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Marketing AI: Forge the Future of Smart Marketing
Realize the promise of generative AI in marketing for enhanced content delivery
AI, but specifically generative AI, has the power to augment, accelerate and create new content, and transform how marketing operates. By 2025, 30% of outbound marketing messages from large organizations will be synthetically generated. Download this guide to:
Understand the drivers of, and barriers to, generative AI in marketing
Discover ways to apply generative AI to enhance content marketing and CX
Get recommendations for how to capitalize on generative AI’s potential and navigate its limitations
Reap the content, client and ops benefits of AI in marketing
As enterprises look toward a future where smarter marketing leads to deeper, more valuable connections between customers and brands, successful CMOs leverage AI in the following ways.
- Enhance Content and CX
- Guide Customer Decisions
- Streamline Operations
Drive creativity and personalized experiences through generative AI
The ability to create original content, synthetic data, models of physical objects, and code to improve response time to customer engagement is providing breakthrough innovation opportunities for marketing.
Generative AI learns from existing artifacts to generate new, realistic artifacts that reflect the characteristics of the training data without repeating them. It produces new content artifacts such as video, narrative, speech, training data and product designs. It can generate within the same modality (e.g., picture to picture) or across modalities (e.g., picture to narrative) and produce entirely unique artifacts or improve existing ones.
Generative AI promises a new level of creativity and enhanced experiences using two primary methods:
Augmented generative AI optimizes existing creative workflows collaboratively with human operators that shape the AI’s generation behavior through reinforcement, such as by saying “more like this” generated element or “less like this.”
Automated generative AI produces unique artifacts in bulk with little human involvement beyond shaping the parameters for production. For example, humans set the brand guidelines for automated copy development.
Generative AI in marketing is quickly gaining traction, with varying levels of practical impact.
Specific marketing AI applications include the following:
Text generators can create marketing copy, news stories and job descriptions. Short-form content like subject line creation can support A/B testing.
Images can be generated for logos; human images can be generated for modeling; and images can be altered for different poses, aging and many other aspects.
AI-generated video can showcase event highlights, immersive product experiences and multilingual versions.
Ads can be optimized by assembling content artifacts into combinations to support personalization.
Computer vision (CV) can improve image quality, develop digital twins and create deep fakes.
Avatars and virtual influencers can engage customers on social media and in the metaverse and provide customer support.
Most applications of AI still require people to set the parameters to guide their learning and provide governance. Marketing leaders must consider the implications for their teams, particularly in the areas of data and asset management, skills development and capacity planning.
Along with generative AI’s promise come inherent risks. Issues around ethics, intellectual property and bias are only a few of the potential pitfalls. To avoid these and other unintended consequences, keep in mind the following:
AI will not evolve to regulate itself — so humans will need to regulate it.
The best time to identify relevant risks to your organization is before you implement AI.
Responsible use of AI is a cross-functional effort that requires a foundation of transparency, trust and security to ensure your organization can exploit AI’s benefits while mitigating risk.
Harness the power of influence AI to guide user choices
It’s helping you choose movies. It’s serving you targeted ads. It’s monitoring your emotional state using cameras. And it’s beginning to influence customer behavior.
Influence AI — the use of algorithms to automate digital experience elements and guide user choices at scale — offers digital marketing leaders a competitive advantage by enhancing experiences and choice architecture to nudge customer decisions. Three emerging technologies work together to make this happen:
Generative AI creates media assets like text, images and video through user-based text prompts.
Emotion AI analyzes users’ emotional states via computer vision, voice input and other sensors.
Federated learning brings disparate sources of information together into a single executive function that optimizes multiple processes against an organization’s goals and values.
Scalable influence is about framing and accelerating consumer choices. Savvy organizations recognize that information architecture — the traditional method of designing a website — is a form of choice architecture. AI can play the role of “choice architect,” framing options to maximize influence. Nudges are voluntary. They preserve freedom of choice. But they make decisions easier and subtly “encourage” the choice of one thing over another.
