AI now writes your scripts, makes your images and adapts your campaigns for every audience. It also starts to tell your customers what your brand means before they ever reach your website, moving the judgment that matters back to the top of the company.
Andy Demir | Qalbur Perspective | Growth | 11 minute read
Executive Summary
Between 2025 and 2026, AI moved from being a production tool to being a go-between in how buyers discover and compare brands. The opportunity is real: cheaper creative work, faster adaptation and wider access to execution that once needed large budgets. The risk is that a business mistakes persuasive output for a distinctive, credible story. My argument is that brand storytelling now has to be led across three things at once: the customer experience that earns the story, the content that tells it and the outside systems that retell it. AI can help at every step, but the promises customers meet, and the evidence behind them, remain the responsibility of the people who run the company.
In This Article
- What survives from the older marketing stories
- The economics of telling a story have changed
- One brand, many versions of the story
- AI is also retelling your brand to the buyer
- What makes a synthetic story believable?
- The decisions that belong with senior leadership
- Questions for the Boardroom
- A story worth retelling
- References
What survives from the older marketing stories
When an AI marketing proposal reaches your board, the production case is usually convincing. You can have more creative variations, translation in a fraction of the time, and campaigns that once needed specialist suppliers assembled on a smaller budget. Those are useful gains. What the proposal rarely answers is whether customers will remember whose story they saw, believe what it promised and find that promise kept when they buy.
Giles Lury’s 2013 book, The Prisoner and the Penguin, is a good place to start. It collects seventy-six short marketing stories about recognisable symbols, the people behind brands and the customer experiences that gave those brands meaning. Read in 2026, its lasting contribution is the link between what a business does and what people remember about it. The anecdotes belong to an earlier marketing world, and individual stories should not be read as verified business history, but that link has survived.
What needs updating is the quiet assumption that a memorable success story explains commercial success. An anecdote makes a principle easier to grasp; it rarely tells you how much growth that principle caused. Distribution, product quality, pricing and competitors all played their part. When a story is used to justify an investment, you need both the narrative and an honest account of the business conditions beneath it, or a colourful example becomes a weak investment case.
By storytelling I mean arranging events, people and consequences so that a customer understands why a promise matters. A story can help a buyer imagine a better outcome or recognise a problem they already live with. It needs no founder legend, emotional soundtrack or film about corporate purpose. In a complex business purchase, the story might be how a service team solved a customer’s problem, what the customer avoided as a result and why the supplier can do it again.
The economics of telling a story have changed
You can see in the 2025 evidence why marketing teams moved quickly. In March 2025, Unilever reported that digital twins of its products, accurate virtual models of each item and its packaging, were letting it create product imagery twice as fast and 50% more cheaply. Those are the company’s own production figures, and they say nothing about whether AI made its marketing more effective. They do show how far the cost of executing and adapting an idea can fall once product information and imagery are properly organised. [1]
The pull is strongest when budgets are tight. Nielsen’s 2025 Annual Marketing Report surveyed 1,400 marketers worldwide, all working with annual budgets of at least US$1 million. In Europe, 60% planned to cut advertising spending in 2025, and 59% prioritised revenue growth, compared with 37% who prioritised brand awareness. These are intentions reported by marketers rather than a census of European business, but they explain the pressure under which production savings are being chased. [2]
For a smaller company, this opens up work its production budget used to rule out. AI can turn an approved customer interview into several scripts, propose visual treatments, generate draft scenes and adapt the material for different languages and formats. Used well, that gives you a cheaper way to test an idea before committing serious money to it. The saving loses most of its value when every cut in production cost is turned straight into more output.
Here is the economic complication. Your competitors can buy the same tools, so cheaper execution will not stay an advantage for long. If they also respond by producing more, your customers receive more messages while their attention stays where it was. This is my inference rather than a measured law of advertising, but it points in a clear direction: as production gets easier, your own customer knowledge and recognisable creative choices deserve a larger share of management attention.
One brand, many versions of the story
For you, as a commercial leader, the shift is from approving a campaign to governing an entire family of communications. Generative AI can draft copy, synthesise a voice, create video and produce variations around the same proposition, and when it is connected to customer data and campaign systems, it can help choose or adapt the message for each buyer. These capabilities do not come from a single model, and together they do not amount to a marketing strategy. People still set the objective, supply the evidence and decide what the organisation is prepared to promise.
The benefit of adaptation is easy to see. A finance director wants the economic consequences of a purchase; a technical evaluator wants confidence in performance and implementation. Both can receive different explanations of the same offer without receiving different promises. A service guarantee has to remain the same guarantee whether one version stresses business continuity and another walks through the support process.
