Organisations are rapidly adopting AI-powered technologies to drive business value – few are willing to risk being left behind in the AI revolution. According to Forrester, 84% of AI decision-makers report that their executives are keen to embrace generative AI.
Marketing is among the functions best positioned for AI-driven transformation, largely due to its ongoing reliance on digital marketing data, which provides an ideal foundation for data-trained AI tools. A recent report by PwC suggests that AI-enabled marketing is expected to drive 45% of the total global economy by 2030.
New AI-driven marketing tools are emerging daily, each promising to help marketers work more quickly, intelligently, and efficiently. The challenge for organisations, however, lies in distinguishing between superficial gimmicks seeking to capitalise on the AI trend and genuinely valuable technologies that deliver measurable impact.
This article provides a comprehensive overview of AI in marketing, from its benefits and underlying technologies to practical applications, offering the insight needed to develop an effective AI-driven marketing strategy.
What is AI Marketing?
Artificial intelligence (AI) marketing refers to the use of advanced AI technologies and tools, such as data models, algorithms, and machine learning, to generate customer insights, design and execute marketing campaigns, create custom marketing content, and personalise the customer journey. The goal of AI marketing is to improve marketing performance, increase operational efficiency, and reduce costs by automating manual tasks and accelerating data analytics.
Common examples of artificial intelligence in digital marketing include:
- Chatbots: Automated assistants that respond to customer enquiries in real time.
- Recommendation engines: Systems that suggest products or content based on user behaviour and preferences.
- Targeted advertising: Advertisements tailored to individuals’ search history and online activity.
- Dynamic pricing: Flexible pricing strategies used by e-commerce platforms that adjust according to demand and other factors.
In essence, AI marketing tools leverage advanced technologies and data analysis to execute marketing tasks that would otherwise be highly time-consuming, or even unfeasible for humans, such as uncovering patterns within large-scale datasets.
Types of AI Marketing Solutions
In the business landscape, the term “AI” is frequently used to refer to a wide range of technologies capable of processing vast volumes of data and making autonomous decisions. Below is a brief overview of the actual artificial intelligence tools most commonly employed in marketing.
Machine Learning and Large Language Models
Machine learning (ML) enables systems to learn and improve independently through the use of neural networks. By inputting large volumes of data into ML algorithms, financial institutions can train models to address specific challenges and generate insights that support continuous improvement. In marketing, machine learning enhances a variety of activities, including customer segmentation, personalised marketing, and predictive analytics.
For example, machine learning algorithms can act as an AI-driven marketing tool to identify distinct customer segments based on behaviour, preferences, and demographic data. This enables marketers to tailor their strategies more effectively to specific audience groups.
Large language models (LLMs), such as GPT-4, offer valuable support to marketing teams across a range of functions, including content creation, customer service, and sentiment analysis. LLMs can generate high-quality content for web pages, social media, and email campaigns. They are capable of writing product descriptions, crafting compelling headlines, and creating full-length articles or customer-focused guides. In addition, LLMs also power customer service chatbots that deliver instant, automated responses to customer enquiries.
Natural Language Processing
Natural language processing (NLP) powers AI-driven marketing tools by enabling teams to interpret text written in a natural, human language – meaning any content created by people for other people. This technology is used to analyse social listening data, emails and text conversations, as well as marketing materials from previous campaigns.
Brands can leverage sentiment analysis to monitor social media and assess public perception of their products and campaigns. By examining customer feedback, they are able to identify emerging trends, gain insights into customer emotions, and make informed decisions to refine their digital marketing strategies.
Marketing teams can also apply NLP to analyse large volumes of text data from various sources, including competitor websites, product reviews, and industry reports. This provides a deeper understanding of market trends, competitor strategies, and consumer preferences – insights that can inform both marketing tactics and product development.
Semantic search
Semantic search algorithms form a core component of NLP. They identify and group relevant keywords into semantic clusters while also eliminating duplicates in text mining, resulting in more precise text analysis. This technology plays a crucial role in both AI writing tools and search engine optimisation.
