AI Marketing Automation: 7 Examples and Top AI Marketing Tools

AI marketing automation uses machine learning, predictive analytics, and natural language processing to move beyond rule-based workflows and make real-time decisions that personalise campaigns, score leads, and optimise performance. It analyses behaviour, predicts customer actions, and automates tasks, such as content creation, segmentation, and campaign optimisation, freeing teams to focus on strategy.

According to Salesforce’s Report on AI and the Future of Small Business, 75% of small and growing businesses are already investing in AI. The top examples include running marketing campaigns, creating content, recommending the next best action for a customer, and refining targeting and timing across channels.

Across Australia, AI adoption is accelerating. The Department of Industry’s AI Adoption in Australian Business 2024 report notes that marketing automation and customer support rank among the top five commercial AI use cases, driven by a need for operational efficiency and privacy-focused personalisation.

This guide explains what AI in marketing automation is, outlines seven practical examples, and reviews top AI marketing tools for Australian businesses looking to scale intelligently and responsibly.

What Is AI Marketing Automation?

AI marketing automation is the integration of artificial intelligence technologies into marketing platforms to analyse data, make decisions, and automate tasks in real time. Instead of executing fixed workflows, AI models analyse real-time data, recognise changing customer patterns, and autonomously adjust campaigns to deliver relevant messages and actions across channels.

Where traditional automation follows predefined workflows, AI adds context and adaptability — learning from each interaction, anticipating customer needs, and refining strategies as new data arrives. In practice, this turns automation from a mechanical task engine into a responsive, learning-driven system.

A Comparison Between AI and Traditional Marketing Automation

This shift from rule-based automation to adaptive intelligence highlights why AI delivers greater efficiency, deeper insights, and more meaningful customer engagement across every marketing channel.

Key Benefits of AI for Marketing Automation

• Improved efficiency and productivity: AI automates repetitive marketing tasks, such as lead updates, follow-up emails, and content creation, allowing teams to focus on strategy, creativity, and customer relationships.
• Personalisation at scale: Using behavioural analytics and predictive modelling, AI adapts messages, timing, and offers for each customer, improving conversion across touchpoints.
• Deeper customer insights for smarter campaigns: AI analyses massive volumes of cross-channel data to uncover hidden patterns, such as buying intent, churn risk, or lifetime value, enabling marketers to make data-driven decisions.
• Cost savings through automation: By reducing labour-intensive work and optimising ad spend through predictive performance analytics, AI marketing automation drives down acquisition costs and increases return on investment.

By turning static processes into adaptive, data-driven systems, AI marketing automation enables marketers to move from routine execution to continuous optimisation, a foundation we’ll now see in action through seven real-world examples of AI in marketing automation.

7 Practical Examples of AI in Marketing Automation

AI in marketing automation is applied across the full customer journey, from spotting high-intent leads and creating personalised content to optimising campaigns in real time and monitoring brand sentiment. The illustration below highlights seven practical use cases that show how marketing teams use AI every day to save time, improve results, and deliver more meaningful customer experiences.

Predictive Lead Scoring

Predictive lead scoring uses AI models to identify which prospects are most likely to convert based on engagement signals, demographics, and behavioural patterns. These models continuously refine scores as new CRM data arrives, helping sales teams prioritise high-intent leads and improve conversion rates. When combined with marketing automation platforms like HubSpot, Salesforce, or Odoo CRM, predictive lead scoring becomes a direct link between marketing insights and sales action, helping teams act faster and smarter.

Hyper-Personalised Experience

Delivering a hyper-personalised experience means using AI to tailor content, offers, and product recommendations based on real-time behaviour, past purchases, and individual preferences. Instead of generic campaigns, AI delivers messages unique to each customer’s journey and intent. According to McKinsey’s research, 71% of consumers expect companies to personalise their interactions. This highlights how AI-driven personalisation can lift average order value, strengthen loyalty, and stay efficient across multiple channels.

