst hours rather than consulting engagements. For a department store the constraint is no longer whether the number can be produced but whether anyone in the building is accountable for producing it. The obstacle is now that no one owns the calculation: sustainability teams lack the financial mandate to produce it and finance teams lack the environmental data to check it. Tax is overlooked here as sustainability investments routinely involve credits, depreciation and incentives that never reach the business casel. And the test a CFO should apply is not whether the return is high but whether it was measured on the same basis as the rest of the capital plan.

Analysing what a company discloses is now cheap, acquiring what it does not yet know is not. Reading a set of accounts against a sustainability report is a closed problem with every input present; obtaining primary data from several thousand third-party brands and suppliers is an open one, and as recently as 2023 department stores were still assembling it in spreadsheets, without a traceability tool that covered their needs and without an agreed standard for exchanging Scope 3 data between companies. The measurement excuse has expired for the part of the picture the company controls; for the rest it has become a budget line — and the money that used to go into reporting is the money that should now go into acquiring the data the reporting has been approximating.

What damages financial performance is not sustainability spending but sustainability risk nobody has priced. The reason to do the calculation first is that someone else will do it, using the same disclosures, and a board that has not seen the result before an analyst does will be answering questions rather than asking them

Credits: IADS (Anchita Ranka)

What: AI-driven dynamic pricing is being marketed to food retailers with claims of 2 to 3 percentage points of margin gain, while Canada's three grocery majors publicly rule out the practice.

Why it is important:  The margin case for AI pricing is now quantified and vendor-ready, which shifts the decision from technical feasibility to a deliberate choice between short-term yield and price-trust positioning. 

The pitch came from a Quebec firm at the ALL IN conference in Montreal, which claimed to have lifted a global retailer's profit margins by 2 to 3% through AI-assisted forecasting and dynamic pricing, replacing a strategy that ignored local sensitivities and short-term competitive moves with hourly per-store forecasting. Vendor staff framed the application defensively, citing regional markdowns on slow-selling trousers or celery nearing waste, both standard inventory management. The concern is what happens when the same system identifies a profitable pattern, such as raising rotisserie chicken prices on busy evenings in young-family neighbourhoods or lifting raspberry prices when a competitor runs out of stock.

Metro, Loblaw and Sobeys all confirmed that no dynamic pricing is in place online or in store, and that they do not intend to introduce it, with Loblaw citing customer expectations of fair and consistent prices. Walmart did not respond. In the United States, a Consumer Reports investigation found Instacart prices varying by up to 23% between customers, a practice since discontinued, and a bill now targets electronic shelf labels and surveillance pricing. Canada's NDP motion against personalised dynamic pricing was rejected the same day it was tabled.

IADS Notes: The Quebec vendor's pitch sits inside a debate already well documented. Electronic shelf labels have been defended as pure accuracy infrastructure, incapable of surge pricing or customer tracking, according to NRF in March 2026, yet the same hardware underpins Walmart's machine-learning pricing patents and its rollout across 4,600 US stores, reported by the Financial Times in March 2026 at a moment when several state legislatures were proposing grocery dynamic-pricing bans. Where prices are set from personal data rather than from local demand, the Financial Times noted in May 2026 that the practice attracts both consumer backlash and regulatory attention, and New York has since banned it outright, as Reuters covered in June 2026. The commercial conclusion drawn by The Robin Report in June 2026 is that the reputational and legal exposure now outweighs the margin gain, which is precisely the trade-off the Canadian grocers have resolved in public.

Could AI push up prices right before you buy dinner?

What: Mastercard's global survey reveals a generational split in AI shopping trust: 27% of teens say they'd let a fully AI-run assistant shop for them, versus 16% of parents, with nearly a third of teens trusting AI recommendations over a friend's.

Why it is important: As retailers race to build the "agent-ready" platforms agentic commerce demands, Mastercard's data suggests the harder barrier — consumer trust — is dissolving fastest exactly where it matters most for long-term customer relationships: with teens.

Mastercard's survey of 26,000 parents and teens points to a generational shift in AI-assisted purchasing. Some 27% of teens say they're likely to use a fully AI-run shopping assistant that recommends, selects and buys on their behalf, versus just 16% of parents. Nearly a third of teens trust AI product recommendations over a friend's, and 23% trust AI advice over their own parents' guidance. The generational gap extends to expectations: two-thirds of parents expect AI shopping to define their children's generation, but only 5% expect the same for their own. Teens are also heavier users of AI for everyday research, with 62% turning to it monthly versus 49% of parents.

"AI increasingly helps people decide what to buy; tomorrow it will help do the purchasing for them too," said Brice van de Walle, Mastercard's EVP of core payments Europe. Retailers are already building for this shift: Home Depot has extended its Magic Apron assistant with local store data, Williams-Sonoma is deploying AI across discovery and checkout, and Target has added AI-powered photo search and review tools. KPMG separately found over half of retailers spend $50 million or more annually on digital technology, though 48% cite technical debt as a barrier to further investment.

IADS Notes:  Mastercard's findings echo a pattern already documented at the retailer level. Macy's introduction of its "Ask Macy's" assistant drove users to spend nearly 400% more than non-users (Fortune, March 2026), and Falabella's 60% increase in technology investment has generated more than $78 million in AI-referred sales and over 20% logistics efficiency gains (Perú Retail, September 2026), showing that AI spend is already converting into measurable revenue rather than experimental cost. The infrastructure shift behind teens' growing appetite for a fully autonomous shopping agent is also under way, as agentic commerce pushes retailers to build "agent-ready" platforms capable of handling product search, comparison and purchase decisions on shoppers' behalf (Journal du Net, August 2026). Mainstream adoption nonetheless still hinges on the generational trust gap the survey highlights: earlier reporting found that consumer trust and satisfaction with existing shopping habits remain the primary barriers keeping AI shopping agents from going fully mainstream (BoF, October 2025).

Teens start to trust AI recommendations more than friends, parents

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With increased competition, the proliferation of discounts and the relentless launch of new brands, the beauty model in department stores is under strain. The traditional brand counters on the ground floor are no longer enough for customers seeking agnostic advice. For Falabella, Latin America’s largest department store group with operations across Chile, Peru and Colombia, the answer is Beauty F. In November 2025, they launched this new format, a new in-house beauty space inspired by the Sephora and Ulta Beauty models, built within the department store and then becoming a standalone business. This article analyses Falabella’s bet. Early results show it could help department stores maintain their leadership in beauty, but the investment intensity remains a challenge to building a specialist retailer within a department store. But beyond beauty, Beauty F tells a story of transformation. 

The strategic problem: the new pace of beauty 

How small brands are rewriting the rules of the beauty shelf

Large French-heritage brands occupying department store entrances (such as Hermès, Chanel and Dior) and conglomerate-owned brands (such as L’Oréal’s Lancôme or Puig’s Jean Paul Gaultier and Rabanne) have been successful traffic builders for decades. While they remain beauty floor anchors, as department stores still show to this day, the rise of small, indie beauty brands threatens this old model. Customer habits have changed as brand loyalty erodes, with 70% of consumers worldwide in 2025 having switched brands because they enjoy experimenting.

With more than 25% of all new brands launched in the U.S. being beauty brands, the impact on beauty retailers is significant: conglomerates' market share was 71% in November 2024 and 68% in 2025, with indie brands growing 3% at their expense. Not long ago, they were a niche market with a reduced following. They are now becoming a segment, even though most fail to scale beyond a US$1-US$50 million turnover bracket. In 2024, indie brands grew +22.3% vs. only +6.1% for conglomerates such as L’Oréal and Puig, accelerating from +16.1% the prior year and continuing to siphon market share. Emerging players spark curiosity and drive volume while conglomerates rely more on price increases: specifically, 62% of indie beauty dollar growth stems from more shopping trips, and 64% of conglomerate dollar growth stems from price increases. Overall, indie brands are growing across all beauty categories even though conglomerates still own the lion’s share.

The wake-up call: the agnostic model 

These numbers show how consumer behaviour has shifted, from relying primarily on the Maybelline, Dior or Chanel of the world to expecting renewed brand diversity. In the meantime, Sephora and Ulta Beauty had trained consumers to expect a free-flowing, agnostic experience with open shelves, product testers, expert advice, and constant discovery of emerging names. Even though department store beauty counters, managed by brands, cannot replicate this agnostic model, transforming traditional models into more open, experiential spaces has become a growing concern. So far, however, the question has been more about how to best adapt to this trend than about transforming the floor and the business model.

Falabella is a regional leader, with an average selective market share of approximately 50% across Chile, Peru and Colombia, leading in the fragrances, makeup, and skincare categories. Yet this traditional method of measuring brand power masked a strategic vulnerability. For sure, the department store was capturing significant rental income and foot traffic. But by acting more like a landlord rather than a retailer, the department store was giving customer relationships, brand curation, and trend agility to brands and other beauty retailers. So the risk was losing beauty market share and the younger, trend-engaged consumers most likely to drive the next years of retail growth, as Gen Z is the generation most structurally aligned with indie and new brands. Instead of merely adapting, Falabella gave a structural and strategic answer with Beauty F, facing the challenge of giving up the traditional rental and safer model.

The context of the local cosmetics market reinforced the potential for a beauty rethink. The Chilean beauty market reached US$443 million in 2025, projected to grow by over 10% by 2035. On its side, with a market reaching US$418 million in 2025, Peru maintains an estimated annual growth rate of 6-8%, driven primarily by the dynamism of the skincare and makeup segments. Colombia’s US$264 million market is expected to grow by over 4% by 2035.

Building a beauty retailer from within: Falabella's Beauty F and Glow Bar

Curation, staff and ownership: a beauty retailer built from scratch

Rather than awaiting the arrival of Sephora (both a recurring expectation and a threat to other beauty retailers), Falabella designed and built its own specialist beauty formats, operated fully in-house, on its own terms: Beauty F in Chile and Colombia and Glow Bar, a local adaptation for Peru.

Both formats share the same architecture: an enclosed space within a Falabella department store that functions as a standalone retail business rather than a leased corner or a collection of brand concessions.

A few ownership-based principles distinguish the model from the rest of the department store beauty floors:

• Closed space with a single-entry point.
• Open-shelf layout integrating testing stations, intuitive navigation, and on-floor advisory zones.
• Own sales force, with all staff across every Beauty F and Glow Bar location being Falabella employees, trained beauty specialists, not brand-supplied promoters with competing loyalties.
• Own imports with Falabella dealing directly with brands, importing and curating a much wider and faster-rotating selection.
• Private brands fully controlled by Falabella.
• Focus on younger, trend-driven, celebrity brands with Beauty F opening with over 100 brands across skincare, makeup, fragrances, hair care, and dermo-cosmetics, including a dedicated K-beauty section. Critically, 80+ new brands have been added since launch, of which 15 were first-time entrants to the Latin American market. Hero names, Rare Beauty, The Ordinary, Drunk Elephant, coexist with emerging labels discovered through social media trend cycles.
• Flexible assortment and presentation, as in some cases, legacy brands like MAC have been moved inside the Beauty F space, with the rationale that the concept avoids replicating items (each SKU is either inside Beauty F or in the rest of the beauty floor, not in both). Most brands offer no personalisation, but some, like Fenty, have a branded presence inside.

While Falabella still has traditional beauty counters, this model is that of a specialist retailer, implying additional complexity of managing different business models. Additionally, developing Beauty F is part of Falabella’s strategy to become a specialist in beauty, sports, home equipment, and fashion (particularly with its private labels), through separate business units and P&Ls.