For example, marketing teams using semantic search can analyse customer reviews to identify common themes and sentiments relating to their product and overall customer experience. If customers consistently mention phrases such as “fast delivery” and “customer service”, the algorithm groups these together, enabling the team to emphasise these strengths in future marketing campaigns.
Computer vision
Computer vision allows AI marketing tools to gain insights from non-text digital content, such as images. It powers technologies such as character recognition (OCR), enabling the analysis of information and signatures on cheques, the identification of brand logos in video content, and the extraction of text from images for accessibility.
Retailers can utilise computer vision to analyse customer photos shared on social media. By identifying and tracking their products within these images, they can assess product popularity and customer preferences, enabling more targeted marketing strategies and refined product offerings.
Named entity recognition (NER) and neural networks
Named Entity Recognition is an NLP technique used to identify and classify specific entities within text, such as names of people, organisations, locations, dates, and other defined categories. The process involves parsing text to detect these entities and assigning them the appropriate labels based on their context and type.
For example, NER can detect personal names and preferences within customer data, enabling digital marketers to develop personalised email campaigns or product recommendations. Identifying that a customer frequently references “New York” and “vegan restaurants” allows for more targeted offers aligned with those interests.
Neural networks offer powerful models capable of understanding and processing natural language with a high degree of accuracy. They retain interconnected data points and continuously expand their knowledge base. This ongoing process enables ML models to deliver increasingly precise results and to analyse consumer data more effectively over time through deep learning.
Generative AI
Generative AI tools utilise AI technologies – such as neural networks, machine learning, and natural language processing – to produce original, data-driven content. These tools support marketing teams by automating content creation across multiple marketing channels, enhancing personalisation, and improving the effectiveness of campaign strategies.
For instance, an e-commerce business could utilise generative AI to support its digital marketing campaigns. This might involve sending personalised discount offers and product recommendations to customers, ensuring that marketing emails appear tailored and relevant to each recipient’s interests and purchasing behaviour.
How Can AI Be Used in Marketing?
This section outlines the key use cases for AI in marketing and examines how the technologies mentioned above deliver practical benefits for marketing teams.
Marketing data analysis
AI is widely utilised to analyse marketing data, offering valuable insights into both customer behaviour and campaign performance. By examining large datasets, AI algorithms can identify patterns in behaviour, preferences, and purchasing habits, helping organisations better understand what influences customer decisions and how individuals engage with the brand.
Additionally, AI analyses historical marketing campaign data to evaluate performance. This involves tracking key digital marketing metrics such as click-through rates, conversion rates, and return on investment (ROI). AI supports the identification of areas requiring improvement and uses performance data to optimise live campaigns in real time. For example, machine learning algorithms can adjust advertisement targeting, bidding strategies, and content dynamically based on current results, in order to maximise effectiveness.
Moreover, AI can detect emerging trends and shifts in consumer behaviour by analysing publicly available big data sources, such as social listening data and market research. This enables marketers to stay ahead of the curve and adapt their strategies in line with evolving market conditions.
Audience segmentation
AI marketing tools can effectively group customers according to their behaviour, demographics, and preferences, resulting in more accurate market segmentation. This, in turn, enables the development of tailored marketing strategies that better resonate with distinct customer segments and target audiences.
Lookalike modelling is a technique in which technology identifies shared traits and behaviours among an organisation’s most valuable customers, based on factors such as preferences and purchase history. This insight enables businesses to target new prospects with similar characteristics who are more likely to engage with their products or services.
Content generation
Generative AI tools are increasingly used in marketing to support a wide range of content creation tasks – ranging from planning complex marketing campaigns to designing and drafting copies for emails and landing pages.
Generative AI enhances marketing campaigns by generating engaging communications tailored to each stage of the customer journey. It can craft compelling email subject lines and personalised content aligned with specific buyer personas, enabling intent-driven interactions that elevate the customer experience and support stronger sales performance. Moreover, modern AI marketing platforms are capable of generating audio, images, and video to enrich campaigns with high-quality audiovisual assets.
