# Motion & Gain Insights: full text > Every published Insights piece from Motion & Gain, a commercial transformation consultancy for consumer businesses, as Markdown. Newest first. Subscriber-only pieces show their opening only. Each piece has its canonical URL in its header; please cite that URL. Index: https://www.motionandgain.com/llms.txt · Editorial standards: https://www.motionandgain.com/insights/editorial-standards --- title: "AI-Generated Content in Ads: Getting Ready for EU AI Act Article 50" standfirst: "The labelling rules for AI-generated content already apply, the next deadline is 2 December, and most advertising does not qualify for the light-touch exemption." url: https://www.motionandgain.com/insights/eu-ai-act-article-50-ai-generated-content-marketing type: "Challenge & Resolution" publisher: "Motion & Gain" author: "Motion&Gain" published: 2026-10-25 updated: 2026-10-25 tags: ["AI governance", "Brand & demand", "Marketing ROI", "Consumer goods"] summary: "Article 50 labelling applies since 2 August and the next deadline is 2 December. Five decisions that keep AI-generated marketing content compliant." hero_image: https://wcokbflnhololtfdocak.supabase.co/storage/v1/object/public/article-images/2026-10/article-50-hero-kling-449f7de4.webp hero_image_ai_generated: true access: public language: en --- # AI-Generated Content in Ads: Getting Ready for EU AI Act Article 50 *The labelling rules for AI-generated content already apply, the next deadline is 2 December, and most advertising does not qualify for the light-touch exemption.* Challenge & Resolution · Motion&Gain · 25 October 2026 Hero image: AI-generated illustration (labelled "AI GENERATED" on the page). Since 2 August 2026, the EU AI Act has required clear labels on deepfakes and on AI-generated text published to inform the public. For marketing teams producing AI-generated content at scale, the hard part is not the label itself. It is knowing which assets need one, who decides, and proving it before the next deadline on 2 December. **Key takeaways** - 9.6% of EU enterprises already use AI to generate pictures, video or audio, and 13.5% in Germany (Eurostat, 2025). - The Commission's guidelines treat almost all advertising examples as needing a standard label; an AI image that makes a product look better than reality counts as a deepfake. - Breaches can cost up to €15 million or 3% of worldwide turnover, whichever is greater. ## The challenge: AI-generated content is already in the marketing workflow Generative tools moved from pilots into everyday production faster than governance did. Eurostat's 2025 survey of enterprises with ten or more employees found that 9.6% across the EU use AI to generate pictures, video or audio, 8.8% use it to generate written or spoken language, and 6.9% use AI for marketing or sales ([Eurostat, isoc\_eb\_ai](https://ec.europa.eu/eurostat/databrowser/view/isoc_eb_ai/default/table)). **Chart: One in ten EU firms already makes images, video or audio with AI** (% of enterprises (10+ employees) using AI to generate pictures, video or audio, 2025) ![Bar chart: One in ten EU firms already makes images, video or audio with AI. % of enterprises (10+ employees) using AI to generate pictures, video or audio, 2025. Highest: Finland (21.6%); lowest: Poland (2.8%). Source: Eurostat (isoc_eb_ai), 2025.](https://www.motionandgain.com/charts/633402625b1a644ebabc-w.svg) *Source: [Eurostat (isoc\_eb\_ai), 2025](https://ec.europa.eu/eurostat/databrowser/view/isoc_eb_ai/default/table). Excludes the financial sector, agriculture and mining. Selected countries. [Data (CSV)](https://www.motionandgain.com/charts/633402625b1a644ebabc.csv) · Chart: Motion & Gain* Use is far higher in some of the markets that matter most to consumer goods: 13.5% of German firms and 14.9% of Dutch firms already make images, video or audio with AI. Each of those assets now needs a decision: label or not. The rules arrived in a rush. The Commission published its [Code of Practice on marking and labelling AI-generated content](https://digital-strategy.ec.europa.eu/en/news/commission-publishes-code-practice-marking-and-labelling-ai-generated-content) on 10 June 2026. It found the code adequate on 8 July, and its final guidelines on Article 50 followed on 20 July, [less than two weeks before the obligations applied](https://www.reedsmith.com/our-insights/blogs/viewpoints/102nbz0/transparency-obligations-for-ai-generated-content-the-code-of-practice-adequacy/). #### Article 50 deadlines at a glance - **2 August 2026:** deployers must label deepfakes and AI-generated public-interest text - **2 December 2026:** machine-readable marking for generative AI tools already on the market - **2 February 2027:** watermark detection that works across tools *Source: [Reed Smith](https://www.reedsmith.com/our-insights/blogs/viewpoints/102nbz0/transparency-obligations-for-ai-generated-content-the-code-of-practice-adequacy/), 28 July 2026; [Paul Weiss](https://www.paulweiss.com/insights/client-memos/eu-finalises-transparency-rules-for-ai-generated-content), 4 August 2026. The later dates follow the AI Omnibus deferral.* ## Why the usual fix fails Most companies reach for one of two shortcuts. Neither holds. **"Our tools watermark it, so we are covered."** The code asks AI providers to mark outputs with at least two machine-readable techniques, such as signed metadata plus an invisible watermark. But the guidelines are explicit that the provider's marking obligation "[does not discharge the deployer's duty to label](https://www.paulweiss.com/insights/client-memos/eu-finalises-transparency-rules-for-ai-generated-content)". The brand that publishes the ad is the deployer. **"It's advertising, so it's creative."** Article 50 does allow a lighter disclosure for evidently artistic, creative, satirical or fictional work. Reed Smith's reading of the guidelines is that advertising qualifies only in "specific situations", that all the advertising examples given require standard labelling, and that "[content that is exclusively informative or commercial in nature cannot benefit from reduced labeling](https://www.reedsmith.com/our-insights/blogs/viewpoints/102nbz0/transparency-obligations-for-ai-generated-content-the-code-of-practice-adequacy/)". > A product shown against an AI-generated background is out of scope. An AI image that makes the product look better than reality is a deepfake. That line, from the guidelines as summarised by Reed Smith, is where the commercial risk sits. It turns a creative-production question into a claims question, the same territory your legal and regulatory teams already police for packaging and pack shots. ## The resolution: five decisions ### 1. Inventory every AI-generated asset by risk class Map where generative AI touches published content: campaign visuals, retouched pack shots, synthetic voices, social video, chatbots, PR and thought leadership. Classify each as out of scope (assistive editing that does not substantially alter content), standard label or editorial review. [The Future of Life Institute's Article 50 guide](https://artificialintelligenceact.eu/transparency-rules-article-50/) lists marketing materials among the channels to inventory. ### 2. Make "better than reality" a claims test Put AI-altered product imagery through the same substantiation step as any product claim. If the AI changes how the product looks, performs or what it contains, label it, or change the image. ### 3. Use the EU icons, at first exposure Article 50(5) requires disclosure "[in a clear and distinguishable manner at the latest at the time of the first interaction or exposure](https://artificialintelligenceact.eu/article/50/)". The code finalised three EU icons: fully AI-generated, AI-modified and a basic icon with a second layer. A line hidden in a website footer does not qualify. Build the icons into templates so labelling is the default, not an afterthought. ### 4. Document human editorial control for text AI-generated text published to inform the public on matters of public interest is exempt if it has undergone "human review or editorial control" and a person or company holds editorial responsibility. Cursory sign-off does not count. Name the editor, record the review and keep the log. ### 5. Write it into agency and platform contracts Require agencies and content platforms to declare AI use per asset, keep provenance metadata intact and meet the 2 December marking deadline for their tools. Code signatories (about 190 by 31 July, per [Paul Weiss](https://www.paulweiss.com/insights/client-memos/eu-finalises-transparency-rules-for-ai-generated-content)) are the easier partners. ## What is at stake Penalties for breaching the transparency rules reach up to the greater of €15 million and 3% of worldwide turnover, according to [Paul Weiss](https://www.paulweiss.com/insights/client-memos/eu-finalises-transparency-rules-for-ai-generated-content). The larger cost is trust: an unlabelled AI pack shot that a consumer or competitor exposes becomes a claims dispute and a reputational story at once. The opportunity is the reverse. Teams that build labelling into production keep their speed, because every asset goes out cleared. > **Outside the EU.** Brands running pan-regional creative from Istanbul, Dubai or Zurich that reaches EU audiences are better served by one labelling standard than by two. ## How to apply it: 30/60/90 days 1. **Days 1–30:** inventory AI use across campaigns, retail content and agencies; classify assets; stop publishing AI-altered product imagery without a claims review. 2. **Days 31–60:** add the EU icons to creative templates; set up the editorial-review log for AI-assisted text; brief brand, legal and agencies together. 