Experience design as a commercial tool

The decision to enclose the space is experiential and commercial, not only operational. A high-touch beauty experience runs on a different register than the rest of a department store floor. Spanning 80 to 250 sqm, depending on location, Beauty F is designed around different sensory dimensions: its own soundtrack, distinct from the ambient store environment; a signature scent that creates a fragrance customers associate with the format; and warmer, more theatrical lighting, consistent with prestige beauty standards.
A single monitored entry point signals the transition into a different, specialised space. It also addresses a critical operational reality: beauty is among the most shrink-exposed categories in retail. A controlled threshold is both a material loss-prevention tool and an experience marker.

Proof of concept: what Beauty F has delivered so far

Beauty as a win-back and recruitment engine

One of the most commercially significant Beauty F successes is re-engaging customers who had been absent from Falabella stores for more than 12 months. The customer profile that Beauty F is attracting is 85% female and around 20% aged 18-35, a cohort that has largely abandoned the traditional department store beauty experience. Overall, as Falabella reported to the IADS, the share of young customers and new customer acquisition both doubled since the format launched, and new brands introduced through the format are contributing significantly more than the legacy assortment. The average basket is CLP40,000 ($43, approximately two products per transaction), indicating an engaged purchase rather than a transactional visit. Finally, e-commerce already accounts for a significant share of the Beauty F business.

The speciality beauty format is functioning as a CRM tool, reactivating dormant customers and introducing new demographic segments to the Falabella ecosystem. At a time when customer acquisition costs are escalating and loyalty is increasingly fragile, the ability to generate new visits and convert them is a powerful argument to justify the investment the format requires.

From pilot to platform: from one to 15 locations in nine months

Falabella set Beauty F up as a separate, startup-like company with its own leader and staff. When they opened Beauty F inside Parque Arauco in November 2025, it was presented as a cosmetics and personal care shop-in-shop format, a specialist zone within the department store. In July 2026, Beauty F had 10 stores across Chile and Colombia, with 9 more coming; Glow Bar had 5 in Peru, with 4 more coming, targeting upper-market segments where there is no Falabella store presence. This pace of expansion from zero to fifteen locations in under nine months is possible because the group retains full operational control. The specialist model, kept fully in-house, scales on the group’s own timeline. Falabella sees the standalone ambition as a natural evolution of the project. As a result, a standalone Beauty F is no longer a feature of the department store; it is a competitor in the speciality retail market, occupying the same category space as local beauty retailers such as Aruma and Dermotienda, two of Peru’s most important beauty retailers. This trajectory poses a direct question: when does an internal speciality format become a spinout competitor to the very department store to the point where it could cannibalise the traditional beauty floor? Beauty F is believed to have the potential to transform beauty for Falabella. By becoming a standalone beauty brand, Beauty F is expected to strengthen the traditional beauty floor, benefit the department store’s overall ecosystem, and create a precedent for its international peers. 

The operational friction behind Beauty F's early success

Even successful, the new beauty format comes with operational challenges. CapEx intensity is high due to investments in fixtures and architectural elements, which decreases profitability and limits scalability. A lighter CapEx version is needed to extend the concept without diluting returns. With no comparable historical baseline figures, inventory management and forecasting at launch have been a challenge. In addition to the inventory-related financial burden, stock-outs at the time of opening damaged both the customer experience and commercial performance. Beauty F’s curation is oriented toward selective and prestige brands. Meanwhile, high-frequency, convenience-driven categories such as dermo-cosmetics are underrepresented, creating a missed opportunity to increase frequency, an assortment question for the next openings. Finally, even though the enclosed format reduces retail crime, the smallest and highest-value SKUs still require dedicated product-level staffing, adding costs. 

Beauty F and Glow Bar are an argument about ownership of space, customer relationships and brand curation. The early results support the premise. Dormant customers are returning, younger demographics are engaging, new brands are performing above expectations, and formats are expanding at a pace few in-house retail projects achieve. Yet Falabella is not the first department store to pursue a beauty spin-off strategy and claim success for it. In 2020, Harrods launched H Beauty, a new concept of standalone beauty boutiques designed to attract younger customers and to claim territory before Sephora entered the market in 2023. Six years later, results are mixed, as exemplified by the absence of new openings and the recent Bristol store closure. The UK beauty market is certainly crowded with department stores and multi-brand retailers such as Space NK, Boots, Lookfantastic, Superdrug, Cult Beauty and Sephora. But also, H Beauty did not transform the beauty model as it mostly operated as a smaller department-store beauty floor with branded boutiques and a multi-brand attempt, mixing own-bought brands and concessions. Functioning as a startup company with its own staff, store concept and merchandise, Beauty F is about transformation. If Falabella’s bet proves right, Beauty F could be more than a retail success story. It could be a renewed way of selling beauty and, for a legacy department store, a beacon of retaining leadership in the beauty category.  

Credits: IADS (Christine Montard)

What: Economists Philipp Carlsson-Szlezak and Paul Swartz argue the AI capex boom poses a manageable macroeconomic risk today, sized at roughly $315 billion, or 1% of US GDP, once import-heavy spending is stripped out.

Why it is important: Retail leaders navigating AI investment decisions and board pressure can use this framework to separate genuine systemic risk from investor losses, rather than reacting to headline-grabbing multi-trillion-dollar figures.

Estimates of AI data-center capex now range from hundreds of billions to several trillion dollars, fueling fears of overcapacity and recession. The authors argue the more useful question is not when the bubble will pop, but what it entails and how it could damage the economy. Combining hyperscaler and private AI-lab spending yields roughly $630 billion in 2026, but around half goes to imported semiconductors, leaving a domestic GDP impact closer to $315 billion, or 1% of GDP, rising to 1.5% by 2028.

Three channels could transmit damage: a direct activity stop if capex halts, a wealth effect from an equity sell-off given household equity holdings near 30% of wealth, and a credit crunch if debt losses spread. Lasting structural damage requires losses to hit the banking system specifically, as in 2008. Today, hyperscalers are funding capex mainly through cashflow and non-bank debt, and banks have limited direct exposure, containing the systemic risk for now.

IADS Notes: The capex boom's macroeconomic linkages are already visible in retail practice. Retailers' growing reliance on hyperscaler infrastructure for AI deployment is illustrated by THG Ingenuity's launch of an AI stylist built on Google Cloud's Gemini platform (Retail Week, July 2026), a partnership tying measurable commercial gains directly to continued hyperscaler investment. On the demand side, an analysis of Canadian consumer spending (BCG, July 2026) found that headline growth is increasingly propped up by savings drawdowns, asset gains and borrowing rather than income, leaving middle- and lower-income households most exposed to tightening credit conditions — a fragility that would amplify any equity-market wealth effect from an AI-driven correction. On the leadership side, guidance on balancing board pressure for AI adoption against organisational readiness (BCG, September 2026) reinforces the case for engaging deliberately with the AI boom rather than sidestepping it, framing success around disciplined experimentation rather than headcount reduction.

The questions you should be asking about the AI bubble

What: BCG argues no industry consensus exists on whether AGI is achievable, and lays out the technical bottlenecks (data exhaustion, unverifiable rewards, lack of on-the-job learning, compute limits) that keep AI's capabilities jagged rather than general.

Why it is important: It gives CEOs a concrete framework, the jagged frontier, for deciding where AI should replace tasks versus where humans remain essential, at a moment when related coverage shows most leaders still lack the readiness to make that call well.

BCG defines AGI as technology achieving virtuoso-level competence across the full breadth of human capabilities, a threshold it says does not exist today. Its "jagged frontier" concept, first identified by BCG Institute researchers in 2023, explains why AI excels at some tasks while remaining below average at others, and why this unevenness has so far prevented mass unemployment. Software engineering illustrates the pattern: AI has advanced dramatically in code generation, yet engineers remain essential for architecture tradeoffs and accountability.

Whether this jaggedness persists is contested among AI leaders. Dario Amodei and Demis Hassabis see scaling, with possible architectural innovation, as sufficient; Yann LeCun and Fei-Fei Li argue current LLMs cannot reach general intelligence at all. A 2025 AAAI survey found 76% of academic researchers skeptical that scaling alone will succeed. BCG also flags regulatory uncertainty, citing recent US government interventions restricting frontier model access, as a further constraint on how AGI capability would ultimately be deployed even if achieved.

Applying a Pascal's wager framework, BCG concludes CEOs should prepare for continued jagged AI progress rather than betting on an imminent leap to AGI, since that preparation pays off under either scenario.

IADS Notes: Recent coverage has repeatedly stressed that AI capability is not the binding constraint on retail transformation, organisational readiness is. Harvard Business Review, September 2026 found that middle managers fall into distinct psychographic profiles, from skeptics to catalysts, and that adoption stalls or accelerates depending on whether leadership matches its intervention to the profile blocking each manager. This echoes BCG's July 2026 CEO survey, which found that nearly nine in ten CEOs report AI gains in targeted areas but that only a minority manage to scale them, with the gap traced to weak accountability structures and underdeveloped change management rather than technology limits. HR Dive, July 2026 sharpened the same point at the leadership level: only 3% of surveyed C-suite and HR leaders felt highly prepared to guide AI-enabled work, even as deployment accelerated ahead of workforce and management readiness. Set against this, Forbes, July 2026 argued that AI is deepening a structural divide in retail itself, strengthening scale-driven giants and DTC brands while squeezing mid-market and department-store players caught between the two, raising the stakes of the readiness gap for exactly the audience this BCG piece addresses.

What CEOs need to know about artificial general intelligence (AGI)

What: BCG's executive coaching lead outlines three steps for balancing board pressure to adopt AI with organisational readiness: treat it as a people problem, focus on two or three high-impact use cases, and redefine success around a 70% experimentation win rate.

Why it is important: It reframes the AI payoff around capabilities that were previously impossible, like faster claims resolution and deeper personalisation, rather than headcount reduction, at a moment when retail AI coverage keeps circling back to job cuts.

A BCG executive coach addresses a common question: how to balance board pressure for AI with teams not yet ready. Both instincts are correct. Waiting has become the riskier choice, but moving fast isn't the same as moving recklessly, and leadership's job is telling the two apart.
Three steps help. First, treat AI as a people challenge rather than a technology one: technology is roughly 10% of the equation and data another 20%, while 70% is organisational and human. Apparent resistance often reflects fear about job security and hard-won expertise, so leaders should make clear AI is meant to extend expertise, not replace it, and give people low-risk ways to build familiarity in daily work.
Second, avoid spreading AI across dozens of initiatives. Point it at two or three problems that matter most, and aim for what the author calls "aperture expansion": capabilities that were previously impossible, rather than treating cost-cutting as the primary goal.
Third, redefine success. AI is statistical, not deterministic; a 70% success rate should count as a win. Failed experiments deserve a blameless postmortem, and pilots should start where being mostly right is useful and a human stays in the loop, avoiding regulatory or safety-critical territory.

IADS Notes: The emphasis on treating AI adoption as a people challenge rather than a technology rollout echoes wider retail coverage. HR Dive, July 2026 found that only 3% of surveyed C-suite and HR leaders felt highly prepared to guide AI adoption, even as deployment outpaces workforce and management readiness. Bain & Company, December 2025 similarly reported that retailers moving AI from pilots to production are finding that leadership engagement and workflow redesign, not the technology itself, determine success. The caution against framing AI primarily as a cost-cutting exercise is reinforced by Forbes, October 2025, which linked Amazon's and Target's job cuts to a broader shift toward redefining productivity around value creation rather than headcount reduction. This aligns with an earlier framework from BCG, November 2023, which argued that generative AI transformation begins with a workforce diagnostic to prioritize high-value use cases before technology deployment.