Content Planning and Creation

AI-powered content tools help marketers research topics, build outlines, and create first-draft copy for emails, ads, and landing pages at speed. Instead of starting from scratch, AI provides data-backed ideas and first drafts, while humans refine tone, messaging, and accuracy. For example, a marketer can generate blog outlines, email sequences, and ad variations based on audience insights and past campaign data.

Customer Segmentation

AI builds precise customer segments based on predicted behaviour and value, such as likelihood to purchase, churn risk, or projected lifetime value. By analysing large datasets across CRM, sales, and digital touchpoints, AI uncovers micro-segments that traditional methods often miss. For example, AI can automatically detect customers likely to upgrade, refer a friend, or churn, and place them into relevant nurture journeys.

Chatbots and Conversational AI

AI chatbots and assistants support customers across channels by answering questions, suggesting products, and guiding purchases. They use natural-language understanding to provide instant, on-brand responses 24/7. For example, a chatbot can help a user find products, check order status, and escalate to a human agent when needed.

Campaign Performance Optimisation

AI automatically analyses campaign performance and adjusts creative, targeting, and timing to maximise results. Unlike static A/B tests, AI continuously learns and improves campaigns in real time. For example, AI can shift budget to high-performing ads, tailor send-times for each contact, or update messaging based on engagement signals.

Sentiment Analysis and Brand Monitoring

AI-driven sentiment analysis tracks customer feedback, social mentions, and review tone to identify reputation risks and emerging themes. It helps marketers understand customer emotions and act quickly. For example, AI can flag a sudden spike in negative feedback about delivery delays so the team can respond before it escalates.

These examples show how AI strengthens every stage of the marketing lifecycle, from prospect targeting to brand reputation. Next, we evaluate the leading AI marketing tools that make these capabilities accessible to marketing teams of Australian businesses.

Top 5 AI Marketing Automation Tools for Australian Businesses

AI marketing tools help teams scale content, personalise campaigns, and automate workflows by combining CRM data, machine learning, and automation features in one platform. These platforms support everything from predictive scoring and segmentation to AI-generated content and customer journey orchestration. Below are five leading solutions trusted by SMEs and mid-market organisations looking to elevate automation and customer experience.

HubSpot Marketing Hub

HubSpot Marketing Hub combines CRM-native AI, automation, and content tools to help businesses scale campaigns, accelerate sales workflows, and personalise engagement across the customer journey. With AI deeply integrated into the platform, teams can research prospects, generate content, analyse performance, and automate repetitive tasks without leaving their CRM.

Key AI capabilities include:

• Breeze AI Assistant for meeting preparation, research, content creation, and strategic recommendations using your CRM context.
• Breeze Agents that automate workflows across marketing, sales, and service.
• Prospecting Agent to identify buying signals, perform account research, and send tailored outreach using CRM data and brand tone.
• Conversation intelligence & sales analytics to capture key call insights and guide sales coaching and reporting.

Salesforce

Salesforce brings AI directly into the CRM and Marketing Cloud through Einstein and Agentforce, enabling teams to plan, launch, and optimise campaigns using real-time customer data across the entire lifecycle. With unified data from Data Cloud and enterprise-grade AI orchestration, Salesforce helps organisations scale personalisation and automate complex journeys while maintaining governance and human oversight.

Key AI capabilities include:

• Agentforce campaign planning that generates campaign briefs, audience segments, and key messages.
• AI-powered content creation & channel execution across email, SMS, and WhatsApp, ensuring messages stay relevant and aligned with brand tone.
• Customer intent & predictive scoring to prioritise leads, accounts, and buying signals for sales and marketing alignment.
• AI recommendations & offers that adjust dynamically based on business goals and real-time affinity signals.

Klaviyo

Klaviyo delivers AI-driven marketing automation built for e-commerce brands, helping teams launch campaigns faster, personalise at scale, and convert more shoppers with automated insights and content. Its AI engine learns from store behaviour, purchase patterns, and engagement signals to create campaigns, recommend products, and automate customer support.

