The Trendiest Trend Among All Marketing Trends in 2026

• The defining marketing trend of 2026 is AI transforming almost every existing marketing activity and business decision, not creating another channel or format

• Television, leaflets, loyalty programmes, personalised offers, retail media, influencers, research, creative production and measurement are all being redesigned around AI

• Corporate leaders know they must adopt AI, but most still lack a clear framework for where machines should replace, support or remain limited

• AI delivers the strongest results in repetitive, data-rich and measurable activities, especially when outputs can be reviewed and corrected before reaching customers

• The rejection of AI slop shows that efficiency can damage brands when automated communication feels cheap, deceptive, generic or emotionally empty

• The strongest companies will not use the most AI, but will know exactly what to automate, augment and keep distinctly human

 

For most of modern marketing history, every major transformation appeared to have a recognisable centre: television, direct mail, search advertising, social media, mobile applications, loyalty programmes or retail media.

Each era seemed to introduce a new channel, platform or method for reaching consumers, attracting attention, influencing consideration and moving customers away from competing brands.

The defining marketing shift of 2026 is different because it is not replacing one channel with another or creating one universally dominant new advertising format.

It is transforming almost all of them simultaneously, turning AI into an operating layer across research, creativity, media, personalisation, commerce, customer experience and measurement.

A television campaign can now involve AI-assisted audience research, concept development, script generation, storyboarding, localisation, editing, media allocation and performance analysis across multiple markets.

A supermarket leaflet can be optimised through predictions about regional demand, promotional elasticity, product selection, page position, print volume, household relevance and expected store traffic.

A loyalty programme can estimate which customer may respond to which offer, through which channel, at which moment and with what level of incentive.

A retail media network can use AI to select audiences, generate advertising variations, allocate bids, adjust budgets and determine when a commercial message should appear.

Influencer marketing can use AI to identify creators, predict brand fit, test scripts, localise content, generate synthetic presenters and reproduce successful formats at industrial scale.

AI is therefore not becoming another marketing channel; it is becoming the intelligence, production and optimisation layer operating across almost every marketing channel.

That distinction explains why the most fashionable marketing trend of 2026 is not simply AI advertising, but the broader AI transformation of marketing itself.

AI Is Not the Next Marketing Channel

The easiest way to misunderstand the current transformation is to reduce it to synthetic images, automated copywriting, artificial presenters or AI-generated video advertisements.

Those applications attract attention because their outputs are immediately visible, yet visible content generation represents only one component of a much larger operating-model transformation.

McKinsey’s June 2026 analysis describes five connected AI capabilities shaping marketing: insights, creativity, personalisation, agentic commerce and continuous orchestration across execution and optimisation.

The strategic question is therefore no longer, “What is the next marketing channel?” but, “How should AI change the way every existing channel operates?”

Television, physical stores, printed communication, search, social media, creators, applications, loyalty programmes and retail media all remain commercially relevant across different markets and categories.

What is changing is their underlying economics, speed, precision and decision logic, including what gets produced, who receives it and how performance is continuously improved.

The channel remains visible to customers, while the AI layer increasingly determines how intelligently, efficiently and responsively that channel performs behind the scenes.

Every Marketing Channel Is Being Reconsidered

In television and video advertising, AI is entering consumer research, concept generation, script development, previsualisation, editing, translation, localisation, versioning, audience selection and effectiveness measurement.

One approved campaign platform can now produce numerous languages, durations, formats and market-specific executions faster than traditional production models could deliver economically.

In printed retail communication, AI can help determine which products deserve prominence, which offers may drive store visits and how content should differ by location.

It can also influence page layout, promotional combinations, print quantities, household targeting and distribution priorities without requiring printed leaflets to disappear entirely.

In loyalty marketing, AI can move companies beyond broad demographic segments towards behavioural predictions concerning purchase probability, churn, switching, discount sensitivity and communication timing.

This makes personalised offers potentially more relevant, but it also creates risks involving privacy, perceived manipulation, discriminatory outcomes and excessive dependence on promotional incentives.

In retail media, AI can connect retailer data, consumer behaviour, advertiser objectives, creative assets and campaign performance through continuously adjusted targeting, placement, bidding and allocation.

The result is not one isolated AI revolution, but simultaneous AI transformations occurring across almost every element of the marketing and commercial system.