How CEOs balance AI urgency and risk

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AI capability is becoming more accessible, while the cost of production systems depends on the models, context, tools, loops and human review required by each workflow. For department stores, advantage will come less from access to a model than from control of the data, definitions, customer relationship and judgment that make its output useful.

Boston Consulting Group’s June 2026 analysis describes AI intelligence as increasingly scalable and accessible. Its July 2026 cost framework exposes the other side of that shift. Agentic systems may retrieve documents, make several model calls, use tools, carry context across steps and repeat actions before an output is accepted. Human review and correction add another layer of cost. Access to intelligence is getting cheaper while the cost of an accepted outcome can remain variable and difficult to predict.1

That tension matters because the cheapest model call does not necessarily produce the cheapest useful result. The relevant cost is what the store spends to reach an answer a buyer or a merchandiser can act on without sending it back. It also shifts the strategic decision from model access to organisational control. The store must decide which data enters the workflow, which commercial definitions it uses, how its output is evaluated, who can override it and whether the customer relationship remains with the store.

The argument for “homegrown” AI begins there. Homegrown AI can combine external models, private cloud and selected local infrastructure. What the department store retains is the business logic beneath those systems and the option to run workloads locally when economics, sensitivity or control justify it.

What homegrown AI means 

From token cost to business value 

For a department store, the bill that matters is the cost of an accepted outcome. BCG proposes measuring the economic return from an AI outcome against both the tokens consumed and the human effort required to initiate, review, correct, approve and operate the workflow.

Model price alone is therefore an incomplete guide. A more capable model that answers a buyer’s stock query correctly the first time can cost less than a cheaper one that takes three attempts and a merchandiser’s correction. For high-volume routine work, such as tagging product copy, the cheaper model usually wins. The comparison has to be made on the whole task, at the required quality, speed and level of risk.2

The three kinds of AI spend also behave differently on the profit-and-loss account. Building a reusable merchandising assistant is closer to an investment, running internal reports is an operating expense, and customer-facing inference behaves more like a cost of goods sold, rising with every conversation. Treating all three as one technology line hides where AI protects margin and where it quietly consumes it.

Reducing the time required to prepare a trading report does not automatically create value. The return appears only when the saved time changes a commercial decision: earlier action on inventory, more time with suppliers or better service. If the buyer still places the same order on the same day, the only thing that improved is the report.

Owning the intelligence layer 

BCG’s token-competition argument weakens the case for treating access to a foundation model, a general-purpose model supplied by an external provider, as a lasting source of advantage.

For a department store, the part a supplier cannot bring is the store’s own meaning. Available inventory has to account for merchandise moving between locations. Concession sales have to be attributed consistently across the store, department and brand. Returns have to be read differently when they signal poor fit, product quality or a commercial problem. Those definitions shape what buyers, merchandisers and store teams can do with an AI answer. A general model arrives without them, and no supplier can supply them. Paolo Pedersoli of Jakala made the same argument at the NRF Big Show 2026, in a session titled “AI without semantic capital is of no use”: as powerful models and generic data become widely accessible, the scarce asset is an organisation’s own codified definitions and decision logic, made machine-readable rather than left as tribal knowledge.3

BCG’s 10–20–70 framework, which Forbes reported the consultancy was urging in a January 2026 report on agentic AI, assigns around 10% of AI-transformation effort to algorithms, 20% to technology and data and 70% to people and processes4.

Model location and organisational control are separate decisions, but location still carries consequences. For stable, high-volume workloads on controlled data, local infrastructure can offer tighter custody of sensitive information and greater cost predictability. It also brings hardware utilisation, energy, engineering, security and replacement costs. External models avoid much of that infrastructure burden but introduce dependence on provider pricing, interfaces and governance.

The trade-off is workload-specific. High-volume, stable work on sensitive data, such as enriching product records or answering associate policy questions across the estate, is the kind that can justify local infrastructure. Occasional, bursty or experimental work rarely does. In both cases, the commercial definitions and evaluation standards should remain portable enough for the store to change the technology underneath them.

The operating model beneath the technology

When AI scales the wrong process

An IADS member provides a department-store example of AI built around explicit ownership of the underlying information. At a recent E-commerce Directors Meeting, the department store described moving ownership of data quality from IT to its e-commerce specialists, pairing AI enrichment tools with human validation: the enrichment tools produced usable attributes only once the people who trade the products owned the definitions behind each attribute. The reported improvement in search and conversion followed that change in ownership and human validation alongside AI enrichment.

BCG found that the organisations producing stronger returns were redesigning end-to-end processes, concentrating on a limited number of high-value applications and tracking whether the gains reached the profit-and-loss account. Adding AI to an unchanged process may increase speed while leaving conflicting definitions, unclear ownership or badly designed incentives untouched. The NRF Big Show 2026 department-store panel drew the same line between AI applied to legacy systems and AI-native design, citing a 90% reduction in the cost of product data management as the kind of gain that follows only when a process is rebuilt rather than accelerated.5

A report assembled from incompatible systems can still produce conflicting answers. Product information can be returned quickly while remaining incomplete or inconsistently governed. A workforce system can reduce scheduled labour against a target that damages service. In each case, execution improves before the organisation has agreed what a good result means.

Starbucks provides a separate warning from the wider retail sector. Reuters reported that the company rolled out automated inventory counting across more than 11,000 company-operated North American stores in September 2025 and discontinued it in May 2026 after persistent misidentification and missing items forced employees to check or repeat its work.6

Reuters’ January 2026 reporting places those failures inside a supply chain already affected by fragmented suppliers, outdated systems, forecasting problems and limited store storage. The counting errors created new verification work for employees, while the fragmented suppliers, forecasting problems and storage constraints remained untouched. Nine months of automation had changed the counting process without resolving the supply chain beneath it.

Where build-or-buy moves the work

In Korea, Lotte shows why build-or-buy is the wrong binary. The department store used Strategy’s commercial technology but deployed eight analytics agents on a governed semantic layer applying common definitions across departments. Strategy reports more than 9,500 AI-supported analyses and answers delivered up to ten times faster in six months.7

Lotte answered the technology question by buying and was left with the harder half. The definitions its eight agents run on had to be written, agreed and policed inside the business, and they now live on a supplier's platform, which is where portability has to be defended rather than assumed. Buying a model or platform does not remove the work of deciding what inventory, sales, customers and performance mean.

Department stores should vary a system’s authority according to the consequences of an error and the amount of human interpretation the task requires. Product and policy retrieval can tolerate more automation because the answer can be checked against an approved source. A buying recommendation that changes an order still needs the buyer who understands the category and trading context. A staffing recommendation needs a store manager able to judge the service consequence.8

Recruitment, promotion, discipline and worker monitoring carry still higher stakes. The EU AI Act, which IADS identified as a governance framework for retail in its 2024 analysis of AI accountability, reflects that higher threshold by classifying specified employment uses (recruitment, promotion or termination, certain task-allocation systems and worker monitoring) as high-risk, subject to the regulation’s conditions and exceptions. AI may organise evidence and identify inconsistencies, but accountability for the final decision must remain with qualified people.

At the NRF Big Show 2026, the IADS panel on what AI can and cannot do for department stores placed governance at leadership level rather than inside IT alone. Data quality and taxonomy were identified as foundational constraints, while the complexity of product assortments and databases was singled out as an opportunity for automation. The warning was equally clear: efficiency gains should not be converted into cuts to sales staff or store investment but redirected towards customer experience and differentiation. The 2024 IADS Exclusive Navigating the AI maze in retail beyond the black box argued for proportionate oversight concentrated on high-risk areas, and for AI that supports rather than replaces human judgment.9 The authority given to the system should fall as the commercial, human or legal consequence of being wrong rises.

The customer relationship is part of the infrastructure

Distribution without disintermediation

The internal intelligence layer is only one part of AI sovereignty. The other sits between the department store and the customer. At NRF 2026, futurist and author Jason del Rey described AI platforms moving beyond product research towards transactions, with conversational interfaces experimenting with completing purchases inside the chat rather than sending customers to the retailer’s own website. He treated the shift as under way rather than settled. His practical conclusion was narrower than the threat: experimenting on external platforms is worth doing so products remain discoverable, but the priority is making the store’s own digital experience at least as good as the assistant’s. He described abandoning a retailer’s own site and getting a faster, more accurate answer from ChatGPT.

IADS members had already identified the underlying issue at the 2025 E-commerce Directors Meeting. Across their different app and omnichannel strategies, participants agreed that owning the primary digital doorway mattered to protecting brand equity and margins against marketplace giants. NRF added another layer. IADS summarised REI chief executive Mary Beth Laughton’s approach as “selective openness”: participating in external AI platforms while deciding which content, data and expertise remain exclusive to the retailer’s own channels.

The completed order tells a department store what the customer bought. The conversation that preceded it can contain the occasion, budget, preferences, rejected alternatives and future intent. If an external agent keeps that context and sends the store only an order, the richer part of the customer relationship sits outside the business.

Walmart offers one response. The March 2026 Forbes analysis describes the company placing its own assistant inside external interfaces while retaining more of the customer journey and checkout. The relevant lesson for department stores is control over continuity: an outside platform can introduce the customer, while identity, consent, loyalty history and the ability to continue serving that customer remain with the store.10

Selective openness buys reach by surrendering some control over discovery. The strategic choice is what the department store is willing to distribute and what it still needs customers to return to its own channels to receive. Based on IADS’s e-commerce and NRF report, IADS’s position is that department stores should use external AI agents as distribution channels without allowing them to become the permanent owner of the customer relationship.

What a general platform cannot reproduce

Physical and digital relationships remain intertwined for younger consumers. Edelman Gen Z Lab’s 2024 Rarely Heard Voices research found the cohort divided between enthusiasm and resistance towards AI. Seven in ten Gen Zers reported fact-checking online information even while shopping, while 49% of older Gen Zers preferred making purchases in stores and 40% of 14- to 17-year-olds shopped equally online and in store.11

The research also describes a “trust loop” in which brand action, advocacy and continued purchasing reinforce one another. That relationship extends beyond a single conversion.

Product comparison is where a general AI platform is strong. City-level presence, an alterations counter and a relationship built over several buying seasons sit outside its reach, and as more product discovery moves into AI interfaces, those capabilities carry more of the reason a customer chooses the department store itself.

The workforce is part of the intelligence layer

AI does not create expertise

Much of the knowledge that makes a department store function is not captured in its databases. It sits in how buyers interpret the relationship between sales and returns, how store managers balance labour targets against service requirements, and how associates adjust their approach to different customers. That judgment accumulates across products, seasons, trading cycles and exceptions. Transactional data records what happened; the experience of the people around it often explains why it happened and whether the same response should be used again.

A controlled experiment at IG Group, a financial-services company, helps isolate the role of domain knowledge. The study involved 78 employees divided among experienced writers, marketing specialists with adjacent knowledge and technologists more distant from the task. AI helped the adjacent specialists close much of the gap with experienced writers, but it did not turn employees without the relevant domain knowledge into experts. The researchers described an “AI wall”: the further users were from the required knowledge, the less able they were to evaluate and improve the output.12

Although the setting was not retail, the mechanism is relevant to department stores: employees still need enough underlying knowledge to recognise missing context, weak reasoning and commercially inappropriate answers. AI can accelerate people who already possess enough domain knowledge to judge the result; without that foundation, it risks increasing dependence rather than competence. The IADS NRF 2026 report reached the same conclusion from a retail perspective: Luc Julia, former co-inventor of Siri and Chief Innovation Director at Renault, and Pascal Malfoy, CEO of ADEO (Leroy Merlin), argued that AI does not remove the need for professional expertise, and that the strongest results come from combining domain knowledge with intelligent tools.

