A Comprehensive Guide to AI in Marketing: What businesses and brands need to know

Artificial intelligence (AI) is changing how businesses market themselves – and how customers find, research and choose the brands they buy from.

AI is already influencing which adverts consumers see, how businesses predict customer behaviour and how marketing teams analyse performance. Now, with the growth of ChatGPT, Google AI Overviews and other conversational search experiences, it’s also changing the way people discover information.

For businesses, the implications go far beyond generating social media captions or writing blog posts faster.

AI presents opportunities to improve efficiency, make better decisions and reach customers more effectively. But it also introduces new challenges around website traffic, brand visibility, data privacy, content accuracy and trust.

In this comprehensive guide to AI in marketing, we’ll explain the technology, explore its practical applications and look at what businesses should be doing to adapt.

What is AI in marketing?

AI in marketing refers to the use of artificial intelligence technologies to support, automate, analyse and optimise marketing activities. This can include everything from predicting which customers are most likely to purchase to generating campaign content, automating workflows and improving advertising performance.

AI applications in marketing generally fall into three categories:

1. AI used by marketers: Tools such as ChatGPT, Gemini and AI-powered design platforms that help with research, content creation, analysis and productivity.
2. AI built into marketing platforms: Machine-learning algorithms powering Google Ads, Meta advertising, email personalisation, recommendation engines and customer analytics.
3. AI changing consumer behaviour: Technologies such as Google AI Overviews, AI Mode, conversational assistants and voice search that influence how people discover, compare and choose brands.

Understanding all three is essential to developing an effective modern marketing strategy.

Understanding artificial intelligence: AI, machine learning and LLMs

Before exploring practical applications, it’s worth understanding the technology behind AI and the terminology commonly used.

What is artificial intelligence?

Artificial intelligence is a broad term describing computer systems capable of performing tasks associated with human intelligence.

These tasks include recognising patterns, interpreting language, making predictions, solving problems and generating content.

AI doesn’t necessarily mean a system can think or reason like a human. Different AI technologies are designed to perform different tasks, and their capabilities vary significantly.

What is machine learning?

Machine learning (ML) is a subset of artificial intelligence.

Rather than relying exclusively on manually programmed instructions, machine-learning systems identify patterns in data and use those patterns to make predictions or decisions.

For example, an advertising platform might analyse historical conversion data to estimate which users are most likely to complete a purchase.

The system can then use these predictions to influence advertising delivery and bidding.

This is an example of predictive AI, which has been used in marketing for many years.

What is generative AI?

Generative AI is a category of artificial intelligence that creates new content based on patterns learned from training data.

This can include:

• Written content, including articles, emails and advertising copy.
• Images, illustrations and design concepts.
• Video and audio.
• Computer code.
• Structured information, summaries and reports.

Tools such as ChatGPT and Gemini have made generative AI accessible to people without technical or programming experience.

However, generating content is not the same as independently verifying its accuracy.

What are large language models (LLMs)?

A large language model (LLM) is an AI model trained on extensive amounts of text and other data to understand and generate language.

LLMs work by processing information in units called tokens and predicting how sequences of tokens should continue.

This allows them to answer questions, summarise documents, draft content, analyse information and assist with complex tasks.

Examples of products using LLMs include ChatGPT, Google Gemini, Claude and Perplexity.

Modern AI systems may also incorporate tools for web retrieval, calculation, coding and other tasks, extending their capabilities beyond generating text.

Predictive AI vs generative AI: What’s the difference?

Predictive AI	Generative AI
Estimates likely outcomes	Creates new content or outputs
Predicts conversion likelihood	Drafts advertising copy
Identifies customers at risk of leaving	Summarises customer feedback
Forecasts demand or sales	Generates campaign concepts
Supports automated bidding	Produces content variations

The distinction matters because many businesses associate AI exclusively with generative tools, overlooking the predictive systems already influencing their marketing performance.

How is AI used in marketing?

AI can support virtually every stage of the marketing funnel, from initial research and customer acquisition through to retention, reporting and optimisation.

AI for market research and marketing strategy

AI can accelerate the research and planning stages of marketing by helping businesses organise information, identify themes and explore potential opportunities.