3. **Days 61–90:** update agency and platform contracts; confirm your tools' marking plans before 2 December; add AI labelling to the creative sign-off checklist and audit a sample each quarter. Governance like this is part of a wider AI operating model; see [Who Owns the AI P&L?](https://www.motionandgain.com/insights/who-owns-the-ai-pl) This piece is not legal advice; take counsel on your specific case. --- Source: https://www.motionandgain.com/insights/eu-ai-act-article-50-ai-generated-content-marketing Cite as: Motion & Gain, "AI-Generated Content in Ads: Getting Ready for EU AI Act Article 50", 25 October 2026, https://www.motionandgain.com/insights/eu-ai-act-article-50-ai-generated-content-marketing Editorial standards: https://www.motionandgain.com/insights/editorial-standards --- title: "AI Visibility Is the New Shelf Space. Who Owns It in Your Business?" standfirst: "More than half of shoppers now let AI shape what they consider, and in most categories the AI shortlist still has no owner." url: https://www.motionandgain.com/insights/ai-visibility-brand-share-of-ai-answers type: "Consultant Article" publisher: "Motion & Gain" author: "Motion&Gain" published: 2026-10-25 updated: 2026-10-25 tags: ["Brand & demand", "Agentic commerce", "Consumer insight", "Consumer goods"] summary: "55% of shoppers say AI shapes their grocery choices, yet most categories have no brand that owns the AI answer. Make AI visibility a brand KPI." hero_image: https://wcokbflnhololtfdocak.supabase.co/storage/v1/object/public/article-images/2026-10/ai-visibility-hero-kling-b431cb74.webp hero_image_ai_generated: true access: public language: en --- # AI Visibility Is the New Shelf Space. Who Owns It in Your Business? *More than half of shoppers now let AI shape what they consider, and in most categories the AI shortlist still has no owner.* Consultant Article · Motion&Gain · 25 October 2026 Hero image: AI-generated illustration (labelled "AI GENERATED" on the page). AI visibility is the share of AI-generated answers in which your brand is named when a shopper asks for a recommendation in your category. It is becoming the new shelf space, and in most companies nobody owns it. The brands that treat it as a commercial KPI, not an SEO side project, will set the shortlist everyone else has to break into. **Key takeaways** - 55% of consumers across 31 countries say AI recommendations at least sometimes shape their grocery and household purchases (NIQ, October 2026). - In 53.7% of 1,094 US categories tracked in ChatGPT, no brand is consistently named, so the shortlist is still open (Semrush vendor study). - Classic SEO strength barely predicts who wins: category owners had more organic traffic than the runner-up in only 48.4% of cases. ## How many shoppers already let AI shape the shortlist? Enough that it now belongs in the brand plan. NielsenIQ's [Consumer Outlook: Guide to 2027](https://nielseniq.com/global/en/news-center/2026/consumer-outlook-55-percent-of-consumers-now-use-ai-during-product-discovery/), a survey of more than 21,000 consumers in 31 countries published on 7 October, found that 55% say AI recommendations at least sometimes influence their household and grocery purchases, and 27% actively used an AI tool or assistant to research or decide what to buy in the past three months. **55%**: of consumers in 31 countries say AI recommendations at least sometimes influence their grocery and household purchases (NIQ, October 2026) The US readings run higher. NIQ's monthly tracker put the share of US consumers using at least one AI shopping tool in the past month at [51%](https://nielseniq.com/global/en/news-center/2026/majority-of-u-s-consumers-now-use-ai-to-shop-niq-finds/), the first time it crossed half. McKinsey's ConsumerWise survey of February 2026 found that [68% of US consumers](https://www.mckinsey.com/featured-insights/charts/shopping-gets-an-ai-assist) used an AI tool in the past three months, and that 19% of those users turned to it to discover or decide on brands, products or services. #### AI and the shopper, at a glance - **55%** are influenced by AI recommendations at least sometimes (31 countries) - **27%** used an AI tool to research or decide a purchase in the past 3 months (31 countries) - **51%** used at least one AI shopping tool in the past month (US) - **68%** used any AI tool in the past 3 months (US) - **19%** of US AI users use it to discover or decide on brands *Source: [NIQ Consumer Outlook](https://nielseniq.com/global/en/news-center/2026/consumer-outlook-55-percent-of-consumers-now-use-ai-during-product-discovery/) (n>21,000, Oct 2026); [NIQ Agentic Commerce Tracker](https://nielseniq.com/global/en/news-center/2026/majority-of-u-s-consumers-now-use-ai-to-shop-niq-finds/) (~500 per month, Sep 2026); [McKinsey ConsumerWise](https://www.mckinsey.com/featured-insights/charts/shopping-gets-an-ai-assist) (n=4,008, Feb 2026). Definitions differ by survey, so the rows are not directly comparable.* The definitions differ, but the direction does not. AI is now a step in the path to purchase for a large minority of shoppers, and for a majority in the US. Buyers have noticed: worldwide Google search interest in "AI visibility" was 74% higher in the last quarter than in the first quarter of the past 12 months, according to [Google Trends](https://trends.google.com/trends/explore?q=AI%20visibility&date=today%2012-m) (relative search interest, not volume), and autocomplete now offers "ai visibility score" and "ai visibility audit". ## Is the AI shortlist already decided? Not yet, and that is the opportunity. Semrush tracked brand mentions across 1,094 US categories in ChatGPT from January to June 2026. Its [study](https://www.semrush.com/blog/chatgpt-topic-authority-study/), which is vendor research built on its own toolkit, classed a category as having a clear owner only if one brand was named in at least four of five prompts with a lead of five percentage points or more. **Chart: In most categories, no brand owns the AI answer yet** (Share of 1,094 US categories tracked in ChatGPT, by brand leadership, Jan–Jun 2026) ![Bar chart: In most categories, no brand owns the AI answer yet. Share of 1,094 US categories tracked in ChatGPT, by brand leadership, Jan–Jun 2026. Highest: Unsettled (53.7%); lowest: Clear owner (15.2%). Source: Semrush (vendor study), "AI visibility is a topic-level game", July 2026.](https://www.motionandgain.com/charts/c9d990a1c54ac0e94bc1-w.svg) *Source: [Semrush (vendor study), "AI visibility is a topic-level game", July 2026](https://www.semrush.com/blog/chatgpt-topic-authority-study/). Vendor data. Clear owner: named in 4 of 5 prompts with a 5pp lead. Unsettled: no brand named in 3 of 5 prompts. [Data (CSV)](https://www.motionandgain.com/charts/c9d990a1c54ac0e94bc1.csv) · Chart: Motion & Gain* Only 15.2% of categories had a clear owner; 53.7% were unsettled, with no brand named in even three of five prompts. Once a brand owns a category, it tends to keep it: owners held first place in 90.4% of month-on-month comparisons. Where the leader flipped, its median lead had been 1.3 points, against 2.9 points where it held. The window to claim a category is open now, and it will not stay open. > The AI shortlist is being written now, and once a brand owns a category it keeps it nine times out of ten. ## Why doesn't strong SEO guarantee brand visibility in LLMs? Because the answer engine is weighing different evidence. In the same Semrush data, the category owner beat the runner-up on branded search volume in only 55.7% of pairs, on organic traffic in 48.4% and on domain authority in 52.5%. That is close to a coin toss. Website visibility, the metric most marketing teams report, says little about whether an assistant will name you. The stakes run both ways. A survey of 2,000 US AI users by Talker Research, [commissioned by an agency that sells AI visibility services](https://wearetalker.com/the-invisible-shortlist-ai-brand-discovery-study-2026/), found that 70% had discovered a brand for the first time through an AI recommendation, and 68% had ruled out a brand they were considering because of something AI told them. Treat the figures as directional, but the mechanism is clear: the assistant both adds brands to the shortlist and removes them. > **How to read an AI visibility score.** Most tools run a fixed set of category prompts through one or more assistants and count how often each brand is named, cited or recommended. Ask three questions of any score: which prompts, which assistants and markets, and whether it separates a passing mention from a recommendation. ## Who should own AI visibility? This is where most companies stall. Brand teams think it is SEO. Digital teams see a content problem. eCommerce sees it as a retailer issue. In our view, AI visibility belongs with brand equity, alongside salience and consideration, because that is what it measures: whether your brand comes to mind when the shopper delegates the remembering to a machine. It sits in the corridor we call [Create Superior Product Portfolio & Brands](https://www.motionandgain.com/ai-transformation#framework), next to pricing power, and it feeds awareness and quality trial in consumer engagement. The redesign is in the decision, not the tool. A brand that scores AI visibility monthly by category and market, and lets that score move content, claims and product-data budgets, will compound. A brand that buys a dashboard and files it under digital will not. We made the same argument for agent-led buying in [Agents Can Now Buy. Have You Decided What to Sell Them?](https://www.motionandgain.com/insights/what-to-sell-ai-shopping-agents): the agent rewards the brand whose offer is legible to it. ## What to do now 1. **Run an AI visibility audit of your top ten categories** (CMO, 30 days): 20–30 real shopper prompts per category, in each priority market and language, across the assistants your shoppers use. Record named, cited and recommended separately. 2. **Put an AI visibility score in the brand-health tracker** (Head of Consumer Insight, 60 days), reported next to salience and consideration, with a target per category. 3. **Fix the evidence the assistants read** (eCommerce and Brand, 90 days): complete product data, specific claims with proof, consistent retailer listings and credible third-party reviews. 4. **Defend where you lead, attack where it is unsettled** (Category Directors, 90 days): concentrate effort on the categories with no owner and on those where your lead is under three points. 