When production becomes faster, the scarce capability shifts towards judgment: whether a markdown recommendation suits the season, what trading context the model was never given, and who answers for the decision if it is wrong. Experience cannot be produced at the speed of a model response.13

The IADS 2024 White Paper on middle management treats reverse mentoring as one tool among several, alongside traditional and peer mentoring. That combination is particularly relevant to AI. Younger employees can bring familiarity with emerging interfaces and experimentation; experienced colleagues bring organisational memory, customer understanding and the ability to recognise exceptions. The IG Group experiment suggests why both matter: fluency with the tool did not compensate for distance from the underlying domain.

When efficiency consumes the apprenticeship

The workforce risk extends beyond adoption. It concerns what employees no longer learn once AI begins performing the work. First drafts, routine comparisons, straightforward customer requests, and low-risk recommendations can look expendable. They are also how people develop judgment: by doing work repeatedly, having it corrected and seeing the consequences of low-risk mistakes. Automating those activities without redesigning learning improves output today while weakening the organisation’s future expertise. Keeping some formative work in human hands also has a cost: the department store pays people to perform work that a competitor may automate. That choice should therefore be deliberate. The organisation needs to know which tasks are still buying experience, not merely labour.

BCG’s June 2026 study When everyone uses AI, companies risk losing critical skills, a global survey of 70 C-suite leaders and senior executives, found that half were already observing de-skilling, while more than 60% expected it to become a material threat within three to five years. The capabilities considered most exposed included judgment and decision making, problem understanding and framing, creative thinking, analysis and causal reasoning, and solution generation and evaluation, the same capabilities employees need when evaluating AI-generated work.14

The risk is especially relevant to department stores because much of their management and service capability has traditionally been developed through progression. Employees acquire judgment through customer-facing work, operational responsibility, recurring trading cycles and exposure to exceptions before moving into broader roles. The IADS 2024 White Paper on middle management describes a path in which sales associates could move through store management and potentially into director roles, gaining broader customer, operational, and commercial responsibility along the way. Centralisation and repeated reductions in the in-store management layer have already damaged succession planning and narrowed that traditional route into management.

AI can narrow it further by removing the lower-risk tasks and recurring exceptions through which employees learn to interpret evidence, make decisions under uncertainty and understand the consequences of being wrong. The risk is less that AI erodes leadership skills directly than that future managers assume accountability for work they have had fewer opportunities to perform and judge.15 When formative tasks are automated without being replaced by structured learning, an already fragile route from execution to expertise becomes narrower still.

A department store cannot rely indefinitely on experienced buyers and store managers to check AI output if it has automated the work through which the next generation of them would have been trained.

The recent IADS Exclusive, The $10 trillion management problem, reached the same conclusion from a management perspective. Technology reaches the shop floor through managers, yet manager engagement has fallen sharply, even as organisations expect those managers to translate AI into new working practices.16

The IG Group experiment, BCG’s de-skilling findings and Gallup’s evidence on manager support point to the same requirement for AI training. Shop-floor employees need more than familiarity with an interface. They need enough product and operational knowledge to identify a plausible but wrong answer, escalate uncertainty and reject output when the commercial context does not fit. A human in the loop adds little protection when that person lacks the experience or standing to challenge the system.

What department stores should build first

Start with governed knowledge

At NRF 2026, IADS reported a consistent message across several AI sessions: begin with a concrete business problem, clear objectives and the data required to solve it. Luc Julia and Pascal Malfoy argued for specialised, domain-specific applications rather than broad AI deployments, while the department-store panel identified product assortments, databases, data quality and taxonomy as practical areas where the sector’s complexity can be reduced.

BCG’s workflow framework and NIST’s AI Risk Management Framework add the evaluation discipline. BCG begins with a defined outcome, while NIST requires organisations to establish intended purpose, responsibilities, risks, measurement and continuing oversight. Applied to a department store, these principles favour a bounded and verifiable first project rather than a broad autonomous system.17

An associate-facing product-and-policy assistant fits those conditions. Three things already on the record point to it: the NRF department-store panel named product assortments, databases, data quality and taxonomy as the sector's most automatable complexity, the same panel cited a 90% reduction in the cost of product data management, and the quoted IADS member has shown that a department store can take ownership of that data layer without outsourcing it. It can answer questions about products, services, returns, delivery, appointments and approved procedures. Its answers can be grounded in approved product, service and policy information, the source can be shown to the associate, and unresolved questions can be escalated.

Lowe’s provides an adjacent retail example. Its Mylow Companion was deployed across more than 1,700 stores to give associates access to product, inventory and project information. A department-store version would have to accommodate the additional complexity of concessions, services, appointments, returns and store-specific policies.18

Preparing that knowledge is where the pilot becomes difficult. The IADS member treated data quality as a question of commercial ownership rather than a purely technical clean-up exercise, moving responsibility from IT to its e-commerce specialists and retaining human validation. The same issue would arise in an associate assistant: POS, CRM, inventory and product systems may disagree over whether an item is available, which department or concession owns the sale, or how a return should be classified. Store policies can also differ by service, market and location. The department store therefore has to decide which definition takes precedence, who owns it and how quickly an outdated answer is removed. None of this is free. Reconciling definitions, keeping formative work in human hands and staffing real oversight are costs the store carries before any savings appears, and they compete for the same budget as stores, people and logistics.

NIST requires systems to be tested before and during operation and reassessed against their intended purpose. For a first department-store pilot, that means testing the same set of real associate questions across the viable local, cloud, hybrid and non-AI options. The comparison should measure answer quality, response time, cost per accepted answer, escalation and manual correction, while also recording employee rework and variation across stores and languages.

Measure rework, not activity

The pilot should also test whether experienced employees consider the answers commercially useful, whether newer employees can explain why an answer is correct and whether the assistant builds understanding rather than conceals gaps.

Oversight should follow the retail decision itself, applying the NIST requirements set out above. An associate needs to see the product or policy source behind an answer and know when to escalate it. A buyer reviewing an AI recommendation needs the sales, returns and stock evidence behind it. A store manager needs the authority to reject a staffing recommendation that meets a labour target but leaves the floor unable to serve customers. Decisions affecting recruitment, promotion or discipline require stronger human control again.

Scale only the question types the pilot answered reliably, and leave the rest with people. The test of the pilot is whether associates keep using it once the launch attention fades, whether the cost per accepted answer is one the business would pay at scale, and whether the people supervising it can explain why an answer was right.

Owning the intelligence layer buys one thing above all: the freedom to replace the model.

A department store that keeps its commercial definitions, evaluation standards, workflows and customer identity separate from a supplier can move as the technology moves. It can adopt a better model, bring a sensitive workload closer to home or renegotiate with a provider without reconstructing the business around that decision. Once those assets are embedded inside one platform, switching becomes slower, more expensive and more dependent on the supplier.

Homegrown AI should preserve that freedom. Where a workload runs can change with economics, sensitivity and control. The product history, customer logic, evaluation standards and operating definitions that give the business its meaning must remain portable and under the department store’s control. The model can change. The department store should not have to rebuild itself around every change.

Credits: IADS (Maya Sankoh)

What: Airlines are adopting retail-style loyalty mechanics — gamified rewards, no-purchase engagement, and partner-driven monetisation — to keep customers connected between trips.

Why it is important: Airlines are proving that gamification and partner-driven rewards can turn loyalty into a revenue line in its own right, reinforcing the case for department stores to treat loyalty as a business rather than a cost centre.

Airlines are increasingly borrowing from retail's loyalty playbook, creating reasons for customers to engage with their brands outside active travel. TUI's new Smiles Rewards Club guarantees a monthly win without requiring a booking, while British Airways' Avios currency now draws more than 80% of its revenue from external partners such as Amex and JPMorgan Chase, rather than from flying itself. Virgin Atlantic frames the shift as building lasting relationships through experiences loyal members could not otherwise buy.

Personalisation and reduced friction are central to this shift. British Airways has replaced boarding passes with Face ID on some routes and now allows passport scanning instead of manual checks, while both airlines stress that data should surface relevant offers rather than drive engagement for its own sake. Jet2's myJet2Perks and Riyadh Air's Sfeer scheme extend the same logic without a formal points currency, offering discounts and non-expiring rewards regardless of whether a trip is booked.

The push is partly generational: OAG research shows only 65% of Gen Z travellers are enrolled in an airline loyalty scheme, against 89% of Baby Boomers, yet Gen Z redeems rewards more often while staying loyal to fewer providers, booking across multiple airlines on price or route.

IADS Notes:  Loyalty programmes across department stores and multi-brand retailers have been moving in a similar direction over the past several months. Reporting from Forbes in February 2026 found that nearly a third of Sephora's 46 million loyalty members now engage through gamified shopping mechanics, a precedent for the guaranteed-win, no-purchase-required model TUI has since applied in travel. Fashion Network noted in April 2026 that M&S had overhauled its Sparks scheme around spendable cash rewards, AI-driven personalisation and partner-based bonuses, including one tied to Virgin Atlantic bookings, closely mirroring the airline-retail crossover now emerging in loyalty economics. Fashion United reported in January 2026 that Harvey Nichols had restructured its own loyalty tiers as part of a wider brand repositioning, tying rewards innovation directly to broader transformation efforts. And Retail Dive covered, in June 2026, the linking of J.C. Penney's and Aéropostale's loyalty programmes under the Catalyst Brands portfolio, a coalition-style structure comparable to the cross-brand points transfers airlines run with retail and financial partners.

Miles better: the new business of airline loyalty

What: Southeast Asia split into two speeds in Q2 2026, with tech- and trade-led economies such as Vietnam, Malaysia and Singapore accelerating while Thailand and the Philippines lost momentum amid a Middle East-driven energy shock.

Why it is important: Vietnam and Singapore's continued strength — despite regional headwinds — reinforces their standing as priority growth markets for retail investment and expansion.

Southeast Asia's six major economies diverged sharply in the second quarter of 2026. Vietnam led with 8.39% GDP growth, followed by Malaysia at 6.0%, Singapore at 5.9% and Indonesia at 5.29%, while Thailand slowed to 1.9% and the Philippines to 2.3%. Exports were the standout driver across all six markets, powered by global demand for electronics and technology-related goods, with industrial activity strengthening in Malaysia, Singapore, Vietnam and the Philippines.

The Middle East energy shock complicated the picture, pushing inflation higher in five of the six economies and pressuring the Indonesian rupiah, Philippine peso and Thai baht. Policy responses diverged accordingly: Indonesia and the Philippines raised interest rates, Singapore tightened its exchange-rate policy, while Malaysia, Thailand and Vietnam held their settings steady.

Looking ahead, technology-led exports and improving manufacturing indicators provide a strong platform for the second half, but how effectively individual economies translate that momentum into broader domestic growth will depend on how inflation and currency pressures evolve.

IADS Notes: The World Bank's downgraded 2026 growth outlook for Southeast Asia had already begun weighing on major retail conglomerates by early spring, with stagnant sales and softer tourist arrivals pressuring groups such as Central Retail, Makro-Lotus and Big C (The Diplomat, April 2026). Against this backdrop, the region's AI adoption has produced uneven results: early movers have captured measurable efficiency and service-quality gains, but regulatory fragmentation and workforce readiness continue to limit how many retailers can scale AI beyond pilot projects (The Diplomat, April 2026). Performance has nonetheless diverged sharply by market. Singapore's retail sector kept accelerating even as petrol and living costs climbed, reinforcing its position as a regional benchmark for resilience (Inside Retail, June 2026), while Vietnam's retail sales moved into double digits on the back of a tourism boom and strong GDP growth, prompting Central Retail to commit to 30 new stores in the market (Inside Retail, April 2026).