For example, marketers can use AI to:

• Analyse customer reviews and identify recurring feedback.
• Summarise interviews and survey responses.
• Explore audience segments and customer pain points.
• Develop customer personas and messaging hypotheses.
• Structure competitor research.
• Brainstorm campaign concepts and content themes.
• Identify questions requiring further investigation.

However, AI-generated insights should be treated as hypotheses rather than established facts.

A model can identify patterns in the information provided, but it cannot automatically determine whether a sample accurately represents an entire market.

Strong marketing strategies still depend on combining AI-assisted analysis with reliable data, customer feedback, commercial understanding and human judgement.

AI for customer segmentation, CRM and personalisation

AI is increasingly used to help businesses understand customer behaviour and deliver more relevant communications.

Predictive models can estimate:

• Customer lifetime value (CLV).
• Likelihood of purchase.
• Customer churn risk.
• Lead quality.
• Product preferences.
• Suitable follow-up offers.

For example, an ecommerce business could use purchasing history and engagement data to identify customers who may be ready to reorder.

An email marketing platform could then help personalise product recommendations or optimise the timing of communications.

The objective isn’t simply to increase the amount of personalisation. It’s to make marketing more relevant while respecting customer privacy and consent.

AI for marketing analytics and reporting

Marketing teams often spend considerable time gathering data, producing reports and explaining performance.

AI can help by identifying unusual changes, summarising performance trends and drafting commentary based on supplied data.

For example, a business could combine information from Google Analytics 4 (GA4), Google Ads and Meta Ads into a central reporting dashboard, then use AI to help interpret changes in performance.

However, AI should not be trusted to invent calculations or explain changes without evidence.

A decline in website traffic doesn’t automatically mean a campaign failed. Likewise, an increase in conversions doesn’t necessarily prove that an individual channel caused the improvement.

Marketers still need to understand attribution, seasonality, conversion quality and the limitations of their data.

How AI is transforming paid advertising

One of the most established uses of AI in marketing is paid advertising.

Platforms such as Google Ads and Meta have used machine learning for years to predict user behaviour and optimise campaign delivery.

How do AI advertising algorithms work?

When an advertising opportunity becomes available, platforms evaluate multiple signals to determine which eligible advertisement should appear.

Depending on the platform and campaign, these signals may include:

• Campaign objectives and budget.
• Advertising creative and messaging.
• Search intent or page context.
• Device and placement.
• Historical engagement and conversion signals.
• Predicted likelihood of conversion.
• Expected conversion value.

AI models use available information to estimate which advertising opportunities are most likely to deliver the desired outcome.

This means advertisers increasingly guide algorithms through objectives, creative assets and conversion data rather than manually controlling every individual impression.

AI in Google Ads: Smart Bidding, Performance Max and AI Max

Google Ads includes several AI-powered capabilities, each serving a different purpose.

Smart Bidding uses machine learning to optimise bids towards conversion-focused objectives, such as target cost per acquisition (CPA) or target return on ad spend (ROAS).

Performance Max is a goal-based campaign type that uses Google’s automation to deliver advertisements across eligible Google advertising inventory.

AI Max for Search introduces additional AI-powered optimisation capabilities within Search campaigns, including expanded matching and creative or landing-page optimisation options.

It’s important not to confuse AI Max with Google AI Mode, which is a consumer-facing search experience rather than a Google Ads campaign type.

Advertising can also appear within supported AI-powered search experiences, depending on campaign eligibility, market and available placements.

Despite these advances, successful paid advertising still requires human strategy.

Businesses must define meaningful conversion objectives, supply accurate data, develop effective creative and evaluate performance against commercial outcomes.

An algorithm optimising towards a poorly configured conversion event can become highly effective at generating the wrong result.

For example, imagine a business running a Google Ads campaign designed to generate enquiries.

If the campaign is optimised towards every contact form submission, Google’s algorithms may become effective at generating a high volume of leads – but those enquiries won’t necessarily be commercially valuable.

A better approach would be to distinguish between general enquiries and qualified leads, using reliable CRM data where possible to help the platform understand which conversions contribute to meaningful business outcomes.

