AI in Marketing: The Future of Smart Marketing

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 (GenAI) 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.

GenAI promises a new level of creativity and enhanced experiences using two primary methods:

• Augmented GenAI 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 GenAI 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.

GenAI 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 provide customer support.

Today’s marketing teams cite a gap in marketing roles and skills as the biggest challenge to successful AI execution.

Though GenAI has boosted marketing productivity and creativity, benefits remain inconsistent. CMOs struggle to move beyond GenAI pilots to unlock the full potential of the technology.

To successfully implement AI at scale, marketing teams must build core competencies and AI skills, utilize key tools and work with external teams to implement innovative use cases and experimentation.

1. Develop the foundational capabilities of internal marketing teams. CMOs should focus on building skills in three key areas: practical AI skills for marketing (e.g., AI use-case identification, GenAI tech fluency), critical marketing core competencies (e.g., digital and data literacy; brand positioning and differentiation; business and financial acumen) and addressing cultural resistance to AI.

2. Blend internal and external resources to differentiate. CMOs who foster AI-driven collaboration with agencies and external partners unlock significant value. These teams can provide access to specialized expertise, martech partners, and consultancies.
   

3. Pilot innovative use cases with partners and highly skilled internal staff. CMOs can advance innovative, cutting-edge GenAI use cases by pairing external partners with highly skilled internal team members. These outside agencies and martech partners are often more equipped to handle the risk of investing heavily in experimental use cases. Additionally, despite vendor “no-code” claims, internal teams may still lack the key skill sets to successfully implement AI use cases.

CMOs who leverage deep customer and market insight, bold position and innovation are 2.6 times more likely to surpass revenue and profit targets. However, it is challenging to effectively embed these strengths across fragmented teams and unclear processes.

Insights teams can be a useful tool for CMOs, but structuring them to deliver real value can prove challenging. These teams often exist in silos, which means their valuable intelligence remains underused — or even duplicated by another team — and they fail to deliver strategic differentiation.

• Build an insights infusion team. Today’s CMOs oversee a broad range of responsibilities, which leaves them with limited time to focus on enhancing customer experience. Instead, CMOs should delegate insight integration to a dedicated team. This approach frees up the CMO to focus on vision setting and market shaping for planning growth strategy.

• Identify stakeholders served by the team. First, decide which stakeholders the insights infusion team serves and what is provided to its internal clients. The final list will depend heavily on the size of the organization and the skill sets within the team. However, start by reviewing the current stakeholder list, interview stakeholders to identify current and future needs, and broaden the list to include potential needs from enterprisewide initiatives.