Customer personalisation
AI can support the mapping of the customer journey by analysing interactions with the brand across various touchpoints. This provides a comprehensive view of how customers engage, enabling the development of marketing campaigns tailored to specific stages of the journey and targeted customer segments.
AI can also make use of customer data insights to deliver highly personalised content and offers. For example, AI-driven marketing tools can support the creation of content that reflects diverse social perspectives, helping to ensure messaging is both inclusive and accessible to a broader target audience. This might include personalised email campaigns, product recommendations, and targeted advertising, each tailored to individual preferences and behaviours.
Marketing automation
AI marketing tools are increasingly used to automate various digital marketing tasks and processes:
In email marketing, AI can segment mailing lists based on user behaviour and preferences, automating the creation of personalised email content for each segment. AI algorithms analyse previous email interactions to determine the most effective times to send messages to individual recipients, helping to boost open rates and engagement. Meanwhile, AI-driven chatbots and automated email responders manage common customer enquiries and provide immediate assistance, enhancing the efficiency of customer service operations.
AI-powered automated digital marketing campaigns deliver personalised content dynamically, based on user behaviour, preferences, and real-time interactions. Additionally, AI streamlines the process of A/B testing various campaign elements, such as headlines and calls to action, by automatically analysing performance data to determine the most effective variations.
Finally, AI supports the automation of lead generation and nurturing by evaluating and scoring prospects according to their behaviour and interactions, allowing high-potential leads to be prioritised for follow-up. AI also creates and delivers targeted content and offers based on levels of engagement, helping to guide leads through the sales funnel with minimal manual input.
Media buying and advertising
AI supports media buying by automating the placement of advertising through programmatic platforms. This technology optimises advertising spend and targeting in real time by drawing on customer history, preferences, and contextual data to deliver more relevant advertising and improve conversion rates. Users of platforms such as Google Ads will likely be familiar with similar AI-driven features that assist with the auction process.
AI not only refines targeting and bidding strategies; it can also adjust advertising creatives in response to performance data to improve return on investment.
Moreover, AI is particularly effective in retargeting, as it continuously learns from each customer interaction or conversion. This ongoing optimisation enables the delivery of more tailored content and marketing strategies, leading to more meaningful engagement and higher conversion rates.
Reputation management
AI supports reputation management by monitoring online brand mentions across various platforms, including social media, review sites, and news outlets. It uses sentiment analysis to assess public perception and identify potential risks or opportunities.
AI marketing tools can issue real-time alerts in response to negative sentiment or emerging trends, enabling timely action and more proactive reputation management. In addition, AI can assist in crafting and distributing positive content to strengthen the brand's image.
Competitive intelligence
AI can assist in analysing competitors’ digital marketing strategies and evaluating their effectiveness. By examining their digital presence, including social media activity, advertising campaigns, and content performance, AI helps to identify emerging trends, successful tactics, and potential gaps in competitors' approaches.
For example, AI may identify that a competitor's advertisements centred on sustainability are performing particularly well, encouraging the incorporation of similar themes into future campaigns to maintain a competitive edge.
Benefits of AI in Marketing
One of the analyst reports on AI in marketing, published in 2023, included the following insights from a business leader: “Clients are not viewing genAI as a technology solution. They are seeking smarter, more efficient, more precise ways of accomplishing tasks, which AI can provide.”
The advantages offered by AI marketing are the main area of interest for many digital marketers and business leaders. This section presents a brief overview of the most notable benefits AI marketing can deliver for businesses.
Effective marketing decisions supported by data
AI is particularly effective at analysing data and extracting valuable insights from unstructured sources, turning them into actionable intelligence that can inform digital marketing strategy. This capability enables marketers to evaluate across diverse marketing channels, such as social media posts, customer reviews, emails, and website interactions, that traditional methods may struggle to process efficiently.
