AI in marketing: How AI is changing the future of marketing

AI has evolved from being an experimental add-on for marketing teams to becoming a core part of how many brands and marketers plan, create, deliver and evaluate their work. Professionals at every level, from students considering a marketing career to experienced chief marketing officers, are seeking to understand what this shift means for them.

Generative AI adoption among marketing teams has grown from roughly 50% in 2024 to nearly 9 in 10 teams using it in at least one workflow today. This represents one of the fastest technology adoption curves marketing has experienced in recent years.

However, adoption is only the beginning of the story. We will explore how AI is being used in marketing, its benefits and limitations, and what its continued development could mean for brands and marketing professionals.

How is artificial intelligence (AI) used in marketing?

In discussions about AI, three important forms of the technology (descriptive AI, predictive AI, and generative AI) are often confused or treated as interchangeable.

Descriptive AI identifies and summarises patterns in historical and current data. It helps marketers understand what has happened and what is happening across customers, campaigns and markets.
Predictive AI uses patterns learned from data to forecast likely outcomes, such as campaign performance, customer demand or the probability that a customer will leave.
Generative AI creates new content, including text, images, video and audio, based on patterns learned from large datasets. A tool that drafts a social media caption from a short brief is an example of generative AI in action.

Each form of AI supports a different aspect of marketing. Understanding these distinctions makes it easier to see how AI fits into a marketer’s day-to-day work.

In practice, AI now supports every stage of the marketing process:

Research: Descriptive AI can process large volumes of customer and market data and identify patterns much faster than manual analysis, using platforms such as Adobe Analytics or Google Analytics.
Strategy: Predictive AI can help forecast how a campaign, audience segment or channel mix is likely to perform, using tools such as Google Analytics 4 or Salesforce Einstein.
Customer analytics: Predictive AI can estimate customer behaviour, such as which segments are most likely to purchase, disengage or churn.
Campaign execution: Descriptive and predictive AI can support faster campaign decisions by adjusting bids and directing advertising budgets towards the audiences and placements performing most effectively, as seen in tools such as Google Ads Smart Bidding.
Reporting: Generative AI can help organise performance data and produce initial summaries, reducing the time required to prepare routine reports. Examples include Microsoft Copilot and AI-assisted reporting tools within major analytics platforms.
Content generation: Generative AI tools such as ChatGPT, Adobe Firefly, Canva Magic Studio and Mailchimp can assist with initial drafts of blog posts, advertisements, images, social media content and email copy.
Workflow automation: Generative AI, particularly when combined with agentic AI, can support repetitive and multi-step tasks such as follow-up emails, customer tagging and campaign administration through platforms such as Salesforce Marketing Cloud, HubSpot and Zapier.

The myth of AI replacing marketers altogether

In practice, the shift is less dramatic than predictions of widespread job replacement often suggest. Marketing teams that have incorporated AI into their workflows report saving several hours each week. This is time that can be redirected towards activities that AI cannot independently manage well, including strategy, creative direction, stakeholder engagement, and team collaboration.

AI may handle more repetitive execution, but marketers still decide what is worth executing, why it matters and whether the result is appropriate for the brand and its customers.

The benefits and limitations of AI for marketers

Benefits

Improved efficiency: Tasks that once took hours, such as preparing a performance report or developing the first version of a campaign, can often be completed in minutes with AI assistance.
Stronger personalisation at scale: AI can tailor messages to individual customer segments, or even individual customers, at a scale that would be difficult to manage manually. For example, a retail brand could adjust email content according to each customer’s browsing and purchasing history rather than sending the same message to an entire mailing list.
Improved customer experience: Faster response times, more relevant recommendations, and AI-assisted customer support can help customers find what they need with less friction.

Limitations

AI systems still require people to review their outputs and decisions, particularly in relation to the following areas:

Accuracy and bias: AI systems can get things wrong and reflect biases present in the data they were trained on. A marketer still needs to review AI-generated content or AI-driven audience targeting accuracy.
Privacy: AI-powered personalisation depends heavily on customer data. Organisations have a responsibility to ensure that this data is collected, stored and used transparently, securely and lawfully.

Human input matters

AI should be used to support marketers rather than replace their professional judgement. Teams are most likely to gain value from AI when they combine it with clear human oversight, subject-matter expertise, and accountability.

This is also consistent with emerging research suggesting that experienced humans can continue to outperform AI in some creative tasks, including aspects of the Alternate Uses Test (divergent-thinking exercise where participants generate as many uses as possible for a common object, used to measure creative idea generation). AI can accelerate ideation, but human experience, context and judgement remain essential to evaluating whether an idea is meaningful, original and appropriate.

AI marketing trends – What's next?

AI agents and workflow automation

AI agents extend beyond basic automation by undertaking multi-step tasks with a degree of autonomy. For example, an AI agent could retrieve campaign data, analyse the results and prepare a draft presentation summarising its findings—a process that might otherwise take a marketer several hours.

The adoption of agentic AI remains at an early stage. Gartner’s 2026 survey of technology leaders found that approximately 17% of organisations had deployed AI agents, while more than 60% expected to do so within the following two years. This suggests that agentic AI may experience one of the fastest adoption curves among emerging technologies.

AI-generated content

Generative AI can accelerate the development of initial drafts for written content, images, and video. However, it has not removed the need for human review. Marketers using AI-assisted content must still edit, verify, and approve the material before it is published.

The ease of generating content also increases the importance of originality, authenticity, and brand consistency. Producing more content does not necessarily mean producing better or more effective content.

Answer Engine Optimisation (AEO)

AI-generated answers are changing how people search for and consume information. As a result, Answer Engine Optimisation (AEO) has emerged alongside traditional Search Engine Optimisation (SEO).

AEO focuses on making content clear, authoritative, and easy for AI-powered search systems to interpret and reference when generating direct answers. This involves structuring information logically, answering common questions clearly and developing reliable authority signals that make a source credible and worth citing.

How will AI impact the future of marketing?

AI is changing marketing roles rather than eliminating the marketing function altogether. The activities most exposed to automation are generally execution-heavy tasks, including manual reporting, basic advertising copy and repetitive campaign administration.

Roles centered on strategy, brand judgement, creativity, customer empathy and stakeholder relationships are likely to remain more durable because these areas continue to depend heavily on human understanding and contextual judgement.

A McKinsey report estimates that agentic AI could eventually support as much as two-thirds of current marketing activities, including content generation, synthetic audience testing and media planning. The greatest pressure may initially be felt in entry-level positions where responsibilities are concentrated around content drafting, routine data extraction and campaign administration.

At the same time, new roles are emerging alongside those being transformed or automated. Demand is growing for professionals who can oversee AI systems, provide effective instructions, evaluate outputs and manage marketing operations that combine creative judgement with technical capability.

For people entering marketing, as well as those already working in the profession, the direction is increasingly clear: AI fluency is becoming a foundational marketing capability. Knowing how to brief an AI system, guide its work, question its recommendations and verify its outputs is becoming a standard professional expectation.

Our Marketing courses are designed with this shift in mind, helping students develop the strategic judgement and commercial mindset required for the next phase of marketing.

AI in marketing: How AI is changing the future of marketing

Table of contents

How is artificial intelligence (AI) used in marketing?

The myth of AI replacing marketers altogether