5. **Name one accountable owner** (CCO, 30 days), with a budget line, rather than splitting AI visibility across SEO, PR and eCommerce. ## The bottom line A majority of shoppers in many markets now let AI shape what they consider, and in more than half of categories the AI shortlist has no clear owner. That will not last. Treat AI visibility as brand equity, measure it monthly, and give it an owner before a competitor makes the category theirs. ### Questions leaders ask #### What is a good AI visibility score? There is no universal benchmark yet. Use Semrush's owner threshold as a working bar: named in four of five category prompts, with a lead of five points or more over the next brand. #### Is generative engine optimization just SEO with a new name? It overlaps, but the evidence differs. In Semrush's data, organic traffic predicted the category owner in fewer than half of cases, so product data, claims and third-party proof matter more than rankings alone. --- Source: https://www.motionandgain.com/insights/ai-visibility-brand-share-of-ai-answers Cite as: Motion & Gain, "AI Visibility Is the New Shelf Space. Who Owns It in Your Business?", 25 October 2026, https://www.motionandgain.com/insights/ai-visibility-brand-share-of-ai-answers Editorial standards: https://www.motionandgain.com/insights/editorial-standards --- title: "Price Has Stopped Carrying Growth. Rebuild Revenue Growth Management" standfirst: "After five years in which price did the work, consumer goods growth now depends on buying back volume, and revenue growth management has to be rebuilt to do it." url: https://www.motionandgain.com/insights/revenue-growth-management-volume-led-growth type: "Challenge & Resolution" publisher: "Motion & Gain" author: "Motion&Gain" published: 2026-10-18 updated: 2026-10-25 tags: ["Revenue growth management", "Pricing & promotions", "Portfolio & innovation", "Consumer goods"] summary: "EU food prices rose 38% since 2019 while volumes barely moved. Five revenue growth management decisions to win volume back without giving away margin." hero_image: https://wcokbflnhololtfdocak.supabase.co/storage/v1/object/public/article-images/2026-10/revenue-growth-management-volume-led-growth-full-91240e5b.webp hero_image_ai_generated: true access: public language: en --- # Price Has Stopped Carrying Growth. Rebuild Revenue Growth Management *After five years in which price did the work, consumer goods growth now depends on buying back volume, and revenue growth management has to be rebuilt to do it.* Challenge & Resolution · Motion&Gain · 18 October 2026 Hero image: AI-generated illustration (labelled "AI GENERATED" on the page). For five years, price did the work in consumer goods. In Europe, food prices rose 38% between 2019 and 2025 while food-store sales volumes rose less than 2%. That model is running out of road, and revenue growth management has to be rebuilt to win volume back without giving the margin away. **Key takeaways** - EU food prices rose 38% from 2019 to 2025; volume in food-led retail rose 1.9%, and fell in Italy and Germany (Eurostat). - PepsiCo's Q3 2026 growth split shows the turn: in North American beverages, pricing added 3 points and volume took 3 away. - Private label now holds a 50% unit share across Europe's six largest grocery markets (Circana), while 34% of branded units sell on promotion. ## The challenge: growth bought with price The numbers from [Eurostat](https://ec.europa.eu/eurostat/databrowser/view/sts_trtu_a/default/table) are stark. Rebased to 2019, the EU's food price index reached 138 in 2025, against 127 for all items. The sales volume of supermarkets and other food-led stores reached 102. **Chart: Europe's food prices rose 38% since 2019; food-store volumes barely 2%** (Index, 2019 = 100, EU27: HICP food and all items (annual average); sales volume of food-led non-specialised retail, 2019–2025) ![Line chart: Europe's food prices rose 38% since 2019; food-store volumes barely 2%. Index, 2019 = 100, EU27: HICP food and all items (annual average); sales volume of food-led non-specialised retail, 2019–2025. Food prices from 100 to 138; All-items prices from 100 to 127; Food-store sales volume from 100 to 102 (2019 to 2025). Source: Eurostat, HICP annual data (prc_hicp_aind) and retail trade volume (sts_trtu_a, NACE G47.11, calendar adjusted).](https://www.motionandgain.com/charts/358cd209d1c03b655fba-w.svg) *Source: [Eurostat, HICP annual data (prc\_hicp\_aind) and retail trade volume (sts\_trtu\_a, NACE G47.11, calendar adjusted)](https://ec.europa.eu/eurostat/databrowser/view/sts_trtu_a/default/table). Rebased to 2019 = 100 by Motion & Gain from Eurostat indices (HICP 2015 = 100; volume 2021 = 100). Nominal price indices. [Data (CSV)](https://www.motionandgain.com/charts/358cd209d1c03b655fba.csv) · Chart: Motion & Gain* Food-store volume in 2025 was still 0.2% below 2019 in Germany and 2.8% below in Italy; France (+6.9%) and Spain (+7.4%) did better. Shoppers paid more and bought roughly the same amount. Many of them traded down: [Circana reports](https://www.circana.com/post/private-label-reaches-record-50-unit-share-across-europe-s-six-biggest-grocery-markets) that private label reached a 50% unit share across France, Germany, Italy, the Netherlands, Spain and the UK for the first time. | Market | Units | Value | | --- | --- | --- | | Spain | 59% | 52% | | Netherlands | 56% | 55% | | UK | 52% | 44% | | Germany | 52% | 44% | | France | 46% | 36% | | Italy | 36% | 31% | | Six markets | 50% | 42% | *Private label share of grocery sales, by units and by value. Source: [Circana, "Private label reaches record 50% unit share across Europe's six biggest grocery markets"](https://www.circana.com/post/private-label-reaches-record-50-unit-share-across-europe-s-six-biggest-grocery-markets), April 2026; more than 230 FMCG categories.* Now the price lever is visibly weakening at the top of the industry. PepsiCo's [third-quarter results](https://investors.pepsico.com/docs/pepsico-5v9wci20/media/Files/investors/q3-2026-earnings-release.pdf) on 8 October show organic revenue up 3.1%, with effective net pricing contributing 3 points and volume 0.5. The split by division is the real story. **Chart: At PepsiCo, price carried North American beverages; volume carried Asia** (Contribution to organic revenue growth, percentage points, by division, Q3 2026 (12 weeks to 5 Sep 2026)) ![Grouped bar chart: At PepsiCo, price carried North American beverages; volume carried Asia. Contribution to organic revenue growth, percentage points, by division, Q3 2026 (12 weeks to 5 Sep 2026). Organic volume, Effective net pricing across 6 categories. Source: PepsiCo, Q3 2026 earnings release, organic revenue performance table (p. A-7).](https://www.motionandgain.com/charts/0e1dc7ea1c36d67f73a1-w.svg) *Source: [PepsiCo, Q3 2026 earnings release, organic revenue performance table (p. A-7)](https://investors.pepsico.com/docs/pepsico-5v9wci20/media/Files/investors/q3-2026-earnings-release.pdf). Effective net pricing includes pricing actions, sales incentives and pack/country mix (PepsiCo definition). Rounded as reported. [Data (CSV)](https://www.motionandgain.com/charts/0e1dc7ea1c36d67f73a1.csv) · Chart: Motion & Gain* In North American beverages, pricing added 3 points and volume subtracted 3: organic revenue went nowhere. In North American foods, PepsiCo went the other way, investing in affordability. Pricing took 1 point off, volume and volume share improved, and core operating margin fell 280 basis points, which PepsiCo attributes to affordability investments, the lap of a prior-year asset-sale gain and higher marketing spend. The company has cut its core constant-currency EPS growth outlook for 2026 to 1–2%, from the low end of 4–6%. ## Why the usual fix fails The reflex is a deeper promotion. It rarely works. Brands already lean on promotion far more than retailers' own labels: Circana finds 34% of branded unit sales in those six markets were on promotion, against 14% for private label. Another round of depth buys short-term volume, trains shoppers to wait for the deal and hands margin to the retailer. The second reflex, an across-the-board price cut, is worse. It gives money to shoppers who were not going to leave and does little for those who already have. Both fixes treat price as one lever. Revenue growth management (RGM) works because it treats price, pack, promotion, mix and trade terms as a system. > The next phase of revenue growth management is not finding more price. It is buying back volume at the lowest cost per unit. ## The resolution: five decisions ### 1. Put volume back on the scorecard Report every brand's growth as a price, volume and mix bridge, by pack and channel, every month. Set volume and household-penetration targets alongside margin. Owner: CFO and head of RGM. ### 2. Rebuild the price-pack ladder around entry points Affordability is a pack decision before it is a price decision. PepsiCo says its portion-control multipacks, now more than $3.5 billion in annual net revenue, [delivered both volume and net revenue growth](https://investors.pepsico.com/docs/pepsico-5v9wci20/media/Files/investors/q3-2026-prepared-management-remarks.pdf) in the quarter. Map each category's ladder against private-label price points and fill the gaps with packs, not discounts. Owner: brand and RGM directors. ### 3. Move promotion money from depth to effectiveness Score every promotion on incremental volume per euro of investment, then cut the bottom quartile and reinvest in the mechanics and stores that work. This is where AI earns its place: elasticity by store cluster, pack and week, read continuously rather than once a year. Owner: trade marketing. ### 4. Fund affordability with productivity, explicitly PepsiCo says it has "elevated enterprise-wide productivity savings through automation, network optimization, digitalization and simplification" to support its growth priorities, and is adding structural cost reductions. Name the cost pool that pays for each price-pack move before you make it. Owner: CFO. ### 5. Tie trade terms to volume growth Shift retailer investment from fixed allowances to pay-for-performance terms linked to distribution, display and unit growth. Owner: head of sales and key accounts. ## Evidence: what the trade-off looks like | PepsiCo Q3 | Vol. / price | Margin | | --- | --- | --- | | N. America foods | +0.5 / −1 pp | −280 bp | | N. America drinks | −3 / +3 pp | −15 bp | | International | n/a | +105 bp | *Source: [PepsiCo, Q3 2026 prepared management remarks](https://investors.pepsico.com/docs/pepsico-5v9wci20/media/Files/investors/q3-2026-prepared-management-remarks.pdf) (margins, p. 8) and [earnings release](https://investors.pepsico.com/docs/pepsico-5v9wci20/media/Files/investors/q3-2026-earnings-release.pdf) (p. A-7). Volume and effective net pricing contributions to organic revenue growth; change in core operating margin. Foods pursued affordability, drinks pursued price. International organic revenue grew 8%.