Language adds another layer. Translating an English customer story into French, Polish or Arabic changes the words, but whether it is commercially relevant depends on the actual buyer, the contract and the service you can deliver locally. Delivery terms, warranty cover and support availability have to be checked market by market. AI can help with translation and adaptation, yet fluent prose tells headquarters nothing about local credibility. Your regional teams need the authority to challenge a message that sounds convincing and misstates what they can deliver.
This is where Lury’s emphasis on images and icons becomes useful again. If hundreds of variants each shift the visual identity, the tone and the central promise, personalisation can dissolve recognition. Kantar’s analysis of hundreds of advertisements that used generative AI found that those with distracting or unnatural visuals performed worse on branded cut-through, and that branding was weaker on average, mainly where the use of AI was obvious to viewers. That argues for disciplined creative testing. It does not show that AI-made advertising is inferior as such. [3]
Manage this badly and the cost lands on the people below you. A creative team measured mainly by the number of assets it produces has little reason to spend time seeking an unfamiliar customer insight. A regional marketer correcting weak translations can quietly become a permanent reviewer of machine output. If you approve the efficiency programme, decide where the released capacity goes: better research, stronger creative development or closer contact with customers. Otherwise, the organisation becomes faster at repeating what it already knows.
AI is also retelling your brand to the buyer
The bigger change for market access happens outside your marketing department. A buyer can now ask an AI assistant which suppliers to consider, how products compare or whether a brand’s claims hold up. Depending on the system, the answer may draw on retrieved web pages, reviews and other material as well as on what the model learnt in training. Either way, the explanation belongs to the intermediary. It can omit the context your team considered essential or describe your offer in someone else’s words.
Pew Research Centre’s analysis of the browsing data of 900 US adults shows what that does to access. In March 2025, people who saw an AI summary on a Google results page clicked a traditional search result on 8% of visits, against 15% when no summary appeared. This was observational research on US Google use, so it is neither a causal estimate for European commerce nor a forecast for every assistant. It does show why you cannot assume that appearing in search will bring the customer to the narrative your team built so carefully. [4]
The shift is real, but it needs proportion. Kantar’s Media Reactions 2026 study, published on 2 October 2026 and based on interviews with over 800 senior marketers and 23,000 consumers, found that 32% of consumers use AI assistants to research brands and products and only 23% trust their recommendations. In the same study, a net 75% of marketers said they plan to increase investment in AI assistants in 2027, so marketing spending is outpacing consumer trust. That supports experimenting with AI-mediated discovery, and it should temper any assumption that your customers have already handed their buying decisions to a machine. [5]
One practical consequence follows: your story needs evidence that survives being separated from the campaign. A precise product page, a customer case the customer has agreed to put their name to and a clear explanation of service coverage give a buyer something firmer to examine than a broad claim to market leadership. None of this guarantees that an assistant will include you or describe you accurately. It improves the material from which customers and intermediaries form their view.
It also changes how much the people behind the scenes matter. A support call that solves a customer’s problem may turn into a review, a forum discussion or a recommendation to another buyer, and that material can feed the account others give of your brand. Marketing has always depended on reputation; AI adds another route by which scattered experiences are pulled together into a compact judgement. Your service team takes part in the story through its work, whether or not it ever appears in a campaign, which makes service quality a brand decision for the executive committee as much as an operational one.
What makes a synthetic story believable?
At board level, authenticity needs a sharper meaning than “made by a human”. Human advertising can mislead, and synthetic imagery can explain a product accurately. A generated illustration of a process is different from a generated customer presented as someone who bought the service, because the latter asks the audience to believe that an experience happened. The boundary that matters is the claim being made about reality, and that is where your policy should draw its lines.
Dove shows what a brand-specific boundary can look like. In April 2024, marking twenty years of its Campaign for Real Beauty, Dove committed never to use AI to represent real women in its advertising. Less than a year later, its parent company, Unilever, reported that Dove was among the brands whose product imagery was now produced using AI-driven digital twins. The same group adopted AI for product shots while ruling it out for the representations at the centre of the brand’s promise. That is the kind of line a leadership team can deliberately draw, rather than leaving it to each campaign. [6] [1]
The strongest argument against caution is commercial: customers often care more about whether an advertisement is useful, entertaining or relevant than about how it was made. Kantar’s analysis supports part of that. Advertisements that used generative AI spanned the full spectrum of effectiveness, including the strongest, and elicited stronger emotional reactions, though not always positive ones. The production method alone provides a poor basis for approving or rejecting the creative. [3]
Consumer attitudes complicate the picture further. IAB’s January 2026 research with Sonata Insights, which surveyed 505 US Gen Z and Millennial consumers and 104 US advertising executives, found that executives overestimate how warmly younger consumers feel: 82% of the executives believed these consumers were positive about AI-generated advertisements, while only 45% of the consumers were. Yet 73% of the same consumers said that knowing an advertisement was made with AI would either increase their likelihood of buying or make no difference. Disclosure seems to carry less downside than many marketers fear, even as scepticism about AI advertising grows. These are stated attitudes from one market and two generations, not observed sales. [7]
So the choice is harder than a blanket preference for human- or machine-produced. Fiction, illustration and real testimony need different treatment. A synthetic character can work when the audience understands its role; a fabricated testimonial borrows authority from an experience nobody had. A real employee’s account deserves consent and a faithful presentation of what that person meant. The ease of producing a voice or a face raises the responsibility attached to using one.
