Artificial intelligence (AI) holds immense potential for transforming the marketing landscape. From understanding customer needs to predicting behaviors and personalizing content, AI can dramatically enhance marketing strategies. However, to harness this potential, Chief Marketing Officers (CMOs) need to understand the various AI applications available today and how they might evolve. This article will guide marketing executives through the current state of AI in marketing and present a framework to help them plan and implement AI-driven strategies effectively.

The Current State of AI in Marketing

Marketing stands to gain significantly from AI's capabilities. Core marketing activities such as understanding customer needs, matching products and services to these needs, and persuading people to buy can be significantly enhanced by AI. A McKinsey analysis found that marketing was the domain where AI could contribute the greatest value. A 2020 Deloitte global survey of early AI adopters showed that three of the top five AI objectives were marketing-oriented: enhancing existing products and services, creating new products and services, and enhancing customer relationships.

Well-Established AI Applications in Marketing

AI has already laid claim to a few key areas in marketing.

1. Chatbots: Used for lead development, customer support, and cross-selling or upselling.
2. Digital Ad Placement: Also known as programmatic buying, AI helps place ads more effectively.
3. Customer Service: AI assists in managing customer inquiries and providing personalized responses.

The Framework for AI in Marketing

AI applications in marketing can be categorized based on two dimensions: intelligence level and whether they are stand-alone or part of a broader platform.

Types of AI Intelligence

1. Task Automation: These applications perform repetitive, structured tasks that require relatively low levels of intelligence. Examples include systems that automatically send welcome emails or basic chatbots that follow predefined decision trees.

2. Machine Learning: These algorithms are trained with large amounts of data to make complex predictions and decisions. Examples include image recognition, text analysis, customer segmentation, and sales propensity models. Machine learning and its more advanced form, deep learning, are becoming powerful tools in marketing, driving applications like programmatic ad buying and e-commerce recommendation engines.

Stand-Alone vs. Integrated AI

1. Stand-Alone Applications: These AI programs operate independently of the primary customer interaction channels. For instance, a color-discovery app that uses AI to recommend paint colors but requires customers to make the actual purchase outside the app.

2. Integrated Applications: These AI solutions are embedded within existing systems, making them less visible but highly effective. Examples include AI-driven product recommendations on e-commerce sites or predictive sales lead scoring within CRM systems.

The Four Types of Marketing AI

Combining the two dimensions (intelligence level and structure) yields four categories of AI applications:

1. Less Advanced, Stand-Alone AI: Basic consumer service chatbots and email automation systems.
2. Less Advanced, Integrated AI: Inbound customer call routing and CRM-linked marketing automation systems.
3. More Advanced, Stand-Alone AI: Machine-learning apps like AI-driven skincare advisors and advanced chatbots.
4. More Advanced, Integrated AI: Predictive sales-lead scoring, CRM-based sales coaching, and programmatic ad buying.

Implementing AI in Marketing: A Stepped Approach

1. Start Simple: For firms with limited AI experience, begin with simple, rule-based applications. For example, an AI tool that guides human service agents.

2. Progress to Machine Learning: Once basic AI skills are acquired and sufficient customer data is gathered, move towards machine learning applications. An example is using AI to personalize clothing recommendations based on user preferences and feedback.

3. Integrate AI: Over time, integrate AI within existing marketing systems rather than relying solely on stand-alone applications. This approach maximizes the potential of AI, as evidenced by companies like Salesforce, which integrates AI capabilities into its CRM products.

Future Trends and Considerations

As AI continues to evolve, marketers should keep an eye on several trends:

1. Advanced Machine Learning: As technology advances, AI will become more adept at handling complex tasks such as real-time customer interactions and personalized marketing at scale.

2. Ethical AI Use: As AI becomes more integrated into marketing strategies, maintaining customer trust through ethical use of AI and data privacy will be crucial. Establishing ethics and privacy review boards can help ensure AI applications are deployed responsibly.

3. Continuous Learning and Adaptation: Marketers should adopt a mindset of continuous improvement, regularly refining AI models and strategies based on new data and changing market conditions.

AI Marketing Strategies

While AI in marketing holds great promise, CMOs should be realistic about its current capabilities. AI can perform many narrow tasks but is not yet capable of running an entire marketing function. However, its capabilities are rapidly expanding, and it will ultimately transform marketing. By starting with simple applications and gradually integrating more advanced AI solutions, marketers can effectively harness AI's potential to drive business growth. Establishing a long-term AI strategy today will position companies to take full advantage of AI's future advancements.