* Neither path is free. Price held beverage margin roughly flat (−15 bp) but lost 3 points of volume; affordability rebuilt volume in foods at a visible margin cost. The aim of the five decisions is to move the frontier: more volume per point of margin invested. ## How to apply it 1. **Days 1–30:** build the price, volume and mix bridge for your top ten brands over three years, by market. Identify where volume has fallen while price rose. 2. **Days 31–60:** map price-pack ladders against private-label price points in your three largest markets. Rank promotions by incremental volume per euro. 3. **Days 61–90:** launch two entry-pack moves and one promotion reallocation, each funded by a named productivity saving, with volume targets set in advance. Pricing the shopper is off the table; we explain why in [this week's article on AI dynamic pricing](https://www.motionandgain.com/insights/ai-dynamic-pricing-product-not-person). Pricing the pack, the promotion and the trade term is where the volume is. --- Source: https://www.motionandgain.com/insights/revenue-growth-management-volume-led-growth Cite as: Motion & Gain, "Price Has Stopped Carrying Growth. Rebuild Revenue Growth Management", 18 October 2026, https://www.motionandgain.com/insights/revenue-growth-management-volume-led-growth Editorial standards: https://www.motionandgain.com/insights/editorial-standards --- title: "AI Dynamic Pricing: Price the Product, Not the Person" standfirst: "AI dynamic pricing is reaching the grocery shelf just as regulators draw the line at personal data; the value, and the safe ground, is in pricing products, packs and promotions." url: https://www.motionandgain.com/insights/ai-dynamic-pricing-product-not-person type: "Consultant Article" publisher: "Motion & Gain" author: "Motion&Gain" published: 2026-10-18 updated: 2026-10-18 tags: ["Pricing & promotions", "Revenue growth management", "AI governance", "Consumer goods", "Retail"] summary: "AI dynamic pricing is spreading fast, and US states and the EU are drawing the line at personal data. Where the value is, and the rules to set now." hero_image: https://wcokbflnhololtfdocak.supabase.co/storage/v1/object/public/article-images/2026-10/ai-dynamic-pricing-product-not-person-full-e8fb97a8.webp hero_image_ai_generated: true access: public language: en --- # AI Dynamic Pricing: Price the Product, Not the Person *AI dynamic pricing is reaching the grocery shelf just as regulators draw the line at personal data; the value, and the safe ground, is in pricing products, packs and promotions.* Consultant Article · Motion&Gain · 18 October 2026 Hero image: AI-generated illustration (labelled "AI GENERATED" on the page). AI dynamic pricing is moving from airline seats to the grocery shelf, and the rules are arriving faster than the tools. The winners will use AI to price products, packs, promotions and trade terms with more precision, and will refuse to price individual shoppers. That line, not the algorithm, is now the strategic decision. **Key takeaways** - 28% of more than 2,200 business leaders surveyed by Simon-Kucher already use AI for real-time price optimisation; 41% use it to track competitor prices. - Legislators in at least 30 US states considered pricing bills this year; Maryland, New Jersey and Connecticut have restricted the use of personal data to set prices. - EU law already requires traders to tell consumers when a price has been personalised by automated decision-making, and the Digital Fairness Act could go further. ## Is AI dynamic pricing already mainstream? Closer than most boards assume. In a [global Simon-Kucher survey reported by the Associated Press on 18 October](https://abcnews.com/Technology/wireStory/consumer-apprehension-grows-us-retailers-adopt-ai-assisted-137357982), covering more than 2,200 business leaders in 28 countries and 39 industries, 41% said they use AI software to gather market intelligence, including competitors' prices, and 28% said they use it for real-time price optimisation. **28%**: of 2,200+ business leaders use AI for real-time price optimisation (Simon-Kucher survey, reported by AP, October 2026) Shoppers have noticed. The same AP report describes growing consumer unease about electronic shelf labels, app-based pricing and algorithmic tools, even though the evidence of abuse in grocery is thin. One retail technology executive told AP that changing prices throughout the day "is exceedingly rare in grocery and general merchandise today"; retailers typically update electronic shelf prices weekly. A web-data firm that tracked 2025 US price changes found about half were decreases. Search behaviour tells the same story. Worldwide, the top query related to "dynamic pricing" on [Google Trends](https://trends.google.com/trends/explore?q=dynamic%20pricing&date=today%2012-m) over the past 12 months is "AI dynamic pricing", and "Maryland dynamic pricing ban" is a breakout query. In the US, the top question around ["surveillance pricing"](https://trends.google.com/trends/explore?q=surveillance%20pricing&geo=US&date=today%2012-m) is simply "what is surveillance pricing", with state bans in New Jersey and New York among the breakout searches. Google Trends measures relative search interest, not volume, but the direction is clear: shoppers now have a word for the thing they fear. > **Two different things.** *Dynamic pricing* changes the price of a product for everyone, based on demand, stock, time or competition. *Personalised* (or "surveillance") pricing sets a different price for an individual, based on their data: purchase history, browsing, location, inferred income. Most of the commercial value sits in the first. Most of the regulatory and reputational risk sits in the second. ## Where is the regulatory line being drawn? On the person, not the algorithm. The rules taking shape on both sides of the Atlantic leave product-level dynamic pricing largely alone and target the use of personal data to set individual prices. ### The rules at a glance - **New York, in force since 10 Nov 2025:** the Algorithmic Pricing Disclosure Act requires a notice when an algorithm sets a price using personal data. - **New York, passed, governor has until 31 Dec 2026:** the One Fair Price Act bans prices generated from personal data; loyalty, coupons and group discounts are exempt. - **Maryland and New Jersey, enacted:** restrict personal data in setting grocery prices, with exceptions for certain loyalty and promotional offers. - **Connecticut, enacted:** restricts personal data in pricing across a broader range of retail. - **US federal (FTC), proposed:** an enforcement policy warning that undisclosed personal-data pricing could breach consumer-protection law. - **European Union, in force:** traders must tell consumers when a price is personalised by automated decision-making. *Sources: [Wilson Sonsini, June 2026](https://www.wsgrdataadvisor.com/2026/06/new-york-legislature-passes-ban-on-personalized-pricing/) (New York); [AP, 18 October 2026](https://abcnews.com/Technology/wireStory/consumer-apprehension-grows-us-retailers-adopt-ai-assisted-137357982) (Maryland, New Jersey, Connecticut, FTC); [Covington, Inside Privacy](https://www.insideprivacy.com/consumer-protection/digital-fairness-act-series-topic-3-personalized-advertising-and-pricing/) (EU). Summaries, not legal advice.* New York's disclosure law already requires the words "THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA". The One Fair Price Act, [according to Wilson Sonsini](https://www.wsgrdataadvisor.com/2026/06/new-york-legislature-passes-ban-on-personalized-pricing/), would allow dynamic pricing that does not use personal data, provided its use, frequency and conditions are disclosed "clearly and conspicuously". Penalties would reach $20,000 per repeat violation. According to the National Conference of State Legislatures, cited by AP, legislators in at least 30 states considered pricing bills this year. Europe is already a step ahead on disclosure. The Consumer Rights Directive requires traders to tell consumers when a price has been personalised on the basis of automated decision-making, [Covington notes](https://www.insideprivacy.com/consumer-protection/digital-fairness-act-series-topic-3-personalized-advertising-and-pricing/), though not the parameters behind it. Options floated for the Commission's Digital Fairness Act include restricting personalisation based on behavioural predictions such as willingness to pay. For brands selling across the EU, Switzerland, Turkey and the GCC, the safe planning assumption is simple: personal-data pricing will need to be disclosed everywhere and will be banned in some places. > The question is no longer whether AI sets prices. It is whether it prices the product or the person. ## Where does AI pricing actually pay in consumer goods? Away from the individual. Walmart's chief executive John Furner put the principle in one line in a blog post last month, as [AP reports](https://abcnews.com/Technology/wireStory/consumer-apprehension-grows-us-retailers-adopt-ai-assisted-137357982): "We price the product, not the person." For a consumer-goods manufacturer, that is also where the money is. The largest pools of pricing value in FMCG are promotion depth and timing, price-pack architecture and trade terms. Promotions alone are a large share of the business: across Europe's six biggest grocery markets, [Circana finds](https://www.circana.com/post/private-label-reaches-record-50-unit-share-across-europe-s-six-biggest-grocery-markets) 34% of branded unit sales were sold on promotion, against 14% for private label. None of these decisions requires knowing who the shopper is. All of them improve with AI that reads elasticities by store cluster, pack and week. **Chart: Most of the value in AI pricing sits away from the person** (AI pricing uses in consumer goods and grocery: commercial value vs exposure to trust and regulatory risk) ![Quadrant chart: Most of the value in AI pricing sits away from the person. AI pricing uses in consumer goods and grocery: commercial value vs exposure to trust and regulatory risk. Trust and regulatory exposure vs Commercial value. Promo depth and timing 2.5, 8.5; Price-pack architecture 2.0, 8.0; Trade-term optimisation 2.0, 7.5; Markdown of perishables 3.5, 7.0; Store-cluster pricing 5.0, 6.5; Competitor price monitoring 3.0, 6.0; and 3 more. Source: Motion & Gain assessment.](https://www.motionandgain.com/charts/c72c872ac2a545772e03-w.svg) *Source: [Motion & Gain assessment](https://www.motionandgain.com/ai-transformation#framework). Scores: Motion & Gain assessment, informed by the US state rules and EU consumer law discussed in this piece; not survey data. [Data (CSV)](https://www.motionandgain.com/charts/c72c872ac2a545772e03.csv) · Chart: Motion & Gain* Read the matrix as a sequencing plan. Promotion optimisation, price-pack architecture and trade-term optimisation sit in the top left: high value, low exposure. Store-cluster pricing and loyalty-member offers carry value but need governance, because the line between a cluster and a person can blur. Intraday surge pricing and prices built from personal data sit bottom right: modest value in grocery, maximum risk to trust. ## Why pricing governance is a commercial capability Because trust is a pricing asset. Brand equity and price premium depend on shoppers believing the price is fair. One opaque experiment can cost more in perceived fairness than a year of optimisation earns. As we argued in [The Override Gap](https://www.motionandgain.com/insights/the-override-gap), AI pays where the decision is redesigned around it. In pricing, that redesign must include the rules the model may not break. The companies in AP's report that faced lawsuits or investigations, from Target's geofenced app prices to Instacart's price tests, each disputed the allegations or ended the practice. The lesson for consumer-goods leaders is not that AI pricing is dangerous. It is that every AI price needs an owner who can explain it to a shopper, a retailer and a regulator. ## What to do now 1. **Write a pricing-data policy.