Management Knows It Must Move

Boards, chief executives and marketing leaders increasingly understand that ignoring AI is not a credible strategy when competitors, investors and employees expect visible progress.

They face pressure to generate growth without proportional budget increases, improve productivity, accelerate execution and prevent slower decision-making from becoming a structural disadvantage.

Gartner reported in May 2026 that CMOs were allocating an average 15.3 per cent of marketing budgets to AI initiatives, while 70 per cent considered AI leadership critical.

However, only 30 per cent reported mature or fully developed readiness, showing that executive ambition is running substantially ahead of organisational capability.

McKinsey found that nearly 60 per cent of marketers used AI several times weekly, yet fewer than 10 per cent captured value across end-to-end workflows.

Only 28 per cent considered their organisations to be fundamentally rewiring marketing teams and workflows, while most were layering tools onto existing processes.

This is the defining management contradiction of 2026: leaders know they must act, but they still lack a complete map for acting intelligently.

Most large companies are therefore not executing a finished AI strategy; they are operating portfolios of experiments intended to discover where measurable value actually exists.

Experimentation Has Become the Strategy

Experimentation is not necessarily evidence of weak leadership because no executive team can reliably predict every valuable AI application before testing it inside real workflows.

Results depend heavily on company-specific data, technology architecture, employee capability, brand positioning, regulatory exposure, customer expectations and the cost of reviewing mistakes.

Automated product descriptions may create significant value for a retailer managing hundreds of thousands of items, while adding little to a premium brand built around distinctive language.

An AI assistant may resolve routine enquiries efficiently, yet damage trust when consumers face complicated complaints, financial loss, health concerns or emotionally sensitive circumstances.

Personalisation may increase response rates, while excessive personalisation may feel intrusive, manipulative or unfair when consumers cannot understand why different people receive different treatment.

AI-generated influencers may allow hundreds of inexpensive content variations, while simultaneously making a brand appear deceptive, artificial or unwilling to support genuine human creators.

The correct answer cannot be determined through enthusiasm or fear; it must be discovered through disciplined testing, explicit hypotheses and commercially meaningful measurement.

Companies must compare AI-assisted performance against the existing human baseline, identify failure modes and stop experiments whose apparent savings disappear after supervision and correction.

Possible Is Not the Same as Valuable

One of the most dangerous instructions an executive can give is, “Introduce AI wherever possible,” because technological possibility is not equivalent to strategic value.

The better instruction is, “Introduce AI wherever it demonstrably increases net business value after all material costs, risks and downstream effects are included.”

Net value must incorporate software, integration, data preparation, governance, human review, error correction, reputational exposure, customer dissatisfaction and organisational complexity.

AI may reduce content-production costs while dramatically increasing the volume of forgettable communication that weakens brand distinctiveness and overwhelms already saturated audiences.

It may improve short-term conversion while teaching customers to wait for discounts, weakening pricing power and damaging long-term customer economics.

It may automate customer support while increasing frustration, escalation and churn among customers whose problems do not fit the system’s standard decision pathways.

AI is exceptionally capable at optimisation when the objective has been defined correctly, but it cannot guarantee that management selected the correct objective.

A system instructed to maximise clicks may produce increasingly provocative messages, while a system instructed to reduce service costs may obstruct access to human assistance.

The machine may achieve the selected metric while damaging the wider business, demonstrating why optimisation must remain subordinate to strategy and accountability.

Where AI Is Already Exceptionally Strong

AI produces its most convincing results when sufficient relevant data exists, the task occurs frequently, the expected output is definable and success can be measured.

The case becomes stronger when a competent person can review or correct outputs quickly, preventing low-quality or inaccurate material from reaching customers.

This combination appears frequently in marketing through advertising variations, translation, summarisation, segmentation, recommendation systems, media optimisation, demand prediction and large-scale performance analysis.

McKinsey estimates that generative AI could increase the productivity of marketing expenditure by between 5 and 15 per cent, although potential does not guarantee realised savings.

Gartner reported that marketing leaders expect AI-driven automation of marketing work to rise from 16 per cent in 2026 to 36 per cent by 2028.

These projections matter because marketing teams face constant pressure to deliver more output, greater personalisation and faster learning without equivalent increases in resources.