This data-driven approach ensures that marketing strategies are based on accurate, up-to-date information and carefully tailored to align with customer needs and expectations, ultimately resulting in more effective campaigns and improved customer satisfaction.
Higher ROI for your campaigns
AI marketing tools enable marketers to identify actionable insights from campaign data with near real-time efficiency. They can also help determine the most effective channels for media buying and the optimal advertising placements, based on customer behaviour.
As part of this process, AI-driven attribution modelling allocates credit to various marketing touchpoints, providing insights into the effectiveness of various channels and campaigns. Moreover, AI-powered predictive analytics can forecast customer behaviours – such as likelihood of churn and purchase, enabling the development of personalised engagement and retention strategies.
Overall, these capabilities support more efficient campaigns, helping to reduce costs and optimise budget allocation.
Increased customer engagement and loyalty
According to Forrester’s Marketing Survey 2023, 77% of global B2B marketing decision-makers agree that buyers and customers now expect a personalised experience across sales and marketing Interactions.
AI tools empower marketers to elevate personalisation by enabling the customisation, repurposing, and continuous updating of the content to better align with customer needs. These tools support the rapid creation of content variations and allow teams to locate assets or content components through real-time, AI-powered search, driven by large language models fine-tuned on internal data.
The resulting personalised content and interactions contribute to stronger customer relationships and foster long-term loyalty.
Enhanced productivity through automation
AI in marketing enhances business productivity by automating routine tasks, such as email marketing, social media posting, and advertising management. This allows marketing professionals to focus on more strategic initiatives, thereby improving overall efficiency.
Improved transparency across marketing activities
AI enhances transparency in marketing activities by providing detailed insights and real-time analytics. AI agents can quickly collect and analyse large volumes of data, identifying key metrics and performance indicators. This data is then used to generate comprehensive reports that evaluate campaign effectiveness, customer behaviour, and ROI.
With AI, marketers can more easily track and assess the effectiveness of their strategies, enabling informed decision-making and providing clear visibility across all marketing initiatives.
Increased profit
Well-executed campaigns, driven by advanced data and stronger customer engagement, play a key role in driving profitability. The combination of accurate targeting, enhanced engagement, and greater operational efficiency ultimately contributes to increased revenue growth and improved financial performance.
9 Strategies for Implementing AI in Marketing
1. Define objectives
Before getting started, it is essential to define the goal or objective to be achieved. Is the aim to improve the effectiveness of marketing campaigns? Or perhaps to accelerate specific marketing activities and increase the overall volume of operations?
It may also be worth beginning by prioritising a particular area of marketing when introducing AI – whether that is email marketing, social media, or marketing analytics. By focusing on one area, the implementation of AI becomes more manageable and allows for a trial period to assess how it can add value to the marketing team.
2. Understanding the limitations of generative AI
Many marketers commend AI for its ability to generate personalised content, such as text and video. However, it is important to acknowledge the current limitations of these tools, particularly in relation to content quality. For example, Generative AI still struggles to produce accurate visual details, such as realistic human fingers in images.
AI should be applied selectively, focusing on areas where it can deliver high-quality results, while avoiding reliance in situations where its performance may be limited.
3. Audit infrastructure and ensure robust data management
Before adopting an AI marketing platform, it is essential to evaluate existing tools and infrastructure to identify opportunities for AI integration.
It may be beneficial to produce a report outlining potential areas for implementation, expected outcomes, and the resources required. As AI capabilities largely depend on the information on which they are trained, it is important to assess the quality, quantity, and accessibility of valuable data. Reliable, well-structured inputs enable AI systems to generate valuable insights and support informed strategic decisions.
4. Adhere to data privacy laws
Compliance with data protection laws is crucial when deploying AI in marketing, as the use of customer data for training and implementation must not violate privacy regulations. One of the greatest challenges facing AI marketing solutions is ensuring that customer data is managed both securely and ethically. It is imperative that organisations prioritise the protection of customer privacy throughout the AI training process in order to avoid substantial fines and potential legal consequences.