Southeast Asia quarterly economic review: Tech tailwinds drive a two-speed region

What: A study of 3,000 U.S. consumers found AI-generated video ads underperformed human-made ones by 14% on short-term sales potential and 17% on long-term brand equity, even though consumers could not reliably tell them apart.

Why it is important: It gives retail marketing leaders an evidence-based framework for where AI creative works (conventional, product-driven work) and where human judgment is still required (emotional nuance, brand storytelling).

A study co-led with Ipsos tested 20 video ads for major brands, half made by humans and half entirely by AI from the same creative briefs. Consumers could not reliably distinguish the two, but the AI versions performed measurably worse commercially. The Chewy example illustrates why: the human-made spot built a precise emotional device around a dog aging over the years, while the AI version told a more generic, less specific story.

AI performed best on conventional, product-driven briefs with clear structures, as seen with Febreze and Herbal Essences, where AI versions matched human ones on sales potential. It performed worst on briefs requiring emotional specificity or narrative surprise, such as a Fiat spot that lost the human version's humor. The recommendation is to stop measuring AI creative by cost or speed and instead track its actual effect on persuasion, memory, and brand equity, scaling only what performs as well or better than human-made work.

IADS Notes: Earlier coverage has tracked AI's expanding but uneven role across the marketing function. Journal du Net's July 2026 reporting framed AI-generated product visuals as a measurable commercial lever tied directly to conversion and return rates, treating output quality as a performance question rather than a production shortcut — a framing that anticipates this study's insistence on measuring efficacy over cost. The regulatory dimension has also been building: Reuters reported in June 2026 that Eurocommerce lobbied to exempt non-misleading AI-generated retail advertising from the EU AI Act's disclosure requirements, while Inside Retail's coverage the same month noted that Korea moved in the opposite direction, mandating disclosure of AI-generated content and endorsers to protect consumer trust. Separately, BCG's June 2026 analysis described a parallel shift at the delivery layer, with AI agents increasingly assembling personalized offers and micro-journeys in real time rather than following fixed campaign structures — a reminder that the effectiveness question this study raises about *what* AI creates sits alongside a broader transformation in *how* that creative gets deployed to customers.

AI-generated ads perform worse than human-made ones, even when customers can't tell them apart

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Nine million people registered for pre-sale tickets for Céline Dion’s September 2026 concerts. Compared with the total seating capacity across her 16-concert Paris residency, that’s nineteen wannabe attendees for every seat. That’s probably why, last April, Galeries Lafayette CEO Arthur Lemoine told French newspaper Le Monde that he expected a “significant positive impact” at the Boulevard Haussmann flagship this autumn.

His reasoning drew on a specific precedent: during Taylor Swift’s six French concerts in 2024 — four at the very same La Défense Arena, plus two in Lyon — Galeries Lafayette registered a measurable uplift in traffic. Lemoine specifically cited the impact of the Lyon dates reaching Haussmann, a remarkable observation given that Lyon is 470 km from Paris. If concerts in a different city generated spillover, the implication for 16 Dion concerts in the French capital itself is clear.

Lemoine’s comment was not wishful thinking. It was grounded in a pattern tracked across sporting events and entertainment phenomena over the past several years, and one that the data increasingly support: landmark cultural events function as demand shocks for premium retail, particularly for department stores positioned as destinations in their own right.

Céline Dion in Paris: fans’ hearts will go on

Céline Dion’s Paris residency is shaping up as one of the most significant entertainment events in Europe in 2026.

The residency comprises 16 concerts at Paris La Défense Arena (Europe’s largest indoor venue), spread across five weeks in September and October 2026. The total capacity is capped at approximately 30,000 per show (the arena holds up to 45,000, but production design limits seating), implying a total audience of roughly 480,000 concertgoers over the run. Over 9 million pre-sale registrations were recorded before the artist pre-sale opened on 7 April — a figure that signals massive unmet demand and, crucially, a large pool of international fans who will compete for hotel rooms, restaurant tables, and retail experiences. Six additional dates were added within 48 hours of pre-sale opening, on the back of what organisers described as “phenomenal demand.” Marriott Bonvoy is the official hotel partner, offering VIP experience packages — a direct indication that the event is structured to drive multi-day tourism stays, not just single-evening attendance.

The audience profile matters as much as the volume. Céline Dion’s fanbase skews older and more affluent than that of a typical pop tour. Her career spans nearly four decades; her peak commercial period — the mid-1990s to mid-2000s — means her core following is now aged 35–60, a demographic that aligns closely with the primary customer profile of flagship department stores: internationally mobile, with high disposable income, and inclined to frame travel around cultural experiences. This is further amplified by the emotional narrative of Dion’s return to the stage after years of public struggle with Stiff Person Syndrome[1], a rare neurological disorder — a story that has generated worldwide media coverage and deepened the event’s symbolic resonance. Over 200 million records sold worldwide make her the best-selling Canadian recording artist and the best-selling French-language artist of all time.

In the past, Céline Dion’s two Las Vegas residencies at the Colosseum at Caesars Palace (2003–2007 and 2011–2019) grossed a combined $681 million (€578m) in ticket sales and attracted over 4.5 million spectators across 1,141 performances. Her second residency averaged $693,000 per show (€588,000). That Las Vegas track record demonstrates that a stationary artist can serve as a sustained tourism magnet, generating consistent demand for hospitality, retail, and dining over multi-year periods[2].

What Swiftonomics already taught us

The “Swiftonomics” phenomenon is documented (read the 2024 IADS Exclusive here), and provides the most granular evidence base for projecting the retail impact of the Céline Dion residency — all the more so because Taylor Swift performed at the very same venue.

Taylor Swift played four concerts at Paris La Défense Arena in May 2024, launching the European leg of her Eras Tour, followed by two additional shows in Lyon in June. Around one million people registered for tickets in France. Arthur Lemoine’s specific reference to the Lyon dates generating a measurable impact at Haussmann — despite Lyon being 470 km away — suggests an additional echo in Paris, beyond the four La Défense shows. As a consequence, the potential impact of Celine Dion’s concerts might be at two levels: a direct effect from Paris concerts, as demonstrated by the Taylor Swift concerts, and a spillover effect from unmet demand, as demonstrated by the distant Lyon dates[3].

Globally, the Eras Tour (2023–2024) grossed approximately $2.2 billion (€1.9bn) in ticket revenue across 149 shows worldwide and generated an estimated $7 billion (€5.9bn) in total retail economic impact across North America, according to Chain Store Age. It is estimated that the total economic footprint — including indirect spending by non-ticket holders drawn to the cultural moment — likely exceeded $10 billion (€8.5bn).

The per-show economics are striking. In 2024, North American concertgoers spent an average of $1,572 (€1,335) per show for the full experience (tickets, travel, lodging, outfits, merchandise, food and beverage), up 21% from $1,303 (€1,107) in 2023. 64% of Eras Tour attendees made concert-related purchases in categories including apparel, footwear, accessories, cosmetics, and crafts.

At the city level, the pattern was consistent across virtually every stop. In the U.S., Pittsburgh saw $46 million in direct spending from two concerts (€39m), with 83% of attendees coming from outside the county and hotel occupancy reaching 95%. Chicago set an all-time hotel revenue record of $39 million (€33m) during one concert weekend. Denver’s two shows added $140 million (€119m) to Colorado’s GDP. Los Angeles saw $320 million (€261m) in total economic impact from six shows, with 3,300 temporary jobs created. In the UK, Swift’s shows were estimated to boost London’s economy by £300 million (€344m) and Edinburgh’s by £77 million (€89m). In Ireland, concert-related spending increased by 88%.

What K-pop also suggests

Asian department stores have developed the most sophisticated playbooks for converting concert traffic into retail revenue, driven by the economic power of K-pop fandoms.

BTS — Seoul, March 2026

BTSnomics 2.0, as the Korean press has cheerfully named it, is exactly the playbook European department stores now need to study.

According to Inside Retail Asia, following BTS’s comeback concert at Gwanghwamun Square (21 March 2026), department store sales surged by over 40% during the comeback week. Duty-free merchandise sales rose by up to 430%. Brands endorsed by individual BTS members saw improved sales, such as Calvin Klein (endorsed by Jungkook). Five-star hotels near Gwanghwamun sold out entirely, with rates reaching ₩1.5 million (€865) per night. The economic impact of the single Gwanghwamun concert is estimated at approximately $177 million.

Korean department stores prepared proactively and systematically. Lotte Department Store launched discount events for foreign passport holders at its Myeongdong and Jamsil branches from 19–29 March, coordinating with online travel platforms to issue 5% discount coupons. Hyundai Department Store transformed The Hyundai Seoul into a “K-pop sanctuary,” with monthly pop-ups for idol group merchandise starting with EXO and ENHYPEN, a K-culture experience zone for foreigners, and a foreigner-exclusive integrated membership programme (“H.Point Global”). Hyundai had also been developing packaged products that linked hotels, shopping, exhibitions, and experiential content for foreign tourists for a few years already.

BlackPink — Hong Kong, January 2026

The K-pop group’s three-night “Deadline” world tour finale at Kai Tak Stadium drew over 356,400 mainland Chinese visitors to Hong Kong over one weekend — a January record. Hotel occupancy near the venue exceeded 90%, with some properties charging over HK$7,000 per night (€760). Retailers in the Kai Tak area, including SOGO, reported an uptick in both concert-related merchandise and general tourism goods. Industry leaders called on authorities to develop more systematic celebrity tie-ups as a tourism lever.

The “ticketless economy” model

Analysts tracking BTS have noted that many fans travel internationally to concert cities without tickets, simply to participate in the cultural moment — spending on hotels, food, fashion, and experiences. This pattern of “ambient consumption” around a major event is exactly what Galeries Lafayette could also bank on. With 9 million registrations for Céline Dion’s 480,000 available seats, the ratio of unmet demand to supply virtually guarantees a significant pool of non-ticket-holding visitors in Paris during the five-week residency window.

What Paris 2024 Olympics actually did to sales

IADS benchmarked the retail impact of recent major sporting events, including the London 2012 and Paris 2024 Olympics. The Paris Games, in particular, provided a real-world case study for understanding how large-scale events affect department store performance.

During the Games (29 July – 11 August 2024)

In France, national FMCG sales rose 2.7% in value — a swing of 3.8 percentage points against the January–July trend of -1.1%. The effect was amplified in proximity to competition sites. Décathlon’s flagship in Paris Madeleine saw a 28% increase in footfall; its Villeneuve-d’Ascq campus near Lille registered 22% more visitors. Carrefour’s central Paris convenience stores recorded a 25% increase in sales, with mascot sales tripling initial estimates to over 560,000 units. The sports equipment category surged by 7.2% in supermarkets, against a year-to-date decline of 17.2%. Analysts found that weather accounted for only 18% of volume growth in Paris and 37% nationally; the remainder was attributable to the event itself and the influx of tourists.

However, for department stores, the story was more nuanced. Galeries Lafayette had initially budgeted for a 5–10% dip in summer sales due to traffic restrictions and disruption. In practice, the impact was less severe: July was down but above expectations, and August was “almost flat.”

September–December 2024 — the “post-Olympic glow”

The real benefit came after the closing ceremony. As Arthur Lemoine confirmed in a November 2024 interview with WWD, September saw “high growth” as tourists charmed by the television spectacle of the Games came to discover — or rediscover — Paris. October sales at Haussmann rose 15% year-on-year, powered by a mix of domestic and international customers. The store was on track for its 4% annual growth target, and Lemoine expressed confidence in Christmas trading. The Banque de France, in its September 2024 note, attributed an estimated 0.25 percentage points of GDP growth in Q3 2024 to the Olympic effect, with market services (hospitality, retail, dining) identified as the principal contributor to the rise.