** State which data AI pricing may use (product, store, time, stock, competitor) and which it may not (individual identity, inferred income, location history). Owner: CCO with legal. Horizon: 30 days. 2. **Inventory every algorithm that touches price.** Include retailer-side tools, retail media and D2C. Flag anything that uses personal data. Owner: head of RGM. Horizon: 60 days. 3. **Redirect AI investment to the top-left quadrant.** Promotion depth and timing, price-pack architecture and trade terms. Owner: RGM director. Horizon: 90 days to first scaled use case. 4. **Build an explanation standard.** For every AI-set price or promotion, be able to say in one sentence why it is what it is. Owner: pricing analytics lead. Horizon: 90 days. 5. **Align loyalty offers with the carve-outs.** Make member pricing a transparent, opt-in discount, not a hidden personal price. Owner: CRM lead. Horizon: 90 days. ## The bottom line AI dynamic pricing will spread through consumer goods because the economics are compelling. The regulatory line is already visible: price the product, the pack, the promotion and the store, and leave the person out. Brands that draw that line themselves will keep both the margin and the trust. See how we approach this in our [AI transformation framework](https://www.motionandgain.com/ai-transformation). ## Questions leaders ask ### What is surveillance pricing? Surveillance pricing is setting an individual's price from their personal data, such as purchase history, browsing or location. It differs from dynamic pricing, which changes the price for everyone. ### Is dynamic pricing legal in Europe? Yes. Product-level dynamic pricing is legal, but EU consumer law requires traders to disclose when a price has been personalised by automated decision-making, and further limits are under discussion in the Digital Fairness Act. ### Do loyalty discounts count as personalised pricing? Under New York's One Fair Price Act and the Maryland and New Jersey grocery rules, bona fide loyalty and promotional discounts are exempt. Transparent, opt-in member pricing is the safer design. --- Source: https://www.motionandgain.com/insights/ai-dynamic-pricing-product-not-person Cite as: Motion & Gain, "AI Dynamic Pricing: Price the Product, Not the Person", 18 October 2026, https://www.motionandgain.com/insights/ai-dynamic-pricing-product-not-person Editorial standards: https://www.motionandgain.com/insights/editorial-standards --- title: "Agents Can Now Buy. Have You Decided What to Sell Them?" standfirst: "AI shopping agents went live at checkout this month, and most consumer brands still have no deliberate policy on the range, prices and terms those agents see." url: https://www.motionandgain.com/insights/what-to-sell-ai-shopping-agents type: "Consultant Article" publisher: "Motion & Gain" author: "Motion&Gain" published: 2026-10-18 updated: 2026-10-18 tags: ["Agentic commerce", "Revenue growth management", "D2C & eCommerce", "Consumer goods", "Operating model"] summary: "AI agents can now buy on shoppers' behalf. Brands need an owner and a policy for the range, prices and terms agents see, before platforms set them." hero_image: https://wcokbflnhololtfdocak.supabase.co/storage/v1/object/public/article-images/2026-10/3ebd9b6c6ef16dd81f5b-w-cc9580a5-365a48b3.svg access: public language: en --- # Agents Can Now Buy. Have You Decided What to Sell Them? *AI shopping agents went live at checkout this month, and most consumer brands still have no deliberate policy on the range, prices and terms those agents see.* Consultant Article · Motion&Gain · 18 October 2026 AI agents can now search, compare and buy on a shopper's behalf, at scale, inside the platforms consumers already use. The brands that win this channel will not be the ones with the cleverest chatbot. They will be the ones that decide, deliberately, what an agent is allowed to see and buy from them, and who owns that decision. **Key takeaways** - Mastercard, TikTok and Shopify all launched agent-buying features within three weeks, yet only 28% of merchants will offer agents their full range on the same terms as other channels. - AI-referred shoppers now bring 53% more revenue per visit than other traffic to US retail sites. A year earlier, they brought less than half as much. - Food takes 16.3% of EU household spending, but only 11% of Europeans bought it online in the last three months. Grocery is where Morgan Stanley expects agents to unlock the most. Most consumer businesses have not made that decision yet. In the last three weeks the market made it harder to postpone. ## What changed in the last three weeks? The infrastructure for agent-led buying went live almost at once. On 30 September, [Mastercard expanded Agent Pay](https://www.mastercard.com/us/en/news-and-trends/press/2026/september/new-trust-and-intelligence-services.html) with a probability score that estimates whether a transaction was started by an AI agent, now in testing in the US. On 5 October, [TikTok introduced a conversational shopping assistant](https://techcrunch.com/2026/10/05/tiktok-rolls-out-an-ai-shopping-assistant-and-one-click-checkout/) and one-click in-feed checkout, initially for advertisers in eligibility-based testing. On 13 October, [Shopify opened its Catalog](https://www.tokenpost.com/news/technology/31472), which it says holds billions of products, to personal AI agents that can search, prepare checkouts, place approved orders and reorder within spending limits the shopper sets. Agents are not waiting for brands to get ready. Security firm HUMAN, quoted by [Digiday](https://digiday.com/marketing/in-graphic-detail-where-brands-stand-when-ai-agents-do-the-shopping/), logged 5.6 million requests from Meta's Muse shopping agent in under two days of tracking it, a volume that took ChatGPT 11 months to reach; 84% targeted product and search pages. Public curiosity, meanwhile, has cooled. Worldwide searches for "AI shopping" peaked in mid-May and over the past three months ran 47% below late 2025, on [Google Trends](https://trends.google.com/trends/explore?q=agentic%20commerce%2CAI%20shopping%20agent%2CAI%20shopping&date=today%2012-m)' relative scale. The hype is fading just as the plumbing arrives: exactly when a channel's rules get set without anyone noticing. **28%**: of merchants are willing to offer AI agents their full product range on the same terms as other channels (PYMNTS merchant survey, cited by FinTech Weekly, October 2026) That is the gap. The pipes are ready; the commercial policy is not. The same PYMNTS survey, as [reported by FinTech Weekly](https://www.fintechweekly.com/magazine/articles/agentic-commerce-launch-month-liability-risk-2026), found that 93% of merchants believe the AI or agent provider should bear the loss when an agent makes a wrong purchase. Checkout.com's own merchant research, cited in the same piece, puts agent-involved transactions at just 3% today, while 89% of merchants say they are preparing. Those are self-reported vendor figures, but the direction is clear: almost everyone is preparing, and few have decided what "ready" means. Three percent will not last. [Morgan Stanley](https://www.morganstanley.com/insights/articles/agentic-commerce-market-impact-outlook) expects agentic shoppers to spend $190 billion to $385 billion online in the US by 2030, 10% to 20% of online retail; [Bain](https://www.bain.com/insights/2030-forecast-how-agentic-ai-will-reshape-us-retail-snap-chart/) says $300 billion to $500 billion; [McKinsey](https://www.mckinsey.com/industries/retail/our-insights/the-agentic-commerce-opportunity-how-ai-agents-are-ushering-in-a-new-era-for-consumers-and-merchants) up to $1 trillion of revenue orchestrated by agents. **Chart: Every major forecast puts US agent-led shopping in the hundreds of billions by 2030** (Forecast range for 2030, US, $bn) ![Dumbbell chart of 2030 forecasts for US agent-led shopping: Morgan Stanley $190bn to $385bn, Bain $300bn to $500bn, McKinsey $900bn to $1,000bn. Definitions differ between forecasters. Sources: Morgan Stanley, Bain, McKinsey.](https://www.motionandgain.com/charts/3af4c796f0c44ca82334-w.svg) *Source: [Morgan Stanley (Dec 2025), Bain (Dec 2025), McKinsey (Oct 2025)](https://www.morganstanley.com/insights/articles/agentic-commerce-market-impact-outlook). Forecasts, not outcomes. Definitions differ: Morgan Stanley, agentic shopper spending in US e-commerce; Bain, the US agentic commerce market; McKinsey, revenue orchestrated by agents in US B2C retail (goods only). [Data (CSV)](https://www.motionandgain.com/charts/3af4c796f0c44ca82334.csv) · Chart: Motion & Gain* The forecasts measure different things; read them as ranges, not targets. They agree on timing: agents will move a material share of online retail this decade, on terms being written now. ## Why is this a commercial decision, not a technology project? Because an agent is a new kind of shelf, and someone has to set its terms. > **The agent offer** is the range, prices, pack sizes, promotions, return policies and product data a brand deliberately exposes to AI shopping agents. Today most brands have an agent offer by accident: whatever their product feeds, retailer listings and site markup happen to say. Nobody in commercial leadership signed it off, yet it already shapes outcomes. Jellyfish, also cited by Digiday, found that when Alexa for Shopping showed toys from 177 brands, nearly every one came from Amazon's own store, while ChatGPT recommended 24 retailers and Google's AI Mode 37. Each agent has its own routes to product; a brand absent from them is simply not considered. > An agent does not browse your brand story. It reads your data, your price and your terms, then decides. The value of getting it right is showing, and fast. Adobe's analysis of more than a trillion visits to US retail sites, reported by [Digiday](https://digiday.com/marketing/in-graphic-detail-where-brands-stand-when-ai-agents-do-the-shopping/), found that AI-referred shoppers generated 53% more revenue per visit than other traffic in July. A year earlier, other visits were worth 128% more than AI-referred ones. **Chart: In a year, AI-referred shoppers became the most valuable visitors** (Revenue per visit to US retail sites, index: other traffic = 100, July 2025 and July 2026) ![Slope chart: revenue per visit from AI-referred shoppers to US retail sites rose from 44 to 153 between July 2025 and July 2026, indexed to other traffic at 100. Source: Adobe Analytics via Digiday; Motion & Gain calculation.](https://www.motionandgain.com/charts/d16aeed3d1279b10770b-w.svg) *Source: [Adobe Analytics, reported by Digiday (Oct 2026)](https://digiday.com/marketing/in-graphic-detail-where-brands-stand-when-ai-agents-do-the-shopping/). Motion & Gain calculation from Adobe’s figures: in July 2025 other visits were worth 128% more than AI-referred ones (100 ÷ 2.28 = 44); in July 2026 AI-referred visits were worth 53% more. Per-visit values, not shares of revenue. [Data (CSV)](https://www.motionandgain.com/charts/d16aeed3d1279b10770b.csv) · Chart: Motion & Gain* These are per-visit figures, not shares of sales. But the reversal says AI-referred shoppers arrive further down the funnel: the agent has already compared. What it compared was your agent offer, deliberate or not. ## Will shoppers actually let agents buy? For small purchases, increasingly yes. In [Global Payments' survey](https://www.financialcontent.com/article/bizwire-2026-9-23-consumers-expect-ai-to-make-15-of-their-purchases-within-five-years-global-payments-research-finds) of about 8,000 consumers in seven countries, the share comfortable letting AI spend up to $50 more than doubled in a year in each of the four categories it reported. | Comfortable letting AI spend up to $50 on | 2025 | 2026 | Change | | --- | --- | --- | --- | | Cinema tickets | 32% | 82% | +50 pp | | Meal delivery or pickup | 30% | 78% | +48 pp | | Meal vouchers and gift cards | 28% | 77% | +49 pp | | Subscriptions and memberships | 27% | 69% | +42 pp | *Source: [Global Payments, Agentic Commerce Report](https://www.financialcontent.com/article/bizwire-2026-9-23-consumers-expect-ai-to-make-15-of-their-purchases-within-five-years-global-payments-research-finds), press release, 23 September 2026. Surveys by The Lantern of 8,000 consumers (August–September 2025) and 8,027 (May 2026) in the US, UK, France, Brazil, China, Singapore and Australia. Company-commissioned research; the question wording is not published.* The same survey finds 69% comfortable letting AI spend up to $100 on groceries and everyday essentials. Comfort is not the same as handing over the decision, though. [Gartner](https://www.gartner.com/en/newsroom/press-releases/2026-05-27-gartner-survey-finds-consumers-want-ai-shopping-help-but-not-ai-purchase-decisions) found that willingness to let AI make the purchase decision topped out at 11%, even in lower-stakes categories such as personal care and household supplies, and a third of Global Payments' respondents want to approve every transaction. For brands, that makes the terms (spending limits, approvals, substitutions and returns) as much a part of the agent offer as the price. ## Which decisions actually need redesigning? Four, and they sit squarely in the commercial function. For consumer goods, the first is the sharpest. Bain expects spec-driven requests ("cheapest four-pack of Energizer AA batteries by tomorrow") to move to agents first, and Morgan Stanley's survey has groceries and packaged goods leading AI-driven purchases. In Europe, those are the categories people buy online least. **Chart: Food is Europe’s biggest basket, and one of its least bought online** (EU27: people who bought each category online in the last 3 months, 2024 (%), and change since 2022 (pp). Bubble size: share of household spending, 2026) *Source: [Eurostat (isoc\_ec\_ibgs; HICP item weights, prc\_hicp\_iw)](https://ec.europa.eu/eurostat/databrowser/view/isoc_ec_ibgs/default/table). Bubble size uses the HICP weight of the closest COICOP 2018 group (e.g. food and non-alcoholic beverages; personal care products); mapping by Motion & Gain. Highlighted: everyday consumer goods. [Data (CSV)](https://www.motionandgain.com/charts/3ebd9b6c6ef16dd81f5b.csv) · Chart: Motion & Gain* Food and non-alcoholic drinks take 16.3% of EU household spending, more than three times clothing and footwear. Yet only 11% of people in the EU aged 16 to 74 bought food or drink online in the last three months, against 42% for clothes, and that share grew just 1.3 points in two years ([Eurostat](https://ec.europa.eu/eurostat/databrowser/view/isoc_ec_ibgs/default/table)). If agents unlock replenishment, the household's largest basket will go online through a channel where few brands have set range, packs or prices. - **Range.** Which SKUs should agents be able to buy directly, and which should stay in channels where the brand controls the experience? A full-range, same-terms policy is a choice, not a default, as the 28% figure shows. For consumer goods, start with replenishment items that shoppers already specify by brand, size and pack. - **Price and promotion.** Agents compare relentlessly. Price-pack architecture built for a physical shelf may expose awkward per-unit comparisons once a machine lines products up. RGM teams need a view of how each pack and promotion reads to an agent. - **Terms and liability.** Returns, substitutions and wrong orders will happen. With 93% of merchants expecting the agent provider to carry the loss, and providers unlikely to accept that wholesale, brands and retailers need a negotiated position, not an assumption. - **Channel conflict.** Agents will shift volume between retailers, marketplaces and direct channels without asking; FinTech Weekly reports Amazon began blocking purchases through Meta's Muse in September. Brands selling through retailers need a position on where agent demand should land before platforms decide for them. This is why AI pays where the decision is redesigned, not where a model is bolted on. Plugging a catalogue into an agent protocol takes weeks; agreeing range, price and liability rules for a channel between retailers and consumers takes leadership. The question of [who owns the AI P&L](https://www.motionandgain.com/insights/who-owns-the-ai-pl) arrives here in its most practical form. ## What to do now 1. **Name an owner for the agent offer** (CCO, with eCommerce and RGM). Within 30 days, one accountable leader for what agents can see and buy, with a cross-functional team behind them. 2. **Audit what agents see today** (eCommerce lead). Within 60 days, test your top categories in the major assistants and agent platforms. Record where you appear, at what price, from which seller, with which product claims. 3. **Write an agent range and pricing policy** (RGM director). Within 90 days, decide which SKUs, packs and promotions are agent-eligible, and how per-unit comparisons will read. Start with replenishment lines. 4. **Agree terms with key retail partners** (Sales director, customer teams). Within 90 days, open the conversation on agent-led orders, spending limits, approvals, returns, substitutions and who carries the cost of errors. 5. **Fix the product data** (Data and digital lead). Within 90 days, make structured attributes, pack sizes, ingredients and policies complete and consistent across feeds. For an agent, the data is the packaging. ## The bottom line Agentic commerce moved from demos to default settings in a single month. Volumes are still small, which is exactly why now is the time to decide. Brands that treat the agent as a commercial channel, with an owner, a range, a price logic and terms, will shape how agents buy. The rest will be shaped by them. ## Questions leaders ask ### What is agentic commerce? Agentic commerce is buying in which an AI agent searches, compares and completes purchases on a person's behalf, usually within limits the person sets, such as a spending cap or approval step. ### What are UCP and the Agentic Commerce Protocol? Open standards that let AI agents complete checkouts. OpenAI's Agentic Commerce Protocol (ACP) powers Instant Checkout in ChatGPT; Google's Universal Commerce Protocol (UCP), [announced in January 2026](https://www.checkout.com/blog/openai-acp-google-ucp-difference), runs at launch on surfaces such as AI Mode in Search and the Gemini app. For most brands the protocol matters less than what they expose through it. ### Is it too early to act if agent transactions are only a few percent? No. The rules of a new channel are set while it is small. Range, pricing and liability positions agreed now are far cheaper than renegotiating them once agent volume is material. --- Source: https://www.motionandgain.com/insights/what-to-sell-ai-shopping-agents Cite as: Motion & Gain, "Agents Can Now Buy. Have You Decided What to Sell Them?", 18 October 2026, https://www.motionandgain.com/insights/what-to-sell-ai-shopping-agents Editorial standards: https://www.motionandgain.com/insights/editorial-standards --- title: "Who Owns the AI P&L?" standfirst: "The gap nobody wants to name." url: https://www.motionandgain.com/insights/who-owns-the-ai-pl type: "Consultant Article" publisher: "Motion & Gain" author: "Motion&Gain" published: 2026-07-29 updated: 2026-09-15 summary: "Consumer goods has decided AI matters. It has not decided who is accountable for making it pay — and the 2026 data says that single unresolved question is the industry’s largest value leak. The gap…" hero_image: https://wcokbflnhololtfdocak.supabase.co/storage/v1/object/public/article-images/2026-07/img-1798-ec7a30da.webp access: public language: en --- # Who Owns the AI P&L? *The gap nobody wants to name.* Consultant Article · Motion&Gain · 29 July 2026 Consumer goods executives have decided AI matters. It has not decided **who is accountable for making it pay** — and the 2026 data says that single unresolved question is the industry’s largest value leak. > The Gap Nobody Wants to Name > There is a comfortable story circulating in consumer goods boardrooms: AI adoption is going well, pilots are multiplying, budgets are up, and the value will follow. The 2026 evidence does not support the last clause. Across four independent datasets published this year — Deloitte’s survey of 200 retail and CPG executives, the Consumer Goods Forum’s research with BCG, EY’s study of 850+ senior executives across 24 markets, and NVIDIA’s annual industry survey — the same fracture appears. Conviction is universal. Investment is rising. Returns are largely unmeasured, and where measured, unimpressive. This is usually diagnosed as a technology problem, a data problem, or a talent problem. It is none of those. **It is an ownership problem, and it is fixable faster than any of the others.