Key AI capabilities include:

• Klaviyo AI Marketing Agent to launch campaigns, flows, and signup forms automatically using only your website.
• Klaviyo AI Customer Agent for 24/7 automated customer support, answering questions, and resolving issues.
• Smart product recommendations are embedded directly into chats and emails to drive cart additions and repeat purchases.
• 40+ AI personalisation features enabling dynamic content, tailored product suggestions, and channel-level optimisation across Klaviyo's B2C CRM.

Odoo Marketing Automation

Odoo brings AI capabilities directly into its unified ERP, CRM, and marketing workflows, allowing marketing and sales teams to automate content and streamline workflows. With AI embedded across modules, from website building to CRM follow-ups, Odoo helps SMEs and mid-market organisations scale marketing and operations without costly integrations or separate AI tools. See our full guide for a deeper look at Odoo’s AI features.

Key Odoo AI capabilities include:

• Automatically generate industry-aligned website layouts and content based on business type, branding, and selected features.
• AI Text Generation (ChatGPT-powered) is embedded across CRM, Marketing, Knowledge, and Website modules to draft emails, landing pages, and campaign copy.
• AI Draft for CRM & support to read prior messages, lead history, and engagement activity and auto-suggest polished replies for leads, tickets, and internal collaboration.
• Localised features for GST, BAS, and accounting integration with Xero, Tyro, and Linkly POS.

Microsoft Dynamics 365 Customer Insights (Copilot)

Microsoft Dynamics 365 Customer Insights brings Copilot AI into CRM and marketing workflows, allowing teams to build segments, create journeys, and generate content. With deep integration into the Microsoft ecosystem, it supports enterprise-grade personalisation, analytics, and governance across marketing and sales operations. Discover Dynamics 365 AI — Copilot, in-app features, and pricing for Australia in our detailed guide.

Key AI capabilities include:

• Natural-language audience building that creates or refines customer segments conversationally using query assist.
• Segment suggestions that discover new audience groups based on behaviour and predicted value.
• AI-generated email content & ideas that draft copy aligned to brand tone and campaign goals.
• Describe a customer journey in plain language, and Copilot builds it automatically (preview).
• Copilot-driven data analysis, interacting directly with customer data to explore patterns and build segments (preview).

Each of these platforms brings AI to life in unique ways, whether through predictive scoring, intelligent content creation, or cross-channel analytics. Yet the value of AI in marketing depends not just on the tool itself, but on how strategically it’s implemented. In the next section, we’ll outline the practical steps for selecting, integrating, and scaling the right platform for your business.

How to Get Started with AI Marketing Automation

Implementing AI in marketing automation starts with clear goals, high-quality data, and choosing tools that align with business maturity and scale. Rather than deploying AI everywhere at once, marketing teams see better results when they begin with one or two high-impact use cases. The image below is a practical, step-by-step framework for organisations building AI-enabled marketing capability with control and clarity.

Step 1: Start with clear goals

Setting clear objectives is the first step toward successful AI adoption. Every AI initiative should solve a defined business challenge, whether improving lead quality, increasing engagement, or reducing campaign costs. To stay focused:

• Identify specific outcomes, such as improving conversion rates or reducing manual reporting and campaign time
• Define measurable KPIs like cost per acquisition (CPA), email open rate, or lead-to-sale ratio.
• Align marketing goals with overall business growth and customer experience objectives.

Step 2: Assess data quality

Assess your data sources, accuracy, completeness, and integration before deploying AI. AI models need clean, centralised customer data to perform effectively. To build a strong data foundation:

• Audit your CRM, ERP, and marketing systems for duplicate or outdated records.
• Consolidate customer data into a centralised platform to eliminate silos.
• Establish data hygiene rules and assign ownership for ongoing data accuracy.

Step 3: Choose the right platforms

Selecting the right technology depends on your business size, goals, and existing digital ecosystem. The best AI marketing tools are those that integrate seamlessly with your current systems and can grow with your business. When evaluating platforms:

• Prioritise integration with your CRM, e-commerce, and accounting systems.
• Compare usability, scalability, and pricing models to ensure long-term fit.
• Choose vendors that support local compliance, such as Australian Privacy Principles (APPs).