However, productivity creates value only when the activity being accelerated deserves to exist and the additional output remains accurate, distinctive and commercially useful.

AI Can Make Marketing More Efficient and Less Effective

One dangerous assumption within current transformation programmes is that more efficient marketing must automatically be more effective marketing, although those outcomes can diverge sharply.

AI can produce more creative assets, but quantity can overwhelm judgment, dilute brand codes and encourage teams to publish material that would previously have been rejected.

It can direct investment towards channels with abundant measurable signals while underinvesting in slower, less attributable activities that build memory, fame and future demand.

It can personalise communications so aggressively that customers feel monitored rather than understood, especially when prices, incentives or service levels appear individually manipulated.

The executive responsibility is therefore not simply making existing activities faster, but determining whether those activities create durable value for customers and shareholders.

The central decision is not whether AI can perform a task, but whether delegating that task improves the complete commercial outcome without sacrificing trust.

The Rise of AI Slop

As generative systems reduce the cost of producing content, they also reduce the cost of producing repetitive, generic, misleading and insufficiently reviewed content.

The increasingly common term “AI slop” describes output perceived as mass-produced, emotionally empty, manipulative or visibly careless, rather than every legitimate use of artificial intelligence.

One June 2026 US consumer measure reported that visible AI-generated marketing increased trust for only 7 per cent, while reducing trust for 31 per cent.

That result should not be universalised across every market, but it demonstrates a substantial asymmetry: visible AI may create more reputational downside than upside.

Platform behaviour provides another warning because YouTube, TikTok, Pinterest and others are strengthening labels, detection, user controls or policies targeting repetitive and low-effort AI material.

The commercial problem extends beyond technical defects because audiences may punish communication simply for looking automated, even when the underlying message is accurate.

A brand does not need to use AI badly to face criticism; it may be criticised because its communication resembles the recognisable aesthetics of bad AI.

The practical requirement is therefore not merely technical correctness, but visible intention, human judgment, originality and a credible reason for the communication to exist.

Influencer Marketing Is Also Being AI-Zed

The AI transformation of influencer marketing already exists, but it is substantially broader than replacing human creators with fully synthetic personalities.

AI is increasingly used to identify potential creators, analyse audience compatibility, predict performance, detect fraud, generate briefs, test hooks and optimise campaign allocation.

It can translate scripts, alter backgrounds, create product demonstrations, reproduce winning formats and generate multiple localised executions from one original creator concept.

At the most visible end, companies are testing synthetic influencers and AI-generated user-generated-content formats that appear to show ordinary people experiencing real products.

The Guardian reported in June 2026 that some brands were quietly deploying AI-generated influencers portraying apparently genuine customer experiences without obvious disclosure.

Business Insider reported in July that brands were recreating successful influencer formats, producing hundreds of posts and using synthetic creators within TikTok Shop campaigns.

This development demonstrates that influencing is being automated at three levels: creator selection, content production and synthetic replacement of the creator themselves.

The first two applications can improve efficiency without necessarily weakening authenticity, while the third creates a much more serious credibility problem.

Should Brands Use AI-Generated Influencers?

Synthetic influencers can be strategically useful when their artificial nature is visible, intentional and creatively relevant to the campaign’s central idea.

They may fit entertainment, gaming, fictional brand worlds, humour, experimentation or categories where audiences do not mistake the character for a real customer.

The risk increases sharply when an artificial person imitates genuine testimony, claims product experience, demonstrates unrealistic outcomes or appears to provide independent human endorsement.

The issue is not simply whether viewers can detect the synthetic character, but whether the execution creates a materially misleading impression of real experience.

Business Insider reported that some brands use synthetic creators to test concepts before investing in real creators, which represents a more defensible hybrid approach.

A synthetic execution can cheaply identify promising hooks, while a credible human creator can later provide genuine demonstration, context, trade-offs and accountability.

The safest principle is straightforward: use artificial characters when artificiality strengthens the idea, but never use artificiality to counterfeit authenticity.

How Much AI Should Write Marketing and Sales Copy?

There is no credible universal percentage because the appropriate level depends upon the communication’s purpose, audience, visibility, sensitivity and potential consequences.

AI is highly suitable for research, synthesis, outlining, alternative headlines, first drafts, localisation, shortening, versioning, quality checks and structured testing.