** The numbers that should be on the agenda Conviction is not the constraint. Three-quarters of retail and CPG executives call AI a top strategic priority. Only 16.5% can quantify a return on it. Eighty-two percent plan to increase AI investment over the next twelve months. Neither is budget — yet. Roughly half of companies invest less than 0.5% of revenue in AI despite calling it a priority. Nine in ten retailers plan to raise AI budgets in 2026. The money is coming. The question is where it lands: most current spend flows into IT and data infrastructure rather than value-generating use cases. Scaling is where the industry stalls. Enterprise-wide deployment sits at 7–10% across both retail and CPG. The Consumer Goods Forum and BCG put it more starkly: 76% of CPG companies remain in the “explorer” phase, with only 18% actively scaling impact. Retail is materially ahead — 45% scaling — though 40% have barely begun. Best-in-class organizations report 2–5x ROI. **More than half do not measure return at all.** And the sector is splitting in two. Retail is converting AI into commercial outcomes: 38% report revenue growth impact, against 10% for CPG. CPG’s gains remain concentrated in productivity, cost and product development (27% vs 18% for retail). Manufacturers are extracting efficiency. Retailers are extracting growth. Given that 79% of executives already expect bargaining power to shift further toward retailers over the next two to three years, this is not a neutral asymmetry — it compounds an existing structural disadvantage. The operating model has not moved. Only 11% of consumer products executives say sales and marketing operate as a unified growth engine. **Only 15% say commercial data is fully integrated and routinely used for cross-functional decisions.** 71% agree structural disruption makes rapid transformation essential. > The single most consequential statistic Fifty-four percent of AI strategy ownership sits with technology leaders rather than the P&L owners accountable for business results. Every other number in this article is downstream of that one. When a CTO or CIO owns AI strategy, the organization optimizes for what technology leaders are correctly measured on: platform reliability, data quality, architectural coherence, deployment coverage. Those are genuine prerequisites. They are not returns. Nobody in that structure is incentivized to kill an underperforming use case, because no one’s number improves when it dies. Nobody is forced to choose between two pilots, because neither is competing for the same P&L line. And nobody is accountable when 90%+ of deployments never leave pilot status, because “pilot completed” is a legitimate technology-function success metric. This is not a criticism of technology leadership. It is a description of a misallocated accountability, and it explains the pattern precisely: high conviction, broad piloting, infrastructure-weighted spend, and an inability to name the return. Adoption data confirms the containment. Wide AI adoption never exceeds 36% of the organization outside IT. The capability has been built adjacent to the business rather than inside it. > Four failure patterns In our work with commercial organizations across Europe and the Middle East, four recurring patterns explain most of the value leakage. Each map to an ownership vacuum. **The pilot that has no owner who loses.** A use case is sponsored by a function that benefits from its existence rather than its outcome. It runs indefinitely in a state of “promising.” No one closes it because no one is penalized for its cost. **Governance chosen before accountability**. Retail is consolidating around centralized AI governance (43% of retailers). CPG has not converged — it splits roughly evenly across three governance models. The instinct is to resolve this by picking a model. That is the wrong sequence. Which governance model you choose matters far less than whether the person accountable for the result also controls the decision. Centralized governance with the wrong owner produces the same outcome as federated governance with the wrong owner. **Infrastructure counted as progress.** Data platform milestones are reported to the board as AI progress. They are enabling conditions. A migrated data lake with no commercial decision attached to it is a cost with an option attached, not a return. **Strategy written after execution has already started.** The clearest instance is agentic commerce: 40% of CPGs report no defined approach, while 50–60% are already piloting capabilities and prioritizing API readiness. Teams are building because the technology is available and the pressure is real. The strategy that should govern the build is being written afterwards, if at all. > Five moves that close the gap > **1. Reassign AI strategy ownership to P&L holders** — within one planning cycle. Not an AI council. Not a center of excellence with dotted-line influence. The executive who owns the revenue or margin line owns the AI agenda that affects it, with the technology function accountable for enablement and standards. This is a governance change, not a reorganization, and it can be made in a single quarter. > **2. Force every use case onto a P&L line before funding.** Each initiative names the line it moves, the baseline, the expected delta, and the date the delta is measurable. Use cases that cannot complete that sentence is research, and should be funded from a separate, explicitly capped research budget — not from the commercial AI envelope. > **3. Rebalance the spend mix, not just the spend level.** Since 82% are increasing budgets anyway, the decision that matters is allocation, not quantum. Set an explicit floor for value-generating use cases as a proportion of total AI spend, and report against it quarterly. Infrastructure will always argue persuasively for more; it needs a counterweight with equal standing. > **4. Change the reporting metric from pilots launched to pilots scaled.** With enterprise-wide deployment at 7–10%, “number of pilots” is a vanity metric that actively rewards the wrong behavior. Track the conversion rate from pilot to production, and the time it takes. Treat a killed pilot as a positive outcome, and say so publicly the first time it happens. > **5. Close the commercial gap, not just the technology gap.** The 11% figure on unified sales and marketing, and the 15% on integrated commercial data, are the ceiling on everything else. Integration here is a prerequisite for CPG closing the 38%-versus-10% revenue-impact gap with retail. AI applied to a fragmented commercial system automates fragmentation. The window The Consumer Goods Forum and BCG estimate the AI value opportunity in the sector expands by a factor of 1.7 as capabilities mature toward autonomous models. That is the reward for getting the foundation right. It is also the penalty for not: the multiplier applies to whatever base a company has actually built. The pattern across every dataset published this year is consistent. The sector has resolved the question of whether AI matters. It has not resolved who is accountable for making it pay — and the companies that answer that question first will spend the next twelve months compounding, while the rest continue to pilot. The most valuable decision available to a consumer goods leadership team in 2026 costs nothing and requires no technology.**It is deciding, explicitly and in writing, whose number moves when AI works.** --- Source: https://www.motionandgain.com/insights/who-owns-the-ai-pl Cite as: Motion & Gain, "Who Owns the AI P&L?", 29 July 2026, https://www.motionandgain.com/insights/who-owns-the-ai-pl Editorial standards: https://www.motionandgain.com/insights/editorial-standards --- title: "The Override Gap" standfirst: "Consumer goods companies bought the demand-planning models. The returns showed up only where they redesigned the decision around them." url: https://www.motionandgain.com/insights/the-override-gap type: "Challenge & Resolution" publisher: "Motion & Gain" author: "Motion&Gain" published: 2026-07-28 updated: 2026-07-28 summary: "The gap is not a modelling error. It is a decision that arrived one cycle late. A demand plan is not a forecast. It is a chain of decisions — what to make, what to hold, what to promise a retailer on…" hero_image: https://wcokbflnhololtfdocak.supabase.co/storage/v1/object/public/article-images/2026-07/img-1794-3609117b.png access: public language: en --- # The Override Gap *Consumer goods companies bought the demand-planning models. The returns showed up only where they redesigned the decision around them.* Challenge & Resolution · Motion&Gain · 28 July 2026 > The gap is not a modelling error. It is a decision that arrived one cycle late. A demand plan is not a forecast. It is a chain of decisions — what to make, what to hold, what to promise a retailer on Thursday — and the forecast is only the first link. Over the last five years, every large consumer goods company has upgraded that first link. Far fewer have touched the rest of the chain. The difference between the two groups is most of the value. > The Challenge By the early 2020s, the standard planning stack in consumer goods had stopped holding. A statistical baseline — exponential smoothing, ARIMA, and their descendants — was generated on a monthly cycle, then adjusted by hand before it reached supply. It broke in two places, and only one of them was the model. The model was blind to why demand moved. History-based methods rest on a single assumption: that the best predictor of future demand is past demand. That holds for a stable SKU in a stable channel. It fails precisely where the money is. A promotion, a heatwave, a competitor's price cut, a retailer's planogram reset — by the time any of these appear in the sales record, the event is over. The forecast is reporting yesterday's news at the exact moment it needs to be forward-looking. For manufacturers where a third of volume moves on promotion, that is not an edge case. That is the business. **The override was unmeasured.