The decisions that belong with senior leadership
The distinction I would suggest is between producing a story, earning it through experience and having it retold by others. It is my own framing of the commercial problem rather than an established framework. Each of the three has a different owner inside your business, yet the customer meets their combined result. A persuasive campaign and an unreliable service operation will sooner or later give the market two competing accounts of the same company.
Start with the claims the company makes of itself. Marketing and commercial leadership need one agreed source for performance statements, customer evidence and service commitments, with a named owner who corrects them when conditions change. Brand teams also need to know which visual and verbal elements remain consistent across all variants. The aim is enough control to protect meaning while leaving local teams free to explain the offer in ways their buyers understand.
Then set review according to consequence. An early storyboard can tolerate experimentation because it promises nothing in public. A testimonial, an image that implies product performance or a statement about contractual support needs much closer scrutiny. Drawing that boundary and funding the people who apply it belong with senior leadership. If every asset gets the same approval effort, you waste the speed AI offers; if every claim is delegated, people end up accountable for promises they never approved.
Measurement needs the same discipline. Production cost and turnaround time belong in the operating review, alongside evidence of brand recognition, customer understanding, and incremental commercial results, where these can be credibly assessed. In a long-cycle B2B business, that may mean getting onto shortlists, progressing through procurement and keeping the customer after delivery. A click or an AI mention is an intermediate signal, and it should not reach you as growth until someone has shown its connection to a worthwhile customer outcome.
The trade-off you face is between adapting communication quickly and keeping a promise the organisation can actually support. When supply or costs are uncertain, claims about reliability and value need particular care, because the attractive story is the one the business can keep honouring. Put part of the production savings into the evidence and the customer contact behind the message. That is an investment your competitors cannot copy by subscribing to the same model.
Questions for the Boardroom
- Which claims in our AI-assisted marketing depend on evidence that nobody clearly owns?
- What remains recognisably ours when the same offer appears in hundreds of creative variants?
- How do AI assistants describe us in response to our most important buying questions, and which descriptions need correction?
- Where have production savings improved customer understanding or commercial results, and where have they only increased output?
- Which representations of customers, employees or product performance require an explicit boundary?
A story worth retelling
The old marketing stories still carry a demanding lesson: a symbol becomes meaningful because people attach their own experiences to it. AI gives your company more ways to express that meaning and gives your customers more ways to hear an account of it elsewhere. Both developments underscore the importance of what lies beneath communication. If you want your business to be known for reliability, it has to be visible in the customer’s experience and in evidence that others can examine.
You can delegate much of the production and allow wide freedom in creative execution. The responsibility for what the company asks people to believe stays with you and your board. When the market retells your story, through another customer or an AI assistant, the strongest foundation you can give it is an account that your products, your people, and your decisions can stand behind.
References
- Unilever. (2025, March 18). Unilever reinvents product shoots with digital twins and AI. Company-reported production results.
- Nielsen. (2025, May 20). Nielsen releases its 2025 Annual Marketing Report looking at the power of data-driven marketing.
- Erdem, E., & Šídlová, V. (2025, November 4). Rethinking AI-generated advertising: how real people really react. Kantar.
- Chapekis, A., & Lieb, A. (2025, July 22). Google users are less likely to click on links when an AI summary appears in the results. Pew Research Centre.
- Kantar. (2026, October 2). Net 75% of marketers plan to increase investment in AI visibility, including ads. Media Reactions 2026.
- Dove. (2024, April 10). To mark 20 years of Campaign for Real Beauty, Dove renews its commitment to “real”. PR Newswire.
- Williamson, D. A., Koch, J., & Giegerich, C. (2026, January 15). The AI Ad Gap Widens. Interactive Advertising Bureau with Sonata Insights.
- Lury, G. (2013). The Prisoner and the Penguin: And 75 Other Modern Marketing Stories. LID Publishing.
About the Author
Andy Demir is an international commercial and business leader with more than two decades of experience across complex B2B and industrial markets. Andy writes about executive leadership, strategy, organisational transformation, commercial performance, international business and the changing world of work, combining research with lessons drawn from operating across industries, markets and cultures.