To address this challenge, fostering a culture of ethical AI within the organisation is essential. This includes updating internal policies and procedures to support transparent data practices and ensure compliance with data protection legislation. Implementing clear opt-in and opt-out mechanisms, along with effectively communicating how data is used, are key measures in safeguarding customer information.
5. Prioritise AI governance
Before implementing AI in marketing, brands should prioritise governance to mitigate risks and manage costs. Robust governance includes safeguarding consumer data privacy, complying with copyright regulations, and incorporating human oversight to maintain content quality.
By putting strong governance measures in place, organisations can protect sensitive data, minimise legal risks, and uphold the integrity and effectiveness of AI-generated content.
6. Select the right AI marketing tools
Choose an AI marketing tool that aligns with organisational objectives, enhances marketing initiatives, and supports compliance with data privacy regulations. When evaluating potential vendors, consider the following:
- Functionality: Ensure the tool provides core features such as predictive analytics, personalisation, content generation, customer segmentation, and campaign automation.
- Customisation: Select tools that offer flexibility to adapt to specific marketing requirements and workflows. For more advanced customisation, it may be worth investing in a license for an AI no-code platform. For example, Creatio AI enables organisations to design bespoke AI use cases to automate marketing processes – without the need for software development expertise.
- Integration: Check if the tool integrates seamlessly with your existing marketing platforms, CRM systems, and other tools from your tech stack.
- Ease of use: An intuitive interface that allows for straightforward navigation and minimal training requirements.
- Scalability: The platform should be capable of adapting to future technological developments and evolving marketing strategies.
- Pricing structure: Understand the pricing model and ensure it aligns with available budget allocation.
- Industry relevance: Assess whether the tool has a proven track record within the relevant industry or sector.
7. Provide staff training
When implementing AI marketing within an organisation, it is essential to ensure that the marketing team possesses the appropriate training and expertise to make effective use of these advanced tools. The first step is to assess the team’s current capabilities. This may involve investing in upskilling existing staff, engaging a consultant, or establishing a role to lead AI initiatives.
This shift should be positioned as an opportunity for teams to reskill, adopt emerging technologies, and evolve into more strategic marketers. As one Vice President at a generative AI company told Forrester, “You need your current team — just in different ways. With generative AI, you shift the focus from just producing words to brainstorming, conducting interviews, refining, and editing. It’s about creating quality content with diverse skills.”
Offering comprehensive training on how AI-driven natural language generation tools function can also encourage teams to identify new AI use cases, fostering wider adoption and integration of AI across the organisation.
8. Test AI tools
Finally, it is time to put AI marketing initiatives to the test. Select the prior areas for implementation and proceed with launching the chosen programmes. Establish a clear timeframe and define target KPIs to evaluate performance following the trial period.
For example, when testing AI-generated and AI-placed social media advertisements, consider running a one-month trial. Continuously monitor and refine the content throughout, while keeping a detailed record of the process.
Following the trial, evaluate the effectiveness of AI-generated, human-generated, and AI-assisted content, and use these insights to inform a strategy for moving forward.
9. Train brand-specific models
When supported by an AI marketing platform, custom models can be trained using proprietary data. A comprehensive dataset – that includes past campaigns, emails, messages, website copy, and internal branding – enables the generation of tailored recommendations that reflect the organisation’s unique tone and style.
A robust data infrastructure is vital for the effective deployment of models and the generation of high-quality content, particularly as brands begin to implement dedicated instances of language models.
Implementation Challenges of AI in Marketing
Training AI solutions:
Custom AI models require extensive training to perform specialised tasks. For example, deploying AI to engage customers effectively typically demands access to substantial customer data and may also require data scientists to train the model.
Data quality and accuracy:
The effectiveness of AI depends largely on the quality of the data it is trained on. Inaccurate, incomplete, or poor-quality data can result in unreliable outputs. While generative AI is highly advanced, it can still produce content containing factual inaccuracies. Ongoing human oversight remains essential to ensure accuracy and uphold brand consistency.