** Planners knew the model was wrong, so they corrected it. Nobody measured whether the corrections helped. When one manufacturer ran forecast value added retrospectively across fourteen months of manual adjustments, roughly 40% of them moved weighted error in the wrong direction. The organization was paying skilled people to degrade its own forecast — and had no instrument that would have told it. That is the override gap: the distance between what the system could have known and what the organization actually decided. Why buying a better model didn't close it? The obvious fix — a more sophisticated algorithm — has a poor track record on its own. Three reasons recur. **Opacity breeds override.** A model that produces a number without a traceable reason invites correction. The research found more than half of supply chain planning teams cite explainability as a meaningful barrier to adoption. Unexplained forecasts get overwritten, and every overwrite resets the trust clock. **Sophistication is not accuracy**. Forecast error is frequently a data problem wearing a modelling costume. Fragmented master data, unflagged product transitions, missing promotional calendars — no ensemble compensates for inputs that never described the business correctly. **The planning calendar was never re-cut.** A model that can re-forecast daily, plugged into a process that decides monthly, delivers a monthly decision. The cadence, not the algorithm, sets the ceiling. > The Resolution The companies that got paid made four decisions, not one. They are unglamorous and they are sequential. ***1. Stop running one model across the whole portfolio.*** Segment the SKU base by forecastability, then match the method. The 2026 landscape has settled into three usable layers: gradient boosting with engineered features (LightGBM, XGBoost) on high-velocity items where covariates are rich; time-series foundation models — Amazon's Chronos-2, Google's TimesFM, Salesforce's Moirai, Nixtla's TimeGPT — used zero-shot or lightly fine-tuned across the long tail and new product introductions where there is no history to learn from; and classical methods retained where series are genuinely stable. The honest read on foundation models is that zero-shot performance sits between classical methods and fully trained deep models — impressive for zero training, not yet dominant. Fine-tuning on a few hundred domain points closes most of the gap. Treat them as a way to cover the tail cheaply, not as a replacement for the core. ***2. Feed the model causes, not just history****.* This is where the accuracy actually comes from. Price and promotional mechanic, competitor activity, media weight, weather, retailer POS sell-out, local events, macro indicators. Unilever's weather-driven models lifted forecast accuracy for ice cream in Sweden by around 10 percentage points — a category where a six-day temperature swing is the demand driver and the sales history is noise. Nestlé's Brazilian data team reached roughly 94% accuracy predicting sell-out over twelve-week horizons by modelling drivers rather than extrapolating. ***3. Shorten the loop.*** Move from a monthly consensus cycle to continuous re-forecasting, with retraining triggered by events rather than the calendar. The caution here is real: over-retraining on noisy data fits transient variance and increases error. Weekly retraining earns its keep when external signals move faster than a month; monthly is sufficient for stable, high-volume SKUs. Anchor the schedule to a value-added check before new weights go to production. ***4. Govern the human — don't remove them.*** This is the move most often skipped, and it is the one that converts model accuracy into business outcome. Make forecast value added a standing governance metric, not a diagnostic: measure the naïve baseline, the machine forecast, the planner override and the consensus number separately, by SKU and channel. Publish who adds value and where. Then automate what the data says is safe — Gartner's guidance for 2026 puts touchless forecasting for stable SKUs and automated replenishment parameters squarely in the sweet spot — and route the remainder to a planner with the evidence attached. The planner stops re-typing numbers and starts adjudicating exceptions. > **What lies ahead...** *Agentic planning, for now, in most of the portfolio*. Gartner's 2026 assessment is blunt: **vendors claiming end-to-end autonomous supply chain planning before 2027 are overstating what is possible, and "agent washing" is a live procurement risk.** Cross-enterprise negotiation and dynamic cost trade-offs are poor early candidates. High-volume, low-cost-of-error decisions are good ones. Start there, keep the audit trail, keep the hand-off points explicit. *Any programme that starts with the tool.* The sequencing that works is data foundation, then segmentation, then model, then cadence, then governance. Reverse it and the pilot produces a better number that nobody uses. > **The Bottom Line** Every large consumer goods company now has access to the same models. Ninety-one percent of supply chain leaders say they will use AI for demand forecasting within two years, which means the model is no longer the differentiator — it is the entry fee. What still differentiates is narrower and harder: whether the organization has measured its own override behavior, whether the planning cadence matches the signal speed, and whether inventory and service policy were re-cut to spend the accuracy that was bought. **The forecast was never the hard part. Closing the gap between what the model knows and what the business decides — that is the work.** --- Source: https://www.motionandgain.com/insights/the-override-gap Cite as: Motion & Gain, "The Override Gap", 28 July 2026, https://www.motionandgain.com/insights/the-override-gap Editorial standards: https://www.motionandgain.com/insights/editorial-standards --- title: "From Classroom to Compression Chamber" standfirst: "How AI Is Rewriting Learning — and What CHROs Must Do Now" url: https://www.motionandgain.com/insights/from-classroom-to-compression-chamber type: "Consultant Article" publisher: "Motion & Gain" author: "Motion&Gain" published: 2026-07-26 updated: 2026-09-16 summary: "For centuries, education followed a simple contract: time equals mastery. Four years for a degree. Six months for a certification. Ninety days to onboard. AI is quietly voiding that contract. What…" hero_image: https://wcokbflnhololtfdocak.supabase.co/storage/v1/object/public/article-images/2026-07/educationai-38c7f07a.png access: public language: en --- # From Classroom to Compression Chamber *How AI Is Rewriting Learning — and What CHROs Must Do Now* Consultant Article · Motion&Gain · 26 July 2026 For centuries, education followed a simple contract: time equals mastery. Four years for a degree. Six months for a certification. Ninety days to onboard. AI is quietly voiding that contract. What used to take a semester now takes a weekend. What used to demand a mentor now happens through a chat window at 2 a.m. And the implications for organizations — especially for the people who run them — are enormous. ## The Great Compression AI tutors adapt to a learner's pace in real time, diagnose gaps in seconds, and generate infinite practice problems calibrated to the exact edge of a person's ability. Coding, languages, financial modeling, medical diagnostics, legal research — skills that once required years of apprenticeship are now compressed into weeks. Simultaneously, the repetitive scaffolding of work — drafting first passes, reconciling spreadsheets, summarizing meetings, triaging tickets — is being handled by agents that never sleep. The consequence is a strange inversion. The bottleneck in learning is no longer information or instruction. It is *judgment*— knowing which questions to ask, which outputs to trust, and which decisions matter. Education is shifting from knowledge transfer to taste development. ## What This Means for Organizations Three shifts are already visible: **Roles are dissolving into skills.** Job descriptions built around static competencies are becoming obsolete. What matters is a person's ability to compose AI-augmented workflows around whatever problem lands on their desk that quarter. **The junior tier is under pressure.** The bottom of the pyramid — the analysts, associates, and assistants who traditionally did the repetitive work — is being automated first. This threatens the pipeline that historically produced senior talent through years of pattern recognition. **Learning velocity becomes a moat.** Organizations that can reskill their workforce in weeks rather than years will out-compete those still running annual training cycles. Human capital is becoming a flow, not a stock. ## What CHROs Should Be Doing Today The CHRO's job description just got rewritten, whether or not the CHRO noticed. Five moves matter now: Start by rebuilding the skills taxonomy around capabilities and judgment, not job titles. Titles will keep churning; the underlying human capabilities — synthesis, ethical reasoning, cross-functional translation, customer empathy — will not. Redesign the early-career path. If AI is doing the grunt work that used to season juniors, the organization needs deliberate substitutes: rotation programs, shadowing, structured "reps" on real judgment calls with senior feedback. Invest in an internal learning infrastructure that runs at AI speed. Static LMS platforms and quarterly training calendars are dead. Think adaptive learning agents, always-on coaching, and skills marketplaces that match talent to problems in days. Get serious about AI fluency across the whole workforce — not just engineering. Every function needs to know how to prompt, verify, and delegate to AI. This is the new baseline literacy. Finally, rewrite the psychological contract. When AI compresses learning curves, the deal between employer and employee shifts. People will stay where they grow fastest. Retention will belong to companies that treat every role as a launchpad. ## The Bottom Line The organizations that win the next decade will not be the ones with the biggest AI budget. They will be the ones whose people learn fastest, adapt hardest, and make the best judgment calls at the edges where AI cannot yet reach. CHROs who see this now — and act — will define the winners. Those who wait for the "AI strategy" to be handed down from IT will find their talent already halfway out the door. The classroom is being compressed. The workplace is next. And the CHRO's calendar is where the future gets built — or missed. --- Source: https://www.motionandgain.com/insights/from-classroom-to-compression-chamber Cite as: Motion & Gain, "From Classroom to Compression Chamber", 26 July 2026, https://www.motionandgain.com/insights/from-classroom-to-compression-chamber Editorial standards: https://www.motionandgain.com/insights/editorial-standards