Step 4: Start with one or two AI-driven use cases

Starting small allows your team to learn, measure, and optimise before scaling AI adoption across departments. Focusing on one or two high-impact use cases helps demonstrate value early. Good starting points include:

• Implementing predictive lead scoring to improve sales prioritisation.
• Deploying personalised email campaigns based on customer behaviour.
• Using AI chatbots for instant customer engagement and query handling.

Step 5: Train teams and keep “human-in-loop” oversight

Provide training so teams understand how to use AI and evaluate outputs responsibly. Human judgment remains essential. To foster effective collaboration between humans and AI:

• Provide training on interpreting AI-driven insights and campaign recommendations.
• Encourage marketers to review and refine AI-generated content before publishing.
• Establish workflows where humans approve or adjust AI decisions when necessary.

Step 6: Prioritise ethics and transparency

Ethics and transparency are critical to maintaining trust with customers and regulators. Responsible AI use ensures compliance, fairness, and accountability throughout marketing processes. Key principles to uphold:

• Clearly disclose when communications or recommendations are AI-generated.
• Avoid biased or misleading data inputs in predictive models.
• Regularly review privacy practices to meet standards, such as GDPR and the Australian Privacy Act.

With foundations and implementation steps defined, the next section explores the ethical considerations and challenges in AI-powered marketing automation, from privacy and transparency to bias, governance, and trust.

Ethical & Challenges in AI-Powered Marketing Automation

AI in marketing requires responsible data use, transparency, and a balance between automation and human judgment to protect trust and comply with privacy regulations. As AI becomes embedded in campaign workflows, marketers must safeguard customer data and avoid bias, automation overreach, or intrusive personalisation.

• Data privacy and compliance: Require marketers to manage personal information responsibly, follow regulations such as GDPR and the Australian Privacy Principles (APPs), and secure clear consent for data use. This includes transparent data handling, robust storage practices, and opt-in audience management to avoid reputational and legal risks.
• Bias and transparency in AI models: Businesses regularly audit AI outputs to ensure decisions are fair, explainable, and free from unintended discrimination. Establishing model oversight, documenting decision logic where possible, and reviewing training data sources help maintain accountability.
• Avoiding over-automation and preserving human connection: Balancing AI efficiency with authentic human interaction. While AI can personalise at scale, customers still expect empathy, context, and human support, especially in high-value, service-driven or B2B environments. Guided human review and intentional touchpoints prevent robotic or impersonal experiences.

Responsible AI is not just a technical framework; it is a cultural mindset that combines compliance, transparency, and human empathy. By managing data carefully, monitoring algorithms, and maintaining human oversight, businesses can earn customer trust while still benefiting from automation. Next, we address common questions to clarify practical concerns and support confident adoption.

FAQs – AI-Powered Marketing Automation

Can ChatGPT integrate with Odoo?

Yes. ChatGPT does integrate with Odoo, both natively and through custom API setups. Odoo 19 includes built-in ChatGPT capabilities for drafting emails, website copy, and CRM messages, and businesses can extend this with custom GPT integrations for automated communication, smart assistants, and knowledge-based responses. Learn more about Odoo & ChatGPT Integration in our full guide.

Is AI marketing automation suitable for small businesses?

Yes, AI marketing automation is increasingly accessible to small and mid-sized businesses thanks to cloud-based tools and flexible subscription pricing. Many platforms like Klaviyo, Odoo, and HubSpot allow SMEs to start with essential AI features, such as predictive lead scoring or personalised email campaigns, without large upfront investments.

How can AI be used for marketing?

AI can be used in marketing to automate campaigns, personalise customer experiences, and optimise performance in real time. It analyses customer data to recommend products, predict behaviours, and generate tailored content that aligns with user intent.

AI marketing automation gives modern businesses the ability to personalise at scale, accelerate execution, and optimise performance in real time. With the right tools and governance, teams can turn data into decisions and campaigns into results. Havi helps Australian organisations harness AI-powered automation through platforms, such as Odoo, Dynamics 365, and integrated CRM solutions to build compliant, scalable CRM and marketing systems. Consult with our team to explore the best-fit AI roadmap for your business.