The principle is simple: AI can optimise towards the goal you give it, but it’s the marketer’s responsibility to make sure that goal is commercially valuable.

How does Meta use AI in advertising?

Meta uses machine learning throughout its advertising ecosystem, including campaign delivery, audience selection, placement optimisation and creative enhancements.

Its Advantage+ capabilities allow advertisers to automate elements of campaign management and delivery.

Rather than relying exclusively on narrowly defined audience interests, advertisers can increasingly use broader targeting approaches while Meta’s systems identify potential customers based on available signals.

However, automation works best when supported by reliable conversion data.

Two important technologies are:

Meta Pixel: A piece of code installed on a website to help measure relevant visitor activity, subject to applicable privacy requirements.

Conversions API (CAPI): A server-side integration that allows businesses to send permitted conversion events directly to Meta.

These technologies help advertising platforms understand outcomes such as purchases, enquiries and bookings.

They do not guarantee perfect attribution or eliminate consent obligations.

How do cookies influence AI advertising algorithms?

Cookies are small pieces of information stored by a browser that can support website functionality, analytics and advertising measurement.

First-party cookies operate within the context of the website being visited, while third-party cookies are associated with other domains.

Cookies are not themselves artificial intelligence.

Instead, they can contribute data that advertising and analytics systems use for measurement, attribution and optimisation.

AI-powered advertising systems also use other available signals, including contextual information, platform engagement and modelled conversions.

As privacy expectations and tracking restrictions evolve, businesses need to prioritise reliable first-party data and consent-aware measurement.

In the UK, non-essential advertising and analytics cookies and similar technologies generally require informed consent under the Privacy and Electronic Communications Regulations (PECR), alongside applicable UK GDPR obligations.

The important lesson: Better AI advertising performance doesn’t come from handing over complete control to an algorithm. It comes from providing the right objectives, reliable data, effective creative and ongoing strategic oversight.

AI in SEO: How search behaviour is changing

Artificial intelligence is changing not only how marketers work, but how consumers discover businesses.

Historically, search engine optimisation (SEO) focused heavily on helping websites rank prominently in traditional search engines such as Google and Bing.

While those search engines remain important, consumers now discover information across a much wider range of platforms.

These include:

• Google Search, AI Overviews and AI Mode.
• Conversational AI platforms such as ChatGPT, Gemini and Perplexity.
• Voice assistants such as Siri and Alexa.
• Social platforms including TikTok, Instagram, YouTube and Reddit.
• Ecommerce marketplaces such as Amazon.

Consumers can now ask complex questions, compare products and receive recommendations without necessarily visiting individual websites. Research by Semrush found that searches in Google’s AI Mode averaged 7.22 words, compared with just 4 words in traditional Google Search – an increase of approximately 80%. This reflects a shift towards longer, more conversational queries, where consumers can provide greater context about their needs, preferences and purchasing intentions.

This is contributing to the growth of zero-click search.

What is zero-click search?

Zero-click search occurs when someone finds the information they need directly within a search experience without clicking through to an external website.

For example, someone researching the best CRM software for a small business might previously have visited several websites to compare providers.

Today, an AI-powered search experience can summarise features, highlight differences and recommend potential solutions within a single conversation.

The consumer may become aware of several brands without visiting any of their websites.

How is AI search affecting organic website traffic?

Research indicates that AI-powered search experiences are influencing traditional click-through behaviour.

Pew Research Center (July 2025) analysed browsing activity from 900 US adults and found that users clicked a traditional search result in 8% of visits where an AI summary appeared, compared with 15% of visits without one.

Users clicked a source link within the AI summary itself in just 1% of visits.

Similarly, Seer Interactive’s November 2025 research, examining informational search queries across 42 organisations, reported a 61% decline in organic click-through rates for queries featuring AI Overviews over the study period.

Organic CTR for those queries fell from 1.76% to 0.61%.

Importantly, the research also identified declining click-through rates for queries without AI Overviews, suggesting wider changes in search behaviour rather than a trend attributable solely to AI summaries.

These findings don’t mean SEO is dead or that organic website traffic has become irrelevant.