It is less suitable as the autonomous final author of strategically important communication involving trust, reputation, complex persuasion, legal exposure or emotional sensitivity.

A routine product description can tolerate substantially more automation than a chief executive statement, serious customer apology or complex proposal to an important client.

The closer communication sits to trust, strategic positioning or irreversible reputational consequences, the stronger the human involvement and final accountability should become.

For high-volume, lower-risk content, AI may create most initial material, provided trained reviewers verify accuracy, consistency, claims and potential unintended interpretations.

For important brand or sales communication, AI should function primarily as researcher, challenger and drafting partner, while experienced people determine the argument and final language.

The decisive question is not whether AI touched the text, but whether a capable human mind remained meaningfully present and professionally accountable.

Human Creativity Is Not Merely an Expensive Input

Early corporate AI programmes often framed human work primarily as a cost to reduce, making headcount reduction appear like the clearest proof of transformation.

That framing overlooks the fact that some human contributions create differentiation, cultural relevance, emotional precision, ethical judgment and accountability rather than routine output.

Human creativity can produce strategically unexpected ideas that historical patterns would not predict, while human judgment can recognise when rational recommendations violate brand character.

Human interaction may appear inefficient within a narrow process metric while remaining commercially valuable through reassurance, problem discovery, negotiation and relationship protection.

The better question is not only, “Can AI perform this task?” but also, “What valuable capability disappears when a person stops performing it?”

In some activities, almost nothing valuable disappears; in others, the removed human contribution may have been the main reason customers trusted the experience.

Some Early Workforce Decisions Will Be Corrected

Current evidence does not demonstrate a broad global wave of companies rehiring everyone previously displaced or supposedly replaced by artificial intelligence.

Large organisations continue reducing headcount, redesigning roles and shifting investment towards automation, particularly in execution-heavy and entry-level work.

However, McKinsey notes that some companies cutting staff because they expected AI to fill the gap have struggled with unanticipated complexity and incomplete savings.

Reuters reported in July 2026 that Adecco found AI was changing tasks and roles more than eliminating employment, while warning against destroying junior talent pipelines.

The more probable pattern is uneven correction, with some roles disappearing, others changing and certain responsibilities returning through supervision, escalation and quality assurance.

This is not a retreat from AI, but a correction of the crude assumption that every automated task creates an equivalent and permanent headcount reduction.

Jobs Will Be Decomposed Before They Are Eliminated

The most useful unit of analysis is not the job title, but the collection of tasks, decisions, relationships and responsibilities inside that job.

A marketing manager may research markets, interpret behaviour, prepare presentations, coordinate agencies, approve creative work, manage budgets and advise senior leadership.

AI may perform some activities exceptionally well, accelerate others and remain unreliable where ambiguity, accountability, negotiation or emotional intelligence dominate.

Routine production may be automated, analysis accelerated and recommendations generated, while people spend more time defining objectives, challenging outputs and managing exceptions.

This creates a long-term competence risk when junior employees no longer perform foundational work needed to develop the judgment required for senior responsibility.

Efficiency today can create a capability deficit tomorrow if companies remove learning pathways without designing credible alternatives for developing future experts and leaders.

Transparency Is Becoming a Legal and Commercial Requirement

Within the European Union, Article 50 transparency obligations under the AI Act are scheduled to apply from 2 August 2026.

The European Commission’s July 2026 guidance addresses informing people about certain AI interactions, machine-readable marking and disclosure involving specified generated or manipulated content.

Legal requirements will vary by use case and jurisdiction, meaning companies should obtain appropriate legal advice rather than treating every AI-assisted asset identically.

Compliance nevertheless represents only the minimum standard because a correctly labelled synthetic influencer can remain a strategically poor and reputationally damaging idea.

The stronger test is whether the company would remain comfortable explaining the complete AI process publicly to customers, employees, partners and regulators.

If commercial effectiveness depends upon customers not understanding how an execution was created, the organisation should treat that dependence as a warning signal.

Invisible AI, Visible Humanity

For many brands, the strongest operating model will use more artificial intelligence behind the scenes while preserving greater humanity at customer-facing moments.

Use AI to analyse thousands of reviews, but let experienced people determine what the patterns mean and which response protects long-term trust.

Use AI to generate multiple creative routes, but let accountable leaders select the direction that reflects strategy, cultural context and brand character.

Use AI to personalise timing and relevance, but preserve customer control, transparent choices and safeguards against discriminatory or manipulative treatment.

Use AI to draft routine responses, but provide accessible human intervention when situations become sensitive, unusual, consequential or emotionally charged.

Use AI to reduce administrative work, allowing employees to spend more time listening, creating, negotiating, solving exceptions and accepting responsibility.

Customers do not need to see every algorithm, but they should consistently feel that a real organisation remains accountable for every consequential outcome.

When Content Becomes Abundant, Proof Becomes Scarce

AI is rapidly reducing the marginal cost of producing commercial claims, advertising variations, synthetic demonstrations, review-style videos and persuasive sales communication.

This creates abundance, but abundance does not automatically create trust, especially when consumers struggle to distinguish genuine experience from automated simulation.

As communication becomes easier to manufacture, credible evidence, transparent methodology and independently grounded recognition can become strategically more valuable.

AI can make a claim more visible, polished and personalised, but it cannot transform an unsupported claim into a verified commercial truth.

For organisations using ICERTIAS recognitions, the opportunity is not simply distributing recognition assets more efficiently through AI-enabled marketing systems.

The greater opportunity is explaining what each recognition means, what was measured, within which category and market, and why the evidence should matter.

Best Buy Award can support a value-for-money claim, QUDAL - Quality Medal can support perceived-quality positioning, and Customers’ Friend can support customer-experience credibility.

In an environment saturated with generated claims, research-based signals become more valuable when their methodology is clear, specific and consistently communicated.

What Marketing Leaders Should Do Now

Marketing leaders should begin by selecting economically important workflows rather than purchasing the largest possible collection of disconnected AI tools.

They should map each workflow task by task, distinguishing repetitive execution from strategic judgment, sensitive decisions, exceptions and legally consequential claims.

Each proposed use case should have a defined hypothesis, baseline, expected benefit, risk owner, review process and predetermined condition for stopping.

AI-assisted performance should be compared with current human performance using complete commercial outcomes rather than speed, output volume or labour savings alone.

Organisations should retain named human accountability for brand positioning, important claims, customer fairness, sensitive communication and material reputational exposure.

They should redesign roles before eliminating them, ensuring that supervision, escalation and knowledge-development responsibilities remain adequately staffed and clearly assigned.

Employees must learn not only how to operate AI tools, but how to challenge assumptions, verify evidence, identify hallucinations and overrule recommendations.

Management should measure revenue, margin, customer trust, brand distinctiveness and long-term demand alongside immediate reductions in production cost.

A system saving 20 per cent of content costs while reducing campaign effectiveness by 30 per cent is not productive or economically intelligent.

A synthetic influencer costing less while weakening credibility is not innovation, but a false economy disguised as technological progress.

The Next Competitive Advantage

The dominant marketing trend of 2026 is the AI transformation of almost every part of the marketing operating system.

But that should not be confused with a mandate to automate everything technology makes possible.

Companies understand that they cannot ignore AI. What they still do not fully understand is where AI should replace human effort, where it should strengthen it and where human control must remain decisive.

That uncertainty will define the next several years. Organisations will experiment aggressively, capture real productivity gains, make costly mistakes, reverse some premature workforce decisions and redesign roles around more effective collaboration between people and machines.

The companies that succeed will not be those that deploy the most AI. They will be those that apply it with the greatest precision.

AI is already exceptionally powerful in tasks that are frequent, structured, data-rich, measurable and easy to review. It can accelerate production, improve targeting, strengthen personalisation and optimise execution at a scale no human team could match.

Yet the closer a task moves towards strategy, trust, accountability, emotional intelligence or genuine creative distinction, the more dangerous blind automation becomes.

In the previous marketing era, competitive advantage came from reaching more people, purchasing media more efficiently and producing more content than competitors.

In the emerging era, those capabilities will become increasingly available to everyone.

The real advantage will therefore shift from access to judgment.

Winning companies will know what to automate, what to augment and what must remain unmistakably human.

The greatest mistake is no longer failing to use AI.

The greatest mistake is using it before understanding what actually creates value.

AI will become abundant.

Good judgment will not.