Compliance with privacy laws:
AI relies on personal data, making strict compliance with privacy legislation essential. Failure to adhere to such regulations can lead to significant fines and reputational damage. It is therefore vital to follow frameworks such as the UK GDPR and invest in robust AI governance to safeguard consumer information.
Copyright and legal concerns:
Generative AI has the ability to produce content that closely resembles existing works by drawing on proprietary data analysis, which raises important copyright considerations. As the legal status of AI-generated content continues to evolve, it is essential to understand how copyright law applies and to ensure any content created remains original and compliant.
The Future of AI in Marketing
Looking ahead, marketers are expected to depend increasingly on AI to derive insights from unstructured data and to harness proprietary data to guide generative AI in producing customer-focused content that aligns with their brand identity.
AI marketing is set to evolve in response to the limitations of current AI models. To deliver more accurate and meaningful business insights, AI tools must be developed to uphold principles of fairness, security, reliability, inclusivity, and transparency. Achieving this demands thoughtful design and training using diverse datasets to eliminate bias.
Further regulations around data privacy, copyright, and AI governance are expected, aimed at ensuring the ethical use of AI technologies. As AI continues to evolve, organisations must prioritise security, particularly with 68% of customers emphasising the importance of trustworthiness. To maintain confidence, brands will need to implement robust privacy safeguards and ensure their data is protected effectively.
Looking ahead, AI has the potential to transform the entire marketing process, automating campaign briefs, content creation, and performance analysis, all while keeping human oversight at the core.
Marketing Creatio
Optimise marketing campaigns and drive leads with one AI-native platform
Unlocking the Potential of AI with Creatio
It is a common perception that implementing AI in marketing demands substantial technical resources. According to Computer Weekly, 20% of technology executives may turn to shadow HR practices to enhance talent acquisition, driven by the shortage of skills such as AI, skills that are increasingly scarce and costly to secure. One way to address this challenge is by adopting no-code, AI-powered platforms.
Creatio stands out as a user-friendly and highly customisable AI-powered marketing automation solution. Its unique, composable no-code platform enables users to design and deploy both pre-configured and bespoke AI use cases without the need to engage professional developers.
Creatio's product portfolio includes CRM Creatio and the comprehensive Marketing Creatio platform. The latter is dedicated to marketing automation and supports the entire customer lifecycle, from the initial contact and handover to sales, through to ongoing customer engagement and post-sale marketing activities.
Creatio's AI is a sophisticated AI assistant, built to interpret natural language queries and act as a reliable resource for a wide range of marketing requirements, including:
- Audience segmentation
- Campaign flow design
- Rich content generation
- Lead scoring
- Lead management assistant
- Content localisation and translation
- Content personalisation
- Customer intent and behaviour analysis
- Digital ad assistance
- Customer insights
- Document management and intelligent search
For example, users can provide a narrative outlining the desired marketing campaign, and AI will generate a well-structured campaign flow informed by previous successful campaigns, available playbooks, and established best practices. It considers user preferences, suggesting communication channels, promotional materials, and engagement strategies.
An example of Creatio AI used to create a digital marketing campaign
Marketers can readily brief Creatio AI on the specific segmentation requirements, and it will generate the necessary rules and filters. The system is also capable of employing a lookalike approach, enabling the selection of target audiences with a stronger likelihood of response.
An example of audience segmentation with Creatio AI
In addition to these core capabilities, Creatio AI workplace enables teams to develop new AI use cases simply by instructing the assistant on what process should be automated or what data requires analysis. Creatio AI will then create the appropriate model.
The platform also provides several pre-packaged machine learning models that can be trained on an organisation’s data to deliver more advanced analytics and workflow automation. In addition, it integrates with OpenAI and ChatGPT, supporting everyday tasks such as composing emails and generating blog posts.
Creatio was recognised as a leader in the 2023 Gartner® Magic Quadrant™ for B2B Marketing Automation Platforms Report and was recently included in the Everest Group's Innovation Watch Assessment for Generative AI Applications.