They do, however, demonstrate that achieving a prominent search ranking doesn’t necessarily deliver the same number of website visits it once did.

85% of brands experienced declining organic traffic

The impact is also visible at brand level. Research by Brainlabs, examining organic traffic across 54 client accounts, found that 46 businesses (approximately 85%) experienced a decline in organic sessions, with a collective reduction of 10.5% during the period studied.

However, the research also highlighted an important distinction: while organic traffic declined, referral sessions from AI platforms increased by approximately 163%, and visitors arriving through AI platforms recorded higher rates of meaningful website actions.

This suggests that while AI-powered discovery may reduce the volume of traditional search traffic, some of the visitors who do reach a website may be further along in their decision-making journey.

The findings are based on Brainlabs’ client sample and do not establish that AI caused every decline.

What is AEO and GEO, and how do they differ from SEO?

As AI-powered discovery grows, two terms have become increasingly common: Answer Engine Optimisation (AEO) and Generative Engine Optimisation (GEO).

Discipline	Primary Focus
SEO – Search Engine Optimisation	Improving website visibility within organic search results
AEO – Answer Engine Optimisation	Helping search and answer systems identify clear, useful responses to questions
GEO – Generative Engine Optimisation	Improving brand visibility and representation within generative AI responses

These disciplines are closely connected.

Good technical SEO, helpful content, original expertise and strong brand credibility can support traditional rankings and AI-powered discovery.

Google’s own guidance reinforces that established SEO best practices remain relevant for AI Overviews and AI Mode. There is no special schema or guaranteed optimisation shortcut required to appear in these experiences.

Why brand awareness and digital PR matter more in AI search

If consumers are increasingly discovering businesses through AI-generated recommendations, they may encounter a brand long before visiting its website.

This makes wider brand visibility and authority increasingly important.

Businesses should consider how they appear across the broader digital ecosystem, rather than focusing exclusively on their own websites.

That includes:

• Earning coverage in reputable industry publications.
• Building relationships with relevant journalists and publishers.
• Publishing original research and expert commentary.
• Securing relevant backlinks through digital PR.
• Encouraging genuine customer reviews.
• Maintaining accurate business information across platforms.
• Developing consistent brand messaging and recognition.
• Creating useful content that other sources may reference.

These activities can strengthen brand credibility, support traditional SEO and create additional opportunities for discovery.

However, earning a backlink or third-party mention does not guarantee inclusion in an AI-generated answer.

Research from NP Digital provides further evidence of the relationship between established online presence and AI visibility. In a September 2025 analysis of 1,000 ChatGPT prompts containing external citations, 51.4% of citations came from websites with domains more than 10 years old.

This doesn’t mean newer businesses cannot appear in AI-generated answers, or that domain age is a direct AI ranking factor. Rather, it illustrates the potential advantage of an established digital presence, where businesses have had time to build recognition, earn relevant mentions and develop authority.

For newer businesses, the implication is to invest consistently in reputation-building, digital PR, original expertise and relevant third-party visibility rather than relying exclusively on content published to their own websites.

What is E-E-A-T and why is it important?

E-E-A-T stands for:

• Experience: Demonstrating genuine first-hand experience.
• Expertise: Showing relevant knowledge and subject understanding.
• Authoritativeness: Establishing credibility and recognition within a topic or industry.
• Trustworthiness: Providing accurate, transparent and reliable information.

E-E-A-T comes from Google’s search quality framework. It is not a standalone ranking score or a metadata tag that can simply be added to a website.

For businesses, the practical application is straightforward.

Publish genuinely useful information, demonstrate real expertise, identify authors, substantiate claims, share customer experiences and build a credible reputation.

As AI makes generic content easier to produce, authentic experience and original insight become increasingly valuable differentiators.

From SEO to Search Everywhere

The fundamentals of SEO haven’t disappeared.

In fact, it’s more important than ever that websites are still technically accessible, provide useful information and demonstrate credibility.

What has changed is where consumers search, how they search and how they consume information.

A modern search strategy should consider how a business is discovered across traditional search engines, AI platforms, social media, marketplaces and other relevant digital environments.

We refer to this broader approach as Search Everywhere.