Consider an on-site search for running sneakers. If the footwear company is trying to improve sustainability, the first choices presented on the website will be running sneakers made of recycled materials. The user would need to scroll down to find sneakers that aren’t as environmentally friendly, making the choice of a sustainable sneaker easier.
As data becomes more intertwined and information architecture expertise grows, the potential to alter the competitive landscape and provide increased business insight becomes greater. Understanding customer buying habits, purchasing behaviors, and churned and missed opportunities can enable enhanced personalization and targeted promotions, along with service and product catalog refinements.
Advancements in computer vision (CV) technology are accelerating the potential of influence AI. CV involves capturing, processing and analyzing real-world images to allow machines to extract meaningful, contextual information from the physical world. CV technologies support the use of data and integration into existing platforms to frame options that address customer pain points in near real time. By 2023, more than 80% of organizations will use some form of computer vision to analyze images and videos.
Health and beauty brands have been early adopters of AI to present product choices based on analysis of skin and hair type. They’re also using these techniques to nudge consumers toward greener cosmetics with calculated positioning, such as making them the first and last items in a collection display.
Reduce friction and eliminate redundancy through the use of AI in marketing ops
The use of AI in operations will reduce friction and eliminate redundancy, allowing marketers to shift their budgets and resources to focus on analytics and insights.
Our research indicates that by 2025, organizations that use AI across the marketing function will shift 75% of their staff’s operations from production to more strategic activities. AI can benefit marketing operations in the following ways:
Optimizing segmentation and personalization efforts through increased content modularization and journey orchestration
Driving more agile, resilient, data-based responses through AI-augmented processes
Automating the capture, processing and analyzing of real-world images and extracting meaningful and contextual information to improve image quality, develop digital twins and create deep fakes
Driving faster time to market and an increased focus on data- and insights-based product improvements
Marketing needs to predict and remediate production bottlenecks and campaign performance issues across channels. This is where the combination of human expertise and a logical understanding of the system can help replace or augment operational activities and give way to meaningful and actionable insights.
The shift toward analytic capabilities will support a more dynamic and responsive marketing organization for the future. The business case for operational AI is driven by several key benefits:
Faster content and channel delivery: Without content, any strategy around digital experiences is not viable. The ability to eliminate the content bottleneck through AI will foster an environment of real-time, analytics-based decisions. This in‑the‑moment personalization will increase the influence brands have over customer decisions across advertising effectiveness, content delivery at scale and reputation management, among others.
Improved customer experience and agility: The power of machine learning and analytics can turn overwhelming mountains of metrics into proactive operations that deliver unprecedented levels of availability and efficiency. Those adopting operational AI will use that data to create content and optimize channel experiences more efficiently. This will require cross-functional teams across marketing, CX, data, IT, product and others to deliver time-to-market value for operational AI.
To foster a strong, AI-fueled marketing operations practice, successful CMOs will do the following:
Create a fusion team with a blend of professionals from each practice, fostering open communication and collaboration. A data and IT team partnership will be fundamental to the success of creating, deploying and optimizing a composable stack.
Start small, and let the practice grow toward operationalizing the end‑to‑end solutions or AI platform.
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FAQ on AI in marketing
How will marketing evolve in the age of AI?
The use of AI in marketing operations will evolve the marketing function in the following ways:
Applying AI to content and customer journeys will be fundamental to segmentation and personalization.
AI-augmented marketing operations will become more resilient, agile and data-focused.
Generative design AI will accelerate product time to market.
What are the problems with generative AI and marketing?
In addition to challenges presented by the growing volume, scale and uncertainty around the accuracy of AI-generated content, regulators and advocacy groups are becoming more vocal about concerns associated with manipulative and biased uses of AI. Several brands have come under scrutiny over their use of advanced technology to influence consumers in creepy and inequitable ways.
What should be top of mind when choosing AI marketing tools?
When choosing an AI marketing tool, begin by understanding the types of tools needed and the potential for overlap with solutions already in your martech stack. Support your investigation with clear user stories and the prospective AI solution’s ability to integrate with existing technology investments and achieve stakeholder adoption. Finally, analyze how AI solution providers vary against key competencies.