The lesson is that the greatest retail benefit from a cultural event often comes not during the event itself but in the weeks and months that follow, as the city’s enhanced visibility translates into sustained tourism. The five-week Céline Dion residency (September–October) is ideally timed to generate precisely this kind of post-event tail into the critical Q4 trading period.

So what should department stores actually do?

Based on various IADS studies conducted for its members, we identified three phases of action.

Before the event

The preparatory phase is where most of the commercial upside is either locked in or lost, and its defining discipline is lead time.

Experience from previous large-scale event activations suggests that assortment development and influencer programming need to be underway several months before the event itself — the window in which pre-orders, demand signals, and inventory planning can still be meaningfully shaped:

• The product assortment is the first lever: co-branded capsules or exclusive products developed with brands already aligned with the artist’s aesthetic tend to outperform generic event-themed merchandise because they link the store to the cultural moment without encroaching on the artist’s own merchandising operation.
• The same logic applies to marketing. Campaigns routed through sports or music influencers tend to reach audiences already aware of the event, whereas fashion influencers pull style-conscious consumers into the conversation and meaningfully expand the addressable market. Co-branded activations with partner brands consistently outperform standalone retailer campaigns, which can confuse customers when run alongside brand partnerships. For effective activations, department stores are therefore better served by partnering with brands already in the event’s orbit than by constructing a standalone event-themed identity.

The second lever is cross-category thinking: a landmark event ripples well beyond the obvious merchandise. Electronics (as audiences upgrade their viewing and listening setups), spirits, fragrances, and watches tend to see a lift around cultural moments of this scale, and a department store’s structural advantage over specialist retailers lies precisely in its ability to capture that spend across categories within a single visit.

Foreign-visitor infrastructure is the third axis: multilingual signage, visible tax-free shopping, and passport-linked promotions, following the Korean playbook in which Lotte and Hyundai coordinated directly with online travel platforms. Hotel partnerships close the loop — in the Dion case, Marriott Bonvoy, the official hotel partner, is the natural conduit for embedding store visits into VIP concert packages before guests ever arrive in Paris.

During the event window

Once the residency is underway, the commercial question shifts from attracting the audience to capturing the share of its spend that would otherwise disperse across the city.

The most direct lever is repositioning the store itself as a pre-show destination, reinforced by extended opening hours on concert evenings and by targeted digital advertising to ticket-holders — geotargeted, social, and routed through hotel concierge partnerships, with the Marriott Bonvoy tie-up as the obvious anchor in the Dion case. In-store gathering spaces play the same role the Korean “K-pop sanctuary” format plays around BTS comebacks: livestream viewing areas, themed activations, and photo opportunities that convert ambient fans — many of them without tickets — into dwell time on the sales floor. Assortment should follow the same logic as the preparatory phase: concert-adjacent categories (fashion, beauty, accessories, fragrances) that capture the mood without trespassing on the artist’s official merchandising operation. Food and beverage is the underappreciated driver. Prior IADS work around major sporting events has repeatedly identified F&B as a critical traffic generator and dwell-time extender, and the logic transfers directly to concert nights: pre-show dining experiences, themed cocktails, and post-concert gathering spaces are the mechanisms through which consumer spend is retained within the store rather than surrendered to surrounding restaurants and bars.

After the event

The post-event phase is where the largest long-term opportunity sits, and it is also the phase most often under-exploited.

The Olympic precedent is instructive: Galeries Lafayette’s September–December 2024 performance — with October sales at Haussmann up 15% year-on-year and the store on track for its 4% annual growth target — demonstrated that the “glow” generated by a landmark event can be materially more valuable than the event window itself, because the city’s enhanced international visibility continues to convert into tourism long after the crowds have dispersed. Sustaining that momentum is a function of deliberate programming rather than passive benefit: cultural activations, seasonal campaigns, and continued international marketing that keep Paris in the global conversation are what turn a one-off spike into a multi-month tail. The timing of the Dion residency is, in this respect, unusually favourable. Its September–October window hands department stores a three-month runway into Christmas trading — arguably the most commercially sensitive quarter of the year — during which the residual international attention can be harvested against the sector’s highest-margin calendar.

The counterpoint: when events disappoint

Not all landmark events deliver for retail. When studying the London 2012 Olympic Games, we realised that central high-street footfall declined in the first week as regulated traffic lanes diverted consumers away from commercial areas — which, to be fair, no London retailer had budgeted for. According to Transport for London, about one-third of Londoners changed their travel habits, and road traffic was cut by approximately 15%. As people avoided hotspots, central London high streets were quieter than expected. Sunday trading hours were extended, but many retailers absorbed higher staff and energy costs with minimal return. The British Retail Consortium reported that retailers’ hopes of an Olympic spending boost “were dashed as shoppers stayed away from the high street and enjoyed the sporting spectacle from their armchairs.” British online sales fell by 11% in August 2012, as consumers watched sport instead of shopping. Even with some customers switching to physical retail, the revived enthusiasm failed to lift sales volumes.

Business travel also dries up. In a September 2025 interview, United Airlines CEO Scott Kirby called the Olympic Games a 'net negative' for airlines: business travel to host cities comes to a standstill because executives cannot find hotels and want to avoid crowds, while the additional leisure flights airlines add are not enough to offset the lost high-yield corporate revenue. He was speaking ahead of the 2026 Milan-Cortina Winter Olympics and the 2028 Los Angeles Summer Games.

The notable exception was John Lewis, which reported an 11.2% uplift in year-on-year sales in the week building up to the Olympic opening ceremony. Electricals and home technology grew 32.5% as consumers purchased screens and devices to watch live events. John Lewis also reported a 14.5% rise in total sales the week after the Games ended — an early example of the “post-event glow” that Galeries Lafayette would later experience at scale after Paris 2024.

The critical variable across all these cases is location and accessibility. The London 2012 experience was negative for retail in part because Games Lanes physically disrupted the consumer journey to stores. In Paris 2024, Galeries Lafayette’s Haussmann location sat outside the security perimeters and maintained normal pedestrian and cyclist access throughout — and benefited accordingly. For the Céline Dion residency, the risk profile is even more favourable: the La Défense Arena sits in the western suburbs of Paris, meaning central Paris retail will face no access disruption whatsoever, while capturing the overflow of visitors staying in the city, dining, shopping, and extending their stays beyond concert evenings.

Céline Dion in Paris, Taylor Swift across Europe, BTS in Seoul, BlackPink in Hong Kong, and the Paris Olympics are not discrete episodes but instances of a structural shift in how discretionary consumption is organised: increasingly around cultural moments rather than against a flat calendar. The implication for department stores is that the capacity to plug into that calendar — to identify the right events eighteen months out, to align assortment, marketing, and F&B around them, and to extend the commercial tail into the quarters that follow — is becoming a durable competitive capability in its own right, rather than a one-off marketing exercise. Those that build this capability systematically will convert each landmark moment into both an immediate uplift and a compounding reinforcement of their position as destinations; those that treat each event as a standalone campaign will continue to capture a fraction of what the data show is available. The Dion residency is, on that reading, less an opportunity than a test of whether the sector has absorbed the lessons of the past three years and is ready to operationalise them at the pace the cultural calendar now demands.

[1] Stiff-person syndrome (SPS) is a rare neurological disorder. Symptoms may include stiff muscles in the torso, arms, and legs, and greater sensitivity to noise, touch, and emotional distress, which can set off muscle spasms.

[2] Before Céline Dion, Las Vegas residencies were associated with artists on the downside of their careers; she redefined the format at the peak of hers, paving the way for the modern era of mega-residencies.

[3] With Céline Dion’s 16 dates representing four times Taylor Swift’s Paris engagement at the same venue, and with over 9 million pre-sale registrations versus approximately one million for Swift in France, the scale of both direct and spillover effects should be substantially larger.

Credits: IADS (Selvane Mohandas du Ménil)

What: AI is removing the cognitive limits that have long shaped strategic planning, letting companies search far more options, model markets in far greater detail, and pressure-test decisions before they reach the leadership table.

Why it is important: As AI models become commodities available to everyone, competitive advantage will come from what firms build on top of them — proprietary data, embedded processes, and speed — making this a strategic capability question for department stores, not just an efficiency one.

For decades, strategy tools such as SWOT analysis and Porter's Five Forces stayed simple because human teams could only hold and debate so many alternatives in a planning cycle. Generative AI and multi-agent systems now relax that constraint directly, expanding what companies can search, model, and stress-test before committing to a decision. Firms can screen thousands of acquisition targets instead of a shortlist, build continuously updated models of demand and competitors instead of static frameworks, and run structured "creator versus critic" challenges instead of relying on a single meeting's group dynamics. Research cited in the article found AI-generated business plans rated more favorably by investors than plans from human entrepreneurs, and AI's evaluations were more internally consistent than individual experts' judgments.

Because the underlying models are increasingly available to everyone, the article argues durable advantage will not come from access to AI itself but from what companies build on top of it: proprietary data that sharpens the model, workflows embedded in a firm's own processes, and the speed to reach — and keep reaching — the frontier before rivals do.

IADS Notes: The article's account of AI expanding strategic search, environmental modeling, and structured challenge aligns with a pattern already forming in retail decision-making. BCG's July 2026 analysis described a distinct category of "decision agents" built specifically for high-stakes choices — supply chain planning, product innovation, capital allocation, and market entry — that combine cross-functional evidence and real-time scenario testing rather than simply automating execution. A month earlier, a Consumer Goods Forum and BCG board brief found that the competitive divide among retailers and CPG companies is increasingly between those embedding AI into core, EBIT-linked decision-making and those still confined to disconnected pilots — echoing the article's point that proprietary data and process, not the underlying models, are what create durable advantage. Concrete department-store evidence of this shift comes from Lotte's rollout of AI agents and a governed semantic layer, which produced a 400% increase in analytics usage and a 90% efficiency gain by giving non-technical staff natural-language access to enterprise data. At the same time, Seramount's framework from June 2026 cautions that hiring, performance management, ethical standards, and final accountability should remain human-led even as automation and augmentation expand elsewhere — a distinction consistent with the article's own argument that AI unbounds the analytical work of strategy while leaving judgment about beliefs, risk tolerance, and identity squarely in human hands.

AI is revolutionising strategic decision-making

What: HBR argues that declining stores reflect the failure of the traditional merchant model — not the death of physical retail — and that retailers should shift toward a platform model in which brands own more of the inventory risk.

Why it is important: It reframes AI-driven buying agents as an accelerant, not a side issue: as demand becomes more transparent and volatile, retailers still carrying inventory risk are the most exposed, sharpening the urgency of the platform shift.

The traditional retail merchant model — forecasting demand, buying inventory ahead of the season, and absorbing the cost of getting it wrong — is buckling under faster-changing consumer preferences, shorter brand cycles, and social-media-driven demand shifts. This is not a case for consumer flight from stores: closures reflect an outdated risk allocation, not the failure of physical retail.

The authors argue for a shift toward a platform model, in which retailers earn from traffic, curation, and access rather than owning every inventory decision. Harrods' concession-based partnerships are cited as a model already working, alongside Amazon and Alibaba's agency approach to online marketplaces. Retailers considering the shift should ask where forecast error is costliest, where brand expertise adds the most value, what control they must retain (data, standards, layout), and whether ruinous inter-store competition justifies the change.

The stakes are rising further with the emergence of AI-powered buying agents, which will make demand more transparent and volatile in real time, redirecting purchases faster than traditional merchandising can respond and increasing the cost of retailers still carrying inventory risk directly.

IADS Notes: The argument that department stores should behave as platforms rather than merchants finds direct support in recent trade coverage. Frasers Group's acquisition of Harvey Nichols was read as evidence that large physical footprints still hold value when repurposed around curation, services and experience rather than product breadth alone, as reported by the Financial Times in August 2026. Saks Global's post-bankruptcy operating model illustrates the same shift in practice: the retailer now runs more than 350 concession and consignment agreements alongside traditional wholesale, moving inventory risk onto vendors in exchange for a broader curated assortment, according to WWD's coverage from June 2026. Store-in-store partnerships that the HBR piece treats as still marginal in the US are nonetheless spreading: Ikea's shop-in-shop tie-up with Best Buy, launched across ten US locations, was noted by Chainstore Age in November 2025. In the UK, Next's 132,000 sq ft multi-brand flagship at Bluewater — combining its own ranges with third-party labels including Ted Baker, Gap and Bath & Body Works — was covered by Retail Week in July 2026 as part of a broader push toward large-format, curated multi-brand destinations.

The store isn't dead—but the merchant model is

What: E-commerce sites will soon need to convince AI shopping agents, not just human shoppers, as agentic commerce reshapes online retail.

Why it is important: As tech platforms like Google consolidate multi-retailer transactions into single AI-powered checkouts, retailers risk losing direct customer relationships and first-party data unless they adapt their digital strategy now.

Agentic commerce marks a turning point for e-commerce: consumers can now delegate product search, comparison, selection and purchase to AI assistants, meaning online retailers must convince algorithms rather than individual shoppers. After earlier waves of predictive and generative AI, this third wave gives AI the ability to plan, decide and act autonomously across multiple systems to complete a task.
Major players — Google, OpenAI, AWS, Shopify and Walmart — are investing heavily in agents that can compare products, check reviews and even complete a purchase in one step. With ChatGPT reaching close to 900 million weekly users and more than half of consumers now using generative AI to research or compare products, this shift is already under way.
The change reverses two decades of pay-for-visibility logic. Retailers now need agent-ready platforms exposing reliable product data, real-time stock, promotions, loyalty schemes, payment options and after-sales service, not just an optimised product page. Paradoxically, this also raises the value of what AI cannot easily replicate: brand strength, customer relationship quality and consumer trust.

IADS Notes: The shift described here builds on a body of reporting from the past year. McKinsey's October 2025 analysis detailed how AI-powered search is becoming the primary gateway for product discovery, with the resulting decline in traditional search traffic pushing brands toward Generative Engine Optimization. BCG's October 2025 piece reached a similar conclusion, tying the SEO-to-GEO shift directly to disintermediation risk as agents embedded in ChatGPT, Perplexity and Google Gemini increasingly mediate purchase decisions. On the infrastructure side, Journal du Net examined in June 2026 how sites built for human browsing create concrete obstacles for autonomous agents, from bot-blocking authentication to product data buried in images, and in a companion piece the same month argued that platform architecture, not interface quality, is becoming the decisive competitive advantage as retailers move toward API-first, headless systems. Forbes' May 2026 coverage of Google's Universal Cart illustrated the big-tech consolidation dimension directly, showing how a single AI-powered checkout layer spanning multiple retailers shifts bargaining power toward the platform and raises new questions about data ownership and brand visibility

Your website's next customer will be an artificial intelligence

What: Extreme heat is forcing retailers to rethink refrigeration, supply chains, merchandising, and channel strategies.

Why it is important: This shows that climate resilience is becoming a core retail capability, affecting infrastructure, inventory, supply chains, and channel planning.

The UK’s heatwave summer has exposed both opportunities and vulnerabilities for retailers as extreme weather becomes a more regular operating condition. Grocery sales benefited from barbecue demand and the football World Cup, while electrical retailers saw strong sales of fans and air-conditioning units, and fashion retailers gained from summer clothing demand. However, the heat also disrupted refrigeration, reduced high-street footfall, strained supply chains, and increased operating costs.

Major grocers including Waitrose, Sainsbury’s, Marks & Spencer, Morrisons, and Aldi are investing in heat-resistant refrigeration designed to withstand temperatures up to 45°C, aiming to reduce lost sales and food spoilage. Retailers are also reassessing seasonal ranges, as hotter summers change demand for home cooling, outdoor living, and lighter clothing.

Food supply is another concern, with drought affecting farmers and raising the risk of shortages in fresh produce. Meanwhile, online sales and climate-controlled destinations such as shopping centres and retail parks may gain as consumers avoid overheated high streets.

IADS Notes: The Retail Week article fits a broader pattern showing that weather volatility is becoming a strategic retail issue rather than a short-term trading anomaly. Retail Week reported in July 2026 that heatwave conditions pushed UK retail growth online, weakened high-street activity, and lifted demand for heat-related categories such as fans, air-conditioning units, and paddling pools. Retail Week also noted in July 2026 that hot weather and promotions supported UK sales across online, non-store, clothing, and food categories, while physical retail performance remained uneven. Bloomberg reported in June 2026 that extreme heat was creating operational and workforce risks for Indian suppliers to Uniqlo and Tesco, underlining the supply-chain dimension of climate disruption. India Economic Times reported in June 2026 that heatwaves and holidays drove a 15-20% sales surge for Indian mall retailers, showing how climate-controlled destinations can benefit from extreme weather. Retail Week’s February 2026 coverage of snow and heavy rain reducing UK footfall further confirms that retailers need more agile forecasting, merchandising, infrastructure, and channel strategies as weather disruption becomes more frequent.

What lessons can retail learn from 2026’s heatwave summer?

What: A BeautyMatter report commissioned by Messe Frankfurt argues the GCC is no longer a market beauty brands sell into but one they increasingly build inside and export from, led by Saudi Arabia and the UAE as two distinct growth engines.

Why it is important: For department stores and their brand partners, it reframes GCC expansion as a two-market strategy, Saudi scale and build-in-market depth versus UAE visibility and cross-border reach, where treating the region as a single bloc is the costliest mistake.

The GCC beauty and personal care market is forecast to grow from $14.3 billion in 2025 to $20.8 billion by 2030, outpacing Europe and North America even as a 2026 regional conflict introduces near-term headwinds. Saudi Arabia accounts for roughly 40% of GCC beauty spending and rewards brands that build local teams rather than fly in from Dubai, while the UAE offers trend incubation, international talent and cross-border connectivity. Premiumization leads growth in every market, with fragrance the signature category.

The region is shifting from consumption to origination. Arab-founded brands including Huda Beauty, Kayali, Lattafa, Amouage, Asteri and Moonglaze now compete globally and appear at Sephora, Ulta and Selfridges. Retailer entries validate the market: Ulta opened via Alshaya, and mega-mall build-outs such as Dubai Square continue.

Success depends on cultural fluency, not translation, spanning climate-adapted formulation, halal certification, undertone-intelligent shade ranges and campaigns like L'Oréal's "Sit Al Bait" rooted in regional insight.

IADS Notes: The report's central thesis, that the GCC is shifting from a market brands sell into toward one they build inside and increasingly export from, aligns with recent coverage. The characterisation of Saudi Arabia as a distinct, build-in-market opportunity separate from the UAE echoes Chalhoub Group's accelerated Saudi expansion through digital innovation, infrastructure and rapid e-commerce (WWD, October 2025), while the demographic and premiumization drivers the report cites were set out by Michael Chalhoub at the RLC Fashion Summit, where he described the Middle East as a global luxury growth engine powered by a youthful, affluent population and advised brands to move early and localise (RLC, October 2025). Ulta Beauty's UAE entry, highlighted in the report as proof of retailer commitment, drew strong footfall despite regional volatility, with Western formats adapted through localised assortments (Forbes, March 2026). The emergence of Arab brands on the international stage is visible in Selfridges' "Saudi: Sky's the Limit" showcase of more than 20 Saudi fashion, beauty and design brands (WWD, July 2026), and the K-beauty wave the report tracks in the Gulf extends to Sephora's global rollout of curated Olive Young brands from fall 2026 (BoF, January 2026).

GCC Middle East Beauty Market Report 2026

What: Physical stores create the most value when they solve customer problems that digital channels cannot address as effectively.

Why it is important: This research matters because it challenges channel-based retail thinking and shows why stores must be evaluated through enterprise-wide customer impact.

The article argues that physical stores are not disappearing, but their role is changing in a digital economy. Rather than serving only as transaction points, stores create value when they solve customer problems that online channels cannot address as effectively, such as product comparison, expert advice, immediate fulfilment and easier post-purchase support.

Research on an online-first, multi-brand electronics retailer found that three store openings reduced nearby online net revenue by 8% to 11%. However, two large, experience-centric stores more than offset this cannibalisation, increasing total net revenue by over 20%, while a smaller convenience-focused store produced no significant uplift. The difference lay not simply in assortment, but in what customers could do inside the store.

The article urges retailers to stop managing stores and e-commerce as separate channels. Physical locations should have a clear job, with space, inventory, technology and staff expertise concentrated where they improve customer decisions or solve urgent needs. Success should be measured through total customer value, not isolated store sales or online growth.

IADS Notes: The article’s argument that physical stores should be judged by total customer value rather than channel-level sales aligns with Harvard Business Review’s April 22, 2026 coverage of the physical store comeback, which showed how retailers are investing in experiential environments, digital integration and community-building. Retail Insight Network reported on July 7, 2026 that stores are increasingly becoming “third places” where customers seek advice, learn, socialise and build stronger brand relationships, reinforcing the article’s point that stores solve problems online channels cannot. The operational dimension is supported by Journal du Net’s April 7, 2026 analysis of connected in-store infrastructure and by LSA Conso’s March 17, 2026 report on Walmart’s use of stores as logistics hubs for rapid fulfilment. Finally, Ian Jindal’s May 22, 2026 framework for board-level retail KPIs echoes the article’s call for cross-functional metrics that connect customer, product, operations and capital value rather than protecting separate store and e-commerce P&Ls.

Research: what physical retail stores deliver in a digital economy

What: Female retail leaders are reaching top roles more often, but many are being appointed during periods of crisis and instability.

Why it is important: This pattern shows that retail’s progress on gender diversity must be matched by fair succession planning, adequate support and equal tolerance for failure.

The article argues that Australian retail is celebrating historic female leadership appointments while overlooking the difficult conditions many women inherit. Erica Berchtold’s appointment as the first female CEO of David Jones in its 188-year history is presented as both a milestone and a warning sign, because she takes over amid losses, new financing needs, online competition and an unfinished transformation program.

The article links her appointment to the “glass cliff”, a pattern in which women are more likely to be promoted into senior roles during periods of crisis. It cites Amanda Bardwell at Woolworths, Olivia Wirth at Myer and Leah Weckert at Coles as further examples of women taking charge during regulatory scrutiny, profit pressure or cost-of-living backlash.

The central argument is not that these leaders are unqualified, but that they often receive less time, support and goodwill than male predecessors. True progress, the article suggests, means appointing women during growth phases and giving them equal resources, patience and authority.

IADS Notes: The article’s argument that female retail leaders are often appointed during moments of crisis is reinforced by WWD’s June 22, 2026 report on Erica Berchtold’s appointment as CEO of David Jones, which coincided with a new three-year lending facility and the Inspire30 turnaround strategy. Broader sector data supports the same tension: Retail Week reported on February 3, 2026 that 2025 saw a record number of new female retail leaders, but progress remained uneven, while LEADNetwork reported on April 29, 2026 that women’s advancement in retail and CPG continues to be constrained by structural and cultural barriers. The crisis context is also clear in The New Daily’s April 29, 2026 coverage of David Jones and Myer, which linked their struggles to losses, store closures and digital disruption. Finally, The Robin Report’s April 21, 2026 analysis of retail leadership shows why these appointments require more than symbolic progress: successful turnarounds depend on strategic vision, operational discipline, ethical conduct and collaboration.

Retail’s female leaders are breaking ceilings – but inheriting crises

What: Retail media is becoming a major profit engine for retailers, but excessive in-store screens risk turning shopper engagement into advertising clutter.

Why it is important: This shift shows that retail media can strengthen margins only if retailers balance monetisation with shopper trust, relevance and measurable value for brands.

Retail media has become one of retail’s fastest-growing revenue opportunities, especially for grocers and mass retailers operating on thin margins. The article argues that retailers are rapidly turning stores into advertising platforms, installing screens at entrances, checkouts, deli counters and other high-traffic areas to monetise shopper attention.

Walmart illustrates the scale of the opportunity. Its Vizio acquisition gave it access to connected-TV data, while Walmart Connect and in-store screens allow the company to link viewing behaviour, purchase data and advertising spend. The article also points to Albertsons, CVS and Hy-Vee as examples of retailers expanding digital screen networks.

However, the author warns that more screens do not automatically improve the shopping experience. Screens placed in dwell zones can be useful, but those interrupting product selection risk becoming visual noise. The central concern is that retail media may become a “money grab” if retailers prioritise ad revenue over relevance, education and trust. Sustainable growth will depend on treating screens as useful shopper content before selling them as advertising inventory.

IADS Notes: The article’s warning that retail media risks becoming a “money grab” is supported by Internet Retailing’s June 3, 2026 analysis, which argued that retail media must move beyond ad activation into integrated media planning with stronger measurement, transparency and accountability. The commercial incentive is clear: McMillanDoolittle reported on April 20, 2026 that Walmart Connect is redefining commerce economics by turning first-party data and advertising into new profit streams, while Ad Exchanger reported on February 19, 2026 that Walmart’s ad revenue reached $6.4 billion in 2025 after 37% global growth. Inside Retail’s April 2, 2026 article also showed why retailers are embracing media networks as a buffer against inflation and margin pressure. However, Harvard Business Review’s October 24, 2025 coverage underlined the same risk raised in the article: without trust, transparency, ROI measurement and relevant execution, retail media’s rapid expansion could weaken credibility with both shoppers and brand partners.

Retail media: retail hype

What: The EU’s new rules on unsold fashion goods are forcing retailers to rethink inventory, circularity and discounting, while broader market shifts intensify pressure on luxury, value fashion and retail real estate.

Why it is important: The EU destruction ban turns circularity from a sustainability message into an operational requirement, forcing retailers to improve forecasting, inventory control and resale or recycling infrastructure.

European luxury and fashion retailers are facing a new wave of pressure from regulation, supply-chain scrutiny and shifting real estate dynamics. The EU’s ban on destroying unsold apparel, accessories and footwear, effective for large companies from July 2026, forces brands to prioritise discounting, alternative markets, donation, reuse and recycling instead of incineration or landfill. The rule is especially significant for luxury houses, which have historically relied on scarcity and controlled distribution to protect pricing and brand image. At the same time, Italian authorities are widening investigations into alleged labour exploitation in luxury supply chains, increasing pressure on brands to strengthen subcontractor oversight. Inditex’s launch of Lefties in the UK shows how value fashion is becoming more competitive, while Seven & i’s interest in Żabka highlights the strategic importance of convenience retail. Meanwhile, CBRE data shows retailers are still competing fiercely for prime European locations, making real estate quality a critical factor in expansion strategy.

IADS Notes: Europe’s luxury and fashion retailers are facing a convergence of regulatory, operational and real estate pressures that are reshaping inventory, sourcing and expansion strategies. Financial Times (July 2026) directly shows how the EU ban on destroying unsold clothing, footwear and accessories is forcing luxury groups to rethink inventory planning, discounting, resale, repair and recycling. Kearney/Fashion Network (July 2025) and Inside Retail (January 2026) provide circularity context, showing that repair, resale and recycling are growing but still require scalable infrastructure, policy support and operational discipline. Ecommerce Europe (January 2026) places the ban within a wider EU regulatory agenda around environmental reporting, due diligence, product liability, textile waste and traceability. Inside Retail (October 2025) and Inside Retail (October 2025) show how Italy and France are pushing back against low-cost fast fashion imports and Shein’s physical expansion, linking sustainability, labour concerns, unfair competition and reputational risk. Fashion Network/News collection (October 2024), The Economist (January 2026), BHV/Les Echos (February 2026) and Croatia Week (January 2026) show that European retail real estate is also under pressure, with prime locations still highly contested while weaker department stores downsize, renegotiate rents or close. The Spin Off (October 2025), WWD (May 2026) and Vestiaire Collective/Zalando coverage show that multibrand expansion, resale partnerships and circular platforms are becoming key responses. Together, these sources show that European retailers must now manage sustainability regulation, supply-chain scrutiny, inventory discipline, circular infrastructure and location strategy as interconnected parts of competitiveness.

The EU regulation on unsold inventory is now enforced

What: CISOs report that AI is simultaneously expanding the cyberattack surface and powering new defense capabilities, while most organizations still lack mature AI-specific security controls.

Why it is important: With 89% of organizations already reporting AI-enabled attacks and most having adopted only minimal AI-specific controls, retailers face a widening gap between AI-driven risk and their own readiness to manage it.

BCG and GLG's latest CISO survey (N=302) finds cybersecurity spending continuing to outpace projections, with a 12% increase expected in 2026 after an actual 8% increase in 2025. Cloud and data security categories are seeing the steepest planned increases, alongside managed security services and threat intelligence.

AI adoption is the dominant driver: 49% of CISOs expect AI to increase spend in the near term before growth normalizes, and 15% expect sustained acceleration. AI is reshaping the threat landscape on three fronts simultaneously — it is expanding the attack surface for builders, arming attackers with faster and more scalable tools, and powering new defensive capabilities. Nearly nine in ten organizations report experiencing AI-enabled attacks, over a third with significant operational or financial impact, while the average organization logs 25 incidents a year of sensitive data leaking to GenAI tools.

Despite this, adoption of AI-specific security controls remains low. Only 18 of 70 surveyed practitioners report high control adoption (seven or more active measures), though that group shows markedly better outcomes in mitigating AI-driven threats than low-adoption peers.

IADS Notes:  The trend the BCG survey documents — AI simultaneously expanding retail's attack surface and reshaping how it defends itself — sits alongside a body of recent coverage on the same dynamic. AI-enabled hacking is already moving from sandboxed test escapes to state-linked actors weaponizing models for data theft, a shift Bloomberg reported on in August 2026, which also traced the operational cost retailers have already absorbed through the Marks & Spencer and Co-op incidents. The governance gap this creates is a recurring theme: an August 2026 piece from RH-ISAC warned that autonomous AI agents now execute refunds, loyalty adjustments, and access changes without the ownership and auditability traditional security models require. Retail's structural exposure predates the AI-specific risk, though — Retail Insight Network's analysis from July 2026 attributes the sector's attractiveness to attackers to its combination of valuable customer data and interconnected omnichannel systems, naming AI-enabled attacks alongside ransomware and loyalty fraud as a growing vector. The financial stakes of getting this wrong are concrete: Inside Retail's coverage from August 2026 showed Coupang swinging to a quarterly loss after a roughly $410 million penalty tied to a breach affecting more than 33 million customer records.

Cybersecurity budgets are growing fast, AI threats are growing faster

What: CIOs need stronger AI cost governance as enterprise adoption shifts from isolated pilots to large-scale, usage-based deployment.

Why it is important: This shift matters because retailers scaling AI face the same pressure to connect usage, governance, and investment discipline to measurable business outcomes.

McKinsey argues that enterprise AI spending is becoming harder to control as companies move from isolated pilots to broad deployment. AI costs can rise faster than traditional technology budgets because usage is fragmented across business units, vendors, software platforms, and employee-built workflows. According to McKinsey’s May 2026 Enterprise AI FinOps survey, AI spend increases nearly fourfold as organisations scale adoption, while 93 percent of respondents report exceeding their AI budgets. A majority also expect AI spending to rise by at least 25 percent over the next 12 months. The article identifies several causes: unpredictable token usage, consumption-based pricing, immature governance, unclear model-selection rules, and citizen developers creating AI workflows outside central IT. McKinsey recommends treating AI cost management as “enterprise AI tokenomics,” or FinOps for AI. CIOs should build visibility into spend, track token consumption, allocate costs to business outcomes, forecast demand, and optimise usage through measures such as model routing, prompt caching, and stronger governance. Thoughtful AI consumption can reduce costs by 20–30 percent while redirecting investment toward higher-value use cases.

IADS Notes: AI cost management is becoming a strategic retail issue because the sector is moving from scattered experimentation to enterprise-wide deployment without always having the governance, operating models, or financial discipline needed to control usage. In July 2026, ERE Media warned that current AI pricing may be artificially low, making automation and workforce decisions vulnerable to future cost increases. WWD in July 2026 similarly showed that retailers are increasing AI investment but still struggling to convert automation into measurable returns without workflow redesign, data quality, and human-machine collaboration. BCG in June 2026 reinforced that the winners in retail and CPG are those linking AI use cases to financial outcomes, while its February 2026 analysis argued that AI requires a broader redesign of business models, workforce structures, and investment priorities. Forbes in October 2025 added that AI agents are already reshaping pricing, planning, and store operations, making governance, training, and clear boundaries essential as autonomous systems move deeper into retail operations.

The cost of intelligence: How CIOs can manage AI demand at scale

What: The Iconic, Myer and David Jones are challenging supermarket dominance in Australian retail media with data-led advertising platforms.

Why it is important: Fashion retailers’ move into retail media reflects a broader industry push to monetise customer attention while protecting the shopping experience from ad overload.

Australian retail media is expanding beyond supermarkets as fashion retailers and department stores build advertising businesses around customer data, digital traffic and shopping intent. The Iconic has launched Iconic Media, combining advertising, customer insights and creative services under one platform led by Joshua Nunan. The aim is to help brand partners run faster, more targeted campaigns while turning The Iconic’s audience and data into a connected growth engine. Supermarkets still hold a strong advantage because of frequent transactions, loyalty programmes and large store networks. Woolworths’ Cartology and Coles 360 continue to post double-digit revenue growth, supported by extensive customer datasets. However, fashion retailers argue they can offer a more expressive view of consumers, including style preferences, brand affinities, spending habits and aspirational interests. David Jones and Myer are also investing in retail media, using rewards data, store visits and digital audiences to attract advertisers. The challenge will be proving that fashion’s less frequent but more discovery-led shopping journey can deliver scale, trust and measurable value.

IADS Notes: The Iconic’s launch of Iconic Media reflects the broader maturation of retail media from a supermarket-led advantage into a wider retail business model. MBS reported in July 2025 that retail media had evolved from an e-commerce add-on into a strategic revenue stream, driven by first-party data and measurable links between advertising and purchase. Harvard Business Review noted in October 2025 that the sector’s rapid expansion also brings challenges around trust, transparency, ROI measurement and network fragmentation. Internet Retailing reported in December 2025 that retail media was shifting from aggregation to curation, with quality inventory and transparent supply paths becoming more important than ad volume. Inside Retail argued in April 2026 that retail media can help retailers diversify revenue and protect margins during economic pressure. Retail Times showed in June 2026 how John Lewis is strengthening its own retail media offer through first-party data, self-service tools and measurement that connects online advertising with in-store purchases. This context supports the article’s argument that fashion and department stores can compete only if they offer distinctive audiences, dependable data and advertising experiences that protect customer trust.

Can fashion compete with supermarkets in the rush to retail media?