Rather than measuring success exclusively through keyword rankings and website clicks, businesses should also consider brand demand, visibility, customer trust and the commercial outcomes generated across channels.

How should businesses measure SEO and AI search performance?

As more of the customer journey happens outside a brand’s website, traditional SEO metrics don’t always tell the full story.

Organic traffic, keyword rankings and click-through rates remain important, but businesses should also consider:

• Branded search demand: Are more people searching for the business by name?
• AI search visibility: Is the brand being mentioned or cited in relevant AI-generated answers?
• AI referral traffic: Are visitors arriving from platforms such as ChatGPT or Perplexity?
• Brand reputation: What are customers and independent sources saying about the business?
• Lead quality and conversions: Are marketing activities generating meaningful enquiries, bookings or purchases?
• Revenue and commercial performance: Is increased visibility contributing to measurable business growth?

AI visibility measurement is still developing, and results can vary significantly between platforms, prompts and users.

The objective shouldn’t be to replace established marketing metrics with AI-specific scores. It should be to develop a more complete understanding of how customers discover and choose your business.

AI marketing automation: APIs, Zapier and Make

Another major opportunity for businesses is the ability to automate repetitive marketing tasks without requiring extensive programming knowledge.

What is an API?

An application programming interface (API) allows different software systems to exchange information or request actions through a defined interface.

For example, an API can allow a reporting system to retrieve advertising performance data or a CRM to receive information from a website enquiry form.

APIs are fundamental to many marketing integrations.

They can also have permissions, usage limits and associated costs.

What’s the difference between automation and AI automation?

Traditional automation follows predefined rules.

For example: When a customer submits a website form, create a new CRM record and notify the sales team.

This process doesn’t necessarily involve artificial intelligence.

AI-assisted automation adds a model capable of interpreting or generating information.

For example: When a customer submits a website form, create a CRM record, use AI to categorise the enquiry, identify its likely priority and prepare a suggested follow-up for human approval.

The second workflow uses AI to make sense of the information rather than simply transferring it.

How can marketers use Zapier and Make?

No-code and low-code platforms such as Zapier and Make.com allow users to connect applications through visual workflow builders.

Common marketing applications include:

Lead management: Capture website enquiries, validate information, update a CRM, categorise leads and notify the relevant person.

Marketing reporting: Pull information from multiple platforms into a central spreadsheet or dashboard and prepare performance summaries.

Customer feedback analysis: Group survey responses into common themes and identify recurring opportunities or complaints.

Content workflows: Move approved briefs through drafting, editing, approval and scheduling stages.

Email marketing: Update customer segments or trigger appropriate communications based on permitted customer activity.

AI can also help marketers troubleshoot workflows, understand API documentation and generate small pieces of code when needed.

However, automation still requires careful planning.

A poorly designed workflow can repeat the same mistake across hundreds or thousands of records.

Businesses should establish appropriate permissions, error handling, monitoring and human approval processes before automating important customer-facing activities.

AI content creation: Opportunities, limitations and brand responsibility

Generative AI has dramatically reduced the time required to produce initial content concepts, drafts and creative variations.

For marketers, this can be valuable.

However, producing content faster doesn’t automatically mean producing better marketing.

How can AI support content creation?

AI can assist with:

• Blog outlines and initial drafts.
• Social media content ideas.
• Advertising copy variations.
• Email marketing concepts.
• Campaign messaging.
• Image and video concepts.
• Translations and accessibility descriptions.
• Repurposing existing approved content into different formats.

For example, a marketer might use AI to turn a detailed customer case study into suggested social posts, email content and advertising concepts.

This can accelerate production while allowing the marketer to concentrate on quality, differentiation and strategic relevance.

Should businesses use AI-generated images in advertising?

AI-generated imagery can be useful for developing concepts, producing illustrative assets and exploring creative directions.

However, businesses need to consider how those images represent their products, services and customers.

An AI-generated image of a restaurant meal, for example, could mislead consumers if it appears to represent an actual dish served by the business but differs materially from reality.

Similarly, fabricated testimonials, endorsements, product results or customer experiences can create significant reputational and regulatory risks.

Businesses should also consider intellectual property, likeness rights, accessibility and the commercial-use conditions of their chosen tools.

What are the UK rules around AI-generated advertising?

There is no blanket UK requirement that every advertisement must be labelled simply because AI was used to create it.

However, AI-generated advertising must still comply with applicable advertising and consumer-protection requirements.

The UK Advertising Standards Authority (ASA) and Committee of Advertising Practice (CAP) regulate advertising claims and misleading marketing communications.

Businesses must ensure that advertisements are truthful, appropriately substantiated and not misleading.

Specific platform policies or circumstances may also require disclosure of synthetic or altered content.

In February 2026, the Advertising Association published a best-practice guide for the responsible use of generative AI in advertising, developed through the Government and industry-led Online Advertising Taskforce.

Why AI-generated content still needs a human touch

AI is particularly effective at generating plausible content quickly.

But it doesn’t inherently understand a business’s customers, commercial priorities, lived experiences or brand identity in the same way its people do.

Without human involvement, businesses risk publishing content that is generic, inaccurate, repetitive or inconsistent with their positioning.

Every AI-assisted content workflow should include appropriate checks for:

• Factual accuracy.
• Originality and value.
• Brand tone of voice.
• Customer relevance.
• Legal and advertising claims.
• Visual accuracy.
• Accessibility and inclusivity.
• Copyright and usage permissions.

Our view is that AI should accelerate the production process, not replace the thinking behind it.

The risks and limitations of AI in marketing

While AI presents substantial opportunities, businesses also need to understand its limitations.

AI hallucinations: Why confident answers can be wrong

Large language models generate plausible responses based on learned patterns.

They do not automatically verify every statement before presenting it.

This means an AI system can confidently provide incorrect information, invent a source, misinterpret data or present outdated information as current.

These errors are commonly described as AI hallucinations.

Even when an AI platform can retrieve information from the internet, the sources it finds may be outdated, incomplete or misunderstood.

The age of information is another important consideration. In July 2025, Ahrefs analysed approximately 17 million citations across AI search platforms and found that AI assistants cited pages averaging 2.9 years old, compared with 3.9 years for traditional Google organic search results.

This doesn’t necessarily mean the information was outdated or inaccurate. However, it reinforces the importance of checking publication dates, recent updates and the relevance of sources, particularly when researching rapidly changing subjects such as advertising platforms, legislation and technology.

This is particularly important when using AI for competitor research, advertising claims, financial forecasts, legal information or customer communications.

And this isn’t simply a theoretical concern.

Research published by Neil Patel in 2026, which included a survey of 565 marketers, found that 47.1% encountered AI inaccuracies several times a week. More concerningly, 36.5% reported that incorrect or hallucinated AI-generated content had made it into published work.

The same research found that more than 70% of marketers spent between one and five hours each week fact-checking AI-generated outputs.

These findings demonstrate why AI-generated content shouldn’t be treated as finished work. While AI can significantly accelerate production, the time saved must be balanced against the need for accuracy, quality assurance and human review.

Other risks businesses should consider

Data privacy: Sensitive customer or business information should not be entered into unapproved AI tools without appropriate safeguards.

Bias: AI systems can reproduce patterns and biases found in their training data.

Brand consistency: Excessive reliance on AI-generated content can weaken a distinctive brand voice.

Copyright and ownership: Businesses must understand the rights and restrictions associated with generated content and supplied reference materials.

Misleading analysis: AI can produce convincing explanations of performance that aren’t supported by the underlying data.

Automation failures: Incorrect workflows can repeat mistakes at scale.

How to use AI responsibly in marketing

A practical approach is to establish a clear human-in-the-loop process.

As a team of two at The Business Tea (for now!), AI and automation play an important role in how we operate.

They help us streamline internal processes, accelerate research, organise information and deliver high-quality work for our clients without needing a much larger team.

But there’s one principle we don’t compromise on: if something has been created, amended or even touched by AI, it receives a full human review before it’s used.

We use AI to make our work more efficient, not to remove the expertise, accountability or judgement behind it.

The future of AI in marketing: Predictive, reasoning and agentic AI

AI development is moving beyond tools that simply generate content or predict individual outcomes.

Increasingly capable systems can perform multi-step tasks, use external tools, interpret information across different formats and assist with complex workflows.

What is reasoning AI?

Reasoning-oriented AI systems are designed or trained to perform better on tasks requiring multiple steps of analysis, planning and problem-solving.

They still rely on learned computational mechanisms and can make mistakes.

They should not be understood as independently rational or infallible systems.

What is agentic AI?

Agentic AI describes systems that can plan or carry out sequences of actions using connected tools, subject to permissions and controls.

For example, a future marketing workflow might involve an AI assistant retrieving campaign performance, identifying an anomaly, investigating possible explanations and preparing recommended actions.

With suitable integrations and permissions, an agent may also carry out approved steps within connected platforms.

This introduces opportunities for efficiency, but also makes governance, security and accountability increasingly important.

Why is there a global AI race?

Technology companies and research organisations around the world are competing to develop more capable, efficient and commercially useful AI systems.

This competition spans computing infrastructure, model capabilities, specialist talent, energy, regulation, security and business adoption.

For marketers, the important implication is not necessarily which provider develops the most advanced model.

It’s how improving capabilities will change consumer expectations, business processes, digital discovery and the competitive landscape.

Businesses should prepare for a future in which AI assists not only with researching products and services, but potentially with comparing suppliers and completing actions on a customer’s behalf.

How to introduce AI into your marketing strategy

For businesses wondering where to begin, the best approach is not to adopt every available AI tool.

Instead, start with clear commercial objectives and identify where AI can deliver measurable value.

1. Audit your current marketing activity

Review existing processes, technology, reporting and performance.

Identify tasks that are repetitive, time-consuming or difficult to scale.

Also consider where AI may already be influencing performance through advertising platforms, CRM systems or analytics tools.

2. Identify high-value use cases

Prioritise opportunities that address a genuine business need.

This might include reducing reporting time, improving lead handling, strengthening customer insights or supporting content production.

Start with manageable, lower-risk applications before introducing more complex automation.

3. Review your data and measurement

AI systems are only as useful as the information and objectives supporting them.

Check that conversion tracking is accurate, customer information is appropriately managed and performance measures reflect meaningful business outcomes.

4. Establish guidelines and responsibilities

Define which tools can be used, what information may be shared, who approves outputs and how results should be checked.

Ensure AI-assisted work remains consistent with your brand and applicable legal obligations.

5. Test, measure and improve

Run controlled pilots and compare results against existing processes.

Consider both efficiency and effectiveness.

For example, an AI workflow might save five hours of reporting time each month, but that saving is only valuable if the resulting information remains accurate and useful.

6. Integrate AI into your wider marketing strategy

AI should support your marketing objectives rather than become an isolated activity.

The strongest opportunities often emerge when AI is combined with good strategy, reliable data, effective creative and an understanding of customer behaviour.

The Business Tea’s perspective: AI is a tool, not a marketing strategy

At The Business Tea, we believe businesses should be exploring how AI can support their marketing, but not at the expense of the fundamentals. At the end of the day, everyone (including your competition) is using AI – and you should too… responsibly and where it’s necessary. 

Do you need AI to work out a calculation? No… use a calculator. Can you use AI to get ahead and save time? Yes.

Understanding your audience, developing a compelling proposition, creating a differentiated brand and measuring meaningful commercial outcomes remain essential.

AI can help businesses research faster, automate repetitive processes and make better use of available information.

It can also improve the efficiency of advertising platforms and create new opportunities for reaching consumers.

But it cannot compensate for an unclear strategy, poor customer understanding or unreliable data.

Perhaps the biggest change businesses need to prepare for is not simply how AI helps marketers produce work.

It’s how AI is changing the way customers discover information, evaluate businesses and make purchasing decisions.

That requires a broader view of marketing: one that combines SEO, paid media, brand building, digital PR, content, automation and commercial measurement.

The businesses best positioned to benefit from AI will be those that combine technological capability with genuine expertise, strong brands and sound strategic judgement.

If you’re exploring how AI fits into your marketing, or want to understand how changes in search and digital advertising could affect your business, get